INVESTIGATION INTO THE ELECTROCATALYTIC 

HYDROGENATION OF PYROLYSIS BIO-

OIL:ECONOMIC, LIFE CYCLE AND KINETIC 

ANALYSIS 

 
By 
 

Sabyasachi Das 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 

 

2020 

 

 

 

A DISSERTATION 

Submitted to 

Michigan State University 

in partial fulfillment of the requirements 

 for the degree of 

Chemical Engineering – Doctor of Philosophy 

ABSTRACT 

INVESTIGATION INTO THE ELECTROCATALYTIC 

HYDROGENATION OF PYROLYSIS BIO-OIL: 

ECONOMIC, LIFE CYCLE  AND KINETIC 

ANALYSIS  

By 

Sabyasachi Das 

 

 

Rising concerns about the environmental impacts of fossil transportation fuels have motivated the 

development  of  alternative  sources  of  energy  that  are  renewable  and  environment-friendly. 

Biomass-derived liquid hydrocarbon fuels, offer an immediate “drop-in” alternative for displacing 

petroleum-derived  transportation  fuels,  owing  to  their  ability  to  use  existing  infrastructure. 

However, biomass, as an energy source is disadvantaged in terms of carbon and energy content.   

The Billion Ton Report 2016 predicts 1.3 billion tonnes of harvestable biomass in the U.S. by 

2030. The carbon and energy content of this amount of biomass is not sufficient to support the 

demands of the U.S. transportation sector alone. Furthermore, traditional bioenergy systems like 

cellulosic fermentations to ethanol (CE), lose 1/3rd of the biomass carbon as CO2 and fail to utilize 

lignin  (  ~40%  of  biomass  energy)  for  fuel  production.  This  calls  for  biomass  conversion 

technologies that retain most of the biomass carbon and efficiently capture the inherent biomass 

energy in the produced liquid fuels. This can be achieved via fast pyrolysis that can convert all 

biomass (including lignin) to predominantly liquid bio-oil.  However, this bio-oil is unstable, due 

to the presence of reactive functional groups. This fact combined with its low energy content  

makes it unfit as a fuel or a stable intermediate. In this regard, electrocatalytic hydrogenation 

(ECH) can harness renewable electricity from solar/wind farms and sufficiently hydrogenate the 

pyrolysis bio-oil to generate a stable intermediate that can be transported over long distances. 

Additionally, ECH employs mild conditions that allows it to be implemented at a local small-scale 

 

facility. This offers a key advantage in a bioenergy system, where transporting the low bulk density 

biomass can incur large transportation costs. The denser ECH-ed bio-oil can hence be transported 

at lower costs to conventional hydroprocessing facilities to produce a gasoline/diesel range fuel. 

In this study, a bioenergy system (Py-ECH) was developed that combines fast pyrolysis with ECH 

at decentralized depots, followed by hydroprocessing at a central refinery. The mass, carbon and 

energy flux through the system and the fuel yields were estimated. The fuel yields for the Py-ECH 

system were found to be better than CE in terms of energy, mass and carbon. 

To evaluate the economics of Py-ECH, a full techno-economic analysis was conducted using a 

discounted cash flow rate of return (DCFROR) approach and nth plant assumptions. The minimum 

fuel selling price (MFSP) of the Py-ECH fuel was found to be $ 3.62/gge (in 2018 $) compared to 

$ 3.70/gge (in 2018$) for CE. Through sensitivity analyses, key cost-contributing parameters were 

identified, and a pathway was charted for MFSP reduction to < $3/gge (in 2018 $). 

The environmental impacts of Py-ECH  were investigated by performing a cradle-to-grave life 

cycle  assessment  for  environmental  impact  categories  of  global  warming  potential  (GWP), 

eutrophication potential (EUP) and water scarcity footprint (WSF). While the EUP and WSF for 

the Py-ECH system were lower than that for CE, it was observed that the GWP was dependent on 

the source of electricity in the Py-ECH system. Major improvements were identified that can result 

in a carbon negative Py-ECH system. 

Finally, a kinetic model was developed to examine the kinetics of the electrochemical, surface and 

adsorption/desorption  reactions  for  the  ECH  of  phenol  (a  model  bio-oil  compound)  to 

cyclohexanol. The experiments were performed in a rotating disk electrode setup with Ru/ACC 

catalyst as the working electrode and an Ag/AgCl reference electrode to define the effects of mass 

transport.

 

This thesis is dedicated to my FAMILY 
Thank you for your unconditional support 

 

iv 

ACKNOWLEDGEMENTS 

 

 
These last five years of my life at Michigan State University have been a great learning curve to 

say the least. While the destination seemed steep in the beginning, the journey  towards it has been 

truly enjoyable. This would not have been possible without the contribution of some exceptional 

people I encountered along the way. 

First and foremost, I would like to express my heartfelt thanks and gratitude to my advisor, Dr. 

Saffron, who has been a constant source of support, guidance and inspiration. I have simply learnt 

so much from him. I would  like to extend my gratitude to Dr. Barton, who has been  mentored me 

in  different  roles,  starting  from  the  ChEMS  Graduate  Student  Association  to  serving  on  my 

Graduate Committee. I am also grateful to him for allowing me to work in his lab. I would also 

like  to  thank  Dr.  Jackson  and  Dr.  Miller  for  their  constructive  feedback  and  encouragement 

regarding my work. 

A  special  mention  to  my  collaborators  at  Ford  –  Dr.  James  Anderson,  Dr.  Mike  Tamor,  Dr. 

Timothy  Wallington  and  Mr.  Robert  De  Kleine  for  their  guidance  and  mentoring  during  our 

collaboration work. 

I would also like to thank all my teachers starting from my high school to Jadavpur University to 

MSU. They have been my source of inspiration to keep pursuing my quest for knowledge. 

A huge thanks to all past and present group members (Mahlet, Peyman, Dr. Redko, Zhongyu, 

Rachel and Meheryar) for supporting me whenever required and help create a friendly environment 

for all of us to thrive. I would also like to extend my deep appreciation for Tiffany Owen, Jennifer 

Keddle,  Donna  Fernandez,  Heather  Dainton  and  Brad  Tobin  in  the  Chemical  Engineering 

Department for helping me in navigating through departmental requirements and having me stay 

 

v 

updated on different opportunities. A special thanks to the amazing graduate student community 

in the Chemical Engineering Department. I have been lucky to have made so many awesome 

friends  here-  Alex,  Hong-Kang,  Christine,  David,  Natalia,  Iman  and  Neda.  I  would  certainly 

cherish all those shared memories at the different potluck and game nights.  

A special mention to my family away from home here: Kanchan, Saptarshi, Preetam, Aritra, Sayli, 

Yashesh, Tarang, Vikram, Swati, Abhinav, Kamla, Kokil, Mayank and Yogesh. These guys have 

been a source of constant and unwavering support and have been like a cure to my homesickness. 

This journey would be a lot lonelier without their presence. 

Lastly,  but  definitely  not  the  least,  I  would  like  to  thank  my  parents  (Baba  and  Ambu), 

grandparents (Dadu and Amma) and my sister (Bullu) for their unconditional support through the 

ups and downs of this journey. They have been my source of motivation throughout this journey. 

Finally, I would like to express my thanks to friends and family in the U.S. and India- Anuj, Dipto, 

Didibhai, Jammy, Bhuton Mama, Mishta, Pratik, Souradeep, Anshumaan, Piyush, Satyaki, Pritha 

and so many others,  who have also been an integral part of this journey. 

As I approach the end of this Spartan journey, I’ll probably miss MSU the most. It has become a 

part of my identity. No matter where I go, I will always carry a bit of “Green and White” with me 

and all those cherished memories that have become entangled with it over time. 

 

 

 
 
 
 
 
 
 

 

vi 

TABLE OF CONTENTS 

 
LIST OF TABLES ......................................................................................................................... ix 
 
LIST OF FIGURES ...................................................................................................................... xii 
 
Chapter 1 : INTRODUCTION ........................................................................................................ 1 
 
Chapter 2 : LITERATURE REVIEW ............................................................................................. 8 
Fast pyrolysis ...................................................................................................................... 8 
Bio-oil upgrading ................................................................................................................ 9 
Decentralized processing of biomass at depots ................................................................ 18 
Technoeconomic analysis (TEA) of biomass to fuels ...................................................... 20 
Life cycle assessment (LCA) of biomass to fuels ............................................................. 25 
Kinetic modeling of ECH ................................................................................................. 29 

 
Chapter 3 : PY-ECH SYSTEMS ANALYSIS: DEVELOPMENT OF MASS, ENERGY AND 
CARBON FLUX .......................................................................................................................... 31 
Abstract ............................................................................................................................. 31 
Introduction ....................................................................................................................... 32 
Methods ............................................................................................................................. 36 
Results ............................................................................................................................... 38 
Discussion and Outlook .................................................................................................... 46 
Conclusions ....................................................................................................................... 48 
APPENDIX ....................................................................................................................... 49 

 
Chapter 4 : TECHNOECONOMIC ANALYSIS OF THE PY-ECH SYSTEM .......................... 80 
Abstract ............................................................................................................................. 80 
Introduction ....................................................................................................................... 81 
Materials and Methods ...................................................................................................... 83 
Results ............................................................................................................................... 90 
Discussion ....................................................................................................................... 102 
APPENDIX ..................................................................................................................... 106 

 
Chapter 5 : LIFE CYCLE ASSESSMENT OF THE PY-ECH SYSTEM .................................. 115 
Abstract ........................................................................................................................... 115 
Introduction ..................................................................................................................... 115 
Methodology ................................................................................................................... 118 
Results and Discussion ................................................................................................... 130 
Conclusion ...................................................................................................................... 141 
APPENDIX ..................................................................................................................... 145 

 
Chapter 6 : KINETIC MODELING FOR ECH OF PHENOL ................................................... 150 
Abstract ........................................................................................................................... 150 

 

vii 

Introduction ..................................................................................................................... 150 
Model Formulation ......................................................................................................... 152 
Experimental Setup ......................................................................................................... 158 
Results ............................................................................................................................. 160 
Discussion ....................................................................................................................... 163 
APPENDIX ..................................................................................................................... 165 

 
Chapter 7 : CONCLUSIONS AND FUTURE WORK .............................................................. 171 
Conclusions ..................................................................................................................... 171 
Future Work .................................................................................................................... 173 

 
BIBLIOGRAPHY ....................................................................................................................... 175 
 
 

 
 
 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 

 

 
 
 
 
 
 
 
 
 
 
 

viii 

LIST OF TABLES 

 

Table 2.1 : Causes and undesirable effects of different properties of pyrolysis bio-oil (adapted 
from Bridgwater et al.,)19 ................................................................................................................ 9 
 
Table 2.2 : Technoeconomic analyses of bioenergy systems involving thermochemical 
conversion of biomass, followed by upgrading. The year mentioned in parenthesis in each entry 
of the “Cost” column denotes the $ year; gge stands for gallon of gasoline equivalent. .............. 24 
 
Table 2.3 : LCA studies of biomass pyrolysis ............................................................................. 27 
 
Table 3.1 : Label Definitions for the Process Flow Diagram ....................................................... 50 
 
Table 3.2 :  Feedstock Data for Py-ECH ...................................................................................... 52 
 
Table 3.3 : Operating conditions for grinding .............................................................................. 57 
 
Table 3.4 : Operating conditions for drying ................................................................................. 57 
 
Table 3.5 : Representative Compounds in PyV ........................................................................... 59 
 
Table 3.6 : Fast Pyrolysis Data ..................................................................................................... 59 
 
Table 3.7 : Condensation Data ..................................................................................................... 61 
 
Table 3.8 : Key Data for ECH ...................................................................................................... 63 
 
Table 3.9 : Hydroprocessing Data ................................................................................................ 66 
 
Table 3.10 : Electrolysis Operating Conditions ........................................................................... 67 
 
Table 3.11 : Stream Table for Py-ECH Process ........................................................................... 68 
 
Table 3.12 : Balance on combustor for heat integration in BUD for Py-ECH process ............... 70 
 
Table 3.13 : Extracted Stream Table for CE Process (adapted from Humbird et al.)11 ............... 70 
 
Table 3.14 : Energy Balance in CE process ................................................................................. 72 
 
Table 3.15 : Mass Balance for CE Process .................................................................................. 74 
 
Table 3.16 : Carbon Balance for CE Process ............................................................................... 77 
 
Table 3.17 : Water Balance for CE Process ................................................................................. 78 

 

ix 

Table 4.1 : Summary of the economic assumptions in the technoeconomic model .................... 85 
 
Table 4.2 : Depot Capital and Operating Costs ............................................................................ 94 
 
Table 4.3 : Refinery Capital and Operating Costs ....................................................................... 96 
 
Table 4.4 : Assumptions for Calculating the Total Capital Investment and Total Operating Cost
 ..................................................................................................................................................... 110 
 
Table 4.5 : Range of values of biochar found in literature (adapted from Campbell et al.,)215 . 111 
 
Table 4.6 : Summary of factors determining assumed costs for corn stover transport to depots as 
assumed by Kim et al. 74 ............................................................................................................. 112 
 
Table 4.7 : Summary of supply chain costs for corn stover for CE and Py-ECH systems as used 
in model.225 All values in 2018$/tonne of delivered biomass ..................................................... 112 
 
Table 4.8 : Key ECH and Pyrolysis parameters ......................................................................... 114 
 
Table 4.9 : Installation multipliers for different processing units .............................................. 114 
 
Table 5.1 : Description of energy ratios279 ................................................................................. 139 
 
Table 5.2 : Energy ratios for the CE and Py-ECH systems. ERt stands for Total Energy Ratio; 
RF stands for Renewability Factor; Ey stands for Energy Yield; EROI stands for Energy Return 
on Investment; ERf stands for Fossil Energy Ratio. FE stands for Fossil electricity while RE 
represents renewable electricity .................................................................................................. 140 
 
Table 5.3 : Parameters in the modified Weidema method and the description of the scores 
(adapted from Couillard et al.).248 ............................................................................................... 146 
 
Table 5.4 : Life cycle input data for both Py-ECH and CE systems with their source and data 
quality indicator score. ................................................................................................................ 146 
 
Table 5.5 : Contribution of each operation in the determination of Global Warming Potential 
(GWP) for both CE and Py-ECH systems .................................................................................. 147 
 
Table 5.6 : Contribution of each operation in the determination of Eutrophication Potential 
(EUP) for both CE and Py-ECH systems. .................................................................................. 148 
 
Table 5.7 : Contribution of each operation in the determination of Water Scarcity Footprint 
(WSF) for both CE and Py-ECH systems. .................................................................................. 148 
 
Table 5.8 : Life cycle impact summary for the CE process.  Allocation scenarios 1 and 2 are 
reported to observe the effects of avoiding allocation and applying minimal allocation to 
cultivation ................................................................................................................................... 149 

 

x 

Table 5.9 : Life cycle impact summary for the Py-ECH process.  Allocation scenarios 1 and 2 
are reported to observe the effects of avoiding allocation and applying minimal allocation to 
cultivation. .................................................................................................................................. 149 
 
Table 6.1 : Reaction rate expressions ......................................................................................... 154 
 
Table 6.2 : Expressions for reaction rate constants .................................................................... 156 
 
Table 6.3 : Legend for different variables .................................................................................. 158 
 
Table 6.4 : Parameter values used in generating model current vs potential curve ................... 169 

 

 

 
 
 

 

 
 
 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 
 
 
 

xi 

LIST OF FIGURES 

 

Figure 1.1 : A schematic of the Py-ECH system.5 Biomass is brought from the fields to a 
localized depot, where it undergoes C-efficient upgrading (fast pyrolysis, followed by ECH) to 
generate a stable bio-oil that can be transported to a centralized refinery for further petroleum-
like hydroprocessing to produce a sustainable hydrocarbon fuel for transportation. The CO2 
emissions from combusting this fuel is captured by the plant biomass(the system feedstock) via 
photosynthesis. ................................................................................................................................ 5 
 
Figure 2.1 : Schematic for ECH of bio-oil in a batch divided cell. BO represents the pyrolysis 
bio-oil, whereas SBO represents the ECH-ed stable bio-oil. n denotes the number of moles of 
electrons passed.5 .......................................................................................................................... 14 
 
Figure 3.1 : Comparison of the annual energy balance in the U.S. transportation sector and 
projected harvestable biomass. Projections are made based on the available biomass by 2030 
(U.S. Billion Ton Study 2016). The specific energy for petroleum is 48 MJ kg-1 and for dry 
biomass is 20.6 MJ kg-1. Energy is presented in exajoules (EJ), equivalent to 1018 J. Mass is in 
units of a “billion tonnes”, equivalent to 1012 kg. ......................................................................... 33 
 
Figure 3.2 : Sankey diagrams for energy flow. (a) Pyrolysis–electrocatalytic hydrogenation (Py–
ECH) energy analysis. Yield: 89%; (b) CE energy analysis. Yield: 44%. ................................... 39 
 
Figure 3.3 : Sankey diagrams for mass flow. (a) Pyrolysis–electrocatalytic hydrogenation (Py–
ECH) mass analysis. Yield: 38%; (b) CE mass analysis. Yield: 26%. ......................................... 42 
 
Figure 3.4 : Sankey diagrams for carbon flow. a) Pyrolysis–electrocatalytic hydrogenation (Py–
ECH) carbon analysis. Yield: 63%; (b) cellulosic ethanol (CE) carbon analysis. Yield: 30%. ... 44 
 
Figure 3.5 : Energy, mass and carbon yield comparison of CE and Py–ECH. The energy yield is 
significantly higher because of addition of electrical energy. ....................................................... 45 
 
Figure 3.6 : Process Flow Diagram for Py-ECH Process ............................................................ 51 
 
Figure 3.7 : Process Flow Diagram for Pyrolysis of Biomass ..................................................... 53 
 
Figure 3.8: General Distribution of Functional Groups in Bio-oil18 ............................................ 58 
 
Figure 3.9 : Process flow diagram for pyrolysis .......................................................................... 60 
 
Figure 3.10 : Condenser Flow Diagram ....................................................................................... 61 
 
Figure 3.11 : Schematic of ECH Apparatus ................................................................................. 62 
 
Figure 3.12 : ECH Reactions Considered in this Analysis .......................................................... 64 

 

xii 

Figure 3.13 : Process Flow Diagram for ECH ............................................................................. 65 
 
Figure 3.14 : Hydroprocessor Process Flow Diagram ................................................................. 66 
 
Figure 4.1 : Process flow diagram of the Py-ECH process .......................................................... 84 
 
Figure 4.2 : Illustration of depot distribution geometry. (a) Illustration of depot distribution 
relative to central refinery, for a central refinery with 18 depots. (b) Variation of farm-to-depot 
and depot-to-refinery distance with depot size, for a 2000 tpd central refinery ........................... 90 
 
Figure 4.3 : Variation of MFSP from Py-ECH with depot size. CR denotes capacity of central 
refinery .......................................................................................................................................... 91 
 
Figure 4.4 : Breakdown of raw material supply chain costs from farm to the depot gate for (a) 
Py-ECH system and (b) CE system .............................................................................................. 93 
 
Figure 4.5 : Distribution of costs at depot (a) Installed capital costs contribution analyses at a 
depot (b) Variable operating cost contribution analyses at a depot .............................................. 95 
 
Figure 4.6 : Distribution of costs at refinery (a) Installed capital cost contribution at the refinery 
(b) Variable operating cost contribution at the refinery ................................................................ 98 
 
Figure 4.7 : Tornado plot showing single parameter sensitivity analyses on MFSP ................. 100 
 
Figure 4.8 : Effect of depot size on MFSP for different refinery sizes. Cd denotes depot capacity
 ..................................................................................................................................................... 101 
 
Figure 4.9 : Waterfall chart showing potential reduction in MFSP assuming combinations of 
improvements in selected model parameters. The light green bars indicate the reductions in 
MFSP owing to the stacking up of system improvements. The dark green bars denote the initial 
and final MFSP (after all improvements). The yellow bars denote alternate scenarios, not 
considered in the baseline model. ............................................................................................... 105 
 
Figure 4.10 : Electricity costs from different sources. Red dashed line indicates the MFSP using 
MRO-West U.S. grid electricity, assumed as a baseline in the model.197 .................................. 113 
 
Figure 4.11 : Sensitivity analyses on MFSP for all parameters. Larger the slope, greater the 
sensitivity to that parameter. ....................................................................................................... 113 
 
Figure 5.1 : System boundaries for Py-ECH system ................................................................. 121 
 
Figure 5.2 : System boundaries for CE system .......................................................................... 121 
 
Figure 5.3 : System carbon flow for (a) Py-ECH and (b) CE, for GREET assumed value of 
carbon sequestration. ................................................................................................................... 124 
 

 

xiii 

Figure 5.4 : Global Warming Potential (GWP) Contribution Analysis ; Subscript 1 refers to 
Scenario 1 whereas Subscript 2 refers to Scenario 2.  ‘F’ refers to a fossil fuel electrical grid and 
‘R’ refers to renewable power. .................................................................................................... 131 
 
Figure 5.5 : Eutrophication  Potential (EUP) contribution analysis (a) Total EUP contribution 
analysis; subscript 1 refers to Scenario 1 whereas subscript 2 refers to Scenario 2. (b) EUP 
contribution analysis excluding cultivation and harvesting; F stands for fossil electricity and R 
stands for Renewable electricity. ................................................................................................ 134 
 
Figure 5.6 : Water Scarcity Footprint (WSF) contribution analysis; subscript 1 refers to Scenario 
1 whereas subscript 2 refers to Scenario 2. ................................................................................. 135 
 
Figure 5.7 : Sensitivity of system GWP with % renewable in MROW Electricity Grid. (For 
Scenario 2) .................................................................................................................................. 137 
 
Figure 5.8 : Sensitivity of system Global Warming Potential (GWP) with annual C sequestration 
rate for Scenario 2. ...................................................................................................................... 138 
 
Figure 5.9 : Sensitivity of system EROI with (a) % renewable heat at refinery and (b) % 
renewable electricity ................................................................................................................... 141 
 
Figure 5.10 : Waterfall chart showing the resultant global warming potential of stacking 
improvements in the Py-ECH system. ........................................................................................ 143 
 
Figure 6.1 : Setup for RDE experiments .................................................................................... 159 
 
Figure 6.2 : Experimental setup for catalyst preparation ........................................................... 160 
 
Figure 6.3 : (a) Current vs potential data as obtained from RDE experiments (b) Electrode 
overpotential vs log(current density) .......................................................................................... 162 
 
Figure 6.4 : GC-MS chromatogram of electrolyte solution post ECH of phenol in an RDE .... 162 
 
Figure 6.5 : Current vs potential curve as predicted by model for different values of Rr .......... 169 
 
Figure 6.6 : Effect of increasing mass transfer coefficient and kinetic rate constant for hydrogen 
adsorption on model predicted curve. Blue line denotes experimental data. .............................. 170 
 

 

xiv 

Chapter 1 : INTRODUCTION  

According to the World Energy Council’s 2013 survey report, fossil fuels still account for more 

than 80% of the world’s energy consumption. This trend is expected to continue till 2020, when 

the consumption would be reduced by a mere 4%. The survey estimates reserve of 890 billion 

tonnes  of  coal,  225  billion  tonnes  of  crude  oil  and  210  trillion  cubic  meters  of  natural  gas 

(2011data). In fact, based on the data provided, natural gas is set to last only 55 years, whereas the 

other two sources have more than 100 years left.1 Irrespective of the time span, fossil fuel reserves 

are bound to get depleted, owing to their inherent characteristic of being non-renewable. Moreover, 

fossil fuel energy is a major cause for pollution. The IPCC, in 2014, reported that carbon dioxide 

accounts for 76% of the total global greenhouse gas emissions. About 85% of those CO2 emissions 

originate from fossil fuel and industrial processes.2 This is in addition to other greenhouse gases 

like methane, and nitrous oxide. Therefore, fossil fuel sources are both non-renewable and severely 

polluting. The Energy Independence and Security Act (EISA), passed by the U.S. government, in 

2007, recognizes this  and includes, within its aims, the development and production of clean 

renewable  fuels.3  Among  other  renewable  sources,  it  provides  impetus  to  reduce  baseline 

greenhouse gas emissions (established in 2005) by 50% by developing and producing advanced 

biofuels. These biofuels include ethanol, derived from corn starch or stover, ethanol derived from 

waste material, butanol from renewable biomass, biogas or any other fuel derived from biomass-

based resources.  

Biomass, as a renewable energy source is unique in its potential to be converted into liquid, solid 

or gaseous fuels and is the only renewable source of carbon.4 This is because plants (biomass) 

essentially capture CO2 from the atmosphere at very low costs via photosynthesis. They also store 

solar energy in chemical form via this process. However, the photosynthetic efficiencies are very 

 

1 

low in the range of 1-2% of incident solar energy being actually stored.5-7 Moreover, biomass has 

only about one-third of the specific energy content of crude oil. Therefore, biomass-derived fuels, 

without any energy upgrading, cannot hope to compare to petroleum derived fuels. Additionally, 

the Billion Ton Report, published in 2016, estimates that the total amount of harvestable biomass 

(under $60/dry ton) in the U.S. available by the year 2030, to be about 1.3 billion dry tons. Such a 

quantity is not sufficient for complete petroleum replacement. In fact, it is not even enough to 

support the energy and carbon demands of only the U.S. transportation sector.5  

Traditional and prevalent bioenergy systems such as cellulosic fermentations to ethanol8-12 are 

carbon inefficient. This is owing to the fact that about 1/3rd of the carbon present in the starting 

biomass is lost as carbon dioxide in such processes. The final energy content of the fuel (ethanol) 

also does not match up to that of gasoline-like fuels, because the lignin component of biomass, 

which accounts for 40% of the biomass energy, is not processed. This lignin is instead combusted 

to provide heat and power at the refinery. Only the holocellulose (cellulose and hemicellulose) is 

processed to produce the fuel. On the other hand, thermochemical treatment of biomass processes 

all three components of biomass. These employ high temperatures and pressures for the production 

of a complex mixture of organic compounds. Products from thermochemical conversions can be 

obtained in all three physical states. The relative proportion of each state depends on the process 

conditions employed. Gasification of biomass, as the name suggests, is the process of heating 

biomass to high temperatures, around 700°C in the presence of controlled amount of oxygen, 

followed by the water gas shift reaction to generate syngas.13, 14 Direct combustion of biomass can 

only be used to produce heat.15, 16 Torrefaction, which includes heating the biomass to temperatures 

between 200°C and 300°C in an inert environment, produces a solid product that is 70 wt.% of the 

starting biomass and 90% of the initial biomass energy.17 However, liquid products are beneficial 

 

2 

in terms of their higher density and ease in transportation.8 In this regard, fast pyrolysis is the 

thermochemical treatment of biomass, that produces the largest amount of liquid products, about 

75%. The gas and liquid yields are approximately 13% and 12% respectively. The process employs 

a temperature of 500°C and a vapor residence time of 1 second.18, 19 Heat is provided in the absence 

of oxygen, at a heating rate of about 10-200K/s.19 The liquid product obtained from fast pyrolysis, 

known as bio-oil, is a mixture of many organic compounds and includes functional groups like 

carboxylic  acids,  ketones,  aldehydes  and  phenols.20  These  compounds  are  responsible  for  the 

undesirable properties of bio-oil like high oxygen content (~35%), low heating value (~19 MJ/kg) 

and high acidity (~pH of 2-3). Furthermore, the bio-oil has variable viscosity ranges, between 35-

1000 cP, at 40°C, due to the high reactivity of these compounds.21 All these factors render the bio-

oil unstable and unfit for storage and transportation. Furthermore, energy content of this bio-oil is 

not comparable to that of gasoline or diesel and therefore, there is a need for energy upgrading. 

There are numerous physical and chemical methods for bio-oil stabilization and/or upgrading. 

Physical methods mainly include emulsification, hot vapor filtration and microfiltration, whereas 

hydrotreating, esterification and catalytic cracking are the predominant chemical methods.19, 21 Of 

these, hydrotreating, which involves hydrogenation and hydrodeoxygenation, is the most popular 

and  commercially  available  method  for  bio-oil  stabilization  and  upgrading.  The  use  of  such 

hydroprocessing techniques for stabilizing and upgrading bio-oil also presents the opportunity to 

take advantage of existing petroleum infrastructure.19  

However, it must be noted here that biomass is a distributed source feedstock as compared to a 

point source like petroleum. Biomass is a solid of low energy density and hauling it over large 

distances can incur huge transportation costs. In this regard, a decentralized bioenergy system that 

employs local densification/processing depots near the source and produces an intermediate fuel 

 

3 

that is denser (than biomass) and fit for transport to larger processing facilities, can be a potential 

solution.22 While pyrolysis achieves the required densification by converting the solid biomass to 

a  liquid  bio-oil,  it  requires  a  method  of  subsequent  bio-oil  stabilization.  Furthermore,  such  a 

method  should  be  effective  at  a  small-scale  depot,  near  the  biomass  source.  This  is  where 

hydrotreating fails as an option. The costs of producing H2 gas is very high at the small scale of 

the depot and also poses safety concerns related to handling of flammable gas. The method also 

requires extreme conditions in terms of temperature and pressures. A promising solution, in this 

regard, can be electrocatalytic hydrogenation (ECH).23-33 ECH employs mild temperatures and 

pressures to hydrogenate the unsaturation present in the reactive bio-oil compounds and helps 

stabilize  bio-oil.  Therefore,  the  need  for  H2  gas  storage  and  use  is  avoided.  ECH  employs 

electrolysis  of  water  to  generate  in-situ  hydrogen  ions  that  can  reduce  (via  the  addition  of 

hydrogen) the pyrolysis bio-oil on a catalytic cathode. Through this process, it also helps store 

electrical energy as chemical energy in the reduced bonds of the ECH-ed bio-oil, thereby, slightly 

upgrading  it  in  energy  content.  This  electrical  energy  may  be  derived  from  renewable  energy 

sources  like  wind  farms  and  solar  photovoltaics.  ECH  can,  in  fact,  be  an  answer  to  the 

intermittency  problems  of  such  renewable  electricity  technologies.  It  can  provide  a  time-

insensitive method of storing this wind/solar renewable electricity, that is inherently dependent on 

time  cycles  of  wind/light  availability.5  The  stable  bio-oil  after  ECH,  is  therefore,  fit  for 

transportation to a central processing facility, where it can undergo further upgrading and refining 

to a gasoline/diesel like fuel. It must be noted here that the ECH also reduces H2 gas consumption 

at the central refinery since the stable bio-oil after ECH is already upgraded to some degree. 32, 34   

Therefore,  fast  pyrolysis,  followed  by  subsequent  ECH  stabilization  can  be  a  potent  biomass 

processing technology at local decentralized depots, that ship the ECH-ed stable bio-oil to a central 

 

4 

petroleum-style hydroprocessing facility to produce a final gasoline/diesel like fuel. In this study, 

such a system, nicknamed the Py-ECH system, has been proposed with an aim to improve upon 

popular traditional systems like cellulosic fermentations to ethanol and also, simultaneously chart 

a pathway for the ultimate replacement of fossil fuel systems. A schematic of the proposed Py-

ECH system can be seen in Figure 1.1. 

 
Figure 1.1 : A schematic of the Py-ECH system.5 Biomass is brought from the fields to a 
localized depot, where it undergoes C-efficient upgrading (fast pyrolysis, followed by ECH) to 
generate a stable bio-oil that can be transported to a centralized refinery for further petroleum-
like hydroprocessing to produce a sustainable hydrocarbon fuel for transportation. The CO2 
emissions from combusting this fuel is captured by the plant biomass(the system feedstock) via 
photosynthesis. 
 
In summary, the goal of this study is to investigate the merits of the Py-ECH system as a viable 

alternative to prevalent bioenergy systems like cellulosic fermentations to ethanol, and ultimately, 

as a possible replacement for fossil fuel systems. 

 

5 

The entire study may be divided into four major component studies. Each component study 

achieves a sub-objective, in an attempt to realize the primary goal of the study, as stated earlier. 

These component studies include:  

1.  Systems analysis: The objective of this component study was to determine the different 

process flows and yields of the Py-ECH system and compare them to traditional cellulosic 

fermentations to ethanol. A model was developed that calculated the process flows if corn 

stover biomass is processed by the Py-ECH system to produce a gasoline-like fuel. The 

subsequent fuel yields in terms of mass, energy and carbon of the starting biomass, were 

also determined. A comparison was made with the cellulosic fermentation to ethanol (CE) 

system. 

2.  Investigation  of  Py-ECH  economics:  The  objective  of  this  component  study  was  to 

investigate the economics of the Py-ECH system and identify key cost contributors. A 

discounted cash flow rate of return (DCFROR) analysis was performed to determine a 

minimum fuel selling price (MFSP) of the final fuel assuming nth plant economics. 

3.  Investigation of environmental impacts:  The objective of this component study was to 

investigate the environmental impacts of the Py-ECH system in comparison to cellulosic 

fermentations  to  ethanol  (CE).  A  full-scale  cradle-to-grave  life  cycle  assessment  was 

conducted to determine the global warming potential, eutrophication potential and water 

scarcity footprint of the Py-ECH system and compared to that for the CE system. 

4.  Investigation of ECH kinetics: The objective of this study was to determine the kinetic rate 

constants associated with the ECH of phenol. A mathematical model was developed for 

the ECH of phenol (a model bio-oil compound) to cyclohexanol, in a rotating disc electrode 

 

6 

(RDE)  with  a  Ru/ACC  working  electrode.  Preliminary  experimental  data  (current  vs 

voltage) was collected in this regard. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
 
 
 

 

7 

Chapter 2 : LITERATURE REVIEW 

Fast pyrolysis 

The Py-ECH system combines fast pyrolysis and electrocatalytic hydrogenation of biomass at 

localized depots, followed by petroleum style hydroprocessing at a central refinery. Biomass fast 

pyrolysis has been studied before. Reed studied pyrolysis as a method of biomass conversion to 

fuels  as  early  as  1980.35  While  exploring  different  methods  of  biomass  thermochemical 

conversion, he concluded that pyrolysis is a good pathway for synthesis to liquid fuels such as 

gasoline. Fast pyrolysis is described as a process that employs high temperatures to provide rapid 

heat in absence of oxygen. Specifically, the key features of fast pyrolysis have been described as 

having very high reaction temperatures around 500°C and employing very low residence times of 

less than 2 s.36 The main products of pyrolysis are a liquid mixture of organics and water, along 

with some gaseous products and solid char. The yield of each product is a function of the reaction 

temperature employed. The organic liquid product, which is basically a mixture of carboxylic 

acids, ketones, aldehydes and phenols20 is maximized at a temperature of around 500°C.36  The 

solid char, known as biochar is a high energy density solid, compared to the starting biomass and 

has an energy content of around 18 MJ/kg.37 The biochar product may be combusted to produce 

heat and power or may be land applied for carbon sequestration.38-42 The liquid product, known as 

bio-oil has similar heating value (~16-19 MJ/kg), which is less compared to crude oil (~44 MJ/kg), 

owing to the high oxygen content (~35%) in bio-oil compared to crude oil (<1%).43 Bio-oil, as a 

fuel, has other undesirable properties too and these have been presented in Table 2.1, along with 

the possible reasons. These properties render bio-oil unsuitable for storage, transport or use as a 

fuel. Therefore, there is a need for upgrading, with the aim of alleviating these properties.  

 

 

8 

Table 2.1 : Causes and undesirable effects of different properties of pyrolysis bio-oil (adapted 

from Bridgwater et al.,)19 

Cause 

Organic acids from 

biopolymer degradation 
Secondary polymerization 

reactions 

High ash feed 

Undesirable effect 

Corrosion of vessels and 

pipework 

Slow increase in viscosity; 
potential phase separation 

Catalyst poisoning; 
deposition of solids in 
combustion; erosion and 

corrosion 

Incomplete char separation 

Biomass composition 

Sedimentation; filter 

blockage; catalyst blockage 
Poor stability; non-miscibility 

Property of bio-oil 

Acidity 

Aging 

Presence of alkali metals 

Presence of char 

High !! content 

Water content 

Pyrolysis reactions 

 
This upgrading may be achieved through several chemical and physical methods that stabilize the 

with hydrocarbons 
Lower heating value 

bio-oil via reduction in the reactivity of bio-oil.  

Bio-oil upgrading 

Hydrodeoxygenation (HDO) 

HDO employs hydrogen gas at high pressures (~20MPa) and temperatures around 400°C, in the 

presence of a catalyst, to effect removal of oxygen as water.44 Venderbosch et al., reports the 

formation of two liquid phases, when HDO of bio-oil is carried out under these conditions, in the 

presence of Ru/C catalyst. The organic phase is lean in oxygen content as most of the oxygen is 

transferred to the aqueous phase. The percentage of oxygen reduced from 52.1% in the feed bio-

oil to 14.2% (on a weight basis) for a 2-stage HDO process.45 Water percentage also decreased 

from 29% to 3.5%. Elliott et al., found similar results, when bio-oil derived from pyrolysis of corn 

stover was subjected to a pressure of 200 bar and temperatures around 340°C, in the presence of 

Pd/C catalyst. Oxygen content decreased from 55.43% (on a wet basis) to 11.9%. It was further 

observed that the oxygen content passed through a minimum when temperature of the HDO was 

increased. The yield of the oil layer, however, decreased with increase in temperature.46 Oil yield 

 

9 

and  deoxygenation,  were  therefore  found  to  be  inversely  related  with  respect  to  temperature, 

implying  that  higher  degree  of  deoxygenation  must  be  accompanied  by  lower  oil  yields.  The 

conventional catalysts employed are sulfided transition metal salts, supported on γ-Al2O3.47 These 

catalysts can sometimes lead to the formation of sulfur containing compounds and contaminate the 

bio-oil  being  hydrotreated.48  Gutierrez  et  al.,  investigated  the  effect  of  catalyst  by  using  ZrO2 

supported noble metal catalysts49 for the hydrotreating of guaiacol, a model biooil compound.50 

The performance was found to be superior to conventional sulfided catalysts due to less carbon 

deposition and no sulfur addition.49 However, carbon formation and subsequent deactivation of 

the catalyst is recognized as a major drawback of the HDO process. The carbon forming tendency 

is found to increase with increasing amount of unsaturation and aromatics in the feed. Increase in 

temperature and acidity of the catalyst support also enhances carbon deposition.43 Wildschut et al., 

investigated  the  effect  of  varying  catalyst  and  catalyst  supports  on  the  yield,  extent  of 

deoxygenation and H2 consumption in HDO of fast pyrolysis oil. It was found that Ru/C gave the 

best performance with yields of ~65%, oxygen contents of approximately 6%, HHV of 43 MJ/kg 

and  least  H2  consumption  (400  Nl/kg  dry  pyrolysis  oil).51  It  was  also  found  out  that  carbon 

formation is lower for carbon supports (~4 wt%) when compared to alumina supports (26 wt%). 

Therefore, variation of the parameters and catalyst involved in HDO of bio-oil can significantly 

affect the overall efficiency of the process. However, the fact remains that HDO requires large 

amounts  of  hydrogen  gas  at  high  pressures.  This  is  a  major  barrier  for  this  bio-oil  upgrading 

technique, owing to the distributed nature of biomass feedstock. Often, it is difficult to find an 

enormous source of required H2 at local distributed facilities.52 

 

 

 

10 

Zeolite Cracking 

This upgrading scheme involves the heating of the bio-oil to temperatures around 350°C to 600°C 

at  atmospheric  pressures  in  the  presence  of  zeolite  catalysts.53  The  predominant  reactions  are 

cracking reactions, wherein, the long chain oxygenated hydrocarbons in the pyrolysis bio-oil are 

reduced to shorter molecules. The products are formed in three phases, namely the organic phase, 

the aqueous phase and a gaseous phase.43 The process does not require an external source for the 

hydrogen gas since it is generated in situ due to the water gas shift reaction.53 HZSM-5 is the 

preferred  catalyst  due  to  the  presence  of  a  greater  number  of  acidic  sites  compared  to  other 

silicaalumina and silicalite catalysts. Therefore, the conversion of the bio-oil to reduced aromatic 

compounds is greater for HZSM-5 catalyst.43, 54 The extent of deoxygenation is found to increase 

with  an  increase  in  temperature.  The  reduced  oxygen  content,  however,  is  accompanied  by  a 

decreased  yield  and  an  increase  in  coke  formation  with  rise  in  temperature.43  Moreover,  coke 

formation in zeolite cracking is a major drawback, with amounts to around 26-39 wt%.55 Other 

disadvantages include high total acid number, low H/C ratios and consequently, lesser heating 

value of the upgraded bio-oil.43 

Esterification and Acetalization 

A  primary  reason  behind  the  reactivity  and  instability  of  pyrolysis  bio-oils  is  the  presence  of 

functional groups like carboxylic acids, aldehydes and ketones. These can be converted into more 

stable esters and acetals respectively, by the addition of alcohols in the presence of a catalyst.21 

Wang et al., added methanol to bio-oil, in the presence of two catalysts, namely 732 resin and 

NKC-9  resin.  The  resultant  upgraded  bio-oil  was  noted  to  have  improved  stability.  The  acid 

number  reduced  by  about  86-89%,  compared  to  raw  bio-oil.  The  heating  values  increased  by 

~32%. Furthermore, there was reduction in moisture content (~27-30%) and decrease in viscosity 

 

11 

by approximately 97%.56 Li et.al., investigated the effect of reaction parameters on the extent of 

conversion of esterification and acetalization reactions in the presence of Amberlyst-70 catalyst, 

for mallee woody biomass pyrolysis bio-oil. It was found that the conversion for both reactions 

increased  with  increase  in  temperature  and  catalyst  loadings.57  However,  catalyst  coking,  and 

“poor recyclability limit” are some drawbacks associated with this process.21 

Physical methods 

Of the physical methods employed to upgrade bio-oil, emulsification is a promising route. This 

involves the mixing of the bio-oil with petroleum-based fuels, in the presence of a surfactant. The 

process reduces the viscosity of the pyrolysis bio-oils.21 Ikura et al., performed the emulsification 

of pyrolytic bio-oil with diesel fuel in the presence of surfactant Hypermer 2234. It was found that 

the viscosity and corrosion properties of the emulsions were lower than that of the raw bio-oil.58 

However, the process involves high cost and high energy inputs.59 Char particles in bio-oil are 

associated with trapping alkali metal ions in them, which in turn can act as catalysts for undesirable 

polymerization reactions.21, 60 Generally, cyclone separators are used for separation, whereas hot 

vapor  filtration  is  employed  to  remove  very  fine  particles  (<10  μm).  Compared  to  cyclone 

separators, hot vapor filtration is shown to have a tenfold reduction in viscosity growth rates.21 

Other techniques employ ceramic membranes, under a driving force, to affect the separation of 

very fine char particles (0.02-10 μm). Although efficient, the high costs of the membranes are 

significant challenges faced by this technique.21 

Therefore, in summary, all the physical and chemical methods for stabilization and upgrading of 

pyrolytic bio-oils have significant challenges. HDO is the most promising and popular route as it 

is a product of the extrapolation of the hydrodesulfurization (HDS) technique for petroleum fuels 

to  bio-oils.43  However,  the  extreme  conditions  and  challenges  associated  with  availability  and 

 

12 

storage  of  H2  gas  restrict  its  application  in  bio-oil  upgrading.  In  this  context,  electrocatalytic 

hydrogenation (ECH) offers an alternative route that employs milder conditions and eliminates the 

requirement of a source for H2 gas. The lower operating temperatures and pressure also reduce 

carbon deposition compared to HDO.32  

Electrocatalytic Hydrogenation 

Electrocatalytic Hydrogenation (ECH) is an approach for mild hydrogenation of pyrolysis bio-

oil.61 Pletcher defined electrocatalysis as “the acceleration of a particular electrode reaction by the 

appropriate  choice  of  electrode  material”.62    Over  the  years,  the  most  investigated  electrode 

reaction has been the Hydrogen Evolution Reaction (HER), owing to its immense applications in 

fuel cells and chlor-alkali processes.63 The HER reaction is known to proceed through two steps64, 

1.  Adsorption of hydrogen on the catalyst surface by electroreduction of water molecules. 

""!#+$$+%→%"%&'+"!! 

This is also known as the Volmer Reaction.   

 

 

 

2.  Desorption as molecular H2  

a.  Chemical Desorption 

2%"%&'→2%+"!	 

This is known as the Tafel reaction.  

b.  Electrochemical Desorption ""!#+%"%&'+$$→%+"!+"!! 

 

 

 

 

This is known as the Heyrovsky reaction.  

 

 

 

 

 

 

(1) 

(2) 

(3) 

ECH is employed for stabilization of bio-oil because it offers several advantages over the more 

popular HDO process. The conditions required for upgrading of bio-oil are milder and there is no 

need for H2 gas storage or transport.64 However, when ECH is employed for stabilization and 

 

13 

upgrading of bio-oil, reactions (2) and (3) are undesirable side reactions. The adsorbed hydrogen 

in (1) must ideally react with the unsaturated compounds in bio-oil and reduce them. Li et al., 

describes  the  desired  reactions  between  the  adsorbed  hydrogen  and  adsorbed  unsaturated 

compounds as follows: 

 

 

 

 

 

 

 

 

 

2(")%&'%+(*=,)%&'.→(*"−,")%&'.+2% 

(*=,)%(+.→(*=,)%&'. 
(*"−,")%&'.→(*"−,")%(+. 

 

 

 

 

 

 

 

 

(4) 

(5) 

(6) 

where, Y=Z represents the unsaturated organic compounds present in bio-oil, (Y=Z)adsA is the 

adsorbed organic compound and (YH-ZH)adsA is the adsorbed hydrogenated product.23 

Principle of operation 
 
The principle of operation of an ECH setup, is very similar to that of a general electrolytic cell.  

Figure 2.1 : Schematic for ECH of bio-oil in a batch divided cell. BO represents the pyrolysis 
bio-oil, whereas SBO represents the ECH-ed stable bio-oil. n denotes the number of moles of 

 

electrons passed.5 

 As shown in the Figure 2.1, the process involves oxidation of water to yield oxygen gas and 

hydrogen  ions.  This  is  a  non-spontaneous  reaction,  owing  to  a  positive  Gibbs  free  energy. 

Therefore, an external voltage must be applied. On application, water splits up at the anode to 

 

14 

generate oxygen gas and hydrogen ions, which, under the applied electric field, pass through a 

proton  exchange  membrane  (for  eg.,  Nafion  117)  to  be  adsorbed  as  H  atoms  on  the  catalytic 

cathode. The pyrolytic bio-oil is dissolved in a solvent to serve as the catholyte, in a divided cell 

batch  reactor,  as  shown  in  Fig.  2.1.  It  can  alternatively,  be  circulated  past  the  cathode  in  a 

continuous  solid  polymer  electrolyte  (SPE)  reactor24  The  adsorbed  H  atoms  on  the  catalytic 

cathode, will either hydrogenate the adsorbed organic molecules on the cathode or get desorbed to 

evolve  H2  gas.  As  stated  above,  this  is  an  undesirable  side  reaction  that  adversely  effects  the 

performance of the ECH process for bio-oil upgrading. The anodic reactions are shown in Fig. 1, 

and the cathodic reactions are given by reactions (1) to (6). Nafion-117, which may be used as the 

proton  exchange  membrane  is  a  commercial  product  of  DuPont.  It  is  “a  sulfonated 

tetrafluorethylene copolymer, consisting of a hydrophobic fluorocarbon backbone (–CF2–CF2–) 

to  which  hydrophilic  sulfonate  groups  (SO3-)  are  attached”.  It  is  known  for  its  high  proton 

conductivity and much lower conductivity for other cations.65 

Performance Parameters 

The  performance  of  an  ECH  process  can  be  evaluated  by  determination  of  the  following 

performance parameters:24, 32 

1.  Electrochemical efficiency (EE) 

00=$1234567	87$9	35	:$6$4;3$	ℎ=945:$6;3$9	>4598237

 

353;1	$1$234567	>;77$9
?=@51$7	5A	78B734;3$	25678@$9
C6C3C;1	@51$7	5A	78B734;3$  
D=@51$7	5A	9$7C4$9	>459823
353;1	@51$7	5A	>459823  

2.  Conversion (X) 

3.  Selectivity (S) 

 

15 

5.  Production Rate (PR) 

E)=(C∗00)/(2ℎ;4:$	4$H8C4$9	35	A811=	7;384;3$	1	J:	5A	78B734;3$)	 

6.  Power Consumption (PW) 

4.  Yield (Y) 

*=@51$7	5A	9$7C4$9	>459823
C6C3C;1	@51$7	5A	78B734;3$ 

E*=(C∗K)/(E)) 
where C	C7	2844$63	9$67C3= 
K	C7	;>>1C$9	L513;:$ 

High values for EE, X, S, Y, PR and low values for PW are desirable for better performance. 

Operating Parameters 

Operating parameters in an ECH setup play major roles in determining the performance, based on 

the aforementioned performance parameters. The parameters that have been mostly investigated 

are temperature, electrode materials, pH, starting reactant concentration and current density.32, 33 

Effect of Temperature 

Li et al., studied the effects of temperature on the electrochemical efficiency, product selectivity 

and conversion of guaiacol, during its ECH, in a two-compartment reactor that was separated by a 

Nafion-117  membrane.  It  was  found  that  the  electrochemical  efficiency  passed  through  a 

maximum  at  around  50°C.  Below  this  temperature,  electrocatalytic  hydrogenation  was  the 

predominant reaction, and above this temperature, hydrogen desorption was dominant. Conversion 

was found to increase with temperature. Selectivity of cyclohexanol, the desired product, also 

increased  with  increase  in  temperature  and  the  change  was  more  pronounced  during  the 

temperature rise from 50°C to 80°C.32 Green et al., observed an increase in rate of conversion of 

furfural, another model bio-oil compound, with increase in temperature, when ECH of furfural 

 

16 

was carried out in a continuous electrocatalytic membrane reactor. Electrochemical efficiency was 

found to continuously decrease with rise in temperature, and no maxima was obtained.27 Dabo et 

al., however, found a direct relationship of electrochemical efficiency and temperature for the ECH 

of 4-phenoxy phenol.26 

Effect of pH 

It may be expected that higher values of pH are more suitable for hydrogenation reaction since 

lower pH values should ideally favor the undesirable H2 evolution.23 Investigations, however, point 

to the contrary. Robin et al., investigated the effect of pH on the ECH of phenanthrene, using 

Raney Ni electrodes. It was found that an initial pH of 2.6-2.7, with Boric acid as the buffer, gave 

the best results in terms of electrochemical efficiency, yield and conversion of phenanthrene.29 The 

best results for the ECH of furfural was reported at pH value of 7. The conversion, furfuryl alcohol 

yield and selectivity dropped on increasing or decreasing the pH.23 For the ECH of guaiacol, acidic 

conditions were preferred as was indicated by the increase in conversion, cyclohexanol selectivity 

and electrochemical efficiency.32 

Effect of electrodes 

The electrodes used in the ECH reaction, especially the cathode, which acts as the catalyst, plays 

a major role in deciding the performance of the process. For the ECH of furfural in a divided cell, 

Fe and Ni cathodes were observed to be superior to Cu, Al and stainless steel.23 Green et al., carried 

out the ECH of furfural using both Pd/C and Pt/C electrodes and found a much higher current 

efficiency for the Pd/C cathodes.27 For ECH of glucose to sorbitol, however, the most suitable 

catalysts  were  found  to  be  Zn,  Cd  and  other  late  transition  metals,  compared  to  Pt  and  Pd 

electrodes.25 Ru/C catalyst has also been identified as a suitable catalyst for the ECH of model bio-

oil  compounds.25,  29  It  is  important  to  point  out  that  while  non-precious  metals  have  been 

 

17 

demonstrated  to  have  catalytic  activity  for  ECH  of  certain  model  compounds,  they  are 

incompatible with the acidic Nafion membrane in a continuous SPE reactor.61 Therefore, these 

reactors must employ precious metal catalysts like Pt and Pd. 

Effect of current density 

Electrochemical efficiency was found to increase with an increase in current density for the ECH 

of phenanthrene.29 However, for the ECH of furfural, it was found to decrease with increasing 

current  density.23  Santana  et  al.,  found  that  the  electrochemical  efficiency  for  the  ECH  of 

benzaldehyde passed through a maximum as current density was increased.31 These results point 

to  the  fact  that  there  exists  an  optimum  current  density  at  which  the  highest  electrochemical 

efficiency is obtained.23  

Effect of reactant concentration 

Green et al., observed a slight increase, followed by continuous decrease in conversion and current 

efficiency for the ECH of acetone, as the initial concentration of acetone was increased.61 Similar 

results were also obtained for the ECH of furfural.23 This is, however, not expected as increase in 

starting concentration of substrate should ideally increase the electrochemical efficiency. 

It can be inferred that the performance parameters are complex functions of the various operating 

parameters. The optimization of these operating parameters is, therefore, crucial to the efficient 

and economical performance of the ECH process. Li et al., performed such an optimization and 

found that ECH of model bio-oil compounds like guaiacol, syringol and phenol can be performed 

using Ru/ACC catalyst with best performance at temperatures around 80°C and acidic pH.32  

Decentralized processing of biomass at depots 

Biomass  is  a  feedstock  source  that  is  distributed  in  nature  and  hence  renders  the  need  of 

decentralized chemical processes for handling it.66 Being a solid of low energy density, biomass 

 

18 

can incur huge transportation costs if hauled over long distances.22 Therefore, a good strategy 

would be to reduce transportation costs by establishing local depots that densify the biomass.22 

These depots can then supply the densified biomass to a much larger central refinery and take 

advantage  of  economies-of-scale  to  reduce  final  product  cost.  Petrou  et  al.,  recognizes  the 

importance of local depots while studying biomass supply chains.67 Parkhurst et al., studied a 

system where torrefaction was performed in depots to supply the torrefied biomass to a centralized 

combustion facility.68 Chai et al., performed a study to find the optimum depot capacity for the 

comparison between the production of conventional and torrefied pellets at the depot. Bals et al., 

also  studied  the  economic  influence  of  the  incorporation  of  depots  with  different  biomass 

densification  and  processing  technologies.69  These  included  fast  pyrolysis,  ammonia  fiber 

expansion (AFEX) pretreatment and leaf protein processing. They concluded that fast pyrolysis at 

local depots is a slightly profitable venture for corn stover, even without any animal feed sales. 

Quddus  et  al.,  also  investigated  the  application  of  different  densification  technologies  like 

conventional and high moisture pellet processing and ammonia fiber expansion in depots.70  Roni 

et  al.,  found  out  that  a  distributed  supply  chain  design  can  greatly  increase  the  supply  chain 

collection area (~57%), without a rise in the delivered feedstock cost.71 Furthermore, they also 

concluded that the biomass resource and the depot scale can play an important role in determining 

the economics of these distributed supply chains. Lamers et al., established the difference between 

a “standard depot” and a “quality depot”. While a standard depot is responsible for only basic 

biomass supply chain requirements such as increased bulk density and storage, a quality depot 

improves upon the quality of the biomass feedstock.72 These quality improvements may be in the 

form of lower contamination levels or enhanced feedstock intermediates that have higher energy 

content. Jacobson et al., further elaborates on this idea and shows how incorporation of different 

 

19 

technologies  at  depots  can  help  reduce  overall  cost  for  the  process.73  Kim  et  al.,  compares  a 

centralized  cellulosic  ethanol  system  with  a  decentralized  system  and  reports  that  while  the 

centralized system has better economic performance for smaller biorefineries, the depot-based 

system performs better at larger scale biorefineries.74 Crandall et al., investigated the different rural 

benefits of including biomass depots for woody biomass in Western Oregon and concluded that 

they can positively contribute to the rural economy.75 Several other studies have also investigated 

the role of decentralized processing/densification depots in biomass supply chain optimization and 

determined  their  effect  on  process  economics76-78  and  their  environmental  impacts.79,  80  In  the 

present study, the Py-ECH system is a decentralized bioenergy system that combines fast pyrolysis 

of biomass and subsequent electrocatalytic hydrogenation of the  pyrolysis bio-oils to produce a 

stable  denser  bio-oil  intermediate  that  can,  in  turn,  be  hauled  over  long  distances  to  a  central 

hydroprocessing facility to produce a gasoline/diesel like fuel. 

Technoeconomic analysis (TEA) of biomass to fuels 

For the commercial application of any technology, economics can play a vital role. To evaluate 

the  economic  performance  of  any  bioenergy  system,  technoeconomic  analyses  are  performed.  

These analyses combine the process modeling and engineering design with economic evaluation.81 

While the primary objective is to determine the economic feasibility of the process, these can also 

help identify the important bottlenecks of the system and help minimize the cost. For bioenergy 

systems that produce fuel, TEA generally involves determining a minimum selling price for a 

certain  profitability  or  internal  rate  of  return.40,  82-86  TEA  of  any  bioenergy  system  involves 

estimating the capital (equipment costs) and operating costs. These operating costs may be fixed, 

(such as insurance, rent, salaries etc.) that do not vary with production rate, or they may be variable, 

(such  as  raw  material,  utilities  etc.)  that  change  with  production  rate.87  This  is  subsequently 

 

20 

followed  by  profitability  analyses  such  as  profitability  ratio  analysis,  break-even  analysis  or 

discounted cash flow rate of return analysis (DCFROR) to generate a selling price for the fuel. Of 

the three, DCFROR accounts for the time value of money and equates the net present value of all 

cash flows during the lifetime of the biorefinery, to a value of zero. The fixed discount rate that 

achieves this is known as the internal rate of return.88 For the DCFROR analysis, the annual cash 

flow in year i (CFi) may be expressed in terms of the tax rate (rt), the annual revenue (R), the 

annual expenditure (E) and the annual depreciation D as: 

If K-,+ is the present value of the cash flow of a future year i and 4& is the discount rate, then, 

MN+=(1−4,).(P−0)+4,.Q 
K-,+=MN+/(1+4&)+ 

Assuming IT is the total capital cost, IWC is the working capital, Is is the salvage value of the fixed 

capital cost and n is the total plant life, then the net present value (NPV) of the entire investment 

in the plant over its entire lifetime may be expressed as: 

REK=−S/+TK-,+
0
+12

+ S*+S'
(1+4&)0 

The internal rate of return (IRR) may then be defined as the discount rate that results in an NPV 

of zero.88, 89  

In the present study, the TEA of the Py-ECH system was performed using the DCFROR approach. 

The economic assumptions in this approach were considered to be for a nth plant. A nth plant 

assumes that the technology being used in the plants have already been developed and operated 

and therefore,  does not account for any additional pioneer plant costs.90 Although the Py-ECH is 

a novel approach for processing biomass to fuels, the nth plant assumption still works since the 

technologies employed in this system are not new. TEAs have previously been performed for the 

 

21 

thermochemical conversion of biomass to fuels. Shahabuddin et al., performed a comprehensive 

review of production of hydrogen from biomass gasification and pyrolysis. They determined a 

range of $ 2.8-3.4/kg of H2 for the levelized cost of hydrogen, produced from gasification.91 Patel 

et al., in 2016, conducted a review for the TEAs of different types of thermochemical conversions 

for lignocellulosic biomass.92 These included almost all thermochemical methods like pyrolysis, 

gasification,  liquefaction,  co-firing,  carbonization  and  combustion.  Table  2.2  lists  many  such 

TEAs and highlights the feedstock used, the technology employed and the evaluated cost. Most of 

the TEAs listed in Table 2.2 have been performed for technologies like gasification, pyrolysis and 

hydrothermal liquefaction. The feedstock is mostly lignocellulosic biomass like corn stover, poplar 

and woody biomass. TEAs of biomass gasification generally  involved the production of gaseous 

fuels like H2 or syngas, as the first step. These were subsequently followed by an upgrading step, 

that converted this gaseous intermediate to produce a liquid hydrocarbon fuel.93-95 However, TEAs 

that involved pyrolysis of biomass proceeded to bio-oil upgrading via a liquid intermediate.84, 85, 

95, 96 The upgrading technique, too, was almost always hydrotreating, as can be seen in Table 2.2. 

This is highlighted even more by Sorunmu et al., in 2019, who lists numerous TEAs that utilize 

hydrotreating as the designated technique for pyrolysis bio-oil upgrading.97 Moreover, the H2 used 

for upgrading in these studies, is generated either from natural gas, off gases from the process or 

purchased.84,  85,  96  This  is  different  from  the  Py-ECH  system  that  considers  electrolysis  as  the 

source for generation of hydrogen. Wright et al.84 does consider a scenario, where the hydrogen 

used  for  upgrading  is  produced  via  electrolysis.  However,  they  consider  the  production  of 

hydrogen gas, whereas the Py-ECH system involves the generation of in-situ hydrogen ions for 

electrocatalytic  reduction  of  the  pyrolysis  bio-oil.  Most  of  the  TEA  studies  in  literature  also 

consider a single integrated refinery, housing both the pyrolysis and upgrading systems. 82, 84, 85, 96, 

 

22 

98 This is again different from the Py-ECH system that considers a decentralized system, where 

the pyrolysis and electrocatalytic upgrading is occurring in local depots. Lamers et al., does study 

the economics of biomass processing at depots and investigates different depot scenarios with 

different  processing  abilities.72  Jacobson  et  al.,  also  reports  the  economic  benefits  of  the 

decentralized system for processing biomass in local depots.73 Bals and Dale also perform a similar 

kind of economic analysis for comparing different biomass processing technologies at local depots 

for the production of biofuel intermediates or high value co-products.69 However, all these studies 

are only concerned with the economics at the depot and do not consider a full bioenergy system 

for production of gasoline/diesel range fuels. Moreover, none of these consider the combination 

of fast pyrolysis and electrocatalytic hydrogenation at the biomass processing depots, as in the Py-

ECH system. Previous TEAs have also been performed for electrocatalytic systems in fuel cells.99-

101 These are important for estimation of costs for the electrocatalytic hydrogenation of pyrolysis 

bio-oil, because the capital cost for such systems should be very similar. This is owing to the fact 

that the equipment employed for ECH of bio-oil should be very similar  to that for fuel cell systems, 

although the operation is very different. Orella et al., did develop a technoeconomic model for 

evaluating  the  economics  of  the  electrocatalytic  hydrogenation  of    pyrolysis  model  bio-oil 

compounds.102  This  is  however,  restricted  to  only  ECH  and  does  not  account  for  the  cost  of 

previous pyrolysis or subsequent hydroprocessing.  

To  summarize,  while  previous  TEAs  have  focused  on  fast  pyrolysis  and  upgrading  via 

hydroprocessing, none have evaluated the economics of incorporating the ECH unit as a stabilizing 

technique.  Most  of  these  studies  have  also  been  for  integrated  biorefineries  and  have  not 

considered decentralized systems like the Py-ECH system. Therefore, a full scale technoeconomic 

analysis of the Py-ECH system will address this knowledge gap. 

 

23 

Biomass 
feedstock 
Corn stover 

Corn stover 

Woody 
biomass 

Corn stover 

Woody 
biomass 

Corn stover 

Woody 
biomass 

Poplar wood 

Woody 
biomass 

Table 2.2 : Technoeconomic analyses of bioenergy systems involving thermochemical 

conversion of biomass, followed by upgrading. The year mentioned in parenthesis in each entry 

of the “Cost” column denotes the $ year; gge stands for gallon of gasoline equivalent. 
Source 

Technology 

Remarks 

Cost 

Fast pyrolysis to bio-oil 
followed by upgrading 
to naphtha and diesel 

fuel ranges via 
hydrotreating 

Fast pyrolysis to bio-oil 
followed by upgrading 
to naphtha and diesel 

fuel ranges via 
hydrotreating 
Gas Technology 

Institute’s integrated 
hydropyrolysis and 
hydroconversion to 

gasoline/diesel 

Gasification followed 
by catalytic Fischer 
Tropsch synthesis and 
hydroprocessing to 
liquid hydrocarbon 

fuels 

Hydrothermal 
liquefaction and 

hydrotreating to liquid 

hydrocarbon fuel 
Fast pyrolysis and 
hydroprocessing to 
gasoline/diesel  

Catalytic production of 
methanol from syngas 
(from gasification and 
electrolysis of water) 
Gasification to syn gas 
to methanol to gasoline 
using zeolite catalyst 

Mild catalytic 

pyrolysis, followed by 
upgrading to liquid 
hydrocarbon fuel 

$ 3.09/gge 

(2010) 

$ 2.41/gge 

(2010) 

Wright et al.,84 

When 

upgrading H2 is 
produced by 
electrolysis 

When 

upgrading H2 is 

purchased 

Wright et al.,84 

$ 1.68/gge 

(2007) 

Minimum fuel 
selling price 

(MFSP) 

Tan et al.,82 

$ 4- $ 5/gge 

(2010) 

Discounted 
cash flow, rate 

of return 
analysis 

Swanson et 

al.,94 

$ 4.44/gge 

(2014) 

$ 2.57/gge 

(2011) 

€ 11.8-25.3/ 
GJexergy of fuel 

energy  

$ 1.95/gallon 

(2007) 

$ 3.69/gallon 

(2014) 

State of 

technology 

MFSP 

Zhu et al.,103 

MFSP 

Brown et al.,85 

Methanol fuel 

Clausen et 

al.,104 

Philips et al.,93 

Thilakaratne et 

al.,96 

Discounted 
cash flow, rate 

of return 

Hydrocarbon 
fuel composed 
of aromatics, 
cycloalkanes, 
olefins and 
paraffins 

 

24 

Table 2.2 (cont’d) 

Wood chips 

Straw 

Pine wood 

Corn stover 

Gasification to syngas 
converted to methanol 
to acetic acid, followed 
by hydrogenation to 

ethanol. 

Decentralized pyrolysis 
to biosyncrude (bio-oil 
and biochar) which can 

be upgraded to 
synthetic fuels via 
gasification at central 

refinery 

Fast pyrolysis, followed 
by hydroprocessing of 

bio-oil to 

gasoline/diesel 

Pyrolysis, gasification 

and biochemical 

scenarios to liquid fuels 

$ 2.79/gallon 

(2008) 

35 €/MWh of 
fuel energy 

(2010) 

Carbon 

monoxide and 
hydrogen gas 
were purchased 

Biosyncrude 

price 

Zhu et al.,83 

Trippe et al.,105 

£ 6.25/gge 

(2015) 

Electricity co-

produced 

Shemfe et al.,98 

$ 2-5.50/gge 

(2010) 

Fuel price is 
lowest for 
pyrolysis 

Anex et al.,95  

 
Life cycle assessment (LCA) of biomass to fuels 

The  advantage  of  bioenergy  systems  can  be  best  realized  in  regard  to  their  environmental 

performance. Therefore, any evaluation of a bioenergy system is incomplete without quantification 

of  the  environmental  impacts.  Presently,  the  best  tool  for  such  quantification  is  life  cycle 

assessment (LCA).106-108 A LCA can be used to determine the overall environmental impacts of 

any process, subject to the system boundaries selected. In regard to bioenergy systems, a cradle-

to-grave LCA estimates its environmental impacts, starting from acquiring raw material for the 

system to the product end use.106 Any LCA study has four phases, namely109, 110: 

1.  Goal and Scope Definition: During this phase, the goal and scope of the LCA is used to set 

the  system  boundaries  and  the  purpose  of  the  study.  The  functional  unit,  which  is  a 

quantitative measure of the function/use of the product is also decided in this phase 

 

25 

2.  Life Cycle Inventory Analysis: The technical database is created in this phase. In other 

words, the process inputs and outputs, as defined by the system boundaries, are determined 

here. 

3.  Life  Cycle  Impact  Assessment:  This  phase  is  aimed  at  evaluating  the  potential 

environmental  impacts  of  the  system  via  the  calculation  of  several  midpoint/endpoint 

indicators. While endpoint indicators indicate the environmental impact on specific areas 

of protection, the midpoint indicators represent the impact between the emission point and 

the endpoint. The specific impact categories to be investigated are also decided in this 

phase, depending on the LCA goal. 

4.  Interpretation: In this phase of the LCA, the results are from the impact assessment phase 

are interpreted in view of the goal and scope. Decisions or recommendations can be made 

here about the system being studied. 

Previous  LCAs  have  been  performed  for  the  fast  pyrolysis  of  biomass  and  their  subsequent 

hydroprocessing to gasoline/diesel range fuel. Some LCAs have also studied the generation of 

biochar via slow pyrolysis. These studies have been listed in Table 2.3, highlighting the biomass 

feedstock  used  in  the  study  and  the  employed  processing  technology.  In  all  these  studies,  the 

biomass  is  first  subjected  to  pyrolysis  (fast  or  slow),  followed  by  the  presence/absence  of  a 

subsequent upgrading technique. However, none of these studies combined fast pyrolysis with 

electrocatalytic hydrogenation as a stabilizing method, as in the Py-ECH system. Decentralized 

biomass processing has also not been considered in any of these studies barring Peters et al., who 

compared the environmental performance of localized fast pyrolysis and centralized hydrotreating 

to integrated fast pyrolysis and hydrotreating at single refinery.111 They concluded that the relative 

environmental impacts, in regards to the global warming potential and eutrophication potential of 

 

26 

the two scenarios were heavily dependent on the utilization of the by-product biochar generated 

from pyrolysis. Other studies that investigated the environmental impacts of establishing local 

biomass processing depots did not consider biomass pyrolysis itself at the depots.79, 80, 112 In fact, 

in the system studied by Lan et al., pyrolysis occurred at the central biorefinery, whereas the depot 

was concerned with biomass preprocessing  units such as drying, grinding and pelleting.112 

Biomass feedstock 

Table 2.3 : LCA studies of biomass pyrolysis 

Technology 

Short rotation poplar 

biomass 

Lignocellulosic 
energy crops 
Corn stover 

Corn stover, yard 
waste, switchgrass 
Poplar, willow, 
logging residues 
Forest residues 

Municipal solid waste 

Corn stover 

Corn stover 

Hybrid poplar 

Hybrid poplar 

Rice straw 

Pine trees 
Wood waste 

 

Fast pyrolysis, followed by hydrotreating and 

hydrocracking to gasoline/diesel 

Slow pyrolysis to biochar 

Fast pyrolysis, coupled with anaerobic 

digestion 

Slow pyrolysis to biochar 

Pyrolysis to bio-oil to electricity through co-
combustion in conventional power plants 

Fast pyrolysis and hydroprocessing to produce 

gasoline/diesel 

Pyrolysis or gasification, coupled with gas 
turbine and combined cycle to generate 

Fast pyrolysis, followed by hydroprocessing 

electricity 

to gasoline/diesel 

Fast pyrolysis and hydrotreating (using H2 gas 
produced by catalytic reforming of a portion 

of produced bio-oil) to produce 

gasoline/diesel 

Fast pyrolysis at local plant, followed by 

hydrotreating, hydrocracking, distillation and 

steam reforming at biorefinery 

Fast pyrolysis, followed by hydrotreating in 
biorefinery (1)when pyrolysis is integrated 
with biorefinery and (2) when pyrolysis 

occurs in decentralized plants 

Integrated biomass pyrolysis, gasification and 

methanol synthesis 

Fast pyrolysis to bio-oil 

Flash pyrolysis for power generation 

 

Source 

Iribarren et al.,113 

Peters et al.,114 

Righi et al.,115 

Roberts et al.,116 

Fan et al.,117 

Hsu118 

Dong et al.,119 

Dang et al.,120 

Zhang et al.,121 

Peters et al.,122 

Peters et al.,111 

Im-orb et al.,123 

 

Steele et al.,124 

Zhong et al.,125 

 

 

27 

Table 2.3 (cont’d) 
Corn stover 

Palm empty fruit 

bunch 

Corn stover and corn 

Vienescu 126 et al., 

Chan et al.,127 

Kauffman et al.,128 

Pyrolysis followed by bio-oil upgrading via 

different combinations of esterification, 

ketonisation, hydrotreating and hydrocracking 
Fast pyrolysis compared with hydrothermal 

liquefaction 

Corn to ethanol; stover subjected to fast 

pyrolysis, followed by refining to naphtha and 

diesel range fuel 

 
Moreover, in most of the LCAs listed in Table 2.3, emissions associated with cultivation of the 

biomass are not considered. Only a few include cultivation in their system boundaries.114, 117, 122 

Some  LCAs  that  handle  corn  stover  biomass  only  include  the  emissions  associated  with  the 

collection  and  harvesting  of  stover.115,  120,  121,  126,  128  These  studies  omit  the  emissions/benefits 

associating with cultivation of corn, such as soil carbon sequestration and fertilizer  emissions. 

Such  an  omission  is  only  justified  by  assuming  that  corn  stover  is  a  waste  product  of  corn 

cultivation and only affects it via the withdrawal of nutrients in the corn stover removed from the 

fields.  It  is  only  these  nutrients  that  must  be  replenished  through  excess  fertilizer  application. 

However, since cultivation is an integral part of any bioenergy system, such assumptions need to 

be  investigated  further,  especially  for  biomass  feedstocks  like  corn  stover,  whose  cultivation 

boundaries  intersect  with  other  systems  like  corn  grain  to  ethanol  or  corn  grain  as  food. 

Additionally, some LCAs also do not include end use emissions in the analysis, thereby making it 

a cradle-to-plant gate LCA or a farm gate-to-plant gate LCA. 

Therefore, a full scale cradle-to-grave LCA of the Py-ECH system needs to be performed in order 

to address these knowledge gaps and get a full understanding of the environmental impacts of the 

Py-ECH system.  

 

28 

Kinetic modeling of ECH 

As  discussed  before,  the  novelty  of  the  Py-ECH  systems  lies  in  combining  electrocatalytic 

hydrogenation with fast pyrolysis, as a mild stabilization  method for pyrolysis bio-oils. ECH is 

therefore, integral to the Py-ECH system. However, while pyrolysis of biomass is a sufficiently 

mature technology,18, 19, 84, 85, 129 ECH of bio-oil is still in nascent form. It is mostly studied via lab-

scale batch experiments of ECH of model bio-oil compounds.23, 130-132 28 Although ECH of edible 

oils24, 133 and ECH of furfural to furfuryl alcohol27 in solid polymer membrane reactors have been 

reported, most ECH experiments involve 2-compartment divided batch cells. Also, most of ECH 

literature is focused on finding better catalysts for ECH of different model compounds based on 

product yield and electrochemical efficiency.32, 34, 134 In this regard, Laplante et al., found that for 

the ECH of phenol to cyclohexanol, 5%Pd/Al2O3 catalyst performed better in terms of reactant 

conversion and current efficiency than other Pd catalysts with different supports.135 Greater current 

efficiencies and conversion were observed by Lam et al., for the ECH of guaiacol to cyclohexanol 

using Raney Ni electrodes.34 Li et al., discovered even better efficiencies and rates of conversion 

for  the  ECH  of  phenol  to  cyclohexanol  using  ruthenium  catalyst  on  activated  carbon  cloth 

(Ru/ACC).32 From these studies, it may be concluded that Ru/ACC electrode is a better catalyst 

for the ECH of phenol to cyclohexanol. Also, slightly acidic conditions were more favorable for 

the ECH of guaiacol than basic or neutral conditions and the optimum ECH temperature was found 

to be 80°C.32  Very recently, the ECH of phenolic compounds and dimers, in presence of ruthenium 

catalyst (supported on carbon cloth) was performed by Garedew et al.136, 137 Sanyal et al., in 2020, 

investigated the simultaneous ECH of aldehydes and phenol in presence of catalysts like Ru, Rh, 

Pd and Cu, supported on carbon. While such studies provide great insight into the thermodynamics 

of  the  process,  there  is  work  to  be  done  on  the  kinetic  aspect  of  ECH,  which  is  key  for  any 

 

29 

successful scale-up operation.138 Few kinetic studies have been performed previously. Kinetics for 

the ECH of ethylene over Pd black catalyst was studied by Langer et al.139 Anantharaman and 

Pintauro performed the kinetics for the ECH of glucose to sorbitol in a batch slurry reactor and a 

flow through reactor with Raney Ni powder as catalyst.30 Sedighi et al., carried out kinetic studies 

for ECH of ethylene in a polymer electrolyte membrane (PEM) reactor using Ni catalyst.140 A 

mathematical model for the ECH kinetics of phenol was developed by Bannari et al., employing 

Pd/Al2O3 catalyst.141 Song et al. performed preliminary kinetics for ECH of phenol in a divided 

batch cell with Pd/C, Pt/C and Rh/C catalysts130 and also studied the kinetics for the ECH of 

benzaldehyde to benzyl alcohol over the same catalysts.142 Singh et al., also investigated the effect 

of temperature on the ECH rates of phenol on Pt and Rh catalysts.143 Sherbo et al., studied the 

kinetics  of  electrocatalytically  reducing  phenylacetylene  to  ethyl  benzene  in  a  Pd  membrane 

reactor.144 However, the kinetics of ECH of model bio-oil compounds using Ru/ACC have not 

been performed. This presents a knowledge gap that needs to be addressed. A good starting point 

would be to investigate the kinetics of ECH of phenol. This is because phenol is one of the simplest 

bio-oil model compounds, whose ECH in presence of Ru/ACC has already been studied.136   

This kinetic modeling study for the ECH of phenol over Ru/ACC may be aimed at estimating the 

kinetic rate constants of all related electrochemical, adsorption/desorption and surface reactions, 

while also accounting for mass transfer effects. 

 

 

 

 

 

 

30 

Chapter 3 : PY-ECH SYSTEMS ANALYSIS: DEVELOPMENT OF MASS, ENERGY 

AND CARBON FLUX 

(Reproduced directly from Lam, Das, Erickson, Hyzer, Garedew, Anderson, Wallington, Tamor, 

Jackson, Saffron, Sustainable Energy & Fuels, 2017); https://doi.org/10.1039/C6SE00080K 

Abstract 

The carbon efficiency of bioenergy systems is of critical importance in discussions pertaining to 

biomass availability for the displacement of petroleum. Classical carbohydrate fermentations to 

make simple alcohols are carbon inefficient as they discard 1/3 of biomass holocellulose as CO2 . 

Biomass' lignin is typically burned for heat and power instead of liquid fuel, discarding another 

sizeable fraction of the biomass carbon. Carbon is the backbone element in hydrocarbon fuels and 

these losses limit full utilization of the carbon captured by photosynthesis. The DOE Billion-ton 

Study Update optimistically projects enough biomass carbon to cover 2/3 of the estimated fuel 

usage in the transportation sector by 2030. Fast pyrolysis combined with electrocatalytic energy 

upgrading  using  renewable  electricity  offers  a  more  carbon-retentive  pathway  for  biomass  to 

renewable  fuels.  This  fast  pyrolysis-based  sequence  offers  the    added    benefit      of      fixing   

atmospheric   carbon   in   the   form  of   biochar,   which  provides  a mechanism for long-term 

carbon storage. An associated challenge is that the liquid “bio-oil” from biomass fast pyrolysis 

contains  functional  groups  like  carboxylic  acids,  carbonyls,  and  oxygenated  aromatics.  Their 

presence hinders the storage and transportation of bio-oil. We propose a potential solution with 

localized electrocatalytic hydrogenation as an immediate measure to stabilize bio-oil via oxygen 

removal  and  carbonyl  saturation.  Electrocatalytically  stabilized  bio-oil  can  be  stored  and/or 

transported to centralized refineries for further upgrading. Compared to microbial bioconversion, 

the strategy proposed here enables significantly higher yields of renewable hydrocarbon  fuels and 

 

31 

offers a large-scale mechanism for chemical storage of renewable  but  intermittently  generated  

electrical  energy as transportation fuel. 

Introduction 

It  is  now  widely  recognized  that  drastic  reductions  in  CO2  and  other  greenhouse  gas  (GHG) 

emissions from human activities will be necessary to avert serious environmental, economic and 

social  dislocations  due  to  climate  change.  Reduction  in  CO2  emissions  from  transportation  is 

significantly more challenging than from stationary applications simply because mobile vehicles 

must carry their energy supplies with them. This places a large premium on specific and volumetric 

energy density, where liquid hydrocarbons reign. For example, gasoline carries almost 47 times 

more energy per unit mass (44.4 vs. 0.95 MJ kg-1  on  a  higher  heating  value  (HHV)  basis)  than  

the  most advanced commercially available lithium battery,145 and approximately five times that 

of liquid hydrogen when containers and insulation are included.146 These characteristics, along 

with compatibility with existing infrastructure and vehicles, make   a renewable liquid hydrocarbon 

fuel  highly  desirable.  U.S.  annual  energy  consumption  for  transportation  is  estimated  to  be 

approximately 29 EJ for the year 2015.147 With a transportation energy consumption growth rate 

of <0.1% per year for the years 2012–2040,148 this value is unlikely to change much until 2030. 

Considering a specific energy of 48 MJ kg-1 (HHV), this amounts to about 0.6 billion tonnes of 

petroleum. The projected U.S. annual harvestable biomass by 2030 is estimated optimistically to 

be 1.04 billion dry tonnes,149 carrying only 21 EJ of energy, which would still not satisfy the energy 

demand for transportation (Fig. 3.1). Assuming simple empirical formulae for hydrocarbon fuel 

(CH2 ; carbon ¼ 86% of mass 14) and biomass (CH2O; carbon ¼ 40% of mass 30), it can be seen 

that the carbon content in the biomass feedstock falls short of that needed to meet the hydrocarbon 

fuel needs of U.S. transportation (Fig. 3.1).  

 

32 

In this regard, it must be noted that typical processes for biofuel production can only utilize a 

fraction of the biomass carbon. This carbon loss is illustrated by considering cellulosic ethanol 

production. Though biological and thermochemical processes to convert biomass to ethanol have 

seen impressive advances in recent years,12 their intrinsic stoichiometry loses at least 1/3 of the 

starch/sugar  carbon  as  CO2,  while  concentrating  the  feedstock's  energy  content  into  a  smaller 

fraction of the starting mass. Most of these biofuel strategies are also powered via combustion of 

part of the biomass itself, discarding additional CO2. 

 

Figure 3.1 : Comparison of the annual energy balance in the U.S. transportation sector and 
projected harvestable biomass. Projections are made based on the available biomass by 2030 
(U.S. Billion Ton Study 2016). The specific energy for petroleum is 48 MJ kg-1 and for dry 
biomass is 20.6 MJ kg-1. Energy is presented in exajoules (EJ), equivalent to 1018 J. Mass is 
in units of a “billion tonnes”, equivalent to 1012 kg. 

 

However, the carbon quantity supplied annually from biomass is already below that consumed in 

fuels. The key limiting factor must then be recognized as carbon, rather than the energy content of 

the feedstock. Biomass to fuel conversion should thus aim to maximize  carbon  retention.  This  

makes    a  fuel  process  that  combines  biomass  and  renewable  electricity  particularly  attractive. 

 

33 

Considering biomass carbon as a limited resource, biomass-to-biofuel conversion strategies that 

augment energy content and minimize carbon loss are imperative. One example consistent with 

this approach is fast pyrolysis followed by electrocatalytic hydrogenation (ECH). Biomass can be 

liquefied through fast pyrolysis, a thermal process characterized by rapid heating in the absence of 

oxygen, to produce bio-oil.19, 36 The bio-oil composition is complex and varies with feedstock. It 

typically contains a great variety of lower carbon number organic compounds such as aldehydes, 

ketones,  carboxylic  acids,  aromatics,  and  about  20  wt%  water.  As  a  result  of  these  reactive 

oxygenated moieties, bio-oil has a low energy content (comparable to the starting biomass), and 

is  vulnerable  to  polymerization,  increasing  viscosity,  even  under  ambient  temperature  storage 

conditions. Hydrodeoxygenation (HDO) of bio-oil, as used for petroleum upgrading, is of great 

interest as a strategy to achieve deoxygenation and stabilization of bio-oil. Such a practice could 

potentially  take  advantage  of  the  existing    petroleum    infrastructure.19,  51,  150-160  However,   

transportation of biomass to a distant centralized location for pyrolysis and upgrading would be 

costly and energy inefficient on a large scale. Further, the aforementioned instability of untreated 

bio-oil makes such transport problematic. So far, two common strategies, organic solvent dilution 

and low temperature storage, have been proposed to improve the bio-oil storage life.161 Neither of 

these approaches is economically applicable on a large scale. Bio-oil pipelines would require acid-

resistant  coatings  to  prevent  corrosion  of  metal  surfaces,  but  these  would  not  address  bio-oil 

stability  and  thus  would  only  be  viable  for  on-site  or  short  distance  transport.162  Ground 

transportation of untreated bio-oil would require costly stainless steel containers and refrigeration 

systems. One alternative is to build a small-scale hydrogenation reactor at the pyrolysis site that 

would stabilize bio-oil for transportation. However, the costs of small-scale H2 production are 

undesirably high and the need for flammable/explosive gas handling poses safety concerns. With 

 

34 

a  simpler  and  safer  alternative,  electrocatalytic  hydrogenation  (ECH)  is  proposed  as  a  mild 

upgrading approach that could be implemented practically as an immediate stabilization strategy 

local to the biomass pyrolysis sites. These regional biomass processing depots,22 with facilities for 

pyrolyzing biomass and stabilizing the resultant bio-oil via ECH will henceforth be referred to as 

Biomass Upgrading Depots (BUDs).  

Any strategy for direct biomass-to-fuels conversion is fundamentally a solar energy and carbon 

capture scheme. Plants are naturally evolved as atmospheric CO2 collectors and do so with very 

low  fixed  and  operating  cost.  However,  while  excellent  at  fixing  carbon,  plants  are  relatively 

inefficient  at  capturing  the  sun's  energy  and  storing  it  in  chemical  form,  with  no  evolutionary 

driving force to store more energy than needed for growth and reproduction. Most plants in nature 

have photosynthetic efficiencies of approximately 1–2%; with 98–99% of the incident solar energy 

lost  to  optimal  wavelength  mismatch,  respiration,  reflection  and  transmission.6,  7  Furthermore, 

biomass has only about one third of the specific energy of liquid hydrocarbon fuels; therefore, 

unmodified plant matter is energetically unsuitable as transportation fuel. Human technologies can 

capture solar energy much more efficiently than plants, but they do so in the form of electricity 

which is difficult to store. Commercially available lower-end photovoltaic cells, mostly made of 

single crystalline silicon, have an energy efficiency of 12–15%.163 In other words, even the oldest 

form  of  photovoltaic  device  on  the  market  still  captures  solar  energy  at  least  ten  times  more 

efficiently than plants. Wind electricity  is  also   increasingly  cost   effective.164   It  is a national 

goal  to  replace  fossil  fuels  (petroleum  oil)  with  renewable  fuels,  ideally  liquid  fuels.  Using 

renewable  electricity  to  raise  the  energy  content  of  biomass  increases  carbon  utilization, 

maximizing yields of biomass-derived fuels. In fact, Clausen has demonstrated that fuel yields per 

unit biomass fed in a thermochemical biorefinery do indeed increase when energy is added via 

 

35 

electrolysis.165  However,  that  strategy  uses  H2  gas  from  water  electrolysis  in  conventional 

hydrodeoxygenation  at  relatively  high  temperatures  and  pressures.  In  contrast,  the  direct 

electrocatalytic  reduction  scheme  (at  the  BUD)  outlined  in  this  study  avoids  the  use  of  high 

pressures or H2 gas and operates at near-ambient conditions. It can be argued that for renewable 

fuel production purposes, ECH is a promising strategy to achieve bio-oil stabilization because (a) 

it operates at mild conditions; (b) it avoids the storage and use of H2 gas; (c) the chemisorbed 

hydrogen density and reactivity on the cathode surface can be controlled by current density; and 

(d) most important of all, ECH reduces H2 consumption during on-site petroleum-style hydro-

upgrading.32, 34 Furthermore, due to a mismatch between the times of human power demand and 

the cycles of light and wind availability, renewable electric power generating capacity may go 

unused at some times; energy upgrading of biomass-derived fragments via ECH thus represents a 

much needed time-insensitive method of using and storing alternative energy. Recently, it was 

demonstrated  that  the  aqueous  fraction  of  bio-oil  can  be  stabilized  via  ECH.23  The  resulting 

stabilized  bio-oil  contained  significantly  greater  alcohol  content  and  was  resistant  to 

polymerization and aging.132 

Methods 

The  ECH  scheme  discussed  in  the  preceding  sections  is  a  mild  technique  to  stabilize  bio-oil 

resulting from fast pyrolysis of biomass feedstocks. The stabilized bio-oil can then be transported 

 to  more  centralized  petroleum  refinery  complexes  for  further  upgrading.  ECH  reduction  is 

therefore only one part of the entire biomass conversion process. In this context, it is important to 

adopt a holistic approach that studies the results of implementing ECH in the overall biomass 

conversion process. This study models a complete biomass conversion that incorporates ECH for 

mild upgrading. Henceforth referred to as the Py–ECH process, it starts from the grinding and 

 

36 

drying  of  the  raw  feedstock  and  ends  with  the  production  of  a  gasoline-like  fuel.  The  major 

processes employed are biomass fast pyrolysis (BFP), followed by ECH and hydroprocessing.  

Detailed descriptions of each of these processes, along with model assumptions, can be found in 

the Appendix at the end of this chapter. The values obtained from the developed spreadsheet model 

along with all necessary sources have also been listed in the Appendix. The Py–ECH approach is 

then  compared  to  a  cellulosic  ethanol  (CE)  process  for  the  biochemical  conversion  of 

lignocellulosic  feedstock  to  ethanol  in  terms  of  mass,  energy  and  carbon  fluxes  through  each 

system. The values for these fluxes were extracted from a NREL report published in 2011.11 All 

necessary values were extracted and then scaled for a fair comparison. Both sets of extracted and 

scaled values can be found in Tables 3.13–3.17 in the Appendix. For comparing Py–ECH to CE, 

corn stover was chosen as the biomass feedstock for each process, starting with the same higher  

heating  value  (HHV,  16.498  MJ  kg-1  wet  weight)  and moisture content (20 wt%). The biomass 

feed rate was fixed at 1.0 billion dry tonnes per year for both systems, which approximates the 

predicted available biomass in the United States, by the year 2030, that could be produced at 

approximately $60 per ton.148 All the energy calculations for the Py–ECH process were performed 

on a higher heating value (HHV) basis. The reported energy values are a summation of the HHVs 

at the reference state and the sensible and latent heats required to attain the stream conditions, 

starting from the reference state. The reference state used for the calculations was 25°C and 1 bar 

pressure. 

Renewable hydrocarbons by fast pyrolysis, electrocatalysis and hydroprocessing (Py–ECH)  

The Py–ECH approach is a thermochemical and electrochemical biomass conversion technique 

that creates stabilized bio-oil, biochar and hydrogen gas from corn stover. The primary processes 

involved  are  fast  pyrolysis,  electrocatalytic  hydrogenation  (ECH)  in  BUDs  and  a  subsequent 

 

37 

hydroprocessing step in petroleum refinery complexes. Fast pyrolysis produces the unstable liquid 

bio-oil product that is subsequently stabilized by the ECH process in the BUD. This stable bio-oil 

is  then  transported  to  the  petroleum  refinery  complexes  to  be  further  hydrogenated  at  high 

temperature and pressure to yield the upgraded fuel. Both the ECH and the hydroprocessing steps 

use renewable electricity to generate the H2 for hydrogenation. A detailed description of the entire 

process, along with all necessary assumptions and calculations can be found in the Appendix. 

Cellulosic ethanol (CE) by fermentation 

The CE approach considered in this study follows the process described in detail in NREL's 2011 

report.11 According to a classification approach adopted by Cherubini et al., the CE process can be 

best described as a three-platform (hydrolysate, lignin, and bio-gas) biorefinery for ethanol and 

electricity from corn stover.166 The major processes involved in the CE approach are pretreatment, 

hydrolysis, fermentation, distillation, aerobic digestion and anaerobic digestion. The major product 

is  ethanol;  electricity,  which  comes  from  incinerating  the  non-fermentable  components,  is 

generated  as  a  by-product.  In  fact,  the  energy  requirement  of  the  entire  process  is  more  than 

satisfied by this generated electricity. The entire design for the CE process has been performed by 

NREL using Aspen software. Therefore, for our study, all data required for the analyses were 

extracted from this report. For equal comparison, all the energy, mass and carbon data for the CE 

process were scaled to accommodate 1 billion tonnes of biomass as is processed by the Py–ECH 

system. 

Results 

The energy analyses for the two renewable fuel production  systems are summarized in the Sankey 

diagrams presented in Figure 3.2. Balances for each system component were performed for the 

Py–ECH system, while overall balances were performed for the CE system. The analysis shows 

 

38 

Figure 3.2 : Sankey diagrams for energy flow. (a) Pyrolysis–electrocatalytic hydrogenation (Py–ECH) energy analysis. Yield: 

89%; (b) CE energy analysis. Yield: 44%. 

 

 

39 

that the fuel energy produced by the two systems is markedly different, as almost twice as much 

liquid fuel energy is produced by Py–ECH than by CE using the same biomass input. During CE 

production,  the  energy  content  in  the  primary  fuel  stream  decreases  in  every  stage,  especially 

during fermentation. In the end, the amount of energy (HHV) available in the product ethanol is 

44% of that in the starting biomass. In contrast, the Py–ECH system involves a more modest energy 

loss  through  bio-oil  production  but  then  regains  much  of  that  energy  during  ECH  and 

hydroprocessing, ultimately yielding hydrocarbon fuel products with approximately 89% of the 

biomass energy. 

These differences in fuel energy yield are explained by differences in electrical power utilization, 

heat losses, and excess electricity output. The Py–ECH process is a net consumer of electricity, 

while the CE process generates electricity. In order to neutralize the advantage of excess electricity 

produced in the CE process, a similar electrical power output of 0.979 EJ was added to the Py–

ECH  process  (as  seen  in  Figure  3.2a).  This  was  done  by  raising  the  electrical  power  input 

requirement of the Py–ECH process by an equivalent amount. The most noticeable difference 

between the two systems is the 10.9 EJ of electricity required by the Py–ECH system, while no 

electrical energy is needed by the CE system. As discussed earlier, this electrical energy input 

actually benefits the Py–ECH system, as renewable electricity is converted into chemical energy 

that is stored in the resultant fuel. Net heat losses explain most of the difference, with losses from 

the Py–ECH process being approximately half of the heat loss from the CE process. No overall 

heat input is required for the CE process (due to heat integration) or at the BUD for the Py–ECH 

process as sufficient heat can be provided internally by combustion of the co-product biochar, non- 

condensable gases (NCG), and H2 gas. At the central refinery, the stable bio-oil can be preheated 

using the heat from the outgoing hot water vapor stream. This approach reduces the heat input 

 

40 

required, and subsequently minimizes external fuel combustion required at the central refinery. 

Additional process modifications to improve fuel energy yield are possible. As seen in Figure 3.2a, 

the combustion energy of the NCG and the H2 streams (together indicated by RH in Figure 3.2a) 

is more than sufficient to support the entire heat requirement of the BUD. In fact, the excess energy, 

after  compensating  for  the  heat    requirement,  can  also  be  employed  to  reduce  the  electricity 

requirement of the process. When not combusted, the co- product biochar can be used in a number 

of ways,38 including land application for carbon sequestration. Use of the molecular hydrogen 

coproduct,  generated  during  ECH,  to  further  hydrogenate  bio-oil  in  depots  should  also  be 

considered, though the current analysis only utilizes some of its combustion energy for heat. 

The CE approach is also expected to be more sensitive to the biomass type, specifically as this 

relates to lignin content. Fuel energy yield for woody biomass, which typically contains greater 

lignin content, would be less than for corn stover because energy from the lignin does not become 

liquid fuel in the CE approach but does in the Py–ECH approach. 

The mass balance analyses reveal that the expected Py–ECH fuel mass yield of 38% (Figure 3.3a) 

is greater than the CE fuel mass yield of only 26% (Figure 3.3b). Biochar and NCG formation 

account for a mass loss of about 30% as this is not converted into liquid fuel. The overall mass 

yield for Py–ECH is limited by the high oxygen content of biomass, much of which is converted 

to water during hydroprocessing.  In the CE process  shown in  Figure 3.3b, a smaller fraction of 

the  feedstock  is  converted  into  ethanol  as  lignin  is  burned  for  heat  and  power  and  1/3  of  the 

carbohydrate carbon is converted into carbon dioxide. A striking characteristic of Figure 3.3b, is 

the significant water requirement in the process. Much of the incoming water can be attributed  to  

process  water  consumption,  mostly  lost  during  cooling  water  evaporation.  The  exiting  water 

stream is larger than  the incoming stream as water is generated in the combustor, where the lignin 

 

41 

Figure 3.3 : Sankey diagrams for mass flow. (a) Pyrolysis–electrocatalytic hydrogenation (Py–ECH) mass analysis. Yield: 38%; 
(b) CE mass analysis. Yield: 26%. 

 

42 

 

is combusted. A  more detailed analysis can be found  in the Appendix. For Py–ECH, much less 

water is likely to be used in the process. Water consumption in the BUD can be attributed to the 

ECH unit, which splits water to create hydronium ions that  are then recombined with electrons 

and  incorporated  into  the  bio-oil  and  generated  H2.  However,  the  total  water  consumption  is 

reduced  after  combining  the  hydroprocessing  step,  as    the    water  vapor  generated  during 

hydroprocessing may  be condensed for heat integration. The air required in the Py–ECH process 

for  combusting  the  NCG  and  H2  for  heat  integration,  along  with  the  subsequent  products  of 

combustion  have  been  excluded  from  the  analysis  shown  in  the  Sankey  diagram.  However, 

including them does not dramatically change the results as shown in Table 3.12 in the Appendix, 

which depicts the mass balance that includes air and stack gases. Generally,  the mass balance 

analyses show that the two systems manage matter in greatly different ways. Py–ECH loses mass 

as biochar, which can be used to mitigate climate change, while CE loses carbon as CO2, itself a 

climate gas.  

The  carbon  balance  analysis  provides  additional  insight  into  the  fuel  product  yields  discussed 

previously. In Py–ECH, 63% of the feedstock carbon is retained in the liquid fuel product, while 

only 30% of feedstock carbon is converted to ethanol by CE as shown in Figure 3.4. In fact, for 

the CE process, most of the carbon is lost as CO2 from the combustion flue gas stack. The retention 

of carbon in the final fuel is important as the carbon–carbon and carbon–hydrogen bonds are the 

energy carriers in hydrocarbon fuels.167 In the case of CE, corn stover is considered an optimistic 

case for high ethanol yield when compared to other likely feedstocks. Though not the composition 

used in this study, dried corn stover can contain as much as 81.9% of fermentable holocellulose 

(comprised of cellulose and hemicellulose) and 14.4% of non-fermentable lignin.168 Further, the 

highest sugar recovery, by acid hydrolysis, has been reported to be 98.4%.169 Regardless, 

 

43 

Figure 3.4 : Sankey diagrams for carbon flow. a) Pyrolysis–electrocatalytic hydrogenation (Py–ECH) carbon analysis. Yield: 63%; 

(b) cellulosic ethanol (CE) carbon analysis. Yield: 30%. 

 

 

44 

fermentation converts glucose to ethanol at the expense of liberating 1/3 of the carbon as CO2 , 

which constrains the final ethanol yield accordingly. Py–ECH also loses a portion of carbon as 

non-condensable gases (NCG) and char. However, the NCG (about 12%) released can be utilized 

for heating. The char, on the other hand, which accounts for about 25% of the biomass carbon, is 

a useful by-product that may be densified to form a renewable solid fuel or land applied as a soil 

amendment.170 The liquid bio-oil, with the majority (about 63%) of the carbon, can be stabilized 

via  a  localized  ECH  system.  Through  ECH,  electrical  energy  is  stored  in  the  newly  formed 

chemical  bonds  in  the  upgraded  bio-oil,  increasing  its  energy  density  and  bridging  renewable 

electricity with liquid fuel. The partial removal of bio-oil's oxygen improves its storage stability 

and increases its energy density, which enables and improves the efficiency of transporting bio-oil 

to distant refineries.132 Once there, conventional H2 upgrading of stabilized bio-oil to fuel becomes 

more economically feasible as a result of the locally available hydrogen and improved stability. 

Figure 3.5 : Energy, mass and carbon yield comparison of CE and Py–ECH. The energy yield 
is significantly higher because of addition of electrical energy. 

 

 
In summary, Py–ECH is superior to CE in terms of energy yield, carbon yield and mass yield of 

the final liquid fuel product, as shown in Figure 3.5. The difference in energy yield is primarily a 

result of upgrading bio-oil using renewable electricity and hydrogen that is derived from renewable 

 

45 

electricity. Although fast pyrolysis initially discards almost 31% of the biomass energy and 37% 

of  the  biomass  carbon  as  biogas  and  char,  the  lost  energy  is  mostly  replaced  as  hydrogen 

incorporated during electrocatalysis and hydroprocessing – a key step of energy upgrading that is 

not present in the CE scheme. About 75% of the biomass energy, lost during pyrolysis, is recovered 

after the ECH and hydroprocessing steps. This is because both these processes add electrolytic 

hydrogen  to  the  pyrolysis  bio-oil  and  the  low  molecular  weight  and  high  specific  energy  of 

hydrogen serves as an ideal upgrading ingredient. Further details of the quantitative analysis of 

hydrogen formation and its incorporation into bio-oil can be found in the Appendix. Stabilized 

bio-oil gains little mass while dramatically increasing in energy content and stability relative to 

raw  bio-oil.  Fuel  production  from  stabilized  bio-oil  using  hydrodeoxygenation  requires  less 

hydrogen  than  raw  bio-oil  as  significant  hydrogenation  has  already  occurred  through 

electrocatalysis. In other words, the stabilized bio-oil, leaving the BUD, is upgraded in energy 

content, relative to the raw bio-oil. In fact, as seen in Fig. 3.2a, about 71% of the total upgrading 

is already achieved by ECH before leaving the depot. Further, if the electricity is derived from 

renewable  sources,  then  the  entire  energy  content  of  the  fuel  would  have  been  derived  from 

renewable  sources.  Although  electrocatalysis  has  limited  ability  to  deoxygenate  bio-oil  when 

compared  to  traditional  hydroprocessing,  the  energetic  and  economic  value  of  bio-oil  can  be 

augmented  substantially  under  mild  conditions  by  electrocatalysis  after  pyrolysis  in  regional 

biomass upgrading depots. 

Discussion and Outlook 

Py–ECH systems appear to be advantaged in terms of energy, mass and carbon balances, though 

further work is certainly needed to assess the economics and environmental impacts associated 

with this hydrocarbon fuel strategy. Although the Py– ECH process produces less carbon dioxide 

 

46 

than the CE process, a full life-cycle assessment is needed to ascertain its environmental benefits 

with  and  without  biochar  land  application.  It  is  noteworthy  that  climate  change  potential  is 

significantly  reduced  when  co-product  biochar  is  land  applied.41  In  addition  to  reversing  the 

greenhouse  effect,  biochar  has  a  range  of  potential  applications,38,  171  including  pollutant 

remediation40  or  stationary  power  production,  although  its  optimum  usage  depends  on  many 

factors that are location specific. Biomass upgrading depots are integral features of the Py– ECH 

concept.  For  commercial  adoption,  the  number,  size,  and  design  of  these  depots  should  be 

optimized to balance the logistical advantage of depots with the economies of scale of a large 

refinery complex. Real geographical landscapes must be considered when deciding where depots 

should be located in relation to feedstocks and centralized facilities. Therefore, technoeconomic 

and life-cycle analyses, with system-wide optimization, should be conducted.69  Until direct air-

capture  of CO2 and subsequent reduction processes are proven economically viable at very large 

scale, carbon efficient bioenergy systems may prove essential to meeting future demand for low-

fossil  carbon  transportation  fuel.  As  ECH  of  bio-oils  is  a  nascent  technology,  its  technology 

readiness level will need to elevate before it is ready for commercial application. In this regard, 

bench  and  pilot-scale  studies  of  ECH  reactor  conditions,  such  as  optimization  of  temperature, 

catalyst  activity,  catalyst  reusability,  current  density,  residence  time,  and  proton  exchange 

membrane  properties  must  be  performed  to  maximize  economic  viability.23,  132  A  similar 

progression of experimentation is needed to convert post ECH bio-oil into finished hydrocarbons, 

presumably by catalytic hydroprocessing. Finally, the finished fuel should possess comparable 

energy content and fuel properties to commercial hydrocarbon fuels (e.g. gasoline, diesel, and jet 

fuel) and a robust analytical characterization will be needed to determine the quality of such fuels. 

 

47 

Conclusions 

The incorporation of hydrogen and electricity from renewable sources, such as wind and solar 

energy, significantly improves bio-oil's potential as a sustainable alternative fuel. In this analysis, 

carbon retention in the Py–ECH scheme is twice that of producing cellulosic ethanol. The process 

also  consumes  significantly  less  water  and  thus  the  distributed  Py–ECH  process  offers  many 

system-level benefits. At the scale of this analysis, synthesis of fuel would use very roughly as 

much electrical energy as all existing electricity consumers, while the inventory of precursors and 

fuel products equates to an energy storage system several orders of magnitude larger and cheaper 

than any that could be assembled from electrochemical batteries. The controllable electrical load 

of these distributed conversion plants may play an essential role in managing the electric grid with 

its growing fraction of variable renewable generators. The synergies between carbon efficient use 

of biomass, familiar liquid hydrocarbon fuel and renewable electricity may be the foundation of a 

future fossil-carbon-free energy economy. 

 

48 

APPENDIX 

 

49 

This section details the calculation methods used to determine the values depicted in the Sankey 

diagrams for both the Py-ECH and the CE processes.  

Py-ECH Process 

Table 3.1 : Label Definitions for the Process Flow Diagram 

Label 

A 
B 
C 
D 
E 
F 
G 
H 
I 
J 
K 
L 

 

Process 
Grinding 
Drying 

Fast Pyrolysis 
Condensation 

Electrocatalytic Hydrogenation 

Fuel Storage 

Fuel Transportation 

Electrolysis 

Hydrogen Compression 

Hydroprocessing 
Biochar Storage 
Hydrogen Storage 

Overview of the Process: 

For the Py-ECH process, as shown in Figure 3.6, harvested biomass feedstock is first milled in a 

series of grinders (A) and then dried in a rotary dryer (B) to a moisture of 5%. The ground dry 

biomass (BM) is then fast pyrolyzed in a screw reactor.  The biochar (BC) is collected, while the 

pyrolysis vapors (PyV) are condensed in a condenser (D) to produce the primary product bio-oil.  

The  non-condensable  gases  (NCG)  may  be  used  for  heating  purposes.  The  bio-oil  (BO)  is 

subsequently  fed  into  the  electrocatalytic  hydrogenation  (ECH)  unit,  wherein  it  is  chemically 

reduced  to  produce  the  stable  bio-oil  (SBO).    The  ECH  unit  (E)  is  considered  to  be  a  2-

compartment  electrolytic  cell,  separated  by  a  proton-exchange  membrane.  The  mildly 

hydrogenated product from E is then transported to a petroleum refinery, where it is upgraded to a 

gasoline-like fuel.  This upgrading occurs in a hydroprocessor unit (J) under more severe operating 

conditions.  The compressed hydrogen gas required for the process is provided by the electrolysis 

 

50 

of water in a local electrolyser unit (H). The product from the hydroprocessor unit is referred in the following sections as upgraded bio-

oil (UBO).  

Figure 3.6 : Process Flow Diagram for Py-ECH Process 

 

 

Feedstock: 

To compare the Py-ECH process to NREL’s CE process, the biomass feedstock selected was corn stover.  The empirical formula was 

determined from the ultimate analysis data for corn stover.84  The extracted mass percentages were normalized to exclude all elements 

but carbon, hydrogen and  oxygen. Feedstock moisture content was reduced from 20% for the raw biomass to 5% for the ground, dried 

biomass.

 

51 

The feed rate of biomass was assumed to be 1.0 billion dry tonnes/year,  which is approximately 

the entire biomass potential of the United States, in 2030, that can be produced at $60/dry ton or 

less.172  The higher heating value of corn stover was evaluated from ultimate analysis data.84 The 

feedstock data have been summarized in Table 3.2. 

Table 3.2 :  Feedstock Data for Py-ECH 
Corn Stover 

Feedstock 
Empirical Formula 
Raw biomass moisture % 
Dried biomass moisture % 
Dried biomass HHV 
Dried biomass feed rate 

!"!.#!$#%.&!# 

20% 
5% 

18.860 MJ/kg 

1.050 Billion tonnes/year 

 

The mass and energy balances for all primary unit operations involved in the Py-ECH process 

were performed as described in the next section. 

Major Processes: 
 

1.  Calculation Methods 

A.  Mass Balances: The mass values for each stream were reported in units of Billion 

tonnes/yr. 

B.  Carbon Balances: The carbon values were reported in units of Billion tonnes C/yr. 

C.  Energy Balances: The energy values were reported in units of EJ/yr. The energy 

balances were performed on a higher heating value basis. This implies that the energy 

value of each stream had three components: 

a.  The higher heating value of the stream at the reference state. 

b.  A sensible heat component to raise the temperature of the stream from the 

reference state temperature. 

c.  A latent heat component for any change of phase of all or part of the stream. 

D.  Reference State: The reference state was selected to be 25°C and 1 bar pressure. 

 

52 

E.  Higher  Heating  Values:  The  higher  heating  values  (HHV)  required  for  making  the 

energy balances, were calculated using the Gaur and Reed formula:173 

!!"=(0.3491+!)+(1.1783+")+(0.1005+#)−(0.0151+$)

−(0.1034+%)−(0.0211+&'() 

where Xi’s = mass percentage and HHV is in MJ/kg. 

The  mass  percentages  were  determined  from  ultimate  analysis  data  provided  in  the 

literature.  On certain occasions however, the HHV values were directly extracted from 

literature. 

a)  Thermophysical Properties: The thermophysical properties like specific heat and latent 

heat of vaporization were extracted from the NIST database in Aspen or were estimated 

using Group Contribution Theory.174-178  

b)  Sample  Calculation:  A  sample  calculation  is  shown  below  for  the  energy  balance 

following equation: 

around  the  pyrolysis  reactor.    The  fast  pyrolysis  reaction  can  be  modelled  by  the 

	%&'()**	→,!	-./'0.*&*	1)2'/*+,$	%&'4ℎ)/+,'	!#+,#	"$+,(	!#$ 

where Ni refer to the stoichiometric coefficients. 

Win(Work)

Qin(Heat)

Biomass

NCG
Pyrolysis vapors
Biochar
Figure 3.7 : Process Flow Diagram for Pyrolysis of Biomass 

Pyrolysis Reactor

 

 

For the reactor shown in Figure 3.7, the energy balance is given by: 

 

53 

()*ℎ+,)*% +6-.+7-.
=(/019ℎ+,/01
+:ℎ/01−ℎ/01,2<=+()39ℎ+,)3% +:ℎ)3−ℎ)3,2<=
%
+(349ℎ+,34% +:ℎ34−ℎ34,2<=+(5!9ℎ+,5!% +:ℎ5!−ℎ5!,2<=
+:ℎ5!4−ℎ34!,2<= 
+(34!9ℎ+,34!
%
()*ℎ+,)*% +6-.+7-.−>(-:ℎ-−ℎ6,-<
<+()3:ℎ+,)3%
) 

-
=()4:ℎ+,/01
%
+(34!(ℎ+,34!
%

<+(34:ℎ+,34%

<+(5!:ℎ+,5!%

<

which can be rearranged to: 

………………….. (1) 

where mi refers to mass of the ith stream and ℎ+,-7	 refers to enthalpy of formation of the the 

……………….. (2) 

The heat of combustion for Biomass (BM), Pyrolysis Vapors (PyV), CO, H2 and Biochar 

ith stream. 

(BC) are given by: −(34!ℎ+,34!
%
−(34!ℎ+,34!
%
−(34!ℎ+,34!
%

−(5!4ℎ+,5!4
%
−(5!4ℎ+,5!4
%
−(5!4ℎ+,5!4
%
−(34!ℎ+,34!
%
−(5!4ℎ+,5!4
%

="3,)*−()*ℎ)*% …….……..………….. (3) 
="3,/01−(/01ℎ/01% …………………….. (4) 
="3,)3−()3ℎ)3% ……………..…………. (5) 
="3,34−(34ℎ+,34% ……..…………………(6) 
="3,5!−(5!ℎ+,5!% ……………..………… (7) 

where HC,i refers to the heat of combustion of the ith component. 

 

54 

(3) to (7) are derived by considering the complete combustion of each component to CO2 

and H2O.  The heat of combustion of any compound is the negative of the enthalpy change 

for  the  combustion  reaction.    The  standard  enthalpy  of  formation  of  O2  is  zero,  and 

 

 

 

 

 

 

 

 

 

 

 

 

             

     ………(8) 

therefore does not appear in (3) to (7). Subtracting equations (4) to (7) from (3): 

pyrolysis process.  The heat of combustion of carbon dioxide is zero and therefore is not 

−(5!4ℎ+,5!"
%
−:−(34!ℎ+,34!
%

:−(34!ℎ+,34!
−(5!4ℎ+,5!"
<)*−(−(34!ℎ+,34!
)/01
%
%
%
<)3+(34!ℎ+,34!
−(5!4ℎ+,5!"
+(5!4ℎ+,5!4
%
%
%
=−"6−()*ℎ)*% +(/01ℎ/01% +()3ℎ)3% +(34ℎ+,34% +(5!ℎ+,5!%
where "6=−"3,)*+"3,/01+"3,)3+"3,34+"3,5!  is  the  heat  of  reaction  of  the 
"6=−()*ℎ)*% +(/01ℎ/01% +()3ℎ)3% +(34ℎ+,34% +(5!ℎ+,5!% +(34!ℎ+,34!
%
also, the left hand side of (8) equals −(34!(ℎ+,34!
). 
%
<)*+ 6-.+7-.−∑(-:ℎ-−ℎ6,-<
−(5!4ℎ+,5!"
()*ℎ+,)*% +:−(34!ℎ+,34!
%
%
-
<+
=	((/01:ℎ+,/01
−(5!4ℎ+,5!"
<+:−(34!ℎ+,34!
</01)+9()3:ℎ+,)3%
%
%
%
−(5!4ℎ+,5!"
<−
<−(34!:ℎ+,34!
<+(5!:ℎ+,5!%
<)3=+9(34:ℎ+,34%
:−(34!ℎ+,34!
%
%
%
<= 
(5!4:ℎ+,5!"
%

Adding (8) to (2): 

included. 

 

 

 

 

 

 

 

 

 

As 

…….(9) 

 

55 

⇒()*""1)*+6-.+7-.−>(-:ℎ-−ℎ6,-<
(-ℎ+,-%

=(/01""1/01+()3""1)3+(839""1839 
(-ℎ+,-%
2:;<=;.=>

−∑
?27@A<=>

-

Where (-""1-=∑

………..(10) 

  for  the  complete  combustion 

reaction of the corresponding component with product water in the liquid state.  Note that 

NCG refers to the combined stream of CO, CO2 and H2.  

Therefore, for the pyrolysis reactor: 

Energy associated with incoming biomass (EBM): ()*""1B-7C;>> 

Energy associated with Pyrolysis Vapors (EPyV):  

Energy associated with Biochar (EBC):  

Energy associated with non-condensable gases (ENCG):  

i. 

ii. 

iii. 

iv. 

9(/01""1/01+(/01:ℎ/01−ℎ/01,6<= 
9()3""1)3+()3:ℎ)3−")3,6<= 
:(839""1839+(839(ℎ839−ℎ839,6)< 
6-.=C)*−C/01−C)3−C839−7-. 

v.  Work input required (Win): Electrical energy required by the pyrolysis reactor. 

vi.  Heat required by pyrolysis (Qin): 

Finally, both the sensible and the latent heat components are included in the (ℎ-−ℎ-,6) 

term.  

2.  Grinding 

The raw biomass feedstock is ground from a size of 50 to 200 mm to about 2 mm using 

hammer  mills.  The  grinding  energy  requirement  was  evaluated  from  literature.179  The 

 

56 

entire energy supplied was assumed to be lost as heat and there was no change in energy 

of the biomass. 

Table 3.3 : Operating conditions for grinding 

Temperature 
Pressure 
Grinding Energy 

25°C 
1 bar 
0.215 MJ/kg of dry biomass 

 

3.  Drying 

The ground biomass feedstock is dried in a rotary dryer from an initial moisture content of 

20%  to  a  final  moisture  content  of  5%.  The  minimum  drying  energy  requirement  was 

calculated  by  determining  the  amount  of  heat  required  to  evaporate  moisture  in  the 

feedstock  from  20%  to  5%.  This  heat  was  doubled  to  approximate  the  actual  energy 

required for the process.180  

Table 3.4 : Operating conditions for drying 

Temperature 
Pressure 
Feed Moisture Content 
Product Moisture Content 

100°C 
1 bar 
20% 
5% 

 

4.  Fast Pyrolysis 

In the pyrolysis screw reactor, the dried biomass is heated to a temperature of 500°C at 

atmospheric pressure in the absence of air. The vapor residence time is assumed to be 30 

seconds.  The  pyrolysis  products  were  considered  to  be  pyrolysis  vapors  (forms  bio-oil 

when condensed), biochar, and NCG, with the yields assumed as 70%, 15%, and 15% by 

weight respectively. The NCG was composed of only CO, CO2 and H2. To arrive at a 

stoichiometric balanced reaction for the fast pyrolysis reaction, the empirical formulae of 

biochar and pyrolysis vapors were determined. 

 

57 

a)  Biochar: The empirical formula for the biochar was determined from literature.84 

The HHV was  also calculated using the Gaur and Reed formula by utilizing the 

ultimate analysis data provided.84 

b)  Pyrolysis Vapors: In order to determine an empirical formula for the pyrolysis 

vapors, which is an extremely complex mixture of organic compounds, certain 

representative  compounds  were  selected.  The  mass  fractions  of 

these 

representative  compounds,  corresponding  to  different  functional  groups  were 

estimated from Figure 3.8.  The mass fraction of water was assumed to be 18% 

and the mass fractions of the other representative compounds were normalized 

accordingly.  The  pyrolysis  vapors  were  subsequently  condensed,  and  the 

condensed liquid was referred to as bio-oil.  The model representative compounds 

and the assumed mass percentages are presented in Table 3.5. Subsequently, the 

empirical formula was determined. The HHV was once again calculated using the 

Gaur and Reed formula. 

c)  NCG: The NCG were assumed to be composed of CO, CO2 and H2 only. 

Figure 3.8: General Distribution of Functional Groups in Bio-oil18 

 

 

58 

Table 3.5 : Representative Compounds in PyV 

Mass percentage 

Compound 

Water 

Acetic Acid 

Glycolaldehyde 

Phenol 
Furfural 
Guaiacol 

Levoglucosan 

Acetol 

18.00 
19.17 
9.05 
9.58 
8.52 
10.65 
9.58 
9.05 

stoichiometric equation was determined as follows: 

After evaluation of the empirical formulae of biochar and pyrolysis vapors, the balanced 

!"!.#$#%.&!→0.63!"!.DD#%.E#+0.25!"%.(%#%.!&+0.02!#$+0.02"$+0.10!# 

Table 3.6 : Fast Pyrolysis Data 

Temperature 
Pressure 
Pyrolysis Vapor Empirical Formula 
Biochar Empirical Formula 
HHV of pyrolysis vapors 
HHV of biochar 

500°C 
1 bar 

!"!.DD#%.E# 
!"%.(%#%.!& 

18.811 MJ/kg 
29.594 MJ/kg 

The biomass enters the reactor at 25°C and heat is provided to raise the temperature to 

500°C. The products are evolved at the latter temperature. Assuming no heat loss from the 

reactor and calculating the electrical input required (as shown below), the heat required for 

pyrolysis can be calculated from the difference in enthalpy streams of the reactants and the 

products. While the biochar is sent to storage, the NCG and the pyrolysis gases are fed to 

the condenser. The electrical energy required for rotating the auger in the pyrolysis reactor 

was calculated as follows: 

The auger was assumed to have:181  

•  Shaft Diameter (ds) = 0.064m 

•  Flight Diameter (df) = 0.25m 

•  Pitch length (lp) = 0.229m 

59 

 

 

 

 

As the density of corn stover (r) is 90 kg/m3,182 a 100 dry ton/day facility corresponds to a 

volumetric flow rate of about 0.7 m3/min.  Using the relation, 

6;=K∗0?∗M∗N 

where  Qa  is  the  actual  volumetric  flow  rate  in  m3/min;  A  is  the  cross-sectional  area 

occupied  by  the  reacting  material  in  m2;  n  is  the  rotations  per  minute,  and  h  is  the 

volumetric efficiency (the ratio of actual and theoretical volumetric flow rates).183 

Therefore, 

 6;=9P#=∗:O+$−O>$<∗0?∗M∗N 

Assuming a volumetric efficiency of 35%, n can be evaluated to be approximately 200 

rpm, which corresponds to about 3 kW of power.182  This scales up to about 0.003 EJ/yr 

for processing 1 Billion tonnes/year of dry corn stover. 

It must also be noted that the reactor length required for a residence time of 30s and a pitch 

of 0.229m for the 100 ton/day reactor was found to be less than 75 ft, which is a feasible 

reactor length.184 

Figure 3.9 shows the streams entering and leaving the pyrolysis reactor, information on the 

energy content, mass content, and carbon content is given in the tables at the end of this 

appendix. 

Figure 3.9 : Process flow diagram for pyrolysis 

 

 

60 

 

 

5.  Condensation 

Well water entering at 13.33°C and leaving at 20°C was assumed in order to cool the 

entering gases from 500°C to 25°C.185  The condensed pyrolysis vapors formed the bio-oil, 

whereas the NCG gases were separated.  The NCG gases contain energy and may be burned 

to heat the pyrolysis reactor.  Heat losses are considered in the condenser. 

Table 3.7 : Condensation Data 

Empirical formula of condensed Bio-oil 
HHV of formed bio-oil 
Inlet temperature of cooling water 
Outlet Temperature of Cooling water 

13.33°C 
20°C 

!"!.DD#%.E# 

18.811 MJ/kg 

The streams entering and leaving the condenser are shown in Figure 3.10.  

Figure 3.10 : Condenser Flow Diagram 

6.  Electrocatalytic Hydrogenation 

 

 

The  pyrolysis  vapors  were  fed  to  the  ECH  unit,  where  they  were  electrocatalytically 

hydrogenated to stable bio-oil (SBO).  As this is only a thermodynamic model, the ECH 

unit  was  assumed  to  be  a  2-compartment  electrolytic  cell,  separated  by  a  Nafion-117 

membrane.  Oxidation was performed at the Pt anode and reduction at the Ru cathode, 

which  is  supported  on  Activated  Carbon  Cloth  (ACC).32    The  apparatus  considered  is 

shown in Figure 3.11. 

61 

 

 

The ECH unit uses external electrical power to split water into oxygen and H+ ions at the 

anode.  These  H+  ions  then  migrate  through  the  Nafion  membrane  (a  proton  exchange 

membrane)  and  hydrogenate  the  bio-oil  at  the  catalytic  cathode.    An  undesirable  side 

reaction involving the evolution of H2 was also considered at the cathode.  In summary, 

the reactions involved at the cathode and anode are as follows: 

Cathode Reactions: 

 

 

Anode Reaction: 

Figure 3.11 : Schematic of ECH Apparatus 

1)  Bio-oil+$.'  H++$.' e-→ Stable bio-oil 
2) F'HG+F'eH→FIH$ 
n2H!O→nH"+ne#+n4O! 

 

 
Where n is the number of moles of hydrogen ions required to reduce 1 mole of bio-oil to 

stable bio-oil. 

To find the value of n, the empirical formula of SBO was determined. Here too, certain 

representative compounds were selected. These compounds were selected such that they 

result from the hydrogen addition to one or more compounds selected as part of the bio-oil 

mixture.    The  mass  fractions  for  SBO  were  evaluated  from  the  stoichiometry  of  these 

 

62 

hydrogen  addition  reactions  by  knowing  the  mass  fractions  of  the  representative 

compounds in the bio-oil.  During this reaction, only the number of H atoms changed in 

the empirical formula of bio-oil to make SBO, as only hydrogen addition takes place.  The 

value of n was then determined by balancing the cathode reaction (1).  

Assuming  a  current  efficiency  (electrons  in  desired  products  divided  by  total  electrons 

passed)  equal  to  67%,  the  needed  water  splitting  and  the  amount  of  H2  liberated  were 

calculated.  Using Group Contribution Theory to estimate the Gibbs free energies for the 

reduction reaction for each species, the minimum external voltage required for the ECH of 

bio-oil was found to be 1.07V.  To account for the resistance associated with the electrolyte, 

the electrode overpotentials, and the required activation energies, this voltage value was 

increased  to  a  value  of  1.43V,  by  assuming  a  voltage  efficiency  of  75%.  The  power 

consumption was then determined by the following formula:24 

where i	is	current, V	is	applied	voltage,and	PJ	is	the	power	consumption. 

PW = (i*V) 

The assumed operating conditions and the empirical formulae determined for the BO and 

the SBO are presented in Table 3.8. The reactions considered for the hydrogenation of 

bio-oil to stable bio-oil are presented in Figure 3.12. 

80°C 
1 bar 

!"!.DD#%.E# 
!"'.%!#%.E# 

Water, methanol, ethanol, levoglucosan, 

propylene glycol, ethylene glycol, 
furfuryl alcohol and cyclohexanol 

Table 3.8 : Key Data for ECH 

Temperature 
Pressure 
Bio-oil Empirical Formula 
Stable Bio-oil Empirical Formula 
Stable Bio-oil Model Compounds 

 

 

63 

Figure 3.12 : ECH Reactions Considered in this Analysis 

 

 

 

64 

The balanced overall chemical reaction for the ECH reaction is given as: 

!"!.DD#%.E#+0.51"$→!"'.%!#%.E# 

 

A heat input was considered for the ECH reactor to raise the temperature of the reactant 

streams to the required temperature. Heat losses were considered in the ECH reactor. A 

schematic showing the input and output streams to the ECH reactor is shown in Figure 

3.13. 

Figure 3.13 : Process Flow Diagram for ECH 

 

7.   Hydroprocessing 

 

 

SBO is transported from the depot to a centralized refinery for hydroprocessing. In the 

hydroprocessor, the SBO is reacted with H2 at elevated temperatures and pressures.  For 

simplicity,  hydroprocessing  was  assumed  to  only  produce  octane  and  water  at  these 

extreme conditions. 

The overall balanced reaction for hydroprocessing was determined  as: 
 
 

CH'.%!O%.E#+	0.46H$→0.13CEH!E+0.84H$O 

 

65 

Heat was provided to the reactants balanced reaction for hydroprocessing was determined 

to achieve the desired temperature.  The hydrogen gas required for the process was supplied 

by performing local electrolysis of water. 

The operating conditions for the hydroprocessor are provided in the Table 3.9. 

Table 3.9 : Hydroprocessing Data 
400°C 
20 bar 

Temperature 
Pressure 
Empirical Formula of SBO  CH3.01O0.84 
Empirical Formula of UBO 

 
A schematic for the hydroprocessing unit is shown in Figure 3.14. 

C8H18 

 
Figure 3.14 : Hydroprocessor Process Flow Diagram 

 

8.  Electrolysis 

Electrolysis was used to provide the hydrogen needed by hydroprocessing at the petroleum 

refinery.  The minimum external voltage required was assumed to be 1.23 V, corresponding 

to electrolysis of water.  Here too, the voltage efficiency was assumed to be 75%.186 The 

current  efficiency  is  not  applicable  here  since  hydrogen  evolution  is  the  only  reaction 

occurring at the cathode. The electrical energy required for the process and the required 

heat input to raise the reactant temperatures to the reactor temperature were calculated in 

 

66 

the same way as for ECH.  The operating conditions for the electrolysis unit are given in 

the Table 3.10. 

Table 3.10 : Electrolysis Operating Conditions 

Temperature 
Pressure 

80°C 
1 bar 

 
The  electrolysis  unit  is  followed  by  a  compressor,  which  isothermally  compresses  the 

produced H2 to a pressure of 200 bar, which is then fed to the hydroprocessing reactor.  

9.  Storage 

Storage heat losses have been considered for the biochar storage and hydrogen gas storage. 

These have been determined by estimating the amount of heat liberated when the stored 

substance thermally equilibrates with the surroundings. The surrounding temperature was 

assumed to be 25°C at atmospheric pressure. 

10. Heat Integration 

No  external  heat  source  is  required  for  the  Biomass  Upgrading  Depot  (BUD),  as  the 

evolved NCG and H2 gases have sufficient combustion energy to provide heat to the BUD.  

Biochar that is not needed for combustion energy can be land applied to sequester carbon.  

Finally, the air requirement and the composition of the stack gas for the required combustor 

at the BUD is detailed in Table 3.12. 

The heat needed to preheat the stable bio-oil feed may, in part, be provided by recycling 

thermal energy present in the water vapor product that exits the hydroprocessor.  Heat can 

also be transferred from the hydroprocessor itself during its operation.   

The most important values to construct the Sankey diagrams for the Py-ECH process are listed in 

Table 3.11. The stream numbers mentioned in Table 3.11, correspond to the stream numbers used 

through Figures 3.4, 3.5, 3.8 and 3.9.  

 

67 

CE Process 

For the CE (cellulosic ethanol) process, all values were extracted from the 2011 NREL report 

detailing the cellulosic ethanol biorefinery.11 The extracted values are listed in Table 3.13.   These 

values  were  then  normalized  to  the  scale  assumed  for  the  Py-ECH  process,  to  enable  a  fair 

comparison. The Sankey diagrams for the CE process were drawn for the entire system upon 

formulating overall mass, energy and carbon balances, as shown in Tables 3.14-3.16. The water 

balance for the CE process is included in Table 3.17. 

Stream Tables 
 
This  section  lists  the  carbon,  energy  and  mass  values  of  all  streams  involved  in  the  different 

bioenergy  systems,  i.e.  Py-ECH  and  CE.    Tables  3.11  and  3.12  are  for  the  Py-ECH  process, 

whereas Tables 3.13-3.17 cover the CE process.  

Table 3.11 : Stream Table for Py-ECH Process 

Energy 

Temperature  Pressure 

EJ 

20.623 
0.215 
0.215 
20.623 
0.898 
0.448 
1.219 
19.854 
1.553 
0.003 
16.671 

4.738 

0.065 

 

  
  

  

  

  

°C 

25.000 

25.000 

100.000 

100.000 

 

500.000 

500.000 

  

 

bar 
1.000 

  
  

1.000 

  

1.000 

  

1.000 

  
 

1.000 

1.000 

  
 

Stream 

  
1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 

12 

13 
 

 

Content 

  

  
  

  

  

  

Grinder Biomass In 
Grinding Work In 
Grinding Heat Loss 
Dryer Biomass In 
Drying Heat In 

Dryer Water Vapor 

Out 

Drying Heat Loss 
Dryer Biomass Out 
Pyrolysis Heat In 
Pyrolysis Work In 

Pyrolysis (Py 

Vapors + NCG) Out 
Pyrolysis Biochar 

Storage Pyrolysis 
Biochar Heat Loss 

Out 

 

Mass 
Billion 
tonnes 
1.250 

Carbon 
Billion 
tonnes C 
0.509 

  
  

1.250 

0.197 

1.053 

 

0.895 

0.158 

  

 

  
  

  

  

0.509 

0.509 

 

0.384 

0.125 

  

 

68 

Table 3.11 (cont’d) 

Condenser Cooling 

Water In  

Condenser Cooling 

Water Out  

Condenser Heat 

Condenser NCG 

Loss 

Out 

ECH Bio-Oil In 
ECH Water In 
ECH Work In 
ECH Heat Loss 
ECH Oxygen Out 

0.149 

0.149 

  

0.158 
0.737 
0.369 

0.328 

  
  

ECH Heat In 

ECH Stable Bio-oil 

 
0.764 
ECH Hydrogen Out  0.014 
  

Storage ECH 

Out 

  

  

  

Hydrogen Heat Loss 

Hydroprocessor 

Water Out 

Hydroprocessor 

Heat In 

Hydroprocessor 

Heat Loss 

Hydroprocessor 

Fuel Out 

Hydroprocessor H2 

In 

In 

Compressor Work 

Electrolysis 
Hydrogen Out 

Electrolysis Water 

Electrolysis Oxygen 

Electrolysis Work 

Compressor Heat 

In 

Out 

In 

Loss 

14 

15 

16 

17 
18 
19 
20 
21 
22 
23 
24 
25 
26 

27 

28 

29 

30 

31 

32 

33 

34 

35 

36 

37 

 

 

-0.134 

-0.056 

1.324 

1.408 
13.861 
0.000 
5.639 
0.590 
0.017 
0.125 
17.087 
1.931 
0.011 

1.284 

1.524 

2.418 

18.403 

3.494 

0.161 

3.494 

0.000 

0.000 

3.892 

0.161 

  
  

  

  

  

13.333 

20.000 

  

25.000 
25.000 
25.000 

80.000 

 

25.000 
80.000 

  

1.000 

1.000 

  

1.000 
1.000 
1.000 

  
  

1.000 

 

1.000 
1.000 

  

25.000 

1.000 

  

  

25.000 

1.000 

25.000 

200.000 

  

1.000 

1.000 

1.000 

  

 

25.000 

25.000 

25.000 

  

 

  

  

  

0.061 
0.323 

  
  
  
  
 
0.323 
  

  

  

  

  

0.405 

0.383 

0.025 

0.323 
  

0.025 

0.221 

0.197 

  

 

  

  

  

 

  

  

 
 

69 

Table 3.12 : Balance on combustor for heat integration in BUD for Py-ECH process 

IN 

COMPONENT  SOURCE  Description 

 
Air 
 

 

External 

 

 
 

 

Nitrogen 
Oxygen 
Water 
Vapor 

 

 
 

 
 
 
 

 
 
 

NCG 

Pyrolysis 

CO 
CO2 
H2 
 
 
 
 
 
 
OUT 
COMPONENT  SOURCE  Description 

 
 
 
 
H2 
 
 
 

ECH 

 

Stack Gas 

 

Combustor 

 
 
 
 
 

BALANCE 

 
 
 
 
 
 

 
N2 
O2 
CO2 
H2O 

 
 
 

Qty. 
Billion 
tonnes/yr 

Total 
Billion 
tonnes/yr 

0.661 
0.199 

0.017 
0.877 
0.118 
0.039 
0.001 
0.158 

 

0.007 

 
 
 

Qty. 
Billion 
tonnes/yr 

0.661 
0.066 
0.224 
0.091 
1.041 

 
 

 
 

 

0.877 

 
 
 

0.158 

 

0.007 

 

1.041 

 

Total 
Billion 
tonnes/yr 

 
 
 
 

1.041 

 

0.000 

Table 3.13 : Extracted Stream Table for CE Process (adapted from Humbird et al.)11 

Component 

Stream 

 

 

 

 

Mass  
(calc) 
kg/hr 
IN 

Carbon  Energy  Temp.  Press. 
kmol 
C/hr 

Gcal/hr 

atm 

°C 

Hydrolysis & 
Fermentation 
Chemicals 

 

309.00 

CSL 

211.00 

311.00 

CSL 

948.00 

 

 

 

 

20.00 

1.00 

20.00 

1.00 

70 

 

 

Table 3.13 (cont’d) 

 

 

 

310.00  DAP 

26.00 

312.00  DAP 

116.00 

 

 

 

 

 

Total 

1,301.00 

22.00 

-2.00 

Pre-Treatment 

Chemicals 

 

 

273.00  NH3 

1,051.00 

710.00 

CSL 

1,981.00 

 

 

 

 

 

Total 

3,032.00 

0.00 

-3.00 

20.00 

1.00 

20.00 

1.00 

 

 

20.00 

9.00 

21.00 

5.40 

 

 

Biomass Feedstock 

105.00 

Air for Recovery 

- 

401.00 

 

 

 

104,167.00  3,117.00 

-316.00 

25.00 

1.00 

12,105.00 

 

 

- 

- 

2,845.00 

81.00 

-8.00 

28.00 

1.00 

404.00 

CSL 

8.00 

 

 
 
 

 

 

 

 

 
 
 

 

 

 

157.00 
6.00 
109.00 

67.00 

16.00 

13.00 

376.00 

6.00 

-1.00 

32,853.00 

223,602.00 

 

 

 

 

20.00 

20.00 
28.00 
28.00 

1.00 

1.00 
9.00 
9.00 

20.00 

1.00 

28.00 

3.00 

20.00 

3.20 

 

 

 

 

 

 

Glucose for 

Enzyme Production 
Enzyme Production 

Chemicals 

 
 
 

 

 

 

 

Air for Enzyme 

Production 
Air for WWT 

WWT Chemicals 
Air for Boiler/ 
Turbogenerator 

 
 

 

443.00 
CSL 
406.00 
NH3 
441.00 
NH3 
440.00  Nutri
ents 
442.00 
SO2 
446.00  Antif
oam 
Total 

 

- 

 

- 

 
632.00  Caust
ic 
 

- 
 
 

4,504.00 

0.00 

2.00 

20.00 

2.00 

 

 
 

 

 
 

 

 
 

 

 
 

284,495.00 

 
 

 
 

71 

Table 3.13 (cont’d) 

Chemicals for 

Boiler/Turbogenera

tor 

851.00 

Lime 

895.00 

0.00 

0.00 

25.00 

1.00 

Well water 

904.00 

147,140.00 

0.00 

87.00 

33.00 

5.00 

Recovery Vent 

Ethanol 

Recovery Heat 

Enzyme Production 

Loss 

Vent 

 
 

550.00 
515.00 

 

435.00 

423.00 

 

WWT Evaporation 

622.00 

Brine 

627.00 

WWT Heat Loss 
Boiler/Turbogenera

tor Heat Loss 
Electricity Out 
Electricity Rsrv 
Stack & Ash 

 

 
 

Cooling Tower 

COMPONENT 

 

Biomass 

 

Chemicals 

 

 

 

 

 
 
 

 

 

Total 

 

 
 
 

 
 

OUT 

21,398.00 
21,808.00 

 

1,586.00 

31,801.00 
33,387.00 
221,417.00 

9,929.00 

 
 

 
 

471.00 
941.00 

 

 

 

54.00 
88.00 

4.00 

 
 

 
 
 

 
 

 
 

809.00 

Ash 

5,725.00 

810.00 

821V 

 
- 

Stack  363,445.00 
Stack
-2 
Total  371,750.00  1,680.00 

2,580.00 

 

 

 

137,362.00 

0.00 

Table 3.14 : Energy Balance in CE process 

SOURCE 

 
 
 

IN 
 

Description 

Raw Feedstock 

 

Fermentation 

 

CSL, DAP 

 

72 

0.00 
-139.00 
-24.00 

 

 

0.00 
-1.00 

8.00 
-26.00 
-12.00 

-11.00 
-4.00 

 

 

 

-12.00 
-20.00 

34.00 
35.00 

 

25.00 

28.00 

 

25.00 

100.00 

 
 

 
 

0.90 
2.50 

 

1.00 

1.00 

 

1.00 

1.00 

 
 

 
 

0.00 

1.00 

145.00 

1.00 

100.00 

1.00 

 

 

37.00 

1.00 

Qty. 

(Gcal/hr) 
-316.000 

Norm. 
(EJ/yr) 
20.623 

-2.000 

0.131 

  

 

  

 

Total 
(EJ/yr) 
20.623 

  
  
 

Table 3.14 (cont’d) 

 

Pre-treatment 

 

 
 

Glucose 

 

Air 
 
 
 
 
 
 

COMPONENT 

 

Ethanol 

 

Electricity 

 
 

Losses 

 
 

 
 

 

 

 
 

Enzyme 
Production 

WWT 

Boiler/ Generator 

Recovery 
Enzyme 
WWT 

Boiler/Generator 

SOURCE 

 
 
 

Out 

Reserve 

 

Recovery Heat 

Loss 

WWT Heat Loss 
Boiler/Generator 

Heat Loss 

CW Heat Loss 

Enzyme 

Production Vent 
Boiler Stack & 

Ash 

 

 

 
 
 

 

 
 

Vents &Waste 

Recovery Vent 

Streams 

Ammonia, dil. 
Sulfuric Acid 
CSL, Ammonia, 
SO2 , Corn Oil 

Caustic 

Lime in FGD, 

Ammonia, 
Phosphate 
Glucose for 

-3.000 

-1.000 
2.000 

0.196 

0.065 
-0.131 

  

  
  

0.000 

0.000 

0.261 

Enzyme Production 

-8.000 

0.522 

0.522 

 
 
 
 
 
 
 
 

OUT 
 
 
 
 
 
 
 

Heat Loss 

Heat Loss 
Heat Loss 

Heat Loss 

 
 

 

CO2 ,CO,SOX, 
NOX, H2S,O2 , 

N2,NH3 

 
 

  

  
  
 

  

  

  

  
  
 

  

  

  
  
  
  

  
 

  
  

  

  
  

0.000 
0.000 
0.000 
0.000 

0.000 
0.000 
0.000 
0.000 

0.000 

21.406 

Qty. 

(Gcal/hr) 
-139.000 

Norm. 
(EJ/yr) 
9.071 

Total 
(EJ/yr) 
9.071 

-11.000 
-4.000 

0.718 
0.261 

0.979 

-24.000 
-26.000 

-12.000 
-107.000 

  

0.000 

0.000 

-12.000 

 
 

1.566 
1.697 

0.783 
6.983 

  

0.000 

0.000 

0.783 

 
 

11.029 

  

  

  

  

  
 
 

 

73 

Table 3.14 (cont’d) 

 

 

 
 

BALANCE 

WWT 

Evaporation 

WWT Brine 

 
 
 

CO2 ,CO,SOX, 
NOX, H2S,O2, N2, 

CH4 

Predominantly 

NaNO3 

 
 
 

 

IN 

COMPONENT 

 

Oxygen 

 

 
 
 
 

Nitrogen 

 

 

 
 
 

Water 

 

 

 

 

 

 

 
 
 
 

 

 

 

 
 
 

 

 

 

SOURCE 

 

Air for 
Enzyme 
Production 

Air for 
Recovery 
Section 

Air for WWT 
Air for Boiler 

 
 

Air for 
Enzyme 
Production 

Air for 
Recovery 
Section 

Air for WWT 

Air for Boiler 

 
 

Air for 
Enzyme 
Production 

Air for 
Recovery 
Section 

Air for WWT 

 

74 

-1.000 

0.065 

  

8.000 

 
 
  

-0.522 

 
 
  

0.326 
21.406 

 
 

0.000 

Table 3.15 : Mass Balance for CE Process 

Stream 

No. 

 

Qty. 

(kg/hr) 

Norm. 
Billion 
tonnes/yr 

Total 
Billion 
tonnes/yr 

450 

7440.00 

0.09 

559 

2764.00 

630 
804 

 
 

51061.00 
64966.00 

 
 

0.03 

0.61 
0.78 
1.51 

 

450 

24504.00 

0.29 

9104.00 

0.11 

559 

630 

804 

 
 

168162.0

213957.0

0 

0 
 
 

450 

638.00 

559 

630 

237.00 

4379.00 

2.02 

2.57 
4.99 

 

0.01 

0.00 

0.05 

 

 

 
 

1.51 

 

 

 

 

 

4.99 

 

 

 

 

Table 3.15 (cont’d) 

 
 
 

 

 

 

 

 
 
 

Glucose 

 

Chemicals 

 

 

 

 
 
 

Dry Biomass 

 

Other Solids 

 

 

 

 
 
 

 

 

 

 

 
 
 
 
 

Air for Boiler 

Feedstock 
Fermentation 
Chemicals 

Pre-Treatment 

Chemicals 
Glucose 
Solution 
Enzyme 
Production 
Chemicals 
Wastewater 
Chemicals 
Well Water 

 
 

Glucose Soln. 

 

 

Protein 

Fermentation 
Chemicals 
Enzyme 
Chemicals 
Ammonia  Pre-Treatment 
Chemicals 
Enzyme 
Chemicals 

 

804 
105 

309,310,
311,312 
273,710 

401 

404,443,
406,441,

440 
632 

904 

 
 

401 

 

309,310,
311,312 
404,443,
406,441,

440 

273,710 
404,443,
406,441,

440 

5572.00 
20833.00 
582.00 

139.00 

427.00 

83.00 

2252.00 
147140.0

0 
 
 

2418.00 

 

290.00 

41.00 

0.07 
0.25 
0.01 

0.00 

0.01 

0.00 

0.03 

1.77 
2.19 

 

0.03 

 

0.00 

0.00 

1051.00 

0.01 

115.00 

Sulfuric 
Acid 

 
 
 
 
 

 

 

 

Pre-Treatment 

Chemicals 

 
 

273, 710 

1842.00 

 
 

 
 

Feedstock 

 

105 

 

83334.00 

 

Fermentation 
Chemicals 
Enzyme 
Production 
Chemicals 
Wastewater 
Chemicals 

Boiler 

Chemicals 

309,310,
311,312 
404,443,
406,441,

440 
632 

429.00 

108.00 

2252.00 

851 

895.00 

0.00 

0.02 
0.04 

 

1.00 

 

0.01 

0.00 

0.03 

0.01 

 

75 

 
 
 

 

 

 

 

 

2.19 

 

0.03 

 
 

 

 

 

 

0.04 

 

1.00 

 
 

 

 

 

Table 3.15 (cont’d) 

 
 

COMPONENT 

 

Oxygen 

 

 

 
 
 

Nitrogen 

 

 

 
 

Water 

 
 

 

 

 

 
 
 

 
 

 

 

 

 

 

 
 
 
 

 

 

 
 
 

 
 

 

 

 

 
 
 

 
 

 
 

 
 

0.04 

 

0.04 
9.80 

SOURCE 

OUT 

Stream 

No. 

 

 

Qty. 

(kg/hr) 

Norm. 
Billion 
tonnes/yr 

Total 
Billion 
tonnes/yr 

WWT 

Evaporation 
Stack & Ash 

Enzyme 
Production 

Vent 

Recovery 

Vent 

WWT 

Evaporation 
Stack & Ash 

Enzyme 
Production 

Vent 

 
 

 
 

WWT 

Evaporation 
Stack & Ash 

Brine 
Enzyme 
Production 

Vent 

Recovery 

Vent 
Cooling 
Tower 

Evaporation 

Product 

 
 

622 

44765.00 

809,810,
821V 

11324.00 

0.54 

0.14 

435,423 

5727.00 

0.07 

550 

172.00 

 
 

622 

809,810,
821V 

 
 

0 

0 

168157.0

223985.0

435,423  24504.00 

 
 

 
 

622 

4350.00 

809,810,
821V 
627 

57031.00 
4967.00 

435,423 

774.00 

550 

522.00 

948 - 
949 
519 

 
 

138293 

108.00 

 
 

0.00 
0.74 

 

2.02 

2.69 

0.29 

5.00 

 

0.05 

0.68 
0.06 

0.01 

0.01 

1.66 

0.00 
2.47 

 

 

 

 

 

0.74 

 
 

 

 

5.00 

 
 

 
 

 

 

 

 

2.47 

 

 

76 

Table 3.15 (cont’d) 

Chemicals 

CO/NOx/
SOx/H2S 

 
 
 

 
 
 
 
 

CO2 

 

 

 

 
 
 

Ethanol 

 

Other Solids 

 
 
 
 
 

BALANCE 

 
 

Ammonia 

 
 

Methane 

 
 
 

 

 

 

 
 
 
 
 
 
 
 
 
 
 
 

 

 
 
 

 
 
 

0.01 

 
 

 

 

 

 

1.20 

 

0.26 

 
 
 

0.13 

 

9.81 

 

-0.01 

622 

809,810,
821V 
627 
622 

809,810,
821V 
627 
622 

 
 

284.00 

178.00 
44.00 
1.00 

30.00 
3.00 
3.00 

 
 

622 

3857.00 

809,810,
821V 

 

73396.00 

 

0.00 

0.00 
0.00 
0.00 

0.00 
0.00 
0.00 
0.01 

 

0.05 

0.88 

 

435,423 

2382.00 

0.03 

WWT 

Evaporation 
Stack & Ash 

Brine 
WWT 

Evaporation 
Stack & Ash 

Brine 
WWT 

Evaporation 

WWT 

Evaporation 
Stack & Ash 

Enzyme 
Production 

Vent 

Recovery 

Vent 

 
 

 

 
 

550 

20669.00 

 
 

 
 

Product 

 

519 

 

21673.00 

 

Stack & Ash 

Brine 

 
 
 
 
 

809,810,
821V 
627 

 
 
 
 
 

5805.00 
4907.00 

 
 
 
 
 

0.25 
1.20 

 

0.26 

 

0.07 
0.06 
0.13 

 
 
 
 

 

 

Table 3.16 : Carbon Balance for CE Process 

COMPONENT 

 

Biomass 

SOURCE 

 
 

Norm. 

(kmol/hr)  Biilion tonnes/yr 
3117.000 

0.509 

Total 

Billion tonnes/yr 

0.509 

IN 
Qty. 

77 

Fermentation 
Pre-treatment 

Enzyme Production 

WWT 

Boiler/ Generator 

22.000 
0.000 
6.000 
0.000 
0.000 

81.000 

OUT 
Qty. 

  

  
  

  

  
  
  

0.004 
0.000 
0.001 
0.000 
0.000 

0.013 

  

  
  

Norm. 

  

0.077 

0.009 
0.014 
0.001 
0.274 

  
  
  

  
  
  
  

  

  

0.005 

0.013 

0.526 

Total 

Billion tonnes/yr 

0.154 

  
  

  
  
  

  

0.375 
0.528 

-0.002 

SOURCE 

 
 
 

(kmol/hr)  Biilion tonnes/yr 
941.000 

0.154 

Recovery Vent 

Enzyme Production 

WWT Evaporation 

Vent 

Brine 

Stack & Ash 

471.000 

54.000 
88.000 
4.000 

1680.000 

Table 3.16 (cont’d) 

Chemicals 

 
 
 
 
 

Glucose 

 
 

COMPONENT 

 

Ethanol 

 

CO2 and traces 

 

 
 
 
 
 

BALANCE 

 
 
 
 

 
 
 

 
 

In 
 

 
 
 
 

 

 

 

 

Table 3.17 : Water Balance for CE Process 

Component 

 

Air for Enzyme 

Production 

Air for Recovery 

Section 

Air for WWT 
Air for Boiler 

Feedstock 
Fermentation 
Chemicals 

Pre-Treatment 

Chemicals 

Glucose Solution 

Water Balance 

Stream No. 

 

450 

559 
630 
804 
105 

309,310,311,312 

273,710 

401 

78 

Qty. 
kmol/hr 

Norm. 
Biilion 
tonnes/yr 

Total 
Billion 
tonnes/yr 

638.00 

237.00 
4379.00 
5572.00 
20833.00 

582.00 

139.00 
427.00 

0.01 

0.00 
0.05 
0.07 
0.25 

0.01 

0.00 
0.01 

  

  
  
  
  

  

  
  

Table 3.17 (cont’d) 

 

Enzyme Production 

404,443,406,441,440 

83.00 

0.00 

  

 

 
 
 

Out 

 
 
 

 
 

 
 
 

 
 
 
 
 

Chemicals 
Wastewater 
Chemicals 
Well Water 

WWT Evaporation 

Stack & Ash 

Enzyme Production 

Brine 

Vent 

Recovery Vent 
Cooling Water 
Evaporation 

Product 

 
 

 
 

 
 

  
  

  
  

  
  

2252.00 
147140.00 

4350.00 
57031.00 
4967.00 

0.03 
1.77 
2.19 

  

0.05 
0.68 
0.06 

2.19 

  
  

  
  
  
  

774.00 
522.00 

0.01 
0.01 

  
  

138293.00 

108.00 

1291 
121 
2680 
23869 

2473 

1.66 
0.00 
2.47 

  

0.02 
0.00 
0.03 
0.29 
0.34 

  

0.03 

2631 

  
  
  

0.03 
0.06 

  
  

2.47 

  
  

  
  
  
  
  

  
  

  

  

0.34 

0.06 

-0.01 

632 
904 
  
  
622 

627 

809,810,821V 

435,423 

550 

948 - 949 

519 
  
  

Table 37 

  
  

Table 37 

  
  
  

79 

Generated  Enzyme Production 

Fermentation 

WWT 

Combustor 

Consumed 

 

 
 

Balance 

Pretreatment 
Enzymatic 
Hydrolysis 

 
 
 

 
 
 
 
 

 

Chapter 4 : TECHNOECONOMIC ANALYSIS OF THE PY-ECH SYSTEM 
 
Abstract 

Maximizing fossil fuel displacement and reducing atmospheric carbon dioxide levels requires that 

bioenergy systems exhibit high efficiency of carbon incorporation.  The availability of biomass 

carbon is a constraint globally, and strategies to increase the efficiency of bioenergy production 

and biogenic carbon use are needed.  Previous studies have shown that “energy upgrading” of 

biomass  by  coupling  with  fossil  carbon-free  electricity  through  electrocatalytic  hydrogenation 

offers a potential pathway to near full petroleum fuel displacement even when annual U.S. biomass 

production is limited to 1.3 billion dry tons.  Given that commercial application of such technology 

requires economic feasibility, the present work formulates and describes a technoeconomic model 

of  a  process  that  combines  decentralized  pyrolysis  and  electrocatalytic  hydrogenation  with 

centralized  hydroprocessing  (Py-ECH)  to  produce  a  liquid  hydrocarbon  fuel  and  provides  a 

comparison to a cellulosic ethanol pathway using consistent assumptions. Using a discounted cash 

flow approach, a minimum fuel selling price (MFSP) of $ 3.62/gallon-gasoline equivalent (gge) is 

estimated for the Py-ECH fuel derived from corn stover, considering nth plant economics and a 

fixed internal rate of return of 10%. This is comparable to the MFSP for cellulosic ethanol from 

fermentation with the same feedstock ($ 3.71/gge) and also in the range of gasoline prices over the 

last 20 years that have varied from as low as $ 1/gge to $ 4.44/gge (in 2018$). Sensitivity analyses 

were performed to find key parameters that affect the Py-ECH MFSP, with electricity cost, raw 

material costs, bio-oil yields, and cell efficiencies identified as the most influential parameters. 

With system improvements, a pathway to fuel at a cost of less than $ 3/gge is articulated for this 

Py-ECH route.   

 

80 

Introduction 

     The U.S. Department of Energy’s “2016 Billion-Ton Report” projected the total harvestable 

biomass available in the U.S. to be 1.3 billion dry tons by the year 2030 for biomass obtained at a 

cost of less than $60/dry ton. While this is a large quantity, the carbon and energy content of this 

biomass is insufficient to meet the energy demands of the U.S. transportation sector. This has been 

demonstrated  in the work by Lam et al.,5which also highlights the importance of establishing 

bioenergy systems that utilize renewable energy and carbon efficiently. Prevalent systems such as 

cellulosic ethanol fermentations are inherently carbon inefficient, as one-third of the holocellulosic 

carbon is lost as carbon dioxide.  Moreover, combusting lignin for internal heat and power, also 

diverts  carbon  away  from  incorporation  into  higher-value  liquid  fuels.    These  considerations 

further stress the need to devise bioenergy systems that are more carbon and energy efficient. 

     All liquid biofuel strategies require biomass deconstruction as an early step in processing.  The 

literature on deconstruction is immense and growing, as new techniques involving acids, bases, 

solvents,  enzymes,  heat,  and  combinations  thereof  continue  to  emerge.    Of  the  many  existing 

deconstruction techniques, biomass fast pyrolysis is well-studied and can be done with low capital 

cost and high yield because of short residence times.  Further, it converts a portion of the biomass 

lignin along with the holocellulose into the primary product, bio-oil.18, 19, 129  Regional biomass 

processing depots to produce bio-oil are capable of lowering overall hauling costs because bulk 

density  is  increased,22  however  the  reactivity  of  bio-oil  limits  its  ability  to  be  transported.  

Functional groups such as carbonyls, carboxylates, and alcohols, react to form polymers which 

increase viscosity and form sludges.  Further, bio-oil from pyrolysis is corrosive to metals as it 

contains weak acids and has high total acid number (TAN).   

 

81 

     Being highly unsuitable for transport and storage, bio-oil needs to be stabilized immediately 

after pyrolysis.  Thermal hydrogenation and hydrodeoxygenation has been used for hydrogenating 

and stabilizing bio-oil,19, 51, 150-160 however, these techniques operate under high temperature and 

pressure and are not suited for widespread deployment in small-scale plants (depots).22  A milder 

alternative is electrocatalytic hydrogenation (ECH), which involves the electrolysis of water to 

produce  in-situ  hydrogen  ions  on  the  anode  that  electrocatalytically  react  with  bio-oil  on  the 

cathode.  This technique has been found to successfully hydrogenate and deoxygenate the variety 

of compounds found in raw biomass-derived bio-oil as well as lignin-derived bio-oil.32, 34, 132, 136, 

137, 187  ECH is a promising strategy because it operates at mild conditions, avoids storage or use 

of  hydrogen  gas,  and  also  reduces  hydrogen  consumption  at  the  centralized  refinery  where 

hydroprocessing can be safely utilized to create finished fuels.32, 188  As previously investigated,5 

sequential fast pyrolysis and electrocatalysis (Py-ECH) co-deployed in a biomass upgrading depot, 

followed by petroleum-style hydroprocessing in central refineries, potentially offers a carbon and 

energy efficient strategy for making liquid hydrocarbon biofuels.   

     While such a decentralized biorefinery system shows promise in terms of carbon and energy 

efficiency, its economic metrics must also be investigated.  Previously, technoeconomic analyses 

have  been  completed  for  centralized  pyrolysis  followed  by  hydroprocessing.    Wright  et  al. 

estimated a minimum fuel selling price (MFSP) of $2.41/gge in 2018$ when the merchant H2 is 

purchased at $ 1.47/kg H2 for hydroprocessing.84  However, when a portion of the bio-oil is steam 

reformed to make the H2 gas required for upgrading the remaining bio-oil, the MFSP of the fuel 

increased to $3.55/gge in 2018$.  Brown et al. estimated a MFSP of $2.64/gge in 2018$ for a 

pyrolysis and hydroprocessing facility that processed 2,000 tonnes/day of corn stover.85  Neither 

of these systems considered decentralized upgrading or the use of ECH.   

 

82 

     Orella et al. investigated the technoeconomics of the ECH process alone and developed a model 

to estimate the minimum fuel selling price of reducing guaiacol, a pyrolysis bio-oil representative 

compound to phenol. It was reported that with enhanced current density, decreased selectivity for 

hydrogen evolution and increased faradaic efficiencies for the desired product, the selling price for 

phenol  can  drop  to  $0.42/kg.102  This  is  equivalent  to  a  rough  approximation  of  the  cost  of 

electrocatalytic hydrogenation of pyrolysis bio-oil in the Py-ECH process being studied here.  

In  the  present  work,  a  full-scale  technoeconomic  analysis  has  been  performed  for  the 

combined Py-ECH process that 1) upgrades biomass (corn stover) to a stable fuel intermediate in 

decentralized  depots  which  is  then  delivered  to  a  2)  centralized  refinery  that  uses  traditional 

hydroprocessing to create finished hydrocarbon liquid fuels. The final fuel in this present analysis 

has been assumed to be octane, to represent a gasoline-like fuel. Sensitivity analyses have also 

been carried out to identify key parameters that influence the MFSP, with the objective of guiding 

researchers towards economically relevant process improvements.  

Materials and Methods 

Process Description 
     The Py-ECH process under investigation combines fast pyrolysis of corn stover in depots and 

subsequent electrocatalytic hydrogenation with hydroprocessing in a central refinery to produce 

liquid  fuels  from  biomass.    This  system  has  been  described  in  detail  in  the  supplementary 

information file of the work by Lam et al.5  In brief, the Py-ECH system involves hauling biomass 

from the cultivation fields to regional depots where it is ground and dried prior to feeding to a fast 

pyrolysis reactor at 500°C and atmospheric pressures.  The mass percentages of the pyrolysis 

products  are  70%,  15%  and  15%  for  liquid  bio-oil,  biochar  and  non-condensable  gases 

respectively.  The resultant bio-oil is then fed into an ECH unit where it is electrocatalytically 

reduced at 80°C and atmospheric pressure. Ruthenium metal serves as the electrocatalyst.  ECH 

 

83 

stabilized  bio-oil  is  then  transported  to  a  central  hydroprocessing  facility  where  it  is  further 

upgraded to the finished fuel using hydrogen gas at 400°C and 200 bar pressure in the presence of 

zeolite catalyst.  The hydrogen gas is assumed to be renewably generated by the electrolysis of 

water at the central refinery. A process flow diagram of the Py-ECH process has been provided in 

Figure 4.1.  

Figure 4.1 : Process flow diagram of the Py-ECH process 

 

     

Economics  for  cellulosic  ethanol  produced  via  fermentation  from  corn  stover  feedstock  was 

evaluated  for  comparison,  using  the  prior  analysis  by  Humbird  et  al.,  at  National  Renewable 

Energy Laboratory (NREL).11  The analysis was used as a framework for the Py-ECH analysis, 

using  the  same  assumptions  where  possible  and  appropriate.    To  remain  consistent  with  the 

Humbird et al., report, a 2,000 metric tonnes/day biomass processing scale was assumed as the 

combined input to all depots.  Multiple depots, equally sized, supplied a single centralized refinery 

 

84 

to make finished fuel.    Depot sizes in previous literature have varied from Eranki et al., reporting 

values of 100 tonnes/day22  and  Lamers et al. assuming a maximum of about 215 tonnes/day.72 

The depot size in the present analysis was fixed after an optimization study that minimized the 

total transportation cost. This has been described in later sections of this analysis. The composition 

and moisture content of the delivered corn stover at the depot gate was assumed in accordance 

with the Humbird et al., report.11  All material and energy balances required for the Py-ECH and 

CE  economics  respectively  were  extracted  from  Lam  et  al.,5  and  the  Humbird  et  al.,  report 

respectively.  

Economic Model 

All economic assumptions  for the Py-ECH system were similar to those made for an nth plant in 

the Humbird et al., report for ethanol production via fermentation of cellulose.11  The minimum 

fuel selling price (MFSP) of the fuel produced from the Py-ECH system was determined using a 

discounted cash flow analysis (DCFA) with a fixed internal rate of return. This was performed by 

iterating the MFSP until the net present value of all cash flows for the entire plant life equaled 

zero. It must be noted here that the DCFA was done twice, first at the depot and then at the central 

refinery. For the depot, the raw material was the corn stover biomass and the finished product was 

the ECH stable bio-oil. For the central refinery, the raw material was this ECH stable bio-oil and 

the product was the final gasoline-like fuel. The assumed values for the parameters have been 

summarized in Table 1. 

Table 4.1 : Summary of the economic assumptions in the technoeconomic model 

Value 
30 years 

Midwest USA 

2018 
10% 

35% 

200% double declining balance 

Parameter 

Plant life 
Plant location 
Cost basis year 
Internal rate of return 
Depreciation method 
Federal tax rate 

 

85 

5% of fixed capital investment 

Table 4.1 (cont’d) 
Working capital 
Salvage value 
Construction period 
Startup period 
Revenues during startup 
Variable costs during startup  
Fixed costs during startup 
Loan Terms 
Financing 

0 $ 
1 year 
3 months 

50% 
75% 
100% 

8% APR; 10 years 

40% equity 

 
     The total capital investment was calculated as the sum of fixed capital investment (FCI), land, 

and working capital (assumed 5% of FCI).  The FCI, in turn, was determined as the sum of direct 

and indirect costs, which are functions of the total installed equipment costs.  Installed equipment 

costs were calculated by multiplying the estimated equipment cost with an installation multiplier.  

The assumptions and methods behind estimating these equipment costs have been described in 

sufficient detail in the Appendix at the end of this chapter. Operating costs were determined by 

summing the fixed costs (e.g., employee salaries, insurance, and maintenance costs) and variable 

costs  (e.g.,  raw  materials  and  utilities).  While  the  fixed  operating  costs  were  determined  as 

percentages of the total capital investment, the variable operating costs were either estimated from 

literature  data  or  calculated.  For  instance,  the  raw  material  cost  for  the  Py-ECH  system  was 

estimated from a supply chain analysis, described in the next section. Table 4.4 in the Appendix 

lists all assumptions for determining the total capital investment and the total operating cost. 

By-products 

The Py-ECH system generates two by-products at the depot, along with ECH bio-oil, which is the 

primary product. These two by-products are biochar and H2 gas generated from the ECH unit. 

While the biochar is not utilized in the process; some amount of the ECH H2 gas is utilized, in 

combination with the non-condensable gases from the pyrolyser to meet the heat requirements at 

the depot. Therefore, this excess H2 gas and the total amount of biochar may be sold for additional 

 

86 

revenue to bring down the cost of the final Py-ECH fuel. This is similar to the approach adopted 

by Humbird et al., where they incorporate the revenue from selling excess electricity in the cash 

flows that determine the final MFSP of the fuel. In the present analysis, the cost of H2 gas was 

fixed at $ 2/kg of H2, which is the 2020 delivery cost target of the U.S. Department of Energy for 

H2  gas  generated  from  electrolysis.189  This  also  falls  within  the  range  for  the  cost  of  H2  gas 

generated from steam reforming, which varies from $ 1.25/kg for large systems to $ 3.50/kg for 

smaller systems.190 Meanwhile, biochar selling prices vary over a large range in the literature. 

Campbell et al. performed a review of different biochar production scenarios and found that costs 

varied from $80/tonne to about $13,000/tonne in 2013$191 The selling price of biochar depends 

heavily  on  its  quality  as  determined  by  its  carbon  and  ash  contents  among  other  factors,  the 

biomass source and whether it is a wholesale or retail price.191 Table 4.5 in the Appendix provides 

a range of biochar prices from the literature. Due to the immense variability in the selling prices 

of biochar and limited information of the quality of biochar being generated, it is assumed that 

biochar is not being sold in the present study.  

Supply Chain 

The price of corn stover delivered, for the CE system was taken from the Humbird et al., report 

and scaled to 2018$ using Chemical Engineering Plant Cost Indices.  The costs in that report were 

originally  estimated  from  the  Department  of  Energy’s  Multi-Year  Program  Plan  (MYPP), 

published in 2011.  While the 2016 MYPP has since been published, the present analysis still 

employed the 2011 values, as the 2016 MYPP mostly explores blended feedstock with less focus 

on corn stover as the sole feedstock.  The major components of the feedstock price are harvesting 

and  collection,  feedstock  storage,  preprocessing,  transportation  and  handling,  and  the  grower 

payment. To maintain consistency, the same assumptions were used for the Py-ECH system with 

 

87 

a  few  exceptions.  The  preprocessing  costs  involved  drying  and  grinding  operations,  aimed  at 

making the raw material being delivered, fit for processing at the location being delivered. These 

costs had been handled as separate unit operations at the depot for the Py-ECH system. Therefore, 

these were excluded from the raw material cost for the Py-ECH system. Another key difference 

between  the  two  systems  is  logistics.  While  all  the  corn  stover  is  directly  brought  to  a  single 

cellulosic ethanol biorefinery in the CE system, the transportation in the Py-ECH system occurs in 

two stages.  In the first leg, corn stover is transported over short distances from the farm to the 

depot, whereas in the second leg, the stable bio-oil is transported from the depots to the central 

refinery.  This is bound to have an impact on the transportation component of the raw material 

cost. To estimate the transportation cost associated with delivering raw corn stover to the depots 

in a Py-ECH system, an optimization study was performed to determine the least minimum fuel 

selling price (MFSP) of the fuel.  Previous studies to determine depot sizes had been performed 

by  Kim  and  Dale,  who  calculated  a  farm-to-depot  transportation  distance  from  the  equation 

provided in Table 4.6 of the Appendix.74 Fundamentally, these studies assumed a solar system like 

model, where the refinery is located at the center, with all the depots located in a circular orbit 

around the refinery. Each depot, in turn, have their own collection radius. The depot-to-refinery 

distance is subsequently calculated from the difference in the collection radii of the refinery and 

the depot, as determined from the equation in Table 4.6 of the Appendix. 

In  the  present  analysis  however,  a  square  geometry  has  been  assumed  as  land  is  commonly 

parceled in rectangular fashion. Figure 4.2 (a) shows the assumed depot distribution system. The 

central refinery, represented by the red square is located in the center, while the depots, represented 

by  black  stars  are  scattered  all  around  the  refinery.  The  green  squares  represent  the  biomass 

collection area for the depot located at its center.  It must be noted here that the depot arrangement 

 

88 

shown in Figure 4.2 (a) is one of many arrangements, each of which can result in a different 

average depot-to-refinery distance and consequently, different transportation costs. Therefore, an 

optimization was performed for determining the optimal placement of the depots around the central 

refinery. A depot size was assumed, and the average farm-to-depot distance calculated from the 

geometry. The total number of depots were determined from the central refinery capacity and the 

assumed depot size. The depots and their collection squares were then randomly placed around the 

refinery such that there is no overlap between the collection squares of any two depots. It was also 

assumed that all the depots are located in a square region around the refinery, only 25% of which 

is dedicated for crop cultivation. The white spaces in Figure 4.2 (a) denote the 75% not dedicated 

to crop cultivation. Care was also taken to ensure that no depots were located in a region adjacent 

to the refinery, denoted by the blue boundary, to avoid a situation where the biomass is closer to 

the refinery instead of the depot.  Any biomass in this region can directly be transported to the 

refinery (because of refinery proximity), where it can be combusted for required heat and power, 

offsetting natural gas used in the refinery. However, this offset was not considered in the present 

analysis; all heat requirement at the refinery is derived from burning natural gas. Based on the 

placement of the depots around the refinery, an average depot-to-refinery distance was evaluated. 

This distance was then minimized over successive iterations, each of which considered a different 

random arrangement of depots. The arrangement that resulted in the minimum depot-to refinery 

distance was chosen as the optimal arrangement of depots for that particular depot size. It must 

also be noted that a road winding factor of 1.23 was assumed for transportation by trucks, as 

suggested from literature for truck transportation over distances less than 400 km.74, 192-194  Figure 

4.2 (b) shows how the depot-to refinery distance and the farm-to-depot distance varies with depot 

size for a 2,000 tonne/day central refinery. 

 

89 

Figure 4.2 : Illustration of depot distribution geometry. (a) Illustration of depot distribution 
relative to central refinery, for a central refinery with 18 depots. (b) Variation of farm-to-depot 
and depot-to-refinery distance with depot size, for a 2000 tpd central refinery 

 

It can be seen that the farm-to-depot distance varies as the square root of the depot size as the land 

has been parceled as squares.  The depot-to-refinery distance goes through a slight minimum at a 

depot size of 500 tonnes/day but does not vary significantly because the depots are fairly close 

together when the central refinery size is 2,000 tpd.   

Results 

Supply Chain Costs  

Using the optimization strategy and the economic model outlined in the previous sections, the 

minimum fuel selling price (MFSP) of the final fuel was calculated for different depot and refinery 

sizes.  Figure  4.3  shows  the  variation  in  MFSP  of  the  final  fuel  with  depot  sizes  for  different 

refinery capacities. It can be seen that for all refinery sizes (except for a 1000 tpd central refinery), 

the MFSP passes through a minimum as depot sizes are increased. This is owing to two opposing 

forces at play. As depot sizes increase, the MFSP initially goes down owing to economies-of-scale 

effects.

 

90 

Figure 4.3 : Variation of MFSP from Py-ECH with depot size. CR denotes capacity of central 

refinery 

 

 

However, after a certain depot size, transportation distances and consequently transportation costs 

are large enough that they overpower the benefit of economies-of-scale. Therefore, at lower depot 

sizes, economies-of-scale dominate whereas at higher depot sizes, transportation costs dominate. 

There is, however, a middle zone where these two contrasting effects balance each other. While 

this zone of depot sizes is different for different refinery capacities, it is always contained within 

the depot sizes of 500-1000 tpd. This has been highlighted in Figure 4.3 by dotted lines. For a 1000 

tpd central refinery, a minimum was not observed since transportation distances at this refinery 

scale never become large enough to overpower the economies-of-scale effects. For the present 

analysis,  however,  which  assumes  a  refinery  capacity  of  2000  tonnes/day,  the  minimum  was 

observed around a depot size of 500 tpd.  This is also consistent with Figure 4.2 (b), where the 

depot to refinery distance goes through a minimum at around a depot size of 500 tpd. Assuming 

 

91 

equal sized depots, this corresponds to 4 depots (each having a 500 tpd capacity) supplying one 

central refinery. 

     Having established an optimized capacity and arrangement of depots that together supply the 

single 2,000 tonne/day central refinery, the cost of transporting biomass from the farm to depot 

and transporting the ECH bio-oil from depot to refinery was determined. In this regard, the average 

farm-to-depot and depot-to-refinery distances for the optimized depot arrangement were calculated 

and a trucking cost of $ 1.82/mile (in 2018$) was assumed.195 It must be noted that this cost of 

trucking includes both fixed costs like lease payments, driver wages, benefits, insurance premiums, 

permits, etc. and variable costs like fuel, repairs, tolls, etc.. For a depot capacity of 500 tpd and a 

refinery  capacity  of  2000  tpd;  this  corresponded  to  a  cost  of  $  2.97/tonne  of  biomass  being 

delivered from the farm to a depot and a cost of $ 7.71/ tonne of ECH bio-oil delivered from a 

depot to refinery. This makes up a total of $ 7.82/ tonne of biomass in transportation costs for the 

Py-ECH system. This is lower than the $ 10/tonne of biomass, assumed by Humbird et al., for the 

CE system. This only highlights the advantages of the decentralized system in relation to reducing 

overall transport costs. It must be reiterated here that this is the only difference in supply chain 

costs of the two systems. All other costs related to harvesting, collecting and storing the biomass 

and the grower payment costs are same for both systems. In this regard, the total cost of raw 

biomass delivered from the farm to the refinery gate for the CE system was determined to be $ 

68.33/tonne of dry biomass. The equivalent total cost of raw biomass delivered from the farm to a 

depot for the Py-ECH system was evaluated to be $ 61.30/tonne of dry biomass. The relative 

contributions of the different components of the supply chain in both systems have been shown in 

Figure 4.4 (a) and 4.4 (b). The absolute values have been reported in Table 4.7 of the Appendix. It 

can be seen that the grower payment costs, which include the cost of production of corn stover and 

 

92 

the profit margin of the grower are the largest for both systems. This is followed by the costs 

associated with harvesting, baling and collecting stover. The major difference between the cost 

contribution  of  the  two  systems  is  in  transportation,  which  is  expected  from  the  preceding 

discussion.  While transport accounts for 15% of the supply chain costs in the CE system, it merely 

accounts for 5% in the Py-ECH system. It must be noted, however, that the transportation costs 

for the Py-ECH system shown here only account for the farm-to-depot leg. The depot-to-refinery 

transportation costs were included in the cost of the ECH stable bio-oil being delivered as the raw 

material to the central refinery.  

 
Figure 4.4 : Breakdown of raw material supply chain costs from farm to the depot gate for (a) 

Py-ECH system and (b) CE system 

Depot Costs  

Table 4.2 shows the capital and operating costs at the depot. The total capital investment for a 

single 500 tpd depot is $ 29M, while the annual operating costs are about $ 25M. Capital costs at 

the depot are dominated by the ECH unit followed by the pyrolysis, combustion and drying units. 

Grid electricity and raw materials comprise most of the variable cost. 

 

93 

Table 4.2 : Depot Capital and Operating Costs 

Total Capital Cost (2018$) 

Installed Cost  

Unit 
Drying 
Grinding 
Pyrolysis 
Condensation 
ECH 
Combustion 
Storage 
Total Installed Capital Cost 
Inside Battery Limits Capital Cost 

1.24E+06 
1.67E+05 
1.30E+06 
1.45E+04 
9.92E+06 
1.49E+06 
7.58E+05 
1.49E+07 
1.26E+07 

5.05E+05 
1.14E+06 
5.69E+05 
1.71E+07 

1.71E+06 
1.71E+06 
3.42E+06 
1.71E+06 
1.71E+06 
1.03E+07 

2.74E+07 
2.38E+05 
1.37E+06 

2.90E+07 

8.84E+06 
1.56E+07 
1.97E+04 
2.12E+05 
2.47E+07 

1.45E+05 
1.30E+05 
3.79E+05 

 
Direct Costs 
Warehouse 
Site Development 
Additional Piping 
Total Direct Costs 
  
Indirect Costs 
Proratable Costs 
Field Expenses 
Home Office and Construction 
Project Contingency 
Other costs 
Total Indirect Costs 
  
Fixed Capital Investment (FCI) 
Land 
Working Capital 
  
Total Capital Investment (TCI) 
 

Variable Operating Costs 
Raw Material 
Grid Electricity 
Fresh Water 
ECH Stack Replacement 
Total Variable Operating Costs 
  
Fixed Operating Costs 
Salaries 
Labor Burden 
Maintenance 

Total Operating Costs (2018$/yr) 

 

 

 

94 

 
 
 

 
 

 

 

 
 

1.92E+05 
8.46E+05 

2.55E+07 

 

 

Table 4.2 (cont’d) 
Property Insurance 
Total Fixed Operating Costs 
 
Total Operating Costs 
 

 
Figure 4.5 (a) show that the largest contributor to the installed equipment costs at the depot is the 

ECH reactor, which accounts for 67% of the total installed costs.  This is owing to the high cost of 

the membrane electrode assembly stacks that comprise expensive noble metals, that serve as anode 

(Pt) and cathode (Ru).  Combustion, pyrolysis and drying are the next highest costs, as high-

temperature reaction vessels are needed for these units.  

 
Figure 4.5 : Distribution of costs at depot (a) Installed capital costs contribution analyses at a 

depot (b) Variable operating cost contribution analyses at a depot 

 

The variable operating costs at the depot include the costs of raw materials, electricity, fresh water 

for ECH, and ECH stack replacement. Grid electricity (assumed as $0.0656/kWh)11 accounts for 

about  63%  of  operating  costs,  due  to  the  immense  electric  energy  requirement  of  ECH.  Raw 

 

95 

material  costs  (36%)  are  also  significant  because  of  the  various  supply  chain  costs  already 

described.  ECH stack replacement costs, assumed at 15% of the installed capital costs with a 

replacement schedule of 7 years are comparatively negligible (<1%), as seen in Figure 4.5(b). 

Refinery Costs  

Table  4.3  shows  the  estimated  capital  and  operating  costs  at  the  refinery.  The  total  capital 

investment for the central refinery is $227M for processing the amount of bio-oil generated from 

pyrolysing a total of 2000 tonnes/day of raw biomass from all the depots combined.  The annual 

operating costs are around $ 161M.  

Table 4.3 : Refinery Capital and Operating Costs 

Total Capital Cost (2018$) 

Installed Cost  

Unit 
Hydroprocessing 
Electrolysis 
Storage 
Total Installed Capital Cost 
Inside Battery Limits Capital Cost 

 
Direct Costs 
Warehouse 
Site Development 
Additional Piping 
Total Direct Costs 
  
Indirect Costs 
Proratable Costs 
Field Expenses 
Home Office and Construction 
Project Contingency 
Other Costs 
 Total Indirect Costs 
  
Fixed Capital Investment (FCI) 
Land 
Working Capital 
  
Total Capital Investment (TCI) 
 

 

 

96 

4.79E+07 
6.60E+07 
2.10E+05 
1.14E+08 
1.14E+08 

 
 
 

 
 

 

 

4.56E+06 
1.03E+07 
5.13E+06 
1.34E+08 

1.34E+07 
1.34E+07 
2.68E+07 
1.34E+07 
1.34E+07 
8.04E+07 

2.15E+08 
1.83E+06 
1.07E+07 

2.27E+08 

Total Operating Costs (2018$/yr) 

 

Table 4.3 (cont’d) 

Variable Operating Costs 
Raw Material 
Grid Electricity 
Natural Gas 
Electrolyser Stack Replacement 
Hydroprocessor Catalyst Replacement 
Total Variable Operating Costs 
  
Fixed Operating Costs 
Salaries 
Labor Burden 
Maintenance 
Property Insurance 
Total Fixed Operating Costs 
 
Total Operating Costs 
 

1.00E+08 
4.27E+07 
7.23E+06 
1.41E+06 
2.64E+06 
1.54E+08 

1.14E+06 
1.02E+06 
3.42E+06 
1.50E+06 
7.08E+06 

1.61E+08 

 
 

 

 

 
At the refinery, the electrolysis and the hydroprocessing capital costs are comparable, with the 

electrolyzer costs accounting for 58% and the hydroprocessing costs at about 42% of the total. The 

high costs for the electrolyser are again attributed to the high cost of the membrane electrode 

assembly stacks in the electrolyser. The capital cost distribution has been shown in Figure 4.6(a).  

The variable operating costs at the refinery include the raw material (stable bio-oil procured from 

the depots), electricity for the electrolyzer to produce H2 gas, natural gas for process heating, 

electrolyser stack replacement costs and hydroprocessing catalyst replacement costs. The chief 

contributor to the operating costs (65% of total) is the raw material purchased from the depots. 

This is essentially the MFSP (considering an internal rate of return of 10%) of the ECH bio-oil at 

the  depot  exit  and  the  cost  of  transporting  the  stable  bio-oil  from  the  depot  to  the  refinery. 

Electricity accounts for 28% of the operating costs at the refinery; while the cost for natural gas 

(5%) is small in comparison. All other costs are negligible. This has been represented in Figure 

4.6(b). 

 

97 

Figure 4.6 : Distribution of costs at refinery (a) Installed capital cost contribution at the 

refinery (b) Variable operating cost contribution at the refinery 

 

Minimum Fuel Selling Price 

Technoeconomic analysis of the Py-ECH system, using a DCFA approach with an internal rate of 

return of 10% yields a MFSP of $1.17/gge for the stable bio-oil produced by the depot and a MFSP 

of $3.62/gge for fuel produced by the refinery, both in 2018$. 

In comparison, MFSP for ethanol in the cellulosic ethanol system was calculated to be $2.47/gallon 

in 2018$ ($2.15/gallon in 2007$). This corresponds to $3.71/gge in 2018$.  Therefore, under the 

current assumptions, Py-ECH fuel is slightly cheaper than the ethanol produced from cellulosic 

fermentations. Moreover, the Py-ECH fuel may be functionally more attractive than ethanol, since 

it  has  greater  energy  density  than  ethanol  and,  as  a  hydrocarbon,  it  has  greater  potential  for 

blending  at  higher  levels  into  existing  gasoline  or  diesel  fuel-based  transportation  systems 

(distribution system and vehicles). 

 

98 

Sensitivity Analyses 

Effect of model parameters 

Sensitivity analyses were performed to determine the key parameters affecting the MFSP of the 

product fuel at the refinery outlet (Figure 4.7). Electricity cost, raw material cost, bio-oil yield, 

internal rate of return (IRR), electrocatalytic cell efficiencies, catalyst price and thickness, costs 

associated with catalyst replacement, capital costs at both depot and refinery and the selling price 

of by-product hydrogen were the parameters that were investigated. The values of these parameters 

were increased and decreased by either 50% or 25% and the results have been shown in the form 

of a tornado chart in Figure 4.7. A change of 25% for a parameter was only employed when a 

change of 50% from the value used in the model was impractical, e.g., a 50% increase from a bio-

oil yield of 70% is not possible. Since the percentage change is not the same for all parameters, 

the  tornado  chart  may  not  be  the  most  reliable  tool  in  ranking  their  sensitivity  accurately. 

Therefore, another plot has been provided in Figure 4.10 of the Appendix, where the slopes of the 

plotted lines can provide a more accurate description of the relative ranking of the sensitivities. 

Higher the slope, more sensitive the parameter. A tornado plot is however easier to read. As evident 

from  Figure  4.7,  electricity  cost  and  raw  material  costs  are  the  most  sensitive  parameters  in 

determining MFSP. This is intuitive since the Py-ECH system is a major consumer of electricity 

and any change in the price of electricity will benefit the system greatly. Similarly, the other major 

input is the biomass raw material and reduction in its cost would certainly decrease the MFSP. 

Bio-oil yield, current and voltage efficiencies, the assumed internal rate of return, and catalyst 

price and thickness are also important parameters; followed by the selling price of the by-product 

excess H2 gas. This is also understandable as increase in bio-oil yield and the cell efficiencies 

essentially means reduction of losses in the system and an increase in the overall yield of the final 

 

99 

fuel and therefore, a reduction in MFSP. Catalyst price and thickness are also sensitive owing to 

the high cost of the noble metal catalyst, Ru, being used in the system. The catalyst replacement 

costs, whether in the ECH unit at the depot or the hydroprocessor unit at the central refinery are 

not sensitive parameters.  

 
Figure 4.7 : Tornado plot showing single parameter sensitivity analyses on MFSP 

 

Similarly, the MFSP is hardly sensitive to the capital costs at the depot and refinery. From the 

observed list of eight most sensitive parameters, it can be seen that at least five are directly linked 

to the ECH unit. This highlights the significance of ECH in the Py-ECH bioenergy system. Of the 

remaining  three,  two  are  related  to  the  pyrolysis  unit  and  the  last  one,  namely  the  IRR  is  an 

economic parameter. As both the pyrolysis and ECH units are at the depot, the sensitivity analysis 

essentially points towards opportunities at the depot for further optimizing the economics of the 

Py-ECH system. Furthermore, it is evident from the analysis that attention must be focused on 

 

100 

optimizing  the  ECH  catalyst,  process  conditions  and  efficiencies  in  comparison  to  equipment 

costing. 

Effect of Refinery Size 

In order to be consistent with the NREL cellulosic ethanol process analysis, the refinery size was 

assumed  to  be  2,000  tonnes/day.  However,  it  must  be  recognized  that  this  might  not  be  the 

optimum refinery size that generates the minimum MFSP. Therefore, variation of MFSP with 

refinery size for different depot sizes was investigated, as shown in Figure 4.8. Similar to the 

optimization of depot sizes in Figure 4.2, the MFSP goes through a minimum as refinery sizes are 

increased, for different depot sizes.  

Figure 4.8 : Effect of depot size on MFSP for different refinery sizes. Cd denotes depot 

capacity 

 

  

This is again because there are the opposing forces of economies-of-scale and transportation costs 

at work here. At lower refinery sizes, economies-of-scale dominate and at higher refinery sizes, 

 

101 

transportation  costs  dominate.  There  is  also  the  middle  zone  where  these  two  forces 

counterbalance. This happens at refinery sizes of 10,000-12,000 tpd.  

This shows that the Py-ECH system can achieve even lower MFSPs at higher refinery capacities. 

From  Figure  4.8,  a  MFSP  of  $  3.56/gge  can  be  achieved  for  a  refinery  size  of  10,000  tpd, 

employing 10 depots, each having a capacity of 1000 tpd. This is crucial, when viewing the Py-

ECH system, independent of the comparison to the CE system. 

Discussion 

While a MFSP of $3.62/gge for the Py-ECH fuel is better than the $ 3.71 for ethanol in the CE 

system, the long-term goal, of course, is to try and compete with gasoline and diesel prices. In this 

regard, the sensitivity analysis points to areas, where there is further scope of improvement in this 

value. In this regard, Figure 4.7 shows the effect of individual improvements in the parameters, on 

the final MFSP. It would, therefore, be interesting to investigate the effect of stacking up these 

major  improvements  on  the  MFSP.  The  reductions  in  MFSP,  gained  by  stacking  up  these 

improvements have been plotted in a waterfall chart in Figure 4.9. From the sensitivity analysis in 

Figure 4.8, it is clear that the electricity cost, bio-oil yield, raw material cost, cell efficiencies, 

catalyst price and thickness and the internal rate of return are the key parameters. However, it is 

difficult to improve upon the value assumed in the present model for some of these parameters. 

For example, the pyrolysis bio-oil yield has been assumed at 70%, which is among the highest 

values  observed  in  literature.  Raw  material  costs  on  the  other  hand,  have  already  been  fairly 

optimized by optimizing the transport costs via the decentralized approach in the present model. 

There is little scope of improvement there. Also, internal rates of return less than 10%, as assumed 

in the model are unlikely. However, there is great potential in optimizing the cost of electricity. 

While electricity has been assumed to cost at 6.56 ¢/kWh in the model, future costs as low as $0.03 

 

102 

/kWh are projected for wind and solar photovoltaics with advances in materials and manufacturing 

improvements.196  The  U.S.  EIA197  also  reports  the  levelized  cost  of  electricity  from  different 

sources, with and without tax credit. It ranges from 3.91 ¢/kWh (for hydroelectric sources) to 15.70 

¢/kWh (for solar thermal sources), without any tax credit. The effect on MFSP of using these 

different electricity sources have been explored in Figure 4.11 of the Appendix. Furthermore, the 

Wind  Energy  Technologies  Office  at  the  U.S.  Department  of  Energy  estimates  a  price  of  1-2 

¢/kWh  for  electricity  produced  from  wind  sources,  after  applying  the  production  tax  credit. 

Therefore,  a  reduction  of  the  price  in  electricity  to  3  ¢/kWh  is  very  much  feasible.  Such  an 

improvement would result in a 65¢ drop in the MFSP. Similarly, a current efficiency of 67% has 

been assumed in the current model, but An et al., have reported current efficiencies of 70% for 

hydrogenation of soybean oil in a solid polymer electrolyte reactor when hydrogen is generated 

from electrolysis of water.24 Pintauro et al., also observed current efficiencies as high as 97% when 

hydrogen  gas  was  used  for  electrochemical  hydrogenation  of  soybean  oil.198  Therefore  an 

improvement in current efficiency from 67% to 95% may be achievable. This can cause a further 

15 ¢ reduction in MFSP. The catalyst (Ru) price, assumed at $ 263/troy oz, the average 2019 price, 

has ranged from $ 40-270/troy oz over the last 10 years with an average of approximately $121/troy 

oz.199 While prices were as high as $ 180/troy oz in 2011, they dipped to around $ 40/troy oz in 

2016 while again climbing as high as $ 270/troy oz in 2019. Therefore, it is very much plausible 

that there would again be a future dip in prices. Therefore, a future drop to a 10-year average value 

of $ 121/troy oz is a reasonable improvement. Such a reduction in price of catalyst would result in 

a further 8 ¢ decrease in the MFSP. Future improvements in voltage efficiencies and the thickness 

of catalyst layer that would give similar yields and current efficiencies are difficult to estimate due 

to lack of sufficient relevant information and hence, were not considered in this stack up analysis. 

 

103 

It was discussed in the previous section that a change in the refinery size can lead to a lesser MFSP. 

Since the optimum refinery size was evaluated at 10,000 tpd; the change in refinery size from the 

assumed 2,000 tpd to the optimum 10,000 tpd was another improvement that was stacked. This 

resulted in a further drop in the MFSP by 6 ¢/kWh. Finally, although ECH stack replacement cost 

is not a sensitive parameter, it was still investigated to see if there is much change in the MFSP, 

when such an improvement is stacked with the other improvements. The ECH stack replacement 

was changed from 15% of installed capital costs with a replacement schedule of 7 years to 12% of 

installed capital costs with a replacement schedule of 10 years.200 The final MFSP after stacking 

up all the improvements is $ 2.67/gge. This is shown in the green bar at the extreme right. A further 

reduction in MFSP may be achieved by selling the by-product biochar being generated at the depot. 

This was not considered in the basic analysis due to lack of information on the quality of biochar 

that would be generated and inability to fix a price for the vast range of values in literature without 

compromising the integrity of the comparative analysis. Therefore, it is handled as an alternate 

scenario, represented by the yellow bar on the right. If biochar is sold at $ 78.26/tonne (most 

conservative value observed in literature)191, the final MFSP of the Py-ECH fuel can drop to as 

low as $ 2.57/gge. Finally, Figure 4.9 also highlights the benefit from the optimization of the 

transportation costs in the decentralized Py-ECH system. This is shown by the yellow bar on the 

extreme left. If the transportation cost contribution to the raw material cost wasn’t optimized and 

the feedstock cost of $ 68.33/dry tonne (as assumed in the CE system in the Humbird et al., report) 

was used in the Py-ECH system, the MFSP would increase to $ 3.76/gge. In other words, the 

optimization of the transportation costs helped reduce the MFSP by 14 ¢/gge. 

In summary, the present technoeconomic analysis for the Py-ECH system (where four 500  

 

104 

tonne/day depots process corn stover and supply ECH-stabilized bio-oil to a central refinery for 

further hydroprocessing) yields a projected MFSP of $3.62/gge for the final hydrocarbon fuel. This 

MFSP is slightly lesser than that for ethanol from cellulosic ethanol refineries ($3.71/gge) using 

consistent assumptions. Pathways for further reductions in MFSP were determined in a sensitivity 

analysis by identifying key sensitive parameters. These were electricity cost, raw material costs, 

pyrolysis  bio-oil  yield,  ECH  current  efficiencies  and  the  price  and  thickness  of  ECH  catalyst. 

Finally, it was observed that stacking up some of the feasible major improvements can reduce the 

MFSP to as low as $ 2.67/gge and $ 2.57/gge when the by-product biochar is sold at approximately 

$ 80/tonne.   

 
Figure 4.9 : Waterfall chart showing potential reduction in MFSP assuming combinations of 
improvements in selected model parameters. The light green bars indicate the reductions in 
MFSP owing to the stacking up of system improvements. The dark green bars denote the 
initial and final MFSP (after all improvements). The yellow bars denote alternate scenarios, 

not considered in the baseline model. 

 

105 

 
 
 

 

APPENDIX

 

106 

Equipment Costing & Operating Costs 
 
This section provides the assumptions for calculating the equipment costs for the different unit 

operations at the depot stage and the refinery stage. 

Depot Equipment Costs: 

The  depot  was  subdivided  into  six  distinct  areas,  namely:  Drying  and  Grinding,  Pyrolysis, 

Condensation, Electrocatalytic Hydrogenation, Storage, and Combustion. Most of the purchased 

equipment costs at the depot were estimated using the online calculator for “Equipment Costs-

Plant Design and Economics for Chemical Engineers” by Peters and Timmerhaus.201, 202  

Drying and Grinding: 

Drying equipment cost was calculated for a rotary dryer using the rate of water evaporation needed 

to reduce biomass moisture content from 20% to 5% by mass.201, 202  A ball mill was selected to 

reduce the feedstock from >50 mm to 2 mm particles.201, 202 

Pyrolysis: 

The purchased equipment costs for pyrolysis were estimated based on the calculated heat duty of 

the pyrolysis furnace.201, 202  

Condensation: 

For condensation of the bio-oil after pyrolysis, the heat transfer area was calculated from the heat 

transfer rate, the log mean temperature difference of the two fluids, and the heat transfer rate 

coefficient of a shell and tube heat exchanger.203 Using the required heat transfer area, the cost of 

a shell and tube heat exchanger was estimated.201, 202 

Electrocatalytic Hydrogenation (ECH): 

The ECH reactor system have been described in detail in the work by Lam et al.,204 and previous 

literature.23, 32, 34, 136, 137, 196, 198, 205 The ECH reactor for this analysis have been considered to be a 

 

107 

collection of polymer electrolyte membrane (PEM) stacks.30, 198, 205, 206 In these PEM stacks the 

catalytic  Ru  cathode  and  the  Pt  anode  are  pasted  on  two  sides  of  a  Nafion  proton  exchange 

membrane. Capital costs for the ECH reactor system are dominated by the costs of the Pt anode, 

Ru cathode, and the Nafion membrane. The amounts of Pt and Ru required for the ECH reactor 

were estimated from the current densities, bulk densities, catalyst thickness, and the electricity 

requirements to chemically reduce the pyrolysis bio-oil. The surface area and costs for the Nafion 

membrane  were  determined  using  the  Nafion  bulk  density,  acid  capacity,  and  the  electricity 

requirements. It has been estimated that the membrane and electrode costs are only 60% of the 

total stack cost; the stack, in turn accounts for only about 40% of the total electrolyser capital 

cost.207  Therefore,  the  total  electrolyser  capital  costs  were  estimated  from  the  membrane  and 

electrode costs using these percentages. The replacement costs were estimated to be about 15% of 

the installed capital costs, with a replacement schedule of 7 years.200, 207 This cost was incorporated 

in the analysis as an annual variable cost. The installation costs were assumed to be 15% of the 

total uninstalled capital costs.200 Electricity costs were assumed, in accordance with the Humbird 

et al., report at 6.56 ¢/kWh. Similarly, fresh water costs were also assumed from the same report 

at $ 0.22/tonne in 2007$.11 

Storage: 

Storage costs for H2 generated from ECH at the depot were calculated by assuming underground 

storage.208 Stable bio-oil storage at the depot was assumed in shop-fabricated stainless steel tanks 

with walls of thickness 6.35 cm.201 Stainless steel was chosen as the storage material, as only 304L 

and 316L stainless steel satisfy the criterion for corrosion rates of <0.25 mm/year when in contact 

with ECH-treated bio-oil.209 Lu et al., also observed that stainless steel was the most resistant to 

 

108 

corrosion from bio-oil when compared to mild steel, aluminum and brass.210 This, in spite the fact 

that ECH renders bio-oil much less corrosive than pyrolysis bio-oil.  

Combustion: 

A gas-fired furnace was sized to combust the non-condensable gases (NCG) and a fraction of the 

H2 gas to provide heat for all processes at the depot such as pyrolysis.211  

Refinery Equipment Costs: 
 
The central refinery was sub-divided into three units, namely, the electrolysis unit (used to make 

hydrogen for hydroprocessing), the storage unit, and the hydroprocessing unit. The raw material 

for the refinery is the stable bio-oil product delivered from the depots.  

Storage: 

Storage costs for the final hydroprocessed bio-oil were estimated from fabricated stainless steel 

tanks with wall thickness equal to 6.35 cm.201, 202 

Electrolysis: 

Electrolyzer capital costs for making H2 gas for hydroprocessing the stable bio-oil at the refinery 

can vary over a large range. Saba et al., in 2018, conducted a comprehensive review of the cost of 

electrolyzers over the past 30 years and found them to range between € 306 (per kW of HHV of 

H2 gas produced in 2017€) and € 37521(per kW of HHV of H2 gas produced in 2017€), depending 

on type of electrolyzer, assumed efficiency, scale of production and year of estimation.212 Based 

on the values reported by Saba et al.,  to be an estimation by PEM experts, projected electrolyser 

costs in 2030 will be ranging from € 397 to € 955 (per kW of HHV of H2 gas produced in 2017€). 

Therefore, for the purposes of this analysis, an average value of € 676 (per kW of HHV of H2 gas 

produced in 2017€) was chosen. This translates to assuming a cost of $ 1420/(kg/day) of H2 gas 

produced in 2018$. Stack replacement costs were considered to be 15% of installed capital costs 

 

109 

over 7 years as assumed for ECH at the depot. Electricity costs were again, assumed at 6.56 ¢/kWh, 

in line with the Humbird et al., report. 

Hydroprocessing: 

Hydroprocessing costs were estimated by scaling-up the costs associated with a hydroprocessing 

facility  that  subjects  pyrolysis  bio-oil  to  H2  gas  at  elevated  temperatures  and  pressures  in  the 

presence of zeolite catalyst, as determined by Dutta et al., in a joint report by NREL and PNNL in 

2015.213Although the biomass feedstock for the Dutta et al. report, is a blended woody biomass; 

however,  the  hydroprocessing  area  houses  equipment  that  is  very  similar  to  what  would  be 

expected  in  the  hydroprocessing  section  of  the  Py-ECH  plant,  namely,  a  hydrotreater, 

hydrocracker, compressors, product separation columns and heat integration facilities. Therefore, 

when scaled by the amount of hydroprocessor feed, the estimated capital costs should be a good 

approximation for the Py-ECH process. A scaling factor of 0.6 was used to account for the change 

in the amount of feed being handled. The amount of catalyst required was estimated by assuming 

a weight hourly space velocity (WHSV) of 0.5 hr-1, in accordance with the Dutta et al., report, 

purchased at $ 20/lb (in 2011 $), with replacement of the initial fill every 2 years.213 Natural gas 

required for heating purposes at the refinery were estimated at $ 7.86/1000 cubic feet (in 2007$).214 

Table 4.4 : Assumptions for Calculating the Total Capital Investment and Total Operating Cost 
Total Installed Capital Cost is the sum of installed equipment costs  
Inside Battery Limits (ISBL) is the total installed capital cost excluding storage  
 
Direct Costs 
Warehouse 
Site Development 
Additional Piping 
Total Direct Cost (TDC) is the sum of all direct costs 
 
Indirect Costs 
Pro-ratable Costs 
Field Expenses 
Home Office and Construction 

4% of ISBL 
9% of ISBL 
4.5% of ISBL 

10% of TDC 
10% of TDC 
10% of TDC 

 

 

 

110 

10% of TDC 
10% of TDC 

1.6% of total installed capital cost 
5% of FCI 

Table 4.4 (cont’d) 
Project Contingency 
Other costs 
Total Indirect Cost (TIC) is the sum of all indirect costs 
Fixed Capital Investment (FCI) is the sum of TDC and TIC 
Land 
Working Capital 
Total Capital Investment (TCI) is the sum of FCI, land, and working capital 
 
Fixed Operating Cost 
Salaries 
Labor Burden 
Maintenance 
Property Insurance 
Total fixed operating cost is the sum of all fixed operating costs 
Total variable operating cost is the sum of variable costs, e.g. utilities and raw materials 
 
Total Operating Cost is the sum of total fixed and variable operating costs 

0.5% of TCI 
90% of salaries 
3% of ISBL 
0.7% of FCI 

 

 

 
Table 4.5 : Range of values of biochar found in literature (adapted from Campbell et al.,)215 
Biochar Price 

Price Based on 

Technology 

Description 

Citation 

Breakeven price 

Pyrolysis 

When biochar quantity is maximized @ 300ºC 

Breakeven price 

Pyrolysis 

Produced alongside methanol 

When biochar quality is maximized @450ºC 

Produced alongside methanol 

Energy value 

Pyrolysis 

Relative to cost of Central Appalachian coal 

Soil enhancement ability 

Pyrolysis 

78.26 

Energy value 

Fast pyrolysis 

(in 2018$) 
($/tonne) 
230.31 

293.12 

117.44 

68.34 

 
 

 
 

 

 
 

 
 
 
 
 

 
 

 
 

 

 
 

 
 

 

 
 

 
 
 
 
 

 
 

 
 

142.98 

Technoeconomic analysis 

Pyrolysis 

For Sub-Saharan Africa region (Range 99-165) 

224.21 

Technoeconomic analysis 

Pyrolysis 

For Northwestern Europe (Range 155-259) 

2,868.42 

Survey of biochar sellers 

 
 
 
 
 

 
 
 
 
 

 

111 

 

 
 

 
 

 

 
 

 
 
 
 
 

 
 

 
 
 
 
 
 
 
 

 

 

 

 

 

 

 

 

 

Includes fertilizer application cost of $40/tonne 

Includes hauling cost of $8/tonne 

Price for optimal temp of 525ºC 

Yield: bio-oil: 55% and biochar: 20% 

Max revenue: $118.48/tonne 

Assumption: Price/unit energy of bio-oil is equal to 

that of fossil oil 

Price/unit energy of biochar is equal to that of coal 

$75/tonne for pyrolysis at 500ºC 

Discount Rate of 10% 

Discount Rate of 10% 

US average from survey of 23 companies 

Based on the survey of 43 companies worldwide, 23 

in the US 

Does not include shipping or handling costs 

Mix of retail and wholesale prices 
Mix of pure biochar and blends 

 
 
216 
 
216 
 
 
217 
 
218 
 
 
219 
 
 
 
 
 
220 
 
 
220 
 
 
 
39 
 
 
 
215 

Table 4.5 (cont’d) 

Communication with 

1,834.00 

 

88.98 

 
 
 

industry 

 

Energy value 

 
 
 

2,382.94 

Market value 

414.97 

 

 
 
 

Technoeconomic analysis 

 

 
 
 

1,763.81 

Market survey 

Slow 

pyrolysis 

Pyrolysis 

 
 

 
 
 
 
 

 
 
 
 

Wholesale price 

 

Bio-oil produced (38%) sold at $ 192/tonne 

Biochar produced (26%) 

Revenue of $ 93/tonne of forest-based feedstock 

Market value for soil amendment @$2.2/kg; 

possibly retail price 

This is average price in UK 

Min: $222/tonne; Max: $584/tonne 

Includes shipping and handling 

Most often cited price 

Dependent on volume and packaging 

 

 

 

 
 
221 
 
 
222 
 
 
223 
 
 
224 
 

 
Table 4.6 : Summary of factors determining assumed costs for corn stover transport to depots as 

assumed by Kim et al. 74  O+=jk

l>.ℎ2/24

n>=7K:2./%.l<72..p.l;.(1−lL) 

Symbol  Unit 
102 m 
df 
fs 
tonnes/day 
hours 
hr 
 
fa 
w 
 
 
fl 
fcorn 
 

Ystover 
r% 

tonnes/ha 
% 

Meaning 
Farm to Depot Distance 
Facility Size 
Annual operating hours 
Percentage of participating farms 
Road winding factor 
Transportation and Storage loss factor 
Ratio of harvested corn area to total land 
area 
Dry corn stover yield 
Fraction of corn stover collected 

 
Table 4.7 : Summary of supply chain costs for corn stover for CE and Py-ECH systems as used 
in model.225 All values in 2018$/tonne of delivered biomass 

Supply Chain Operation 

 

Harvest and Collection 
Storage and Handling 

Grower Payment 

Transportation and Handling 

TOTAL 

CE system 
($/tonne) 

Py-ECH system 

($/tonne) 

16.64 
3.10 
38.59 
10.00 
68.33 

16.64 
3.10 
38.59 
2.97 
61.30 

 

 

112 

Figure 4.10 : Electricity costs from different sources. Red dashed line indicates the MFSP using 

MRO-West U.S. grid electricity, assumed as a baseline in the model.197 

 

Figure 4.11 : Sensitivity analyses on MFSP for all parameters. Larger the slope, greater the 

sensitivity to that parameter. 

 

 

 

 

113 

Data Inventory 

Table 4.8 : Key ECH and Pyrolysis parameters 

Parameter 

ECH 
Platinum current density 

Platinum thickness 
Platinum density 
Platinum price 

Nafion 117 conductivity 

Nafion 117 price 

Nafion acid capacity 

Nafion thickness 

Ruthenium current density 

Ruthenium thickness 
Ruthenium density 
Ruthenium price 

ECH Current Efficiency 
ECH Voltage Efficiency 

ECH Temperature 

ECH Pressure 

Pyrolysis 

Pyrolysis Bio-oil Yield 
Pyrolysis Biochar Yield 
Pyrolysis NCG yield 
Pyrolysis Temperature 

Pyrolysis Pressure 

Drying 
Grinding 
Pyrolysis 

Condensation 

ECH 

Combustion 

Storage 

At Central Refinery 
Hydroprocessing 

Electrolysis 

Storage 

 

 

 

Source 

Kreuter et al.,226 

Liu et al.,227 

 

 
 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

0.9 meq cations/g dry Nafion 

Value 

 

1000 mA/cm2 

100 nm 

21.45 g/cm3 
29.33 $/gram 

10 S/m 

2222.22 $/m2 

0.1778 mm 
10 mA/cm2 

100 nm 
12.2 g/cm3 
263 $/troy oz 

67% 
75% 
80ºC 
1 atm 

 

70% 
15% 
15% 
500ºC 
1 atm 

1.70 
1.70 
1.80 
2.20 
1.15 
1.80 

 

 
 

1.70 
1.15 
1.80 

114 

Table 4.9 : Installation multipliers for different processing units 

Processing Unit 

Installation Multiplier 

At Depot 

Chapter 5 : LIFE CYCLE ASSESSMENT OF THE PY-ECH SYSTEM  

Abstract 

Quantification of the environmental impacts of bioenergy systems is essential when deciding how 

to replace fossil-based systems. Life cycle assessments are the most reliable and established means 

of  estimating  the  environmental  impacts  of  any  such  system.  Bioenergy  systems  employing 

localized  fast  pyrolysis  combined  with  electrocatalytic  hydrogenation,  followed  by  centralized 

hydroprocessing (Py-ECH) potentially have higher carbon and energy efficiencies vs. traditional 

cellulosic  biorefineries.  In  this  study,  a  cradle-to-grave  LCA  was  performed  to  calculate  the 

environmental  performance  of  this  Py-ECH  system  in  three  impact  categories,  namely,  global 

warming  potential,  eutrophication  potential,  and  the  water  scarcity  footprint.  Results  were 

compared to those for traditional cellulosic ethanol fermentations using both NREL and GREET 

models. Analyses reveal that the Py-ECH system has much lower eutrophication potential and 

water  scarcity  footprint  compared  to  cellulosic  ethanol  biorefineries.  It  also  highlighted  the 

importance of finding sources of renewable electricity in ascertaining favorable global warming 

potentials.  A  sensitivity  analysis  shows  that  the  annual  carbon  sequestration  rate  can  play  a 

significant role in determining system-wide global warming potential. 

Introduction 

Fossil fuel energy, such as the range of liquid fuels derived from crude oil or natural gas, have 

often been shown to be significant contributors to air and water pollution. Such fuels are non-

renewable as the rate of replenishment is much slower than the rate of use. There is a need to look 

for alternative energy production systems that are less polluting and renewable. In fact, the Energy 

Independence and Security Act (EISA),3 passed in 2007, aims to increase the production of cleaner 

renewable  fuels,  as  part  of  the  overall  mission  of  improving  U.S.  energy  independence  and 

 

115 

security. The EISA promotes an increase in the production of biofuels as a cleaner and renewable 

alternative to fossil fuels by producing at least 21 billion gallons of advanced biofuels (with 16 

billion gallons of cellulosic biofuels) by the year 2022. In accordance with EISA, these advanced 

biofuels should reduce 50% of the greenhouse gas emissions compared to the baseline established 

in 2005.    

     Fermentation of lignocellulose-derived sugars into ethanol has been arguably the most studied 

advanced biofuel system since the enactment of the EISA. However, traditional cellulosic ethanol 

systems are inherently carbon and energy inefficient as one-third of the carbon in biomass is lost 

as carbon dioxide and the process does not typically use biomass’ lignin (accounting for 40% of 

biomass energy) for making liquid fuel. These are significant shortfalls, especially vis-à-vis the 

Billion Ton Report of 2016,172 which highlights the importance of carbon and energy efficiencies 

of biofuel production systems. The report suggests that there is not enough energy in biomass (that 

can be procured at $60/dry ton or less) in the entire United states to satisfy the demands of even 

the  transportation  sector  in  2019.  This  was  the  motivation  for  developing  the  concept  of  a 

decentralized pyrolysis and electrocatalysis (Py-ECH) bioenergy system that is advantageous in 

carbon and energy efficiency when compared to traditional cellulosic fermentations to ethanol.5 

The  Py-ECH  system  combines  localized  fast  pyrolysis  and  subsequent  ECH,  with  centralized 

petroleum-style  hydroprocessing  to  produce  “drop-in”  liquid  hydrocarbon  fuels.  While  fast 

pyrolysis deconstructs the biomass to liquid bio-oil, solid biochar and non-condensable gases, the 

ECH employs mild conditions to sufficiently hydrogenate and upgrade the energy content of the 

bio-oil, so that it is stable for storage and transport to a central refinery. At the refinery, the stable 

bio-oil is subjected to high temperatures and pressures to produce liquid fuel-range hydrocarbons 

that is similar to gasoline in terms of energy content. Corn stover was selected for the analysis to 

 

116 

fairly compare with the cellulosic ethanol (CE) process as established by Humbird et al., in 2011,5 

which also used corn stover. In parallel work as this LCA, technoeconomic analyses of the Py-

ECH system revealed a minimum fuel selling price (MFSP) of $3.62/gge (for a fixed internal rate 

of  return  of  10%)  in  comparison  to  $3.70/gge  for  the  CE  system,  in  2018$.  However,  with 

sufficient  improvements  in  technology,  the  MFSP  for  the  Py-ECH  system  could  drop  under 

$3/gge.  

     With a pathway for thermodynamic and economic favorability of the Py-ECH system already 

established, in comparison to the CE system, the present work takes the next step by comparing 

the environmental impacts through LCA. Though many CE LCAs have already been conducted 

using  different  biomass  feedstocks,228-235  this  exercise  was  repeated  to  maintain  consistent 

assumptions  for  the  two  systems  under  consideration.  While  conducting  a  full  cradle-to-grave 

analysis,  including  corn  cultivation  through  end-use  fuel  combustion  in  vehicles,  this  work’s 

primary goal is to compare the two technologies under study, namely, Py-ECH and CE. Previous 

LCA studies have proven the environmental advantages of CE systems over fossil fuel systems. 

The present comparative environmental analysis of the Py-ECH and CE systems, serves as an 

integral step in the commercial application of the Py-ECH system, as a viable alternative to fossil 

fuel  systems.  It  must  be  noted  that  life  cycle  analyses  have  also  been  performed  for  biomass 

pyrolysis  (followed  by  upgrading  using  hydrogen  gas  from  different  sources),  which  have 

highlighted the advantage of pyrolysis upgrading systems over fossil fuel systems.236 Similarly, a 

recent life cycle analysis on a depot based bioenergy system have also shown a pathway to carbon 

negative  cellulosic  biofuels.237  The  Py-ECH  system,  however  combines  biomass  pyrolysis 

upgrading (using mild electrocatalytic hydrogenation) with a decentralized system approach.  

 

117 

Methodology 

A full-scale comparative “cradle-to-grave” life cycle assessment (LCA) was conducted for the Py-

ECH and the CE processes for three environmental impact categories: global warming potential, 

eutrophication  potential,  and  water  scarcity  footprint.  Additionally,  the  energy  return  on 

investment for the two systems was also determined to compare their fossil energy footprint. The 

life cycle inventory was built in spreadsheets, combining appropriate values from well-established 

models like GREET,238 CCLUB,239 and process flow data as provided in the work by Lam et al..5  

Functional Unit 

Since  the  primary  functionality  of  the  two  bioenergy  systems  is  to  produce  fuel  energy,  the 

functional unit chosen for the LCAs was 1 MJ of produced fuel energy. Therefore, all emission 

values calculated in the present analysis are for the production of 1 MJ of fuel energy.  

Life Cycle Impact Categories 

Global Warming Potential (GWP) 

Climate change or global warming potential is a worldwide grand challenge to human existence 

that is primarily caused by carbon dioxide, methane, and nitrous oxide emissions. Traditionally, 

climate change has been measured by the GWP (Global Warming Potential) from the TRACI 

model developed by the U.S. EPA (Environmental Protection Agency).240, 241 Although the GWP 

values in this model are for a 100 year time horizon and are not exactly accurate for a 20 year time 

horizon, they are sufficient for a fair comparison between the two processes.  

Water Scarcity Footprint (WSF) 

Bioenergy  systems  are  inherently  water  intensive  and  therefore,  water  depletion/use  is  an 

important parameter that needs to be investigated. Bayart et al., defines Freshwater Depletion as 

the  “Net  reduction  in  the  amount/availability  of  freshwater  in  a  watershed  or/and  fossil 

 

118 

groundwater stock. Depletion occurs when freshwater consumptive use exceeds the renewability 

rate  of  the  resource  over  a  significant  time  period.”242  In  2018,  Boulay  et  al.,  developed 

characterization factors for water use in LCA, based on the amount of water remaining in a given 

watershed per unit area relative to the global average after all human and ecosystem demands have 

been met.243 These characterization factors are known as “AWARE” (available water remaining) 

factors. Multiplication of the inventory data with these characterization factors gives the potential 

for  depriving  another  user  of  water,  which  is  proportional  to  the  water  use  and  inversely 

proportional to the water availability. The characterization factors range from 0.1 to 100, with 1 

being equivalent to the world average, 0.1 being areas where 10 times more water is available and 

100 being areas with most water scarcity. The average AWARE characterization factor in the 

United  States  for  agricultural  use  and  non-agricultural  use  is  36.49  and  9.51  respectively.150 

However, in this analysis, the AWARE characterization factors used were the average for the 

major  corn  producing  states  in  the  Midwest,  which  are  also  part  of  the  Midwest  Reliability 

Organization  (MRO)-West  e-GRID  subregion,  namely,  Minnesota,  Wisconsin,  North  Dakota, 

South Dakota, Nebraska, and Iowa. The average AWARE factors for these states for agricultural 

use and non-agricultural uses are 10.20 and 9.59 respectively. The expressions used in evaluating 

the Water Scarcity Footprint (WSF) are provided below: 

7qr=j-.!rMNM6O 
1/KstP72L@	;KR=KstP72L@	;KR
1/Kst-
!rMNM6O=
Kst-
Kst-=(Ku)&0)v&0w.−"7!−C7x)
Kst	&*	Ku)&0)v&0&w.	(&Mz*	ty()MO 

K/y)

 

 

 

119 

"7!	&*	"z()M	7)wy/	!'M*z(2w&'M; 
C7x	&*	CMu&/'M(yMw)0	7)wy/	xy|z&/y(yMw 
j-	&*	w'w)0	&MuyMw'/.	'l	*zv*w)M4y	&	(&M	(') 

Eutrophication Potential (EUP) 

Biomass production, which is an integral part of all bioenergy systems, is associated with the use 

of NPK fertilizers. These fertilizers are a major source for eutrophication in aquatic systems, which 

is defined as excess nutrient availability leading to exponential algal and cyanobacteria growth 

that harms marine sytems.244 The eutrophication potentials for the two systems were estimated 

using  the  TRACI  Model,240  assuming  little  variation  in  the  characterization  factors  for  the 

Eutrophication Potential for 20 year and 100 year time horizons. The TRACI characterization 

factor for estimating Eutrophication Potential is a combination of a nutrient potency factor and a 

transport factor.245 While the potency factor is a measure of the effect of a particular nutrient, the 

transport  factor  accounts  for  the  release  of  emissions  into  different  media  (e.g.,  air,  water), 

ultimately  reaching  aquatic  systems.  The  expression  used  in  evaluating  the  Eutrophication 

C}--=y-.!r;-2/P;=:2 

Potential (EUP) is provided below: 

													!r;-2/P;=:2	&*	wℎy	~xK!(cid:127)	4ℎ)/)4wy/&*)w&'M	l)4w'/	l'/	*zv*w)M4y	&	&M	)&/	'/	j)wy/	(yO&z( 

y-	&*	&MuyMw'/.	O)w)	'l	*zv*w)M4y	&	(&M	ÄÅ) 

System Definition 

The results of an LCA analysis can vary greatly depending on the placement of system boundaries. 

For this analysis, the Py-ECH system boundaries are defined by the cultivation of the corn plant 

(for the generation of stover) at the front end and by the combustion of the produced hydrocarbon 

 

120 

 

 

Figure 5.1 : System boundaries for Py-ECH system 

Figure 5.2 : System boundaries for CE system 

fuel at the back end. The CE system boundaries are similarly defined. This has been pictorially 

represented in Figures 5.1 and 5.2, where the two boundaries of the two bioenergy systems are 

shown. The major components of the whole system are depicted in different colors: the supply 

chain (green), processing at refineries (dark blue) or depots (light blue), transport (purple) and fuel 

combustion (black). For the CE system, similar flow diagrams may be found in the Humbird et al., 

report.11 

 

121 

Time Horizon 

A time horizon of 20 years was selected for the LCA to accommodate the transient response of 

the soil organic matter deposition.   

Life Cycle Inventory (LCI) 

The process flow data for the Py-ECH system were adopted from the work by Lam et al..5 For the 

CE system, the process flow data were extracted from NREL’s report published in 2011.11 All key 

LCI  data  were  determined  from  established  and  well-known  models  like  Argonne  National 

Laboratories’  GREET    and  CCLUB  models.  Other  relevant  data,  that  were  absent  from  these 

models were extracted from reliable data sources like reports published by IPCC, EPA, and NREL. 

To qualify the collected inventory, data quality indicators (DQI) were assigned using the modified 

Weidema method.246 Originally Weidema et al. suggested five parameters to evaluate data quality: 

reliability,  completeness,  temporal  correlation,  geographical  correlation,  and  technological 

correlation.  However,  Toffel  et  al.  in  2004,  renamed  the  completeness  parameter  with 

‘representativeness’ and the temporal correlation parameter with ‘data age’. They also split the 

reliability parameter into the acquisition method and independence of data supplier parameters to 

better characterize the data reliability.247 This modified Weidema method has been applied in the 

current study. Table 5.3 of the Appendix, at the end of this chapter, summarizes these parameters 

and describes the meaning of the scores assigned to the data on a scale of 1 to 5.248 All life cycle 

data used in this study have been listed, with their data quality indicators, in Table 5.4 of the 

Appendix. 

Key LCI data and assumptions 

As discussed before, the two bioenergy systems can be broadly classified into 4 major areas. 

These are (a) the Supply Chain, (b) Processing, (c) Transport, and (d) Combustion.  

 

122 

Supply Chain  

The supply chain consists of mainly two sub-processes, namely, the cultivation of the corn and 

the collection of the generated stover. 

Cultivation of corn 

Almost all data pertaining to the cultivation of corn were obtained from the GREET and CCLUB 

models.238, 239 For the present analysis, it was assumed that corn is being cultivated in a continuous 

corn  cropping  system  with  no  tillage.  The  cropland  for  corn  cultivation  is  considered  to  be 

previously used for cropland or pastures. It was assumed that only 60 wt.% of the generated corn 

stover was removed from the fields, an important value because the percentage stover removal can 

impact the final GWP and EUP values.249 GREET provides only two options for stover removal, 

namely 60% and 30% by weight. To increase harvest yield, the 60 wt.% stover removal option 

was selected while also recognizing that retaining a minimum of 30% corn stover on the field 

decreases wind erosion by 70% compared to bare soil.250 GREET was again used for fertilizer 

input values, which includes the  associated emissions for production and soil application. The 

carbon sequestration data were obtained from the CCLUB model and found to be 0.273 Mg C/ha/yr 

for 30% stover removal, which translates to 0.174 Mg C/ha/yr for a 60% stover removal, assuming 

a linear dependence on stover removal. It must be noted that soil carbon sequestration data are a 

function of soil texture, rainfall, tillage, stover removal, soil depth measured, crop rotation system, 

and geographical location. A comprehensive review on the range of  values and their dependence 

on the aforementioned factors has been performed by Alvarez.251 The sequestration values for no 

till  corn  cultivation  varies  greatly  in  literature,  from  0.1  Mg  C/ha/yr  to  about  5  Mg  C/ha/yr, 

depending on these factors.204, 252-258 To account for this range of data, a sensitivity analysis was 

performed to determine the effect of annual carbon sequestration rate on the total GWP. This 

 

123 

carbon  sequestration  rate  is  the  net  accumulation  of  carbon  in  the  soil  over  the  selected  time 

horizon and it accounts for the translocation of carbon, fixed by photosynthesis, the carbon in root 

exudates, the carbon deposited in soil organic matter pools, and the carbon liberated as carbon 

dioxide due to soil respiration and microbial decomposition. Figure 5.3 diagrammatically shows 

the carbon flow for the CE and Py-ECH systems using GREET assumption for the annual carbon 

sequestration rate. 

Figure 5.3 : System carbon flow for (a) Py-ECH and (b) CE, for GREET assumed value of 

carbon sequestration. 

 

The amount of water required for cultivation was extracted again from the GREET model. Water 

 

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can be separated into three categories: blue water (surface water and ground water), green water 

(water associated with precipitation) and grey water (water required to incorporate pollutants being 

discharged into freshwater bodies).259 For cultivation of crops, GREET only includes blue water 

consumption, where consumption is defined as the amount of  freshwater used by the process for 

anthropogenic purposes and not returned due to evapotranspiration or degradation in quality.260 

Green water consumption in cultivation may be neglected because it does not affect blue water 

use.261-264 The underlying assumption is that green water consumption by the land area did not 

change due to crop cultivation. In fact, it is also argued in literature that the effects of green water 

consumption  is  better  handled  as  a  contributing  parameter  when  estimating  emissions  due  to 

change  in  land  use.262,  264  Grey  water  has  also  not  been  considered  because  its  environmental 

consequences  overlap  with  other  impact  categories,  such  as  eutrophication  in  this  study. 

Atmospheric emissions of nitrogen oxides (NOx), contributing to eutrophication potential in the 

cultivation  stage  due  to  fertilizer  application  and  production,  were  extracted  from  GREET. 

Ammonia emissions, due to volatilization of a fraction of the applied fertilizers, were estimated 

from IPCC data.265, 266 All three fertilizer nutrients (N, P, K) were considered; emissions due to N 

and  P  fertilizer  runoff  to  water  resources  were  obtained  from  a  report  published  by  NREL  in 

2005.266 N runoff to surface water was considered in the analysis, as runoff to groundwater was 

found negligible owing to geographic assumptions. The value for N surface runoff (as nitrates) 

was fixed at 24% of total fertilizer N added, as assumed in GREET.266 Phosphorus only contributes 

to  pollution  in  surface  water  due  to  runoff.  There  is  only  negligible  quantities  of  P  in  the 

atmosphere267 and no contribution to ground water pollution owing to the strong sorption of P to 

soil minerals. The P runoff (as phosphates) to surface water bodies was estimated at an average of 

7% of the total phosphorus added as fertilizer, which was found to vary between 1-14%.266 Based 

 

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on the report, it is also assumed that potassium does not partition to air or water and has minimal 

impact on air and water eutrophication. 

Collection of stover 

The  three  basic  operations  associated  with  stover  harvesting  are  windrowing,  baling,  and 

collection.268 A stalk chopper/windrower was selected to avoid collecting foreign material with the 

stover  feedstock.  For  baling,  square  balers  are  assumed,  as  square  bales  are  transported  more 

easily.  Moreover,  square  bales  have  the  advantage  of  higher  bulk  density,  thereby  reducing 

transportation costs.269, 270 For collection, tractors with bale forks are used, which handles a single 

bale at a time.268 All collection equipment were  assumed to run on diesel and the total diesel 

required for the full harvest of stover, excluding grain, were estimated from GREET at 3.58 gallons 

of  diesel  per  acre.  The  emissions  associated  with  burning  diesel  were  also  determined  from 

GREET.  It  must  be  noted  that  stover  collection  emissions  are  calculated  after  accounting  for 

storage,  transportation,  and  farm  handling  losses  of  8.4%,  2%,  and  2%  of  dry  stover 

respectively.238  The  emissions  from  the  decomposition  of  this  lost  biomass  are  dependent  on 

temperature and moisture content of the feedstock271 and  were estimated to vary between 2.3 to 

8.4 g CO2 e/MJ for the cellulosic ethanol processes.272 Therefore, for the CE process, an average 

of 5.35 g CO2 e/MJ was assumed. For Py-ECH, a value of 2.3 g CO2 e/MJ was calculated based 

on the ratio of carbon present in the feedstock biomass for the two processes.  

Processing 

LCI  data  for  the  Py-ECH  process  components  were  extracted  from  the  work  by  Lam  et  al..5 

Similarly, all data for the CE system were obtained from the Humbird et al. report on cellulosic 

sugar fermentations to ethanol by NREL.11 The emissions from electricity and biochar application 

(for the Py-ECH process only) were estimated using the MRO-West electrical grid data, which 

 

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comprises  the  states  of  Minnesota,  Iowa,  West  Wisconsin,  North  Dakota,  South  Dakota,  and 

Nebraska. To estimate the carbon credit from biochar application, the resident carbon percentage 

in biochar that is sequestered in the soil was estimated from literature to vary from 65-100 wt.%.273, 

274 Therefore an average value of 82.5% was assumed for this analysis. Biochar application has 

additional soil benefits, ranging from decreasing fertilizer requirement, reducing NOx emissions, 

and decreasing leaching of soil nutrients,42, 275 which offset emissions. However, these reductions 

are difficult to estimate and were not considered in this analysis.  

     At the central refinery, heat is required for the Py-ECH system. This heat was assumed to be 

provided  by  burning  natural  gas  with  a  heating  value  of  52.2  MJ/kg.  While  carbon  dioxide 

emissions from natural gas combustion at the PY-ECH refinery were measured stoichiometrically 

from methane combustion, associated NOx emissions were estimated from GREET. No external 

heat and power are required by the CE system as it burns the biomass lignin and the wastewater 

treatment sludge to provide sufficient heat and electricity for all plant utilities. In fact, there is an 

associated credit from selling excess electricity to the grid. Similarly, the Py-ECH depots are self-

sufficient in heat and power requirements owing to the heating value of the non-condensable gases 

(NCG) generated during pyrolysis. The NOx emissions due to the burning of the lignin and the 

sludge at the CE biorefinery and the NCG at the Py-ECH depots were estimated using a value of 

0.31 kg/MWhr of heating value of combusted fuel.11  

     Water use in both systems was assessed and compared.  Most of the water in the CE system is 

recycled by treating the wastewater and the only water consumption is the make-up well water to 

compensate for the water lost in cooling tower evaporation. The Py-ECH system utilizes water 

predominantly in the ECH and electrolysis units. While most emissions from the processing stages 

of CE and the Py-ECH are atmospheric in nature, there is one liquid stream (the treated 50% brine 

 

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solution  from  the  wastewater  treatment  plant)  in  the  CE  system11  that  is  considered  a  waste. 

However,  with  recent  advances  in  membrane-based  and  thermal-based  technologies  for  brine 

treatment, the concept of zero-liquid-discharge systems is fast emerging.276 Therefore, no liquid 

discharge stream was assumed in the present analysis. Consequently, no grey water consumption 

or water eutrophication was considered for the processing stage of the two systems. 

Transport 

For  transportation  of  corn  stover,  53  ft  trailer  trucks,  8.5  ft  wide,  and  13.5  ft  high  have  been 

selected.268, 277The transportation weight limit of 80,000 lbs for roadways in Iowa was adopted in 

this analysis. Assuming the average dry weight of a 3 ft x 5 ft x 8 ft bale to be 950 lbs, the average 

wet weight for a similarly sized bale with 20% moisture is approximately 1,200 lb.277 If the weight 

of the trailer is assumed to be 30,000 lbs, then a maximum of 48 bales, amounting to about 50,000 

lb can be transported on a single trip. Because the volume constraints limit the maximum number 

of  bales  to  63,  the  weight  limitations  restrict  the  number  of  bales  per  truck  to  less  than  48. 

Additionally, the average corn stover collection radius from the fields to the biorefinery in the CE 

system is assumed to be 50 miles or 80.5 km.249 For the Py-ECH process, the distance between the 

corn fields and upgrading depots have been optimized for the lowest cost of finished fuel to be 7 

miles  or  about  11.5  km.  This  is  consistent  with  literature  data  that  predict  the  distance  to  be 

between 9 and 55 km.10 Similarly, the mean distance from a depot to the central refinery was also 

estimated to be approximately 51 km, by minimizing the final fuel price . The mean distance for 

the finished fuels from the refinery to the distribution terminals and then to the pumps is assumed 

to be 110 miles,278 which is the same for the Py-ECH and the CE systems. Truck mileage was 

assumed  to  be  5  miles/gallon  for  field-to-depot,  depot-to-refinery,  and  refinery-to-terminal 

 

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transportation  segments.  Fossil  diesel  is  assumed  to  be  the  fuel  for  all  transportation  and  the 

emissions associated with burning diesel were assigned using GREET. 

Combustion 

The  emissions  for  finished  fuel  combustion  were  calculated  for  the  complete  combustion  of 

ethanol and all hydrocarbons to CO2 for the CE and Py-ECH processes respectively. The NOx 

emissions, contributing to eutrophication potential were estimated from GREET. 

Allocation 

Corn stover, the crop residue from corn cultivation after grain harvest, is the selected feedstock for 

this analysis. Corn grain is a major food crop and has its own separate environmental impacts, 

depending on the method for processing corn grain. However, corn grain and stover are connected 

at the cultivation stage of the corn plant. The burdens and benefits due to cultivation emissions and 

below-ground carbon sequestration must be appropriately allocated between the grain and stover. 

Allocation is a controversial topic in LCA as it can lead to drastically differing results depending 

on how it is performed.  LCA methodologies in the literature describes different ways to avoid 

allocation in multifunctional processes, first by process subdivision or by system expansion.279, 280 

If allocation cannot be avoided then burdens must be allocated based on some biological, physical, 

or chemical relationships that link the system functions to process inputs or outputs. If such a 

physical relationship cannot be established, then the allocation can be based on other factors like 

economic value.  

     In the present analysis, allocation is an issue only in the cultivation stage of the two systems 

and therefore, it was decided to consider two scenarios, (a) Scenario 1: with no allocation (b) 

Scenario  2:  with  minimal  allocation.  In  Scenario  1,  allocation  was  avoided  by  making  the 

assumption  that  the  feedstock,  corn  stover  is  a  waste  product  of  the  corn  grain  generation 

 

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process.281 This assumption makes it possible to neglect any cultivation emissions or benefits that 

were shared with the corn grain, like soil carbon sequestration.  The only emissions from the 

cultivation stage in this scenario are from the production and application of the excess fertilizers 

to be added to the soil for removing the nutrients in the harvested corn stover that otherwise would 

be returned to the soil. In Scenario 2, mass-based allocation was performed when subdivision was 

not possible. The grain-to-stover mass ratio in a corn plant is approximately 1:1.282 However, since 

only 60% of the corn stover is harvested and 40% is left on the fields, the mass-based allocation 

percentage were evaluated from GREET to be 34% being allocated to stover. This means that only 

34% of the fertilizer emissions and the net carbon sequestration were allocated to stover in this 

scenario. As with Scenario 1, the second pass harvest emissions for stover “only” were similarly 

accounted for in Scenario 2. Apart from cultivation, emissions from all other stages were the same 

for both scenarios. These two scenarios were examined to reveal which allocation procedure has a 

more pronounced effect on the LCA results.  

Results and Discussion 

Life Cycle Impact Assessment (LCIA) 

The LCIA phase of the LCA quantifies the environmental impacts of the various emissions that 

were compiled in the life cycle inventory phase. In this study, the LCIA was completed for three 

impact categories:  GWP, EUP, and WSF.  

The total GWP, EUP, and WSF for the Py-ECH and CE processes, employing both allocation 

procedures,  is  summarized  in  Tables  5.8  and  5.9  (in  the  Appendix),  respectively  where  the 

contribution of the four primary areas in each impact category has been shown for both scenarios.  

Contribution Analyses 

Global Warming Potential (GWP) 

 

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As global climate change is accelerating, the GWP was calculated and compared for each system 

and allocation scenario. Figure 5.4 shows the contributions of different system components by 

highlighting  13  GWP  contributors  (as  listed  in  the  plot  legend)  for  the  two  systems  for  both 

scenarios. On this figure, the Py-ECH system is shown for the case when fossil electricity is used 

for  upgrading  during  ECH  and  hyroprocessing,  while  a  second  case  shows  Py-ECH  when 

renewable electricity is instead utilized. When renewable electricity is used to power the Py-ECH 

system, it outperforms the CE system in terms of GWP. However, if instead grid electricity is used 

for Py-ECH, the CE system is less impactful to GWP. 

Figure 5.4 : Global Warming Potential (GWP) Contribution Analysis ; Subscript 1 refers to 
Scenario 1 whereas Subscript 2 refers to Scenario 2.  ‘F’ refers to a fossil fuel electrical grid 

and ‘R’ refers to renewable power. 

 

This highlights the importance of clean electrical power in the Py-ECH system, which is expected, 

 

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owing to the large amount of electricity utilized for fuel upgrading. Another major feature of this 

plot is the difference in the amount of biomass feedstock (green bars) used for both systems. This 

difference in needed feedstock results as the CE system has a lower energy yield than Py-ECH and 

therefore  requires  greater  biomass  feedstock  to  make  the  same  amount  of  fuel  energy  (in  this 

analysis the functional unit is 1 MJ). Though somewhat counterintuitive, the CE system exhibits 

greater  benefits  from  biogenic  carbon  fixation  by  the  feedstock.  Though  there  are  increased 

emissions from harvesting, fertilizer application and fertilizer production, with handling a larger 

biomass  feed  in  the  CE  system,  these  emissions  are  counterbalanced  by  a  greater  amount  of 

feedstock carbon being fixed. Additionally, there is more carbon sequestration (cyan bar) from 

generating larger quantities of corn stover. The GWP contribution of the processing components 

for the two systems has been further subdivided here into four sub-components. The first sub-

component (dark blue bars) is the contribution for heat and power generation, which is much 

greater for the CE system because of lignin and wastewater sludge combustion.  For the Py-ECH 

system, these emissions are from heat production needed for performing pyrolysis. The power 

requirement for the Py-ECH system is met by grid electricity (orange bar), which also happens to 

be the second processing sub-component. In fact, the CE system gets a benefit from selling the 

excess  electricity  to  the  grid,  thereby  displacing  fossil  fuel  usage.  The  third  processing  sub-

component  is  the  carbon  dioxide  generated  by  fermentation  of  holocellulose  sugars,  a 

characteristic of the CE system that is not present in the Py-ECH system (light blue bar) The fourth 

sub-component is associated with co-products, which is excess electricity sold to the grid for the 

CE system and land applied biochar for the Py-ECH system. As biochar sequesters carbon when 

land applied, it has a negative value on Figure 5.4 (black bar). The biochar carbon reported is the 

net carbon sequestered after subtracting the non-resident carbon fraction (liberated as CO2) from 

 

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the resident carbon. The transport stage for both processes is negligible in comparison to the other 

stages  and  is  not  visible  in  Figure  5.4.  The  emissions  from  fuel  combustion,  although  not 

negligible,  does  not  distinguish  between  the  two  systems  as  it  is  nearly  equal  for  both.  The 

emissions associated with corn stover losses during harvesting, transport, and storage, are also 

minimal. Finally, there is not much difference between allocation scenarios within a single system 

as only a slight increase in all values results when using mass allocation vs. avoiding allocation.  

Eutrophication Potential (EUP) 

Similar to the GWP, the EUP contribution analyses are shown in Figure 5.5 for both systems, both 

scenarios,  and  the  renewable  electricity  Py-ECH  scenario.  From  Figure  5.5a,  the  cultivation 

component in the two systems dominates EUP in Figure 5.5a. The major cultivation contributors 

are  the  P  and  N  runoff  values  and  the  same  result  is  achieved  for  both  systems.  Fertilizer 

atmospheric  emissions  like  NOx  and  NH3  have  lesser  contributions.  The  contribution  from 

processing (denoted by blue and orange bars) for both systems is minimal, and not observable for 

the Py-ECH system. Transport and fuel combustion EUP are also negligible. To investigate the 

relative contribution of processing, transport, and fuel combustion, Figure 5.5b was constructed 

without the EUP contributions from cultivation. In comparison to Py-ECH, the CE processing 

component has a much higher EUP in part because of greater NOx emissions from the boiler-

combustor  of  the  CE  system,  which  combusts  a  larger  amount  of  fuel  (lignin  and  wastewater 

sludge) than the combustors in the Py-ECH system (NCG in depots and natural gas in refineries). 

Although the Py-ECH has additional emissions from utilization of grid electricity, the rate of NOx 

emissions is not nearly as high. The CE system also has atmospheric NH3 emissions from its 

wastewater treatment plant. From Figure 5.5b, the fuel combustion EUP values are similar for both 

systems and scenarios. Finally, the transport emissions are negligible and do not appear on Figure 

 

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5.5b. In contrast with GWP on Figure 5.4, there is a larger impact of allocation assumptions on 

EUP values. The difference across scenarios disappears when the cultivation stage is excluded, as 

is expected. In summary, it is important to recognize that although cultivation dominates in EUP 

contribution  analyses,  Py-ECH  performs  better  than  CE,  both  at  the  technological  level  and 

overall.

Figure 5.5 : Eutrophication  Potential (EUP) contribution analysis (a) Total EUP contribution 
analysis; subscript 1 refers to Scenario 1 whereas subscript 2 refers to Scenario 2. (b) EUP 

contribution analysis excluding cultivation and harvesting; F stands for fossil electricity and R 

stands for Renewable electricity. 

 

 

Water Scarcity Footprint (WSF) 

The WSF contribution analyses are shown in Figure 5.6 for both systems and scenarios, including 

the renewable electricity scenario for Py-ECH. WSF is only relevant in two components of the two 

systems, the supply chain and processing. There is no contribution in WSF from the transportation 

and fuel combustion areas. The biggest contributor to WSF for both processes is the cultivation 

component (green bar), which includes freshwater (blue water) consumption for agriculture but 

not precipitation, as discussed before. The water demand for cultivation for the CE system is much 

more than for the Py-ECH system, owing to the larger amount of biomass required to produce the 

same amount of fuel energy and therefore, larger land area to be cultivated. Py-ECH does require 

 

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water for the ECH and electrolysis units, however, the fresh make-up water for the CE system to 

compensate for cooling tower evaporation is much larger (blue bars). When powered by fossil 

electricity, the Py-ECH system consumes water because of water use at thermal power plants that 

generate electricity.  

 
Figure 5.6 : Water Scarcity Footprint (WSF) contribution analysis; subscript 1 refers to 

Scenario 1 whereas subscript 2 refers to Scenario 2. 

 

The CE system again benefits by selling the excess electricity it generates back to the grid. When 

Py-ECH  is  powered  by  fossil  electricity,  its  WSF  is  slightly  higher  than  CE  for  Scenario  1 

allocation assumptions. This is because, while Py-ECH suffers from indirect water usage due to 

grid electricity use, the system benefits from selling its excess electricity to the grid. This result 

changes when the Py-ECH system uses renewable electricity sources as the water consumption for 

electricity use is greatly reduced when using solar farms and is zero when using wind farms, as 

 

135 

plotted in Figure 5.6 (Py-ECH1—renewable electricity). Finally, the farming water consumption, 

which is the largest contributor, does not appear in the analysis of Scenario 1, which allocates all 

water  consumption  to  corn  grain,  as  stover  is  considered  a  waste  product.    As  more  water  is 

required  when  growing  more  biomass  over  a  larger  area,  CE2  has  the  largest  WSF.    Again, 

processing yields are very important for reducing environmental impacts. 

Sensitivity Analyses 

Sensitivity analyses are an important tool to investigate “what if” scenarios. These are especially 

relevant for studying dependence of output variables on sensitive input parameters, or when there 

is great variability in a certain input parameter. One result that is featured by the contribution 

analyses is the importance of the electricity source, particularly for the Py-ECH system. If the 

source is grid electricity, which is 29.2% renewable, then the GWP for the Py-ECH system is 

greater in comparison to the CE system. However, if the source is 100% renewable, then the Py-

ECH system performs better. Therefore, the GWP was plotted as a function of the percentage 

renewability of the MRO-West electrical grid and the result is shown in Figure 5.7. The GWP for 

the CE system increases with percentage renewability because greater renewability on the MRO-

grid means a lower amount of fossil electricity to displace, and hence a lower benefit.  On the 

contrary, GWP decreases for the Py-ECH system, because greater percentage renewability leads 

to lower emissions from utilizing the same amount of grid electricity. A key observation from 

Figure 5.7 is that the negative slope of the Py-ECH line is more than the positive slope of the CE 

line because the amount of electricity required by the Py-ECH electricity is greater than the excess 

electricity sold by the CE system to the grid.  A key result is that the CE system performs better 

than the Py-ECH system until the percentage renewability in the electrical grid is about 87.4%. 

Greater percentages make the Py-ECH system perform better in terms of GWP.  

 

136 

Figure 5.7 : Sensitivity of system GWP with % renewable in MROW Electricity Grid. (For 

 

Scenario 2) 

As stated before, there is great variability in literature in the value of annual carbon sequestration 

data. Carbon sequestration varies based on soil texture, rainfall, tillage, stover removal, soil depth 

measured, crop rotation system and geographical location. To investigate the effect of variation of 

this assumption on the total GWP, the annual carbon sequestration was varied from 0 to 2.5 Mg 

C/ha/yr for a continuous corn system, with no tillage, and 60% stover harvest. The two limits were 

chosen as most data fell within this range.252-258 Figure 5.8, which was a result of this exercise, 

shows that for low sequestration values, the Py-ECH, with renewable electricity performs better 

than the CE system. This is owing to the fact that there is less biomass involved in the Py-ECH 

system  for  producing  the  same  amount  of  fuel  energy  and  therefore,  lesser  benefit  from  C 

sequestration. This also explains the difference in the slopes of the two lines, with the CE system, 

depicted by the blue line, having a larger slope. However, the Py-ECH system, when using fossil 

or grid electricity is never better than the CE system, no matter what the annual C sequestration 

 

137 

rate assumption. Two sequestration values from literature, indicated by red dashed lines, have been 

plotted in Figure 5.8.  

Figure 5.8 : Sensitivity of system Global Warming Potential (GWP) with annual C 

sequestration rate for Scenario 2. 

 

The first value is from GREET and has been used in the present analysis. The second value is from 

Follett et al.,283who determined a value of 1.3 Mg C/ha/yr for no till corn stover, with a 50% 

removal, when soil depth up to 150 cm is considered. This amounts to 1.08 Mg C/ha/yr for 60% 

removal, assuming a linear dependence. It is evident that the Follett assumption will benefit the 

CE system more and in fact, make it a more carbon negative system than the Py-ECH system.  It 

is important to note that this result is restricted by the choice of functional unit, in this case 1 MJ 

of fuel produced.  Because Py-ECH requires substantially less biomass and land area, opportunities 

for sequestering carbon by cultivating land not planted with corn for bioenergy production should 

be explored.  For example, if this unplanted land were allowed to become natural forest and was 

 

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included in the Py-ECH system boundary to equalize land area with the CE system, then Py-ECH 

GWP would be far lower than CE for all values of carbon sequestration.  

Energy Ratio 

To  evaluate  the  energy  efficiency  in  terms  of  renewable  energy  produced  vs.  fossil  energy 

consumed, there are several different energy ratios that may be calculated to measure the efficacy 

and renewability of the system (Table 5.1).279 Five such energy ratios have been calculated and 

presented in Table 5.2 for the two different processes and for the Py-ECH scenario when renewable 

electricity is used.  

Energy Ratio 

Total Energy Ratio 

Table 5.1 : Description of energy ratios279 

Abbreviation 

ERt 

Total usable energy output/ Total energy 

Description 

input 

Renewability Factor 

Energy Yield 

Energy Return on Investment 

Fossil Energy Ratio 

RF 
Ey 

EROI 

ERf 

Fuel energy output/ Fossil energy input 

Fuel energy/Feedstock energy 

Total energy of fuel and co-products/Energy 

invested in the process 

Fuel energy + co-product energy output/ 

Fossil energy input 

 
While ERt is a measure of the total efficiency of the system and accounts for both product and co-

product energy, the Ey only calculates what fraction of the feedstock energy resides in the primary 

product, which is the fuel. Therefore, the ERt considers the energy associated with excess hydrogen 

gas in the Py-ECH system and the excess electricity in the CE system. Biochar in the Py-ECH 

system is not considered an energy co-product since it is land applied to sequester carbon. EROI 

is similarly defined as ERt, with the exception that it does not include the energy associated with 

the biomass feedstock input. It is more concerned with the additional energy inputs to the process 

that are essential for manufacturing the fuel and the co-products. However, ERt, Ey, and EROI do 

not distinguish between energy sources for the inputs and do not measure renewability of the 

system. Although there are other ways to determine the renewability of a system, such as Energy 

 

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Renewability Efficiency,279  RF is the most simple and transparent method. It is the ratio of the 

energy of the primary product (the fuel in this case) and all non-renewable energy inputs. The 

higher the RF, the greater is the renewability of the system. ERf on the other hand is the ratio of 

all energy products (fuel and co-products) and all fossil energy inputs. It can be seen from Table 

5.2 that the RF, ERf, and EROI of the CE system is greater than for the Py-ECH system. This is 

because the CE system manufactures its own heat and power by combusting some of its biomass 

feed, thereby greatly reducing non-renewable inputs resulting in a larger RF and ERf. Similarly, 

for the EROI, which excludes the feedstock energy as an input, the CE system gains over the Py-

ECH system, as it is using a part of the biomass feed as heat and power. The Py-ECH on the other 

hand, relies on grid electricity, reducing its RF, ERf and EROI. However, whenever the energy 

ratios include biomass feed as an energy input, the Py-ECH performs better, since it is overall 

more energy efficient than the CE system, and the biomass feed energy is the greatest contributor 

to  all  energy  inputs.  Therefore,  the  Py-ECH  has  a  higher  ERt  and  Ey.  When  using  renewable 

electricity, the RF and ERf of the Py-ECH system increases substantially due to the much lower 

fossil energy inputs. There is, however, no change in the ERt, Ey and EROI of the system.  

Table 5.2 : Energy ratios for the CE and Py-ECH systems. ERt stands for Total Energy Ratio; 
RF stands for Renewability Factor; Ey stands for Energy Yield; EROI stands for Energy Return 

on Investment; ERf stands for Fossil Energy Ratio. FE stands for Fossil electricity while RE 

represents renewable electricity 

Py-ECH (FE) 

Py-ECH(RE) 

0.70 
8.22 
0.91 
1.85 
10.03 

Energy Ratio 

ERt 
RF 
Ey 

EROI 
ERf 

CE 
0.44 
9.66 
0.42 
10.43 
10.40 

0.70 
1.98 
0.91 
1.85 
2.42 

 

The  reasons  behind  the  Py-ECH  system’s  lower  RF  and  ERf  is  the  utilization  of  fossil  grid 

electricity and the combustion of natural gas for heat at the central refinery. If the grid electricity 

is more renewable and the source of heat at the central refinery is made renewable, then the RF 

 

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and ERf of the Py-ECH system markedly increases, as seen in Figure 5.9. It is clear that the CE 

system would have a high RF of about 10, owing to very little fossil energy input, since the biomass 

lignin itself is burnt for heat and power. While Figure 5.9 (a) shows the variation of RF and ERf 

with % renewable heat at the refinery, Figure 5.9 (b) shows the variation of RF and ERf with % 

renewable electricity. If either the electricity or heat source is 100% fossil based, then the Py-ECH 

system can never match the RF and ERf of the CE system. However, if the heat source at refinery 

is 100% renewable, then the Py-ECH system has a better RF and ERf at about 80-90% renewable 

electricity. Similarly, if the electricity source is 100% renewable, the heat source at refinery would 

have  to  be  at  least  25%  renewable  and  5%  renewable  for  the  Py-ECH  system  RF  and  ERf, 

respectively, to overtake that of the CE system. 

Figure 5.9 : Sensitivity of system EROI with (a) % renewable heat at refinery and (b) % 

renewable electricity 

 

Conclusion 

From the present analysis, it is clear that that the environmental performance of the Py-ECH system 

is better than that of the CE system, when the source of electricity is renewable, such as wind or 

solar photovoltaics. The sensitivity analysis in Figure 5.7 shows that this source only has to be 

 

141 

about 87% renewable for Py-ECH to perform better than CE. The eutrophication potential and the 

water scarcity potential for the Py-ECH system is always lower than that for the CE system. Even 

the renewability factor, which measures the amount of renewable energy generated based on the 

amount of fossil energy inputs, also surpasses that of the CE system when the source of electricity 

is about 85% renewable. This is remarkable since the CE system uses the lignin of the feedstock 

biomass to generate its own heat and power and therefore, has a very high renewability factor. To 

summarize, an electricity source that is more than 87% renewable, makes the Py-ECH system 

more environmentally favorable than the CE system in all the aspects that have been studied in 

this analysis. This is significant since the GHG emissions of the CE system, as presented here, are 

itself 10-15% of that for gasoline from crude oil for a 20 year time horizon.284Therefore, the Py-

ECH system, with 87% renewable electricity improves upon the environmental performance of 

the CE system while maintaining a produced fuel with same energy content as that of gasoline. 

The  Py-ECH  system  has  lesser  GHG  emissions  than  that  for  gasoline,  even  without  the 

improvement  in  renewability  of  electricity  sources.  It  must  be  recognized  that  the  Py-ECH 

technology is not yet at the same technology readiness level as the CE process or the production 

of gasoline from crude oil. However, this analysis excludes certain benefits of the Py-ECH system, 

such as the effect of biochar on the reduction of nitrous oxide emissions. Furthermore, the rate of 

biochar degradation, in literature, has been determined to be very slow.285 Kuzyakov et al., in fact 

measured a degradation rate of 0.5% every year under optimal conditions. This amounts to 90% 

biochar carbon retained in 20 years, significantly higher than the assumed 82.5% in this analysis. 

Furthermore, they estimate a mean residence time of 2,000 years under natural conditions and a 

half-life  of  1,400  years,  which  is  significantly  larger  than  the  time  horizon  considered  in  this 

analysis  for  there  to  be  any  biochar  carbon  decomposition.286  Another  key  parameter  that 

 

142 

disadvantages Py-ECH climate change potential compared to CE, is the requirement of greater 

biomass feedstock for the CE system, owing to its lesser energy yield. The extra land area that is 

saved in the Py-ECH system can be dedicated to forestland to sequester more carbon and reduce 

the GWP of the Py-ECH system. In fact, the annual rate of carbon accumulation in forests can vary 

from 0.8 tonnes/ha/yr to 5.1 tonnes/ha/yr, depending on the type of forest.287 The CCLUB model 

from GREET estimates an annual carbon sequestration rates from forests in the United States at 

2.4 tonnes C/ha/yr, which is well within this range. Although this would make the eutrophication 

potential and water scarcity footprints of the two systems almost equal, it would be interesting to 

find out the resultant impact of stacking these improvements on the GWP of the Py-ECH system. 

This has been shown in Figure 5.10 for both allocation scenarios considered in the analysis. The 

annual  carbon  sequestration  rate  for  forests  have  been  assumed  conservatively  to  be  0.8 

tonnes/ha/yr, the lower limit of the range of values found in literature. Higher values will only 

lower the global warming potential further.  

Figure 5.10 : Waterfall chart showing the resultant global warming potential of stacking 

improvements in the Py-ECH system. 

 

From Figure 5.10, it is clear that stacking improvements in the source of electricity and the quality 

of biochar produced can lower the GWP of the Py-ECH system to values that are almost carbon 

 

143 

neutral for Scenario 1. Furthermore, the Py-ECH system can become carbon negative if the excess 

land area that is saved, owing to its better energy yield, can be assigned to forests. Given the 

apparent advantages of a Py-ECH system operated with renewable electricity vs. a CE system for 

the three environmental impacts examined in this analysis, there remains ample opportunity for 

improvements that can lead to carbon negative values for the Py-ECH system. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 

 

144 

APPENDIX

 

145 

Life Cycle Inventory 

Life cycle data was collected from different sources in literature and qualified using data quality 

indicators, as described by the modified Weidema method in Table 5.3.247 The scoring range is 

from 1-5, with 1 indicating that the data is most reliable while 5 is least reliable. Each data was 

qualified  using  a  6-digit  score,  with  each  digit  ranging  from  1  to  5.  Each  digit  represents  the 

reliability of the data in each of the 6 categories, with the first digit representing parameter A and 

the last digit representing parameter F respectively. 

Table 5.3 : Parameters in the modified Weidema method and the description of the scores 

(adapted from Couillard et al.).248 

Parameter 

Description 

 
A  Acquisition method  Method of acquiring the data, 
whether measured directly or 
estimated based on assumptions 
Source of information and its 
bias towards the concerned 
study 

B  Independence of 

data supplier 

C  Representativeness  Degree of application of data to 

even out fluctuations 

D  Data age 

Temporal relevance of data 

E  Geographical 

correlation 

F  Technological 

correlation 

Spatial relevance of data 

Technological relevance of data 
with processes in study 

Scoring range 
1: measured data 
5: non-qualified estimate 

1: independent verified source 
5: unverified source with bias 

1: representative data from 
sufficient large samples 
5: incomplete data from 
relatively small samples 
1: less than 3 years 
5: unknown or greater than 20 
years 
1: data from area under study 
5: data from area unknown or 
different conditions 
1: data from processes under 
study 
5: data from related processes 
but different technology 

 

 

Table 5.4 : Life cycle input data for both Py-ECH and CE systems with their source and data 

quality indicator score. 

Parameter 

Value 

Source 

Technical Data for Py-ECH system 

Technical Data for CE system 
Annual C sequestration rate 

 
 

0.174 Mg C/ha/yr 

146 

Lam et al.,5 

Humbird et al., 

GREET238 

Data quality 

indicator 
(ABCDEF) 

211111 
211321 
211321 

Table 5.4 (cont’d) 

Corn stover storage losses 
Corn stover transport losses 

Corn stover farm handling losses 
Diesel for harvesting corn stover 
Fraction of N leached to surface 

waters 

waters 

NH3 

N2O 

Fraction of fertilizer N emitted as NO 

Fraction of fertilizer N emitted as 

Fraction of fertilizer N emitted as 

system 

Weight limitation on trucks 

Distance from refinery to pumps for 

both Py-ECH and CE systems 

8.40% 
2.00% 
2.00% 

3.58 gallons/acre 

24% of total 
applied N 

P 

0.8% of total 
applied N 
10% of total 
applied N 
1.5% of total 
applied N 

GREET238 
GREET238 
GREET238 
GREET238 
GREET238 

Powers et 

al.,266 

GREET238 

IPCC265 

GREET238 

GREET238 
Kim et al.,249 

80,000 lbs 

110 miles 

Edwards et 

al.,268 

Kumar et al.,278 

211321 
211321 
211321 
211321 
211321 

311421 

211321 

211421 

211321 

211321 
212122 

221311 

212322 

Corn stover yield 

Distance from field to refinery for CE 

2.39 dry ton/acre 

50 miles 

Fraction of P leached to surface 

7% of total applied 

 
Tables 5.5, 5.6 and 5.7 show the values of  contribution of each unit operation to the total GWP, 

EUP and WSF of both systems for both scenarios while Tables 5.8 and 5.9 depict the life cycle 

impact summary of the Py-ECH and CE systems respectively. 

Table 5.5 : Contribution of each operation in the determination of Global Warming Potential 

(GWP) for both CE and Py-ECH systems 

Operations 

 
 

Supply Chain: 

Harvesting 

Fertilizer Application 
Fertilizer Production 

Below ground C sequestration 
Feedstock Biomass to Energy 

Corn Stover Losses 

Processing 

Heat and Power 
Fermentation CO2 

 

Scenario 1 

Scenario 2 

CE 

Py-ECH(F) 

CE 

Py-ECH(F) 

 

2.09 
1.17 
1.19 
0.00 
-64.20 
2.03 

 

12.40 
0.00 

(g CO2/MJ) 
 

 

5.51 
1.26 
8.33 
-13.70 
-235.00 

5.35 

 

132.00 
39.50 

2.09 
4.78 
7.36 
-5.19 
-64.20 
2.03 

 

12.40 
0.00 

(g CO2/MJ) 

 

5.51 
3.09 
3.15 
0.00 

-235.00 

5.35 

 

132.00 
39.50 

147 

Table 5.5 (cont’d) 
Feedstock Biomass to Biochar 

Electricity 
Transport 

Transportation 
Combustion 

Fuel combustion 

 

0.00 
-12.40 

 

1.46 

 

71.00 

 

-13.30 
84.80 

 

0.37 

 

64.10 

 

0.00 
-12.40 

 

1.46 

 

71.00 

 

-13.30 
84.80 

 

0.37 

 

64.10 

 

 
Table 5.6 : Contribution of each operation in the determination of Eutrophication Potential 
(EUP) for both CE and Py-ECH systems. 

Operations 

Scenario 1 

Scenario 2 

 
 

Supply Chain: 

Harvesting 

Fertilizer Application 

Nitrogen runoff 
Phosphorus runoff 
Fertilizer Production 

Processing 

Refinery operations 

Electricity 
Transport 

Transportation 
Combustion 

Fuel combustion 

 

CE 

Py-ECH(F) 

(mg CO2/MJ) 
 

 

CE 

Py-ECH(F) 

(mg CO2/MJ) 
 
 

4.18 
6.86 
111.00 
70.20 
0.35 

 

15.10 
-0.43 

 

1.46 

 

71.00 

 

1.58 
2.60 
42.00 
26.60 
0.13 

 

0.66 
2.94 

 

0.37 

 

64.10 

 

4.18 
28.00 
452.00 
139.00 
0.80 

 

15.10 
-0.43 

 

1.46 

 

71.00 

 

1.58 
10.60 
171.00 
52.70 
0.66 

 

0.66 
2.94 

 

0.37 

 

64.10 

 

 
Table 5.7 : Contribution of each operation in the determination of Water Scarcity Footprint 
(WSF) for both CE and Py-ECH systems. 

Operations 

Scenario 1 

Scenario 2 

 
 

Supply Chain: 

Farming 

Fertilizer Production 

Processing 

Refinery operations 

Electricity 

 

CE 

Py-ECH(F) 

CE 

Py-ECH(F) 

 

0.00 
0.002 

 

0.38 
2.18 

(L H2O/MJ) 

 

12.80 
0.009 

 

2.42 
-0.32 

 

4.85 
0.004 

 

0.38 
2.18 

(L  H2O/MJ) 
 

0.00 
0.006 

 

2.42 
-0.32 

148 

Table 5.7 (cont’d) 

Transport 

Transportation 
Combustion 

Fuel combustion 

 

 

0.00 

 

0.00 

 

 

0.00 

 

0.00 

 

 

0.00 

 

0.00 

 

 

0.00 

 

0.00 

 

 
Table 5.8 : Life cycle impact summary for the CE process.  Allocation scenarios 1 and 2 are 
reported to observe the effects of avoiding allocation and applying minimal allocation to 
cultivation 

GWP 

2 
1 
kg CO2 eq /MJ 

EUP 

1 
kg N eq/MJ 

2 

WSF 

2 
1 
L H2O/MJ 

-2.18E+02 
1.60E+02 
1.46E+00 
7.10E+01 
1.44E+01 

-2.17E+02 
1.60E+02 
1.46E+00 
7.10E+01 
1.54E+01 

1.92E-01 
1.46E-02 
3.02E-05 
1.11E-03 
2.08E-01 

6.24E-01 
1.46E-02 
3.02E-05 
1.11E-03 
6.39E-01 

5.98E-02  1.29E+01 
2.10E+00  2.10E+00 

0 
0 

0 
0 

2.16E+00  1.50E+01 

 
Table 5.9 : Life cycle impact summary for the Py-ECH process.  Allocation scenarios 1 and 2 
are reported to observe the effects of avoiding allocation and applying minimal allocation to 
cultivation. 

 

Allocation 

 

Supply 
Chain 

Processing 
Transport 
Combustion 

TOTAL 

  

  

Allocation 

Supply Chain 
Processing 
Transport 
Combustion 

TOTAL 

 

 

 

 

 

 

GWP 

 2 
 1 
kg CO2 eq /MJ 

EUP 

 2 
 1 
kg N eq/MJ 

WSF 

2 
1 
L H2O/MJ 

2.27E-02  4.89E+00 
2.56E+00  2.56E+00 

0 
0 

0 
0 

2.58E+00  7.45E+00 

-5.77E+01 
8.39E+01 
3.68E-01 
6.41E+01 
9.07E+01 

-5.31E+01 
8.39E+01 
3.68E-01 
6.41E+01 
9.52E+01 

7.29E-02 
3.60E-03 
7.61E-06 
1.07E-03 
7.76E-02 

2.37E-01 
3.60E-03 
7.61E-06 
1.07E-03 
2.41E-01 

149 

Chapter 6 : KINETIC MODELING FOR ECH OF PHENOL 

Abstract 

Electrocatalytic  hydrogenation  (ECH)  can  play  a  key  and  integral  role  in  carbon  and  energy 

efficient bioenergy systems that focus on conversion of biomass to liquid transportation fuels. One 

such  bioenergy  system  is  the  Py-ECH  system  that  utilizes  ECH  as  a  stabilizing  step  for  the 

reduction of highly reactive functional groups like aldehydes, ketones and other aromatics present 

in bio-oil (derived from biomass fast pyrolysis) to more stable alcohols and cycloalkanes. This 

stabilization is essential for the storage and transportation of pyrolysis bio-oils, a key factor in 

decentralized  bioenergy  systems  like  the  Py-ECH  system.  In  this  study,  a  kinetic  model  was 

developed that studies the ECH of phenol to cyclohexanol using a rotating disk electrode (RDE), 

with activated carbon cloth supported ruthenium acting as the catalytic working electrode. The 

goal of the study was to evaluate the kinetic rate constants associated with the electrochemical, 

surface, adsorption, and desorption reactions involved in the ECH of phenol to cyclohexanol. This 

may be achieved by extracting current-voltage data from linear sweep RDE experiments and then 

fitting the developed kinetic model to these data by solving the inverse problem. The development 

of  this  kinetic  model  is  a  crucial  step  in  the  eventual  goal  of  scaling  up  the  electrocatalytic 

hydrogenation process for the commercial application of specific bioenergy systems like the Py-

ECH system. 

Introduction 

Bioenergy  systems  that  employ  fast  pyrolysis  to  convert  biomass  to  transportation  fuels  often 

employ a subsequent upgrading step that converts the intermediate pyrolysis bio-oil to the final 

hydrocarbon transportation fuel.19, 36, 45, 46, 60, 84, 85, 97, 288 Most often, it is a hydroprocessing step 

that subjects the bio-oil to high temperatures and pressures, converting it to a gasoline/diesel range 

 

150 

product. However, the concept of incorporating local biomass processing depots22, 68, 72, 76 into 

bioenergy systems102 requires that new technologies be developed that can stabilize the pyrolysis 

bio-oil  at  these  local  processing  depots,  to  create  an  intermediate  fit  for  long-distance 

transportation. Hydroprocessing is difficult to be implemented at these small-scale local depots 

due to the extreme conditions employed and the need for storage of flammable H2 gas. In this 

regard,  electrocatalytic  hydrogenation  (ECH)  presents  an  opportunity  to  be  utilized  as  a  key 

technology  for  pyrolysis  bio-oil  stabilization,  owing  to  its  much  milder  requirements  of 

temperature  and  pressure.  Bioenergy  systems,  like  the  Py-ECH  system,  have  explored  the 

incorporation of ECH in the overall conversion of biomass to liquid transportation fuels.5 In this 

system, biomass is first converted to a stable bio-oil via fast pyrolysis and subsequent ECH at a 

local  depot  facility,  which  is  then  transported  to  a  central  hydroprocessing  facility  for  final 

conversion to a gasoline/diesel range fuel. 

The kinetics of the ECH of pyrolysis bio-oil is key to its scale-up and subsequent commercial 

application of the Py-ECH bioenergy system. Previous kinetic studies have investigated the ECH 

of different model bio-oil compounds in the presence of different catalysts. Such studies usually 

also involve the kinetics of the hydrogen evolution reaction (HER), which is an accompanying 

side reaction that is undesirable in context of bio-oil ECH. The kinetics of HER have also been 

studied separately by Oshchepkov et al.,289, 290 who investigated the effect of nickel oxide species 

and temperature on HER kinetics. Moreover, some of these have studies have only considered the 

surface and adsorption reactions and not mass transfer effects.130, 139, 140, 142, 144  To minimize the 

effect of slow mass transfer, Singh et al. created a reaction controlled regime by rotating the entire 

electrochemical  cell  at  500  rpm.143  Tourwe  et  al.,  provided  a  theoretical  model  for  evaluating 

kinetics from electrochemical experiments by using a rotating disc electrode setup,291 but studied 

 

151 

metal  deposition  instead  of  electrocatalytic  hydrogenation.    However,  such  a  model  may  be 

adopted and modified for the Py-ECH system to study the ECH of a model bio-oil compound.  

Therefore, in the present study, the kinetics for the ECH of phenol (as model bio-oil compound), 

in the presence of Ru/ACC (ruthenium supported on activated carbon cloth) catalyst was studied. 

Such a selection of catalyst in the Py-ECH system is owing to previous studies that investigated 

different catalysts to determine the conversion and current efficiency for the ECH of model bio-

oil compounds.32, 34, 135 Phenol is selected because of its simplicity owing to the absence of any 

other functional groups or side alkyl chains that can lead to different ECH products, depending on 

the alkyl group length and position.136 ECH of phenol, on the other hand, is known to produce 

cyclohexanol in the presence of Ru/ACC catalyst.32, 136 However, its kinetics in the presence of 

Ru/ACC catalyst have not been investigated. In this regard, the goal of this study was to estimate 

the kinetic rate constants associated with the electrochemical, adsorption, desorption, and surface 

reactions involved in the ECH of phenol. 

Model Formulation 

Reaction Mechanism 

According to Li et al.32 and Dabo et al.,26 ECH of a model bio-oil compound proceeds via the 

following steps: (1) adsorption of atomic hydrogen on the catalyst surface, (2) adsorption of the 

substrate (the model bio-oil compound), (3) reaction of adsorbed substrate and  adsorbed hydrogen, 

(4) additional hydrogenation reactions of substrate depending on its complexity, and (5) desorption 

of the formed products. 

Along with the hydrogenation reaction, there is also the competing H2 evolution reaction that may 

occur via two reaction pathways, namely the Tafel and the Heyrovsky reactions. The Tafel reaction 

is given by:                                                                 

 

152 

The Heyrovsky reaction is given by:        

s(")+s(")→"$+2s 
s(")+"G+yH→"$+s 

where M(H) represents chemisorbed H and M is the active metal site. 

A similar reaction mechanism was assumed for the ECH of phenol on Ru/ACC. 

Model Development 

 

!z//yMw=l(C(w),ÇT,ÇTU,ÄM'jM	2)/)(ywy/*) 

The objective of the model was to develop an expression of the form:291 

Where E(t) is the voltage as a function of time, ÇT,ÇTU are the forward and backward rate constants.  

The model assumptions were as follows: 

•  The reactions are at steady state. 

•  Physical properties like diffusion coefficients and kinematic viscosities are constant. 

•  Solutions are dilute 

•  All cathode reactions are reversible. 

Based on the reaction mechanism described in the last section, the different reactions happening 

at the cathode for the ECH of phenol on Ru/ACC are provided below: 

"'#G+s+yH↔s"+"$# 
-ℎ+s↔(-ℎ)s 
(-ℎ)s+6s"↔(!.")s+6s 
(!.")s↔!."+s 
s"+"'#G+yH↔"$+M+"$# 
s"+s"↔"$+2M 

 

 

153 

(1) 

(2) 

(3) 

(4) 

(5) 

(6) 

The  forward  and  backward  kinetic  rate  constants  for  each  of  the  above  cathode  reactions  are 

represented by ÇT,ÇTU ,where the ‘j’ is the reaction number; M is an active metal site, Ph is Phenol 
reactions, ,K  electroactive  species  in  electrolyte  and ,>  electroactive  species  present  in  an 

and  CyH  is  cyclohexanol.  If  there  are  P  partial  heterogeneous  electrochemical  or  chemical 

adsorbed phase on electrode surface, then:                                      

,=,K+,> 

Σ-V!8 :/-TÜ-<↔Σ-V!8 :2-TÜ-<±MTy 

Also,                                                                                

where i is the index for the considered species and j is the index for the partial reactions. 

where,   ÇT=Ä2Texp9−W%$.$.XO(=)
6[

Under steady state, the rate is given by, uT=ÇTΠ-V!8 :Ü-2#$<−ÇTUΠ-V!8 :Ü-?#$< 
=	; ÇTU=Ä7Texp9W&$.$.XO(=)
6[
ä7T+ä2T=1; &+=rΣTV!? *:TuT, where *:T=±MT 
u\#=ã\#=−(\#[Ü-∗−Ü-]=ΣTV!? :*-TuT<       for i=1,2,…..,Nv 
  u\#= ã\#=0                                                     for i=Nv+1,….,N 

transport flux from the bulk to the surface given by: 

= 

where    (\#=0.62t\#$/'éH!/IΩ!/$ 

At steady state, the rate of transformation of each species at the interface must equal the mass 

Table 6.1 : Reaction rate expressions 

Reaction 

Expression 

é!=Ç!Ü5'4(ê*−Ç!Uê*5 
é$=Ç$Ü/^ê*−Ç$Uê(/^)* 
é'=Ç'ê(/^)*ê*5I −Ç'Uê(305)* 
é#=Ç#ê(305)*−Ç#UÜ305ê* 
é(=Ç(Ü5'4(ê*5−Ç(Uê*-5! 
éI=ÇIê*5$ −ÇIUê*$-5! 

1 
2 
3 
4 
5 
6 

 

154 

The mass transport rate constant (\# can be calculated from values of the diffusion coefficient of 

each species, the kinematic viscosity of the solution, and the rotation speed of the RDE, which can 

be obtained from the literature or experiment. The rate expressions for the reversible cathodic 

reactions may be expressed as in Table 6.1, considering the concentration of water is constant due 

to the assumption of a dilute solution. Now, the rate of transformation of each electroactive species 

at the electrode surface is given by: 

 
From Table 6.1 and the above six equations, it can be concluded, that at steady state: 
 

Ç$Ü/^ê*−Ç$Uê(/^)*−Ç'ê(/^)*ê*5I +Ç'Uê(305)*ê*I=0 
Ç'ê(/^)*ê*5I −Ç'Uê(305)*−Ç#ê(305)*+Ç#UÜ305ê*=0 

 

155 

é5'4(=−é!−é(   
 é*5=é!−é'−é(−éI 
é/^=−é$ 
  é(/^)*=é$−é' 
é305=é# 
  é(305)*=é'−é# 
é5'4(=−é!−é(=−(5'4(ëÜ5'4(
∗
 é*5=é!−é'−é(−éI=0 
é/^=−é$=−(/^[Ü/^∗ −Ü/^] 
 é(/^)*=é$−é'=0 
é305=é#=−(305ìÜ305∗ −Ü305î 
é(305)*=é'−é#=0 

 

 

 

 

 

−Ü5'4(í 

Therefore, we have: 

Ç!Ü5'4(ê*−Ç!Uê*5−Ç'ê(/^)*ê*5I +Ç'Uê(305)*ê*I−Ç(Ü5'4(ê*5+Ç(Uê*-5!−ÇIê*5$
+ÇIUê*$-5!=0 
−Ü5'4(í=0 
Ç!Ü5'4(ê*−Ç!Uê*5+Ç(Ü5'4(ê*5−Ç(Uê*-5!−(5'4(ëÜ5'4(
∗
Ç$Ü/^ê*−Ç$Uê(/^)*−(/^[Ü/^∗ −Ü/^]=0 
Ç#ê(305)*−Ç#UÜ305ê*+(305ìÜ305∗ −Ü305î=0 
ê*+ê*5+ê(/^)*+ê(305)*−1=0 
Ç!=Ä!yï2ñ−ä!M!rC(w)
x~
Ç$U=Ä$U  
Ç$=Ä$ 
Ç'U=Ä'U  
Ç'=Ä' 
Ç#U=Ä#U  
Ç#=Ä# 
Ç(=Ä(yï2ñ−ä(M(rC(w)
x~
ÇI=ÄI 
ÇIU=ÄIU  

ó  Ç!U=Ä!Uyï2ò−ä!UM!UrC(w)
ô 
x~
ó  Ç(U=Ä(Uyï2ò−ä(UM(UrC(w)
ô 
x~

Table 6.2 : Expressions for reaction rate constants 

Backward rate constant 

Forward rate constant 

Reaction 

1 

2 
3 
4 
5 

6 
 

 

 

 
The expression for each of the forward and backward reaction rate constants are provided in Table 

6.  The  bulk  concentrations  may  be  experimentally  measured,  which  hardly  change  during  the 

length of a typical RDE experiment. The mass transfer coefficients, (- are constants that can be 
variables and system parameters Ç-. 

the number of equations can be solved to determine the unknown variables in terms of known 

estimated from literature. Therefore, a system of equations where the number of variables equals 

The model may be simplified by assuming that only adsorption/desorption reactions are reversible. 

All  other  surface  and  electrochemical  reactions  are  irreversible.  This  is  in  accordance  with  a 

previous study of ECH of phenol over Pd, Ni and Rh based catalysts.292 Therefore: 

Ç!U=0;	Ç'U=0;	Ç(U=0 

 

156 

Now, only 6 parameters (six Ki ) are to be estimated. 

Thus, the above system of equations can now be represented as: 

Ç!Ü5'4(ê*−Ç'ê(/^)*ê*5I −Ç(Ü5'4(ê*5−(ÇI)(ê*5$ −ê*5$ /(ê*$-5!))=0 

Ç$U=Ç$Ü/^ê*/ê(/^)* 
Ç#U=Ç#ê(305)*/Ü305ê* 
ÇIU=ÇIê*5$ /ê*$-5! 
(Ü/^ê*−Ü/^ê*
ê(/^)*)(Ç$)−Ç'ê(/^)*ê*5I =0 
Ç'ê(/^)*ê*5I −(Ç#)(ê(305)*−ê(305)*/(Ü305ê*))=0 
Ç!Ü5'4(ê*+Ç(Ü5'4(ê*5−(5'4(ëÜ5'4(
−Ü5'4(í=0 
∗
(Ç$)(Ü/^ê*−Ü/^ê*
ê(/^)*)−(/^[Ü/^∗ −Ü/^]=0 
(Ç#)(ê(305)*−ê(305)*/(Ü305ê*))+(305ìÜ305∗ −Ü305î=0 
ê*+ê*5+ê(/^)*+ê(305)*−1=0 
&+=r(−é!−é()=−r(é!+é()=−r(é'+2é(+éI) 
Therefore:              &+=−r(Ç!Ü5'4(ê*−Ç!Uê*5+Ç(Ü5'4(ê*5−Ç(Uê*-5!) 
Where Ü5'4(, ê*5, and ê* may be expressed in terms of Ç- from the system of equations and 
substituted in this expression. -5!, on the other hand, may be assumed a constant for the time 

The Faradaic current is determined from the rates of partial electrochemical reactions as: 

 Furthermore, we can assume that the adsorption/desorption reactions are always at equilibrium. 

 

 

span of the RDE experiment. 

An expression results of the form: 

!z//yMw=l(C(w),ÇT,ÄM'jM	2)/)(ywy/*) 

 

157 

This expression may be fit with obtained current vs. potential experimental data from the RDE 

experiments to estimate the ÇT by solving the inverse problem. Table 6.3 provides the description 

of variables that have not been described in the study before. 

Table 6.3 : Legend for different variables 

Variables 

Ü5'4( 
ê* 
ê*5 
ê(/^)* 
ê(305)* 
-5! 

XCyH 

XPh 

a 

 

 

 
 

 

 

 
 
 

Description 

Hydronium ion concentration at interface 

Fraction of empty metal sites 

Phenol concentration at interface 

Fraction of sites occupied by atomic H 
Fraction of sites occupied by phenol 

Fraction of sites occupied by cyclohexanol 
Cyclohexanol concentration at interface 

Hydrogen partial pressure 

Transfer coefficient 

Experimental Setup 

 The experimental setup used for the ECH experiments was a rotating disk electrode (RDE) with 

a 3-electrode system setup. The Ru/ACC catalyst was prepared and pasted on a glassy carbon 

working  electrode  using  carbon  conductive  paint.  These  paints  usually  contain  micro-graphite 

particles dispersed in isopropanol that are air-dried rapidly at room temperature and a small amount 

of special polymer to lend extra adhesive properties. A graphite carbon counter electrode was 

employed with an Ag/AgCl electrode as the reference electrode. 30 mL of 0.2 M HCl was selected 

as the electrolyte.32 Pre-electrolysis was performed through a linear scan from 0V to a reduction 

volage of 10V at a scan rate of 20 mV/s using a potentiostat to activate the catalyst for ECH. Once 

the  pre-electrolysis  was  completed,  phenol  was  added  such  that  the  final  concentration  in  the 

electrolyte solution was 20 mM. The ECH was performed at a temperature of 80°C, a rotation 

speed of 2500 rpm, and a voltage scan rate of 5 mV/s, varied from 0V to a reduction voltage of 

5V. 

The experimental setup is shown in Figure 6.1. 

 

158 

Figure 6.1 : Setup for RDE experiments 

 

 

Catalyst Preparation 

The catalyst was prepared in accordance with the method described by Li et al.32 However, the 

activated carbon cloth (ACC) was cut into smaller sizes to paste them on the glassy carbon working 

electrode of the RDE. ACC was cut into 0.6 X 0.6 cm pieces and stirred overnight in deionized 

water and then dried in an oven at 150°C. 32 such cloths were then soaked in a ruthenium salt 

solution of  0.102 g Ru(NH)3Cl6 , 0.196 mL of NH4OH and 1.302 mL of DI water until the solution 

was absorbed completely in 12 minutes to saturate the pores of the ACC.  Next,  overnight drying 

was  performed  at  room  temperature,  followed  by  further  vacuum  drying  for  24  hours.  These 

vacuum dried cloths, 3 at a time,  were then electrocatalytically reduced in a divided cell, separated 

by a proton exchange Nafion membrane, where these functioned as the cathode. Pt wire was used 

as  anode.  0.2  M  HCl  was  used  as  both  catholyte  and  anolyte.  The  experiment  was  run 

galvanostatically at 150 mA and 60°C. The experimental setup has been shown in Figure 6.2. The 

catholyte solution initially turned pink until it faded to a yellowish color, before transitioning to a 

dark blue color. The reduction was stopped when the blue color disappeared. The whole reduction 

 

159 

took about 75 minutes. The reduced cloth was then kept overnight in a drying chamber before 

being used in the RDE. 

Figure 6.2 : Experimental setup for catalyst preparation 

 

 

Results 

Current vs Potential Data from RDE 

Current vs potential data were obtained from the RDE experiment and plotted as shown in Figure  

6.3 (a). The red line denotes the current vs potential data when the substrate phenol was not added 

to  the  electrolyte  solution.  Therefore,  it  only  indicates  the  current  obtained  for  the  hydrogen 

evolution  reaction  (HER).  When  phenol  was  added,  an  increase  in  the  current  was  observed 

(denoted by the blue line), which is indicative of the occurrence of additional reactions such as the 

ECH of phenol to cyclohexanol. As the voltage was varied from 0 V to a reduction voltage of -5V 

, the HER current (denoted by the red line) increased to about -80 mA whereas the total current 

(denoted by the blue line) increased to about -90 mA.  It may be concluded from Figure 6.3 (a) 

 

160 

that the experiments occur in the kinetically controlled regime as no plateau forms in the current 

vs potential plot. This means that enough reactant is reaching the catalyst surface on the working 

electrode and mass transfer does not control the overall kinetics. Furthermore, the small difference 

in current is indicative of the fact that the HER is the dominant reaction. In Figure 6.3 (b), the 

electrode overpotential was plotted against the logarithmic current density. The overpotential was 

determined  by  subtracting  the  equilibrium  potential  from  the  potentials  in  Figure  6.3  (a).  The 

equilibrium  potential,  in  turn  was  estimated  from  the  standard  potential  of  H+/H2,  (since  the 

electrolyte is an aqueous solution) for the experimental conditions of 80°C and pH of 0.69, using 

the Nernst equation. It was determined to be -0.053 V. The current density, on the other hand was 

estimated by dividing the obtained current by the catalyst surface area of 0.36 cm2. It must be noted 

here  that  the  negative  potentials  in  Figure  6.3  (a)  have  been  plotted  as  positive  reduction 

overpotentials in Figure 6.3 (b). From the plot in Figure 6.3 (b), the Tafel slope at higher electrode 

reduction potentials may be estimated. Also, the exchange current density for the overall reaction 

scheme may be estimating by extrapolating the linear part of the curve to an overpotential of zero, 

as  shown  by  the  red  dotted  line  in  Figure  6.3  (b).  The  exchange  current  density  for  any 

electrochemical reaction is similar to a rate constant for a chemical reaction.293 It is a function of 

concentration, temperature, and catalyst surface area. The exchange current density for the overall 

reaction was determined to be 7.5 mA/cm2. The charge transfer coefficient (a), which is defined 

as  the  “fraction  of  electrostatic  potential  energy  affecting  the  reduction  rate  in  an  electrode 

reaction, with the remaining fraction, affecting the corresponding oxidation rate”294 may also be 

estimated from the Tafel slope. This was evaluated to be about 0.08; a value different from 0.5 

denotes lack of symmetry in the anodic and cathodic reactions. 

 

161 

 
Figure 6.3 : (a) Current vs potential data as obtained from RDE experiments (b) Electrode 

overpotential vs log(current density) 

GC/MS of the reaction solution 

Gas  chromatography-mass  spectrometry  measurements  were  made  for  the  electrolyte  solution 

after the RDE experiments were performed. 2 mL of the electrolyte solution was first saturated 

with 0.8g of NaCl, followed by addition of an equal volume of dichloromethane. The organic layer 

was then extracted and subjected to GC/MS. The resulting chromatogram showed a distinct phenol 

peak, as depicted in Figure 6.4.  

Figure 6.4 : GC-MS chromatogram of electrolyte solution post ECH of phenol in an RDE 

 

 

 

162 

However, a very small peak was observed for cyclohexanol. Therefore, although an increase in 

current was observed from the RDE experiment, on adding phenol to the electrolyte solution, the 

GC/MS of the solution could not confirm the conversion of phenol to cyclohexanol. This indicates 

that the time span of a linear sweep scan in an RDE experiment may not  be long enough to detect 

any appreciable amount of cyclohexanol.  

Discussion 

ECH of phenol has been known to produce cyclohexanol in the presence of Ru/ACC catalyst.136 

The present kinetic model was based on this assumption. However, preliminary fitting of current-

voltage data from the RDE experiment to a simplified model, as described in the Appendix at the 

end of this chapter, did not provide a good fit. This may have been caused by low cyclohexanol 

production in the RDE setup, as shown in Figure 6.2, and therefore, the predicted values of the 

model, which accounts for cyclohexanol formation and desorption do not agree with the observed 

values. Moreover, the model assumes elementary reactions whereas the actual reaction orders may 

not be dictated by stoichiometry. Another factor may be the noise in the RDE experiments, which 

may arise from weak electrical connections. In addition, there is the H2 evolution reaction that can 

result  in  bubbles,  which  can  lead  to  fluctuations  in  current.  Furthermore,  the  use  of  carbon 

conductive paint may have introduced impurities in the solution that may have interfered with the 

expected reactions. The carbon paint had to be used to attach the Ru/ACC to the surface of the 

working electrode. This method of attaching the catalyst may have been another source of noise 

in the experiment. A solution is to use a working electrode with a Ru disk inserted into the Teflon 

covering,  discontinuing  the  use  of  carbon  cloth  and  carbon  paint  altogether.  Therefore,  more 

experiments must be performed in the RDE to identify a set of parameters that result in the least 

on the RDE295 instead of linear sweep voltammetry or cyclic voltammetry. Such a method would 

 

163 

probably provide sufficient time for the production of detectable amounts of cyclohexanol as it 

would hold a constant voltage for a fixed amount of time before stepping up to a higher voltage. 

This could lead to the collection of better experimental data, and in turn, better parameter fitting 

for the ECH of phenol using the described model. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

164 

APPENDIX 

 

165 

This section describes a preliminary fitting exercise of a simplified version of the model described 

previously in Chapter 6.  

Model Assumptions: 

•  The reactions are at steady state  

•  Physical properties like diffusion coefficients and kinematic viscosities are constant 

•  The adsorption reaction is at equilibrium 

•  Fast desorption reaction for cyclohexanol 

•  H2 evolution occurs via the Tafel reaction 

Therefore, the cathode reactions may be described as: 

1. "G+s+yH→s" 
2. -ℎ+s↔(-ℎ)s 
3. (-ℎ)s+6s"→!."+7s 
4. s"+s"→"$+2M 

Where H+ are the in-situ generated hydrogen ions from water, Ph denotes phenol, (Ph)M denotes 

hydrogen and H2 denotes evolved hydrogen gas. 

adsorbed phenol, CyH denotes cyclohexanol, M denotes empty metal sites, MH denotes adsorbed 

If õ- are the fractional sites on catalyst surface, occupied by species i, Ä- are the rate constants for 
reaction i, Ç-:_ are the equilibrium rate constant for reaction i and E(t) is the applied voltage, then 
the elementary reaction rates (/-) may be expressed as:  
Ç$:_=õ(/^)*
!/^õ* 
/'=Ä'õ(/^)*õ*5I  

/!=Ç!!5(õ* ; where Ç!=Ä!exp(−W).)XO(=)
6[

) 

 

166 

The ratio x2=2'2*=`'a(,-)/a/0*
`*

/#=Ä#õ*5$  

=Ç2õ(/^)*õ*5#  then represents the ratio of rate of cyclohexanol 

being formed to the rate of hydrogen being evolved. 

flux from bulk to surface. Therefore,  

Now, at steady state, the rate of transformation of species at interface must equal the mass transport 

/5(=−/!=−(5(ì!5(∗ −!5(î 
⇒úb(= ùb(úb(∗
(ùb(+ûcüd) 
where !5(∗ is the bulk concentration of hydrogen ions and !5(is the interface concentration of 
hydrogen ions and (5( is the mass transport coefficient. 

//^=−/$=−(/^[!/^∗ −!/^] 

⇒//^=−Ç$!/^õ*+Ç$Uõ(/^)*=−(/^[!/^∗ −!/^]=0 

⇒!/^=!/^∗  

where !/^∗ is the bulk concentration of phenol and !/^is the interface concentration of phenol. 
where Di is the diffusion coefficient of species i, é is the kinematic viscosity of solution, Ω is the 

ùe=†.°¢£§f1g/h•Hc/i¶c/g 

For a rotating disk electrode, the mass transport coefficient for species i is given by, 

Similarly, for phenol,  

rotation speed and A is the catalyst surface area. 

Also, from site balance,  

The faradaic current (&+) for the set of reactions represented here may be expressed by, 

	õ*+õ(/^)*+õ*5=1 
&+=−Mr/! 

 

167 

⇒ßj=−®©™c´¨≠(−Æc®c©Ø(∞)
±≤

)

ùb(úb(∗
(ùb(+ûcüd)üd 

Methodology: 

The  goal  was  to  estimate  rate  constants  and  equilibrium  constants  that  can  fit  the  obtained 

experimental data. For this, an iterative approach was adopted. 

•  A set of values  for Ä!,Ç$:_,Ä',Ä# were assumed and the values for a set of values  
for Ç!,Ç$:_,Ç2 were then determined 
•  A value was assumed for x2 
•  The õ- were then evaluated  from the set of equations: 
Ç$:_=õ(/^)*
!/^∗õ* 
x2=Ç2:õ(/^)*õ*5# < 
	õ*+õ(/^)*+õ*5=1 

•  The current vs potential curve, as obtained by the model, could then be obtained for 

the set of assumed rate constants and then compared to the experimental data. 

The goal was to iterate the steps outlined above, until a good fit was obtained. 

Preliminary results: 

For the set of values outlined is Table 6.4, the current vs potential curve as predicted by the model, 

for different values of Rr are shown in Figure 6.5. It can be seen that distinct regimes of kinetic 

control and mass transfer control are predicted. As the reduction voltage is increased, the current 

rises in the kinetically controlled regime, until it gets constant for larger reduction voltages in the 

mass transfer controlled regime. Furthermore, for larger Rr values, greater current is observed, 

which is indicative of the fact that greater current is required to reduce phenol to cyclohexanol and 

for the hydrogen evolution reaction.  

 

168 

Table 6.4 : Parameter values used in generating model current vs potential curve 

4E-07 s-1(cm-2 of catalyst surface area) 

Assumed Value 

8E-02 mole-1L 

Parameter 

Ä! 
Ç$:_ 
Ç2 
α! 
Ck(∗  
mk( 

T 

5.44E-4 cm3 s-1(cm-2 of catalyst surface area) 

9E-02 
0.08 
0.53 M 

353 K 

Figure 6.5 : Current vs potential curve as predicted by model for different values of Rr 

 

 

The curves obtained in Figure 6.5 are very different from those obtained experimentally in Figure 

 
 

6.3 (a). In Figure 6.3 (a) the current increases to values around -90 mA, while it only increases to 

around -27 mA in Figure 6.5. Furthermore, there is no mass transfer controlled regime observed 

in  Figure  6.3  (a).The  lack  of  a  good  fit  may  be  due  to  the  numerous  reasons  outlined  in  the 

Discussion section of Chapter 6.  

However, the effect of increasing the mass transport coefficient and the kinetic rate constant of the 

hydrogen adsorption reaction (since it is the only reduction reaction in the model that involves 

electron transfer) was investigated to see if there was better fit between observed and predicted 

data. This can be seen in Figure 6.6. As the mass transport coefficient is increased, the constant 

 

169 

current  value  obtained  in  the  mass  transfer  controlled  regime  increases.  On  the  other  hand, 

increasing  the  kinetic  rate  constant  for  hydrogen  adsorption  reduces  the  reduction  potential 

required for the onset of the mass controlled regime. However, change in either do not provide a 

good fit to the experimental data that only shows a kinetically controlled regime. The possible 

causes behind this lack of fit have been discussed previously in Chapter 6. 

Figure 6.6 : Effect of increasing mass transfer coefficient and kinetic rate constant for 
hydrogen adsorption on model predicted curve. Blue line denotes experimental data. 

 

 

 

 

 

 

 

 

 

 

170 

Chapter 7 : CONCLUSIONS AND FUTURE WORK 

Conclusions 
 
The Py-ECH system, which is a bioenergy system that combines depot-based fast pyrolysis and 

electrocatalytic hydrogenation with centralized hydroprocessing to produce a gasoline range fuel 

was modeled using thermodynamic assumptions from Aspen’s NIST database. The energy, mass 

and carbon flux through the entire system was determined and plotted in Sankey diagrams. The 

calculated fuel yields (in terms of mass, energy and carbon) for the Py-ECH system were found to 

be greater than those for cellulosic fermentations to ethanol. This indicates that incorporation of 

electricity from renewable energy sources into bioenergy systems that thermochemically process 

biomass, can lead to systems that are more carbon and energy efficient than traditional cellulosic 

fermentations to ethanol. It can also be concluded that the Py-ECH system utilizes much less water 

than the cellulosic fermentations to ethanol. Furthermore, biochar is a valuable by-product that is 

an added benefit of the Py-ECH system. 

The technoeconomic analysis of the Py-ECH system revealed that the fuel had a minimum fuel 

selling  price  (MFSP)  of  $  3.62/gge  (in  2018  $)  using  a  discounted  cash  flow  rate  of  return 

(DCFROR) analysis and nth plant economics. The evaluated MFSP was for a gasoline range fuel 

using the Py-ECH system, starting from corn stover biomass. This is less than that for ethanol 

produced  from  cellulosic  fermentations  at  $  3.70/gge  (in  2018  $),  using  consistent  economic 

assumptions. The parameters that the MFSP was most sensitive to were determined to be the cost 

of electricity, the cost of raw material, the pyrolysis bio-oil yield, the ECH cell efficiencies and 

the catalyst price and thickness. Optimization studies showed that the optimum depot capacity and 

refinery capacity that resulted in the least MFSP were a depot size of 500 tonnes/day and a refinery 

size of 10,000 tonnes/day. The analysis showed that by stacking successive realistic improvements 

 

171 

in the Py-ECH system, a MFSP of $ 2.67/gge (in 2018 $) may attained. If the biochar by-product 

may be sold at a price of about $80/tonnne, the MFSP can drop to as low as $ 2.57/gge (in 2018 

$).  

The  life  cycle  assessment  revealed  that  the  eutrophication  potential  (EUP)  and  water  scarcity 

footprint (WSF) is always lower for the Py-ECH system than that for cellulosic fermentations to 

ethanol, making it an environmentally more favorable system in regard to these impact categories. 

The climate change potential or the global warming potential (GWP) is however dependent on the 

source of electricity being used in the Py-ECH system. If the electricity source is 87% or more 

renewable,  then  the  environmental  performance  of  Py-ECH  system  starts  getting  better  than 

cellulosic fermentations to ethanol. The energy analysis revealed that the ratio of total renewable 

energy output to the total fossil energy input of the Py-ECH system, surpasses that of the cellulosic 

ethanol system under two scenarios (a) when there is 100% renewable heat at the central refinery 

and  ~85%  renewable  electricity  or  (b)  when  there  is  100%  renewable  electricity  and  ~5% 

renewable  heat  at  central  refinery.  Furthermore,  a  sensitivity  analysis  of  the  annual  carbon 

sequestration  rate  on  GWP,  revealed  that  at  lower  carbon  sequestration  values  the  Py-ECH 

performs better than cellulosic ethanol processes, in terms of climate change. The trend however 

changes, for larger sequestration values.  

Preliminary kinetic studies for the ECH of phenol to cyclohexanol in an RDE cell, yielded the 

exchange  current  density  and  charge  transfer  coefficient  for  the  overall  kinetics.  GC/MS  of 

electrolyte solution post RDE experiments yielded negligible amounts of cyclohexanol, indicating 

that the time in a linear sweep voltammetry in a RDE setup may not be sufficient enough for 

conversion of phenol to cyclohexanol that can be quantitatively determined. However, increase in 

 

172 

current on adding phenol in the electrolyte solution indicated the occurrence of other reduction 

reactions than only hydrogen evolution. 

Future Work 
 
The  Py-ECH  system,  essentially  integrates  three  technologies,  namely,  fast  pyrolysis, 

electrocatalytic hydrogenation (ECH) and hydroprocessing. Of these, fast pyrolysis of biomass is 

a sufficiently mature technology. Hydroprocessing, in regard to fossil fuels is also very popular, 

and  the  challenge  here  is  in  the  successful  translation  to  the  handling  of  different  bio-oil 

compositions, resulting from different biomass feedstocks; as opposed to relatively similar crude 

oil, in case of petroleum. A major challenge associated with ECH is the continuous operation of a 

reactor that processes bio-oil. In this regard, research into the solid polymer electrolyte reactor as 

a potential solution can be the direction to move forward. There is also scope for other future 

efforts  in  the  successful  implementation  of  the  ECH  of  bio-oil,  such  as  improvement  in  cell 

efficiencies and finding cheaper catalysts. Furthermore, there is future work associated with each 

component study in this analysis: 

1.  The technoeconomic analysis indicates that the key parameters guiding the economics are 

the  raw  material  cost  and  the  electricity  cost.  Research  must,  therefore,  be  focused  on 

optimizing biomass supply chains with local geographical data that can further reduce the 

feedstock  cost.  Research  must  also  be  performed  to  make  cost  improvements  in  the 

generation of electricity from different sources of electrical power. 

2.  The  major  recommendation  from  the  life  cycle  assessment  is  to  incorporate  renewable 

sources to meet electricity requirement in bioenergy systems. In this context, as future 

work, it may be interesting to investigate the holistic effects of incorporating renewable 

electricity from solar/wind farms in regard to the land usage for setting up these farms. 

 

173 

3.  Preliminary kinetic modeling studies indicate that further RDE experiments for ECH of 

phenol  to  cyclohexanol  need  to  be  performed,  possibly  using  chronoamperometric 

techniques to  generate data that can estimate the kinetic rate constants associated with the 

electrochemical, adsorption/desorption and surface reactions. Also, it may be better to use 

a  Ru  disk  embedded  inside  the  Teflon  covering  as  the  working  electrode  instead  of 

Ru/ACC attached to the glassy carbon electrode, using carbon paint, as this might be a 

source  of  noise  in  the  experiment.  Once  the  kinetic  rate  constants  are  predicted  with 

reasonable accuracy for phenol, the process needs to be repeated for other model bio-oil 

compounds. Once sufficient number of model compounds are studied, the rates for ECH 

of pyrolysis bio-oil may be derived out. Such an analysis is key to the scale up of the ECH 

reactor and subsequent commercial application of the Py-ECH system. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 

174 

BIBLIOGRAPHY

 

175 

BIBLIOGRAPHY 

 

Victor, D. G.;  Zhou, D.;  Ahmed, E. H. M.;  Dadhich, P. K.;  Olivier, J. G. J.;  Rogner, 

Congress, U. S., Energy Independence and Security Act (EISA). United States, 2007. 

Demirbaş, A., Biomass resource facilities and biomass conversion processing for fuels 

Lam, C. H.;  Das, S.;  Erickson, N. C.;  Hyzer, C. D.;  Garedew, M.;  Anderson, J. E.;  

Gadonneix, P.;  Nadeau, M.-J.;  Kim, Y. D.;  Birnbaum, L.;  Cho, H.;  Choudhury, A. R.;  

 
1. 
Neto, J. d. C. C.;  Dauger, J.-M.;  Meyers, K.;  Sambo, A.;  Statham, B. A.;  Lieras, J. A. V.;  
Ward, G.;  Wu, X.;  Zatari, T. M.; Frei, C. World Energy Resources-2013 Survey; 2013. 
 
2. 
H.-H.;  Sheikho, K.; Yamaguchi, M. Introductory Chapter. In: Climate Change 2014: Mitigation 
of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the 
Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. 
Sarahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. 
Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.)]; Cambridge, UK and 
New York, USA, 2014. 
 
3. 
 
4. 
and chemicals. Energy Conversion and Management 2001, 42 (11), 1357-1378. 
 
5. 
Wallington, T. J.;  Tamor, M. A.;  Jackson, J. E.; Saffron, C. M., Towards sustainable 
hydrocarbon fuels with biomass fast pyrolysis oil and electrocatalytic upgrading. Sustainable 
Energy & Fuels 2017, 1 (2), 258-266. 
 
6. 
photosynthesis can convert solar energy into biomass? Current opinion in biotechnology 2008, 
19 (2), 153-159. 
 
7. 
http://www.life.illinois.edu/govindjee/whatisit.htm. 
 
8. 
methodology: a case study of corn stover-based fuel ethanol. The international journal of life 
cycle assessment 14 (6), 529-539. 
 
9. 
on corn stover. Biofuels, Bioproducts and Biorefining 2016, 10 (6), 819-832. 
 
10. 
Kim, S.;  Zhang, X.;  Dale, B. E.;  Reddy, A. D.;  Jones, C. D.; Izaurralde, R. C., EISA 
(Energy Independence and Security Act)-Compliant Ethanol Fuel from Corn Stover in a Depot-
Based Decentralized System. Biofuels, Bioproducts and Biorefining 2018. 
 
11. 
J.;  Olthof, B.; Worley, M. Process design and economics for biochemical conversion of 

Kim, S.; Dale, B. E., A distributed cellulosic biorefinery system in the US Midwest based 

Humbird, D.;  Davis, R.;  Tao, L.;  Kinchin, C.;  Hsu, D.;  Aden, A.;  Schoen, P.;  Lukas, 

Zhu, X.-G.;  Long, S. P.; Ort, D. R., What is the maximum efficiency with which 

Govindjee; Govindjee, R. What is Photosyntheis? 

Luo, L.;  Voet, E.;  Huppes, G.; Udo de Haes, H. A., Allocation issues in LCA 

 

176 

Nussbaumer, T., Combustion and Co-combustion of Biomass:  Fundamentals, 

Demirbas, A., Combustion of Biomass. Energy Sources, Part A: Recovery, Utilization, 

Balat, M.;  Balat, H.; Öz, C., Progress in bioethanol processing. Progress in energy and 

Hydrogen Production: Biomass Gasification; Hydrogen and Fuel Cell Technologies 

Kirubakaran, V.;  Sivaramakrishnan, V.;  Nalini, R.;  Sekar, T.;  Premalatha, M.; 

Tumuluru, J. S.;  Sokhansanj, S. J.;  Hess, R.;  Wright, C. T.; Boardman, R. D., A review 

lignocellulosic biomass to ethanol: dilute-acid pretreatment and enzymatic hydrolysis of corn 
stover; NREL/TP-5100-47764; National Renewable Energy Laboratory (NREL), Golden, CO.: 
2011. 
 
12. 
combustion science 2008, 34 (5), 551-573. 
 
13. 
Office. 
 
14. 
Subramanian, P., A review on gasification of biomass. Renewable and Sustainable Energy 
Reviews 2009, 13 (1), 179-186. 
 
15. 
Technologies, and Primary Measures for Emission Reduction. Energy & Fuels 2003, 17 (6), 
1510-1521. 
 
16. 
and Environmental Effects 2007, 29 (6), 549-561. 
 
17. 
on biomass torrefaction process and product properties for energy applications. Industrial 
Biotechnology 2011, 7 (5), 384-401. 
 
18. 
 
19. 
and Bioenergy 2012, 38, 68-94. 
 
20.  Moens, L.;  Black, S. K.;  Myers, M. D.; Czernik, S., Study of the Neutralization and 
Stabilization of a Mixed Hardwood Bio-Oil. Energy & Fuels 2009, 23 (5), 2695-2699. 
 
21. 
and transportation stability of bio-oil. Renewable and Sustainable Energy Reviews 2015, 50, 
859-870. 
 
22. 
key to the logistical challenges of the cellulosic biofuel industry. Biofuels, Bioproducts and 
Biorefining 2011, 5 (6), 621-630. 
 
 
23. 
Li, Z.;  Kelkar, S.;  Lam, C. H.;  Luczek, K.;  Jackson, J. E.;  Miller, D. J.; Saffron, C. M., 
Aqueous electrocatalytic hydrogenation of furfural using a sacrificial anode. Electrochimica Acta 
2012, 64, 87-93. 
 

Yang, Z.;  Kumar, A.; Huhnke, R. L., Review of recent developments to improve storage 

Bridgwater, A. V., Fast Pyrolysis of Biomass: A Handbook Volume 2. CPL Press: 2008. 

Bridgwater, A. V., Review of fast pyrolysis of biomass and product upgrading. Biomass 

Eranki, P. L.;  Bals, B. D.; Dale, B. E., Advanced Regional Biomass Processing Depots: a 

 

177 

Green, S. K.;  Lee, J.;  Kim, H. J.;  Tompsett, G. A.;  Kim, W. B.; Huber, G. W., The 

zhao, B.;  Chen, M.;  Guo, Q.; Fu, Y., Electrocatalytic hydrogenation of furfural to 

Kwon, Y.; Koper, M. T., Electrocatalytic hydrogenation and deoxygenation of glucose on 

Dabo, P.;  Cyr, A.;  Lessard, J.;  Brossard, L.; Menard, H., Electrocatalytic hydrogenation 

24. 
An, W.;  Hong, J.; Pintauro, P., Current efficiency for soybean oil hydrogenation in a 
solid polymer electrolyte reactor. Journal of applied electrochemistry 1998, 28 (9), 947-954. 
 
25. 
solid metal electrodes. ChemSusChem 2013, 6 (3), 455-62. 
 
26. 
of 4-phenoxyphenol on active powders highly dispersed in a reticulated vitreous carbon 
electrode. Canadian Journal of Chemistry 1999, 77 (7), 1225-1229. 
 
27. 
electrocatalytic hydrogenation of furanic compounds in a continuous electrocatalytic membrane 
reactor. Green Chemistry 2013, 15 (7), 1869-1879. 
 
28. 
furfuryl alcohol using platinum supported on activated carbon fibers. Electrochimica Acta 2014, 
135, 139-146. 
 
29. 
Robin, D.;  Comtois, M.;  Martel, A.;  Lemieux, R.;  Cheong, A. K.;  Belot, G.; Lessard, 
J., The electrocatalytic hydrogenation of fused poly cyclic aromatic compounds at Raney nickel 
electrodes: the influence of catalyst activation and electrolysis conditions. Canadian Journal of 
Chemistry 1990, 68 (7), 1218-1227. 
 
30. 
Kinetics of Hydrogen Evolution and Glucose Hydrogenation on Raney Nickel Powder. Journal 
of the Electrochemical Society 1994, 141 (10), 2729-2741. 
 
31. 
Navarro, M., Electrocatalytic hydrogenation of organic compounds using a nickel sacrificial 
anode. Journal of Electroanalytical Chemistry 2004, 569 (1), 71-78. 
 
32. 
electrocatalytic hydrogenation and hydrodeoxygenation of bio-oil derived phenolic compounds 
using ruthenium supported on activated carbon cloth. Green Chemistry 2012, 14 (9), 2540. 
 
33. 
electrocatalytic hydrogenation of lactic acid to lactaldehyde and propylene glycol. Journal of 
Catalysis 2007, 246 (1), 15-28. 
 
34. 
Houdek, C. E.;  Maguire, J. B.;  Saffron, C. M.;  Miller, D. J.; Jackson, J. E., Electrocatalytic 
upgrading of model lignin monomers with earth abundant metal electrodes. Green Chem. 2015, 
17 (1), 601-609. 
 
35. 
Renewable Energy Lab. (NREL), Golden, CO (United States): 1980. 

Reed, T. B. Combustion, Pyrolysis, Gasification, and Liquefaction of Biomass; National 

Li, Z.;  Garedew, M.;  Lam, C. H.;  Jackson, J. E.;  Miller, D. J.; Saffron, C. M., Mild 

Dalavoy, T.;  Jackson, J.;  Swain, G.;  Miller, D.;  Li, J.; Lipkowski, J., Mild 

Lam, C. H.;  Lowe, C. B.;  Li, Z.;  Longe, K. N.;  Rayburn, J. T.;  Caldwell, M. A.;  

Anantharaman, V.; Pintauro, P. N., The Electrocatalytic Hydrogenation of Glucose I. 

Santana, D. S.;  Melo, G. O.;  Lima, M. V. F.;  Daniel, J. R. R.;  Areias, M. C. C.; 

 

178 

Oni, B. A.;  Oziegbe, O.; Olawole, O. O., Significance of biochar application to the 

Laird, D. A.;  Brown, R. C.;  Amonette, J. E.; Lehmann, J., Review of the pyrolysis 

Schmidt, H.-P., 55 uses of biochar. Journal for ecology, winegrowing and climate 

Jirka, S.; Tomlinson, T. 2013 State of the Biochar Industry: A Survey of Commercial 

Tan, X.;  Liu, Y.;  Zeng, G.;  Wang, X.;  Hu, X.;  Gu, Y.; Yang, Z., Application of 

Bruun, E.;  Müller‐Stöver, D.;  Ambus, P.; Hauggaard‐Nielsen, H., Application of 

Bridgwater, A. V.;  Meier, D.; Radlein, D., An overview of fast pyrolysis of biomass. 

 
36. 
Organic Geochemistry 1999, 30 (12), 1479-1493. 
 
37. 
platform for coproducing bio-oil and biochar. Biofuels, Bioproducts and Biorefining 2009, 3 (5), 
547-562. 
 
38. 
farming, posted on December 2012, 29. 
 
39. 
Activity in the Biochar Field; 2014. 
 
40. 
biochar for the removal of pollutants from aqueous solutions. Chemosphere 2015, 125, 70-85. 
 
41. 
biochar to soil and N2O emissions: potential effects of blending fast‐pyrolysis biochar with 
anaerobically digested slurry. European Journal of Soil Science 2011, 62 (4), 581-589. 
 
42. 
environment and economy. Annals of Agricultural Sciences 2019, 64 (2), 222-236. 
 
43.  Mortensen, P. M.;  Grunwaldt, J. D.;  Jensen, P. A.;  Knudsen, K. G.; Jensen, A. D., A 
review of catalytic upgrading of bio-oil to engine fuels. Applied Catalysis A: General 2011, 407 
(1-2), 1-19. 
 
44. 
of biomass. In Proc. Int. Conf. Gasification and Pyrolysis of Biomass, CPL Press: Stuttgart, 
Germany, 1997. 
 
45. 
Stabilization of biomass-derived pyrolysis oils. Journal of Chemical Technology & 
Biotechnology 2010, 85 (5), 674-686. 
 
46. 
Catalytic hydroprocessing of biomass fast pyrolysis bio-oil to produce hydrocarbon products. 
Environmental Progress & Sustainable Energy 2009, 28 (3), 441-449. 
 
47. 
Co(Ni)Mo/γ-Al2O3 sulfide catalysts for gas oil hydrotreating. Journal of Molecular Catalysis A: 
Chemical 2008, 294 (1-2), 20-26. 
 

Prins, W., and Wagenaar, B. M., Review of rotating cone technology for flash pyrolysis 

Venderbosch, R. H.;  Ardiyanti, A. R.;  Wildschut, J.;  Oasmaa, A.; Heeres, H. J., 

Elliott, D. C.;  Hart, T. R.;  Neuenschwander, G. G.;  Rotness, L. J.; Zacher, A. H., 

Sundaramurthy, V.;  Dalai, A. K.; Adjaye, J., Tetraalkylthiomolybdates-derived 

 

179 

Lister, T., Lilga, M., and Lin, Y. 2015 Project Peer Review: Electrochemical Methods of 

Bridgwater, A. V., Catalysis in thermal biomass conversion. Applied Catalysis A: 

Balat, M.;  Balat, M.;  Kırtay, E.; Balat, H., Main routes for the thermo-conversion of 

Gutierrez, A.;  Kaila, R. K.;  Honkela, M. L.;  Slioor, R.; Krause, A. O. I., 

48. 
Şenol, O. İ.;  Ryymin, E. M.;  Viljava, T. R.; Krause, A. O. I., Reactions of methyl 
heptanoate hydrodeoxygenation on sulphided catalysts. Journal of Molecular Catalysis A: 
Chemical 2007, 268 (1-2), 1-8. 
 
49. 
Hydrodeoxygenation of guaiacol on noble metal catalysts. Catalysis Today 2009, 147 (3-4), 239-
246. 
 
50. 
Kim, K. H.;  Eom, I. Y.;  Lee, S. M.;  Choi, D.;  Yeo, H.;  Choi, I.-G.; Choi, J. W., 
Investigation of physicochemical properties of biooils produced from yellow poplar wood 
(Liriodendron tulipifera) at various temperatures and residence times. Journal of Analytical and 
Applied Pyrolysis 2011, 92 (1), 2-9. 
 
51.  Wildschut, J.;  Mahfud, F. H.;  Venderbosch, R. H.; Heeres, H. J., Hydrotreatment of fast 
pyrolysis oil using heterogeneous noble-metal catalysts. Industrial & Engineering Chemistry 
Research 2009, 48 (23), 10324-10334. 
 
52. 
Upgrading Pyrolysis Oils.; 2015. 
 
53. 
General 1994, 116 (1), 5-47. 
 
54. 
Adjaye, J. D.; Bakhshi, N. N., Production of hydrocarbons by catalytic upgrading of a 
fast pyrolysis bio-oil. Part I: Conversion over various catalysts. Fuel Processing Technology 
1995, 45 (3), 161-183. 
 
55. 
biomass into fuels and chemicals. Part 1: Pyrolysis systems. Energy Conversion and 
Management 2009, 50 (12), 3147-3157. 
 
56.  Wang, J.-J.;  Chang, J.; Fan, J., Upgrading of Bio-oil by Catalytic Esterification and 
Determination of Acid Number for Evaluating Esterification Degree. Energy & Fuels 2010, 24 
(5), 3251-3255. 
 
57. 
Li, X.;  Gunawan, R.;  Lievens, C.;  Wang, Y.;  Mourant, D.;  Wang, S.;  Wu, H.;  
Garcia-Perez, M.; Li, C.-Z., Simultaneous catalytic esterification of carboxylic acids and 
acetalisation of aldehydes in a fast pyrolysis bio-oil from mallee biomass. Fuel 2011, 90 (7), 
2530-2537. 
 
58. 
diesel fuel. Biomass and Bioenergy 2003, 24 (3), 221-232. 
 
59. 
upgrading research. Energy Conversion and Management 2007, 48 (1), 87-92. 
 

Zhang, Q.;  Chang, J.;  Wang, T.; Xu, Y., Review of biomass pyrolysis oil properties and 

Ikura, M.;  Stanciulescu, M.; Hogan, E., Emulsification of pyrolysis derived bio-oil in 

 

180 

Green, S. K.;  Tompsett, G. A.;  Kim, H. J.;  Kim, W. B.; Huber, G. W., Electrocatalytic 

Pletcher, D., Electrocatalysis: present and future. Journal of applied electrochemistry 14 

Safizadeh, F.;  Ghali, E.; Houlachi, G., Electrocatalysis developments for hydrogen 

Kang, B.-S.;  Lee, K. H.;  Park, H. J.;  Park, Y.-K.; Kim, J.-S., Fast pyrolysis of radiata 

60. 
pine in a bench scale plant with a fluidized bed: Influence of a char separation system and 
reaction conditions on the production of bio-oil. Journal of Analytical and Applied Pyrolysis 
2006, 76 (1-2), 32-37. 
 
61. 
Reduction of Acetone in a Proton-Exchange-Membrane Reactor: A Model Reaction for the 
Electrocatalytic Reduction of Biomass. ChemSusChem 2012, 5 (12), 2410-2420. 
 
62. 
(4), 403-415. 
 
63. 
evolution reaction in alkaline solutions – A Review. International Journal of Hydrogen Energy 
2015, 40 (1), 256-274. 
 
64. 
Vilar, M.;  Oliveira, J. L.; Navarro, M., Investigation of the hydrogenation reactivity of 
some organic substrates using an electrocatalytic method. Applied Catalysis A: General 2010, 
372 (1), 1-7. 
 
65. 
Chae, K. J.;  Choi, M.;  Ajayi, F. F.;  Park, W.;  Chang, I. S.; Kim, I. S., Mass Transport 
through a Proton Exchange Membrane (Nafion) in Microbial Fuel Cells. Energy & Fuels 2008, 
22 (1), 169-176. 
 
66. 
could aid UN Sustainable Development Goals. Nature Catalysis 2018, 1 (10), 731-735. 
 
67. 
Be Used as an Alternative Fuel—A Case Study. Energy & Fuels 2007, 21 (6), 3718-3722. 
 
68. 
torrefaction in depots to wind with natural gas combustion for electricity generation. Applied 
Energy 2016, 179, 171-181. 
 
69. 
technologies within a local biomass processing depot. Bioresource technology 2012, 106, 161-
169. 
 
70. 
Quddus, M. A.;  Ibne Hossain, N. U.;  Mohammad, M.;  Jaradat, R. M.; Roni, M. S., 
Sustainable network design for multi-purpose pellet processing depots under biomass supply 
uncertainty. Computers & Industrial Engineering 2017, 110, 462-483. 
 
71. 
optimization to evaluate multiple feedstocks for a biorefinery. Applied Energy 2019, 254, 
113660. 
 

Parkhurst, K. M.;  Saffron, C. M.; Miller, R. O., An energy analysis comparing biomass 

Resasco, D. E.;  Wang, B.; Sabatini, D., Distributed processes for biomass conversion 

Petrou, E.; Mihiotis, A., Design of a Factories’ Supply System with Biomass in Order to 

Roni, M. S.;  Thompson, D. N.; Hartley, D. S., Distributed biomass supply chain cost 

Bals, B. D.; Dale, B. E., Developing a model for assessing biomass processing 

 

181 

Kim, S.; Dale, B. E., Comparing alternative cellulosic biomass biorefining systems: 

Crandall, M. S.;  Adams, D. M.;  Montgomery, C. A.; Smith, D., The potential rural 

Eranki, P. L.; Dale, B. E., Comparative life cycle assessment of centralized and 

Jacobson, J.;  Lamers, P.;  Roni, M.;  Cafferty, K.;  Kenney, K.;  Heath, B.; Hansen, J. 

Ng, R. T. L.; Maravelias, C. T., Design of biofuel supply chains with variable regional 

 
72. 
Lamers, P.;  Roni, M. S.;  Tumuluru, J. S.;  Jacobson, J. J.;  Cafferty, K. G.;  Hansen, J. 
K.;  Kenney, K.;  Teymouri, F.; Bals, B., Techno-economic analysis of decentralized biomass 
processing depots. Bioresource Technology 2015, 194, 205-213. 
 
73. 
Techno-economic analysis of a biomass depot; INL/EXT-14-33225; Idaho National Laboratory: 
Idaho, 2014. 
 
74. 
Centralized versus distributed processing systems. Biomass and Bioenergy 2015, 74, 135-147. 
 
75. 
development impacts of utilizing non-merchantable forest biomass. Forest Policy and 
Economics 2017, 74, 20-29. 
 
76. 
depot and biorefinery locations. Renewable Energy 2017, 100, 90-102. 
 
77.  Martinkus, N.;  Latta, G.;  Brandt, K.; Wolcott, M., A Multi-Criteria Decision Analysis 
Approach to Facility Siting in a Wood-Based Depot-and-Biorefinery Supply Chain Model. 
Frontiers in Energy Research 2018, 6 (124). 
 
78. 
Roni, M. S.;  Eksioglu, S. D.;  Searcy, E.; Jha, K., A supply chain network design model 
for biomass co-firing in coal-fired power plants. Transportation Research Part E: Logistics and 
Transportation Review 2014, 61, 115-134. 
 
79. 
distributed biomass processing systems combined with mixed feedstock landscapes. GCB 
Bioenergy 2011, 3 (6), 427-438. 
 
80. 
Nguyen, L.;  Cafferty, K. G.;  Searcy, E. M.; Spatari, S., Uncertainties in Life Cycle 
Greenhouse Gas Emissions from Advanced Biomass Feedstock Logistics Supply Chains in 
Kansas. Energies 2014, 7 (11), 7125-7146. 
 
81. 
 
82. 
fuels from biomass via integrated hydropyrolysis and hydroconversion process—A techno-
economic analysis. Environmental Progress & Sustainable Energy 2014, 33 (2), 609-617. 
 
83. 
Zhu, Y.; Jones, S. B. Techno-economic Analysis for the Thermochemical Conversion of 
Lignocellulosic Biomass to Ethanol via Acetic Acid Synthesis; PNNL-18483; Pacific Northwest 
National Lab (PNNL),: Richland, WA (United States), 2009. 
 

Techno-Economic Analysis. https://abpdu.lbl.gov/capabilities/techno-economic-analysis/. 

Tan, E. C. D.;  Marker, T. L.; Roberts, M. J., Direct production of gasoline and diesel 

 

182 

Brown, T. R.;  Thilakaratne, R.;  Brown, R. C.; Hu, G., Techno-economic analysis of 

Peters, M.; Timmerhaus, K., Plant design and economics for chemical engineers. 

Pintarič, Z. N.; Kravanja, Z., The Importance of using Discounted Cash Flow 

Snowden-Swan, L. J.;  Zhu, Y.;  Jones, S. B.;  Elliott, D. C.;  Schmidt, A. J.;  Hallen, R. 

84.  Wright, M. M.;  Daugaard, D. E.;  Satrio, J. A.; Brown, R. C., Techno-economic analysis 
of biomass fast pyrolysis to transportation fuels. Fuel 2010, 89, S2-S10. 
 
85. 
biomass to transportation fuels and electricity via fast pyrolysis and hydroprocessing. Fuel 2013, 
106, 463-469. 
 
Sokhansanj, S.;  Mani, S.;  Tagore, S.; Turhollow, A. F., Techno-economic analysis of 
86. 
using corn stover to supply heat and power to a corn ethanol plant – Part 1: Cost of feedstock 
supply logistics. Biomass and Bioenergy 2010, 34 (1), 75-81. 
 
87. 
McGraw Hill: New York, 1980. 
 
88. 
Methodology in Techno-economic Analyses of Energy and Chemical Production Plants. Journal 
of Sustainable Development of Energy, Water and Environment Systems 2017, 5 (2), 163-176. 
 
89. 
Cucchiella, F.;  D'Adamo, I.;  Gastaldi, M.; Miliacca, M., A profitability analysis of 
small-scale plants for biomethane injection into the gas grid. Journal of Cleaner Production 
2018, 184, 179-187. 
 
90. 
T.;  Billing, J. M.;  Hart, T. R.;  Fox, S. P.; Maupin, G. D. Hydrothermal Liquefaction and 
Upgrading of Municipal Wastewater Treatment Plant Sludge: A Preliminary Techno-Economic 
Analysis, Rev.1; PNNL-25464-Rev.1; Pacific Northwest National Lab (PNNL): Richland, WA 
(United States), 2016. 
 
91. 
chemical production of hydrogen from biomass and residual wastes: Summary of recent techno-
economic analyses. Bioresource Technology 2020, 299, 122557. 
 
92. 
lignocellulosic biomass thermochemical conversion technologies: A review. Renewable and 
Sustainable Energy Reviews 2016, 53, 1486-1499. 
 
Phillips, S. D.;  Tarud, J. K.;  Biddy, M. J.; Dutta, A., Gasoline from Woody Biomass via 
93. 
Thermochemical Gasification, Methanol Synthesis, and Methanol-to-Gasoline Technologies: A 
Technoeconomic Analysis. Industrial & Engineering Chemistry Research 2011, 50 (20), 11734-
11745. 
 
94. 
biomass-to-liquids production based on gasification. Fuel 2010, 89, S11-S19. 
 
95. 
Anex, R. P.;  Aden, A.;  Kazi, F. K.;  Fortman, J.;  Swanson, R. M.;  Wright, M. M.;  
Satrio, J. A.;  Brown, R. C.;  Daugaard, D. E.;  Platon, A.;  Kothandaraman, G.;  Hsu, D. D.; 

Swanson, R. M.;  Platon, A.;  Satrio, J. A.; Brown, R. C., Techno-economic analysis of 

Shahabuddin, M.;  Krishna, B. B.;  Bhaskar, T.; Perkins, G., Advances in the thermo-

Patel, M.;  Zhang, X.; Kumar, A., Techno-economic and life cycle assessment on 

 

183 

Shemfe, M. B.;  Gu, S.; Ranganathan, P., Techno-economic performance analysis of 

Silveira, J. L.; Gomes, L. a., Fuel cell cogeneration system: a case of technoeconomic 

Sorunmu, Y.;  Billen, P.; Spatari, S., A review of thermochemical upgrading of pyrolysis 

Dutta, A., Techno-economic comparison of biomass-to-transportation fuels via pyrolysis, 
gasification, and biochemical pathways. Fuel 2010, 89, S29-S35. 
 
96. 
Thilakaratne, R.;  Brown, T.;  Li, Y.;  Hu, G.; Brown, R., Mild catalytic pyrolysis of 
biomass for production of transportation fuels: a techno-economic analysis. Green Chemistry 
2014, 16 (2), 627-636. 
 
97. 
bio-oil: Techno-economic analysis, life cycle assessment, and technology readiness. GCB 
Bioenergy 2020, 12 (1), 4-18. 
 
98. 
biofuel production and miniature electric power generation from biomass fast pyrolysis and bio-
oil upgrading. Fuel 2015, 143, 361-372. 
 
99. 
analysis. Renewable and Sustainable Energy Reviews 1999, 3 (2), 233-242. 
 
100.  Rahimi, S.;  Meratizaman, M.;  Monadizadeh, S.; Amidpour, M., Techno-economic 
analysis of wind turbine–PEM (polymer electrolyte membrane) fuel cell hybrid system in 
standalone area. Energy 2014, 67, 381-396. 
 
101.  Wang, J.;  Wang, H.; Fan, Y., Techno-Economic Challenges of Fuel Cell 
Commercialization. Engineering 2018, 4 (3), 352-360. 
 
102.  Orella, M. J.;  Brown, S. M.;  Leonard, M. E.;  Román-Leshkov, Y.; Brushett, F. R., A 
General Techno-Economic Model for Evaluating Emerging Electrolytic Processes. Energy 
Technology 2020. 
 
103.  Zhu, Y.;  Biddy, M. J.;  Jones, S. B.;  Elliott, D. C.; Schmidt, A. J., Techno-economic 
analysis of liquid fuel production from woody biomass via hydrothermal liquefaction (HTL) and 
upgrading. Applied Energy 2014, 129, 384-394. 
 
104.  Clausen, L. R.;  Houbak, N.; Elmegaard, B., Technoeconomic analysis of a methanol 
plant based on gasification of biomass and electrolysis of water. Energy 2010, 35 (5), 2338-2347. 
 
105.  Trippe, F.;  Fröhling, M.;  Schultmann, F.;  Stahl, R.; Henrich, E., Techno-Economic 
Analysis of Fast Pyrolysis as a Process Step Within Biomass-to-Liquid Fuel Production. Waste 
and Biomass Valorization 2010, 1 (4), 415-430. 
 
106.  Curran, M. A., Life Cycle Assessment: a review of the methodology and its application to 
sustainability. Current Opinion in Chemical Engineering 2013, 2 (3), 273-277. 
 
107.  Caro, D., Carbon Footprint. In Encyclopedia of Ecology (Second Edition), Fath, B., Ed. 
Elsevier: Oxford, 2019; pp 252-257. 
 

 

184 

Iribarren, D.;  Peters, J. F.; Dufour, J., Life cycle assessment of transportation fuels from 

108.  Nieuwlaar, E., Life Cycle Assessment and Energy Systems. In Encyclopedia of Energy, 
Cleveland, C. J., Ed. Elsevier: New York, 2004; pp 647-654. 
 
109.  Finnveden, G.;  Hauschild, M. Z.;  Ekvall, T.;  Guinée, J.;  Heijungs, R.;  Hellweg, S.;  
Koehler, A.;  Pennington, D.; Suh, S., Recent developments in Life Cycle Assessment. Journal 
of Environmental Management 2009, 91 (1), 1-21. 
 
110.  Guinée, J. B.;  Heijungs, R.;  Huppes, G.;  Zamagni, A.;  Masoni, P.;  Buonamici, R.;  
Ekvall, T.; Rydberg, T., Life Cycle Assessment: Past, Present, and Future. Environmental 
Science & Technology 2011, 45 (1), 90-96. 
 
111.  Peters, J. F.;  Iribarren, D.; Dufour, J., Life cycle assessment of pyrolysis oil applications. 
Biomass Conversion and Biorefinery 2015, 5 (1), 1-19. 
 
112.  Lan, K.;  Ou, L.;  Park, S.;  Kelley, S. S.; Yao, Y., Life Cycle Analysis of Decentralized 
Preprocessing Systems for Fast Pyrolysis Biorefineries with Blended Feedstocks in the 
Southeastern United States. Energy Technology 2019, 1900850. 
 
113. 
biomass pyrolysis. Fuel 2012, 97, 812-821. 
 
114.  Peters, J. F.;  Iribarren, D.; Dufour, J., Biomass Pyrolysis for Biochar or Energy 
Applications? A Life Cycle Assessment. Environmental Science & Technology 2015, 49 (8), 
5195-5202. 
 
115.  Righi, S.;  Bandini, V.;  Marazza, D.;  Baioli, F.;  Torri, C.; Contin, A., Life Cycle 
Assessment of high ligno-cellulosic biomass pyrolysis coupled with anaerobic digestion. 
Bioresource Technology 2016, 212, 245-253. 
 
116.  Roberts, K. G.;  Gloy, B. A.;  Joseph, S.;  Scott, N. R.; Lehmann, J., Life Cycle 
Assessment of Biochar Systems: Estimating the Energetic, Economic, and Climate Change 
Potential. Environmental Science & Technology 2010, 44 (2), 827-833. 
 
117.  Fan, J.;  Kalnes, T. N.;  Alward, M.;  Klinger, J.;  Sadehvandi, A.; Shonnard, D. R., Life 
cycle assessment of electricity generation using fast pyrolysis bio-oil. Renewable Energy 2011, 
36 (2), 632-641. 
 
118.  Hsu, D. D., Life cycle assessment of gasoline and diesel produced via fast pyrolysis and 
hydroprocessing. Biomass and Bioenergy 2012, 45, 41-47. 
 
119.  Dong, J.;  Tang, Y.;  Nzihou, A.;  Chi, Y.;  Weiss-Hortala, E.; Ni, M., Life cycle 
assessment of pyrolysis, gasification and incineration waste-to-energy technologies: Theoretical 
analysis and case study of commercial plants. Science of The Total Environment 2018, 626, 744-
753. 
 

 

185 

Im-orb, K.; Arpornwichanop, A., Process and sustainability analyses of the integrated 

120.  Dang, Q.;  Yu, C.; Luo, Z., Environmental life cycle assessment of bio-fuel production 
via fast pyrolysis of corn stover and hydroprocessing. Fuel 2014, 131, 36-42. 
 
121.  Zhang, Y.;  Hu, G.; Brown, R. C., Life cycle assessment of the production of hydrogen 
and transportation fuels from corn stover via fast pyrolysis. Environmental Research Letters 
2013, 8 (2), 025001. 
 
122.  Peters, J. F.;  Iribarren, D.; Dufour, J., Simulation and life cycle assessment of biofuel 
production via fast pyrolysis and hydroupgrading. Fuel 2015, 139, 441-456. 
 
123. 
biomass pyrolysis, gasification, and methanol synthesis process for methanol production. Energy 
2020, 193, 116788. 
 
124.  Steele, P.;  Puettmann, M. E.;  Penmetsa, V. K.; Cooper, J. E., Life-Cycle Assessment of 
Pyrolysis Bio-Oil Production*. Forest Products Journal 2012, 62 (4), 326-334. 
 
125.  Zhong, Z. W.;  Song, B.; Zaki, M. B. M., Life-cycle assessment of flash pyrolysis of 
wood waste. Journal of Cleaner Production 2010, 18 (12), 1177-1183. 
 
126.  Vienescu, D. N.;  Wang, J.;  Le Gresley, A.; Nixon, J. D., A life cycle assessment of 
options for producing synthetic fuel via pyrolysis. Bioresource Technology 2018, 249, 626-634. 
 
127.  Chan, Y. H.;  Tan, R. R.;  Yusup, S.;  Lam, H. L.; Quitain, A. T., Comparative life cycle 
assessment (LCA) of bio-oil production from fast pyrolysis and hydrothermal liquefaction of oil 
palm empty fruit bunch (EFB). Clean Technologies and Environmental Policy 2016, 18 (6), 
1759-1768. 
 
128.  Kauffman, N.;  Hayes, D.; Brown, R., A life cycle assessment of advanced biofuel 
production from a hectare of corn. Fuel 2011, 90 (11), 3306-3314. 
 
129.  Bridgwater, A.;  Meier, D.; Radlein, D., An overview of fast pyrolysis of biomass. 
Organic Geochemistry 1999, 30 (12), 1479-1493. 
 
130.  Song, Y.;  Gutiérrez, O. Y.;  Herranz, J.; Lercher, J. A., Aqueous phase electrocatalysis 
and thermal catalysis for the hydrogenation of phenol at mild conditions. Applied Catalysis B: 
Environmental 2016, 182, 236-246. 
 
131.  Lofrano, R. C. Z.;  Madurro, J. M.;  Abrantes, L. M.; Romero, J. R., Electrocatalytic 
hydrogenation of carbonylic compounds using an electrode with platinum particles dispersed in 
films of poly-[allyl ether p-(2-aminoethyl) phenol] co-polymerized with allyl phenyl ether. 
Journal of Molecular Catalysis A: Chemical 2004, 218 (1), 73-79. 
 
132.  Li, Z.;  Kelkar, S.;  Raycraft, L.;  Garedew, M.;  Jackson, J. E.;  Miller, D. J.; Saffron, C. 
M., A mild approach for bio-oil stabilization and upgrading: electrocatalytic hydrogenation using 
ruthenium supported on activated carbon cloth. Green Chemistry 2014, 16 (2), 844-852. 

 

186 

 
133.  Mondal, K.; Lalvani, S., Low temperature soybean oil hydrogenation by an 
electrochemical process. Journal of Food Engineering 2008, 84 (4), 526-533. 
 
134.  Cyr, A.;  Chiltz, F.;  Jeanson, P.;  Martel, A.;  Brossard, L.;  Lessard, J.; Ménard, H., 
Electrocatalytic hydrogenation of lignin models at Raney nickel and palladium-based electrodes. 
Canadian Journal of Chemistry 2000, 78 (3), 307-315. 
 
135.  Laplante, F.;  Brossard, L.; Ménard, H., Considerations about phenol 
electrohydrogenation on electrodes made with reticulated vitreous carbon cathode. Canadian 
Journal of Chemistry 2003, 81 (3), 258-264. 
 
136.  Garedew, M.;  Young-Farhat, D.;  Jackson, J. E.; Saffron, C. M., Electrocatalytic 
Upgrading of Phenolic Compounds Observed after Lignin Pyrolysis. ACS Sustainable Chemistry 
& Engineering 2019, 7 (9), 8375-8386. 
 
137.  Garedew, M.;  Young-Farhat, D.;  Bhatia, S.;  Hao, P.;  Jackson, J. E.; Saffron, C. M., 
Electrocatalytic cleavage of lignin model dimers using ruthenium supported on activated carbon 
cloth. Sustainable energy & fuels 2020. 
 
138.  Guerrero‐Sánchez, C.;  Saldívar, E.;  Hernández, M.; Jiménez, A., A Practical, 
Systematic Approach for the Scaling‐Up and Modeling of Industrial Copolymerization Reactors. 
Polymer Reaction Engineering 2003, 11 (3), 457-506. 
 
139.  Langer, S. H.; Sakellaropoulos, G. P., Kinetics of Electrogenerative Hydrogenation over 
Platinum Black Electrocatalyst. Journal of the Electrochemical Society 1975, 122, 1619. 
 
140.  Sedighi, S.; Gardner, C. L., A kinetic study of the electrochemical hydrogenation of 
ethylene. Electrochimica Acta 2010, 55 (5), 1701-1708. 
 
141.  Bannari, A.;  Proulx, P.;  Ménard, H.; Cirtiu, C. M., Mathematical modeling of the 
kinetics of phenol electrocatalytic hydrogenation over supported Pd–alumina catalyst. Applied 
Catalysis A: General 2008, 345 (1), 28-42. 
 
142.  Song, Y.;  Sanyal, U.;  Pangotra, D.;  Holladay, J. D.;  Camaioni, D. M.;  Gutiérrez, O. 
Y.; Lercher, J. A., Hydrogenation of benzaldehyde via electrocatalysis and thermal catalysis on 
carbon-supported metals. Journal of Catalysis 2018, 359, 68-75. 
 
143.  Singh, N.;  Song, Y.;  Gutiérrez, O. Y.;  Camaioni, D. M.;  Campbell, C. T.; Lercher, J. 
A., Electrocatalytic Hydrogenation of Phenol over Platinum and Rhodium: Unexpected 
Temperature Effects Resolved. ACS Catalysis 2016, 6 (11), 7466-7470. 
 
144.  Sherbo, R. S.;  Kurimoto, A.;  Brown, C. M.; Berlinguette, C. P., Efficient 
Electrocatalytic Hydrogenation with a Palladium Membrane Reactor. Journal of the American 
Chemical Society 2019, 141 (19), 7815-7821. 
 

 

187 

145.  Thomas, G. Overview of storage development DOE hydrogen program; Sandia National 
Laboratories: San Ramon, California, May 9-11, 2000, 2000. 
 
146.  Dunn, D. R. U.S. Energy Information Administration Monthly Energy Review, August 
2016; August 2016, 2016; pp 27-47. 
 
147.  Lynes, M. International Energy Outlook 2016; DOE/EIA-0484(2016); 2016; p 128. 
 
148.  Perlack, R. D.;  Eaton, L. M.;  Turhollow Jr, A. F.;  Langholtz, M. H.;  Brandt, C. C.;  
Downing, M. E.;  Graham, R. L.;  Wright, L. L.;  Kavkewitz, J. M.; Shamey, A. M., US billion-
ton update: biomass supply for a bioenergy and bioproducts industry. 2011. 
 
149.  Langholtz, M.;  Stokes, B.; Eaton, L. 2016 Billion-ton report: Advancing domestic 
resources for a thriving bioeconomy, Volume 1: Economic availability of feedstock; ORNL/TM-
2016/160; U.S. Department of Energy: 2016. 
 
150.  Furimsky, E., Catalytic hydrodeoxygenation. Applied Catalysis A: General 2000, 199 (2), 
147-190. 
 
151.  Czernik, S.; Bridgwater, A., Overview of applications of biomass fast pyrolysis oil. 
Energy & fuels 2004, 18 (2), 590-598. 
 
152.  Bui, V. N.;  Laurenti, D.;  Afanasiev, P.; Geantet, C., Hydrodeoxygenation of guaiacol 
with CoMo catalysts. Part I: Promoting effect of cobalt on HDO selectivity and activity. Applied 
Catalysis B: Environmental 2011, 101 (3), 239-245. 
 
153.  Baliban, R. C.;  Elia, J. A.; Floudas, C. A., Biomass to liquid transportation fuels (BTL) 
systems: process synthesis and global optimization framework. Energy & Environmental Science 
2013, 6 (1), 267-287. 
 
154.  Rinaldi, R.; Schüth, F., Design of solid catalysts for the conversion of biomass. Energy & 
Environmental Science 2009, 2 (6), 610-626. 
 
155.  Serrano-Ruiz, J. C.; Dumesic, J. A., Catalytic routes for the conversion of biomass into 
liquid hydrocarbon transportation fuels. Energy & Environmental Science 2011, 4 (1), 83-99. 
 
156.  Zhao, C.;  Kou, Y.;  Lemonidou, A. A.;  Li, X.; Lercher, J. A., Highly Selective Catalytic 
Conversion of Phenolic Bio‐Oil to Alkanes. Angewandte Chemie 2009, 121 (22), 4047-4050. 
 
157.  Choudhary, T.; Phillips, C., Renewable fuels via catalytic hydrodeoxygenation. Applied 
Catalysis A: General 2011, 397 (1), 1-12. 
 
158.  Elliott, D. C.; Hart, T. R., Catalytic hydroprocessing of chemical models for bio-oil. 
Energy & Fuels 2008, 23 (2), 631-637. 
 

 

188 

159.  Ruddy, D. A.;  Schaidle, J. A.;  Ferrell III, J. R.;  Wang, J.;  Moens, L.; Hensley, J. E., 
Recent advances in heterogeneous catalysts for bio-oil upgrading via “ex situ catalytic fast 
pyrolysis”: catalyst development through the study of model compounds. Green Chemistry 2014, 
16 (2), 454-490. 
 
160.  Saidi, M.;  Samimi, F.;  Karimipourfard, D.;  Nimmanwudipong, T.;  Gates, B. C.; 
Rahimpour, M. R., Upgrading of lignin-derived bio-oils by catalytic hydrodeoxygenation. 
Energy & Environmental Science 2014, 7 (1), 103-129. 
 
161.  Diebold, J.; Thermalchemie, I., NRE Laboratory. A review of the chemical and physical 
mechanisms of the storage stability of fast pyrolysis bio-oils 2000. 
 
162.  Pootakham, T.; Kumar, A., Bio-oil transport by pipeline: A techno-economic assessment. 
Bioresource technology 2010, 101 (18), 7137-7143. 
 
163.  Blaikie, D. Most Efficient Solar Panels. http://sroeco.com/solar/most-efficient-solar-
panels (accessed 11 Mar 2014). 
 
164.  Lantz, E.;  Wiser, R.; Hand, M., The past and future cost of wind energy. National 
Renewable Energy Laboratory, Golden, CO, Report No. NREL/TP-6A20-53510 2012. 
 
165.  Clausen, L. R., Maximizing biofuel production in a thermochemical biorefinery by 
adding electrolytic hydrogen and by integrating torrefaction with entrained flow gasification. 
Energy 2015, 85, 94-104. 
 
166.  Cherubini, F.;  Jungmeier, G.;  Wellisch, M.;  Willke, T.;  Skiadas, I.;  Van Ree, R.; de 
Jong, E., Toward a common classification approach for biorefinery systems. Biofuels, 
Bioproducts and Biorefining 2009, 3 (5), 534-546. 
 
167.  McKendry, P., Energy production from biomass (part 1): overview of biomass. 
Bioresource technology 2002, 83 (1), 37-46. 
 
168.  Demirbaş, A., Calculation of higher heating values of biomass fuels. Fuel 1997, 76 (5), 
431-434. 
 
169.  Sun, Z.-Y.;  Tang, Y.-Q.;  Iwanaga, T.;  Sho, T.; Kida, K., Production of fuel ethanol 
from bamboo by concentrated sulfuric acid hydrolysis followed by continuous ethanol 
fermentation. Bioresource technology 2011, 102 (23), 10929-10935. 
 
170.  Laird, D. A., The charcoal vision: a win–win–win scenario for simultaneously producing 
bioenergy, permanently sequestering carbon, while improving soil and water quality. Agronomy 
journal 2008, 100 (1), 178-181. 
 
171.  Lehmann, J., A handful of carbon. Nature 2007, 447 (7141), 143-144. 
 

 

189 

Joback, K. G.; Reid, R. C., Estimation of pure-component properties from group-

172.  Langholtz, M. 2016 Billion-Ton Report; ORNL (Oak Ridge National Laboratory 
(ORNL), Oak Ridge, TN (United States)): 2016. 
 
173.  Gaur, S.; Reed, T., Thermal Data for Natural and Synthetic Fuels, Marcel Deeker. Inc.: 
New York, NY, USA 1998. 
 
174.  Kolská, Z.;  Kukal, J.;  Zábranský, M.; Ruzˇicˇka, V., Estimation of the heat capacity of 
organic liquids as a function of temperature by a three-level group contribution method. 
Industrial & Engineering Chemistry Research 2008, 47 (6), 2075-2085. 
 
175. 
contributions. Chem. Eng. Commun. 1987, 57 (1-6), 233-243. 
 
176.  Marrero, J.; Gani, R., Group-contribution based estimation of pure component properties. 
Fluid Phase Equilib. 2001, 183, 183-208. 
 
177.  Hukkerikar, A. S.;  Sarup, B.;  Ten Kate, A.;  Abildskov, J.;  Sin, G.; Gani, R., Group-
contribution+ (GC+) based estimation of properties of pure components: Improved property 
estimation and uncertainty analysis. Fluid Phase Equilib. 2012, 321, 25-43. 
 
178.  Goodman, B. T.;  Wilding, W. V.;  Oscarson, J. L.; Rowley, R. L., Use of the DIPPR 
Database for Development of Quantitative Structure−Property Relationship Correlations:  Heat 
Capacity of Solid Organic Compounds. Journal of Chemical & Engineering Data 2004, 49 (1), 
24-31. 
 
179.  Gil, M.;  Gonzalez, A.; Gil, A. In Evaluation of milling energy requirements of biomass 
residues in a semi-industrial pilot plant for co-firing, Proceedings 16th European Biomass 
Conference and Exhibition, Valencia, Spain, 2008. 
 
180.  Kemp, I. C., Fundamentals of energy analysis of dryers. Modern Drying Technology 
2011, 4, 1-46. 
 
181.  Thoreson, C. P.;  Darr, M. J.; Webster, K. E. In Corn Stover Densification Methods and 
their Large-Scale Logistical Impacts—Preliminary Analysis, 2010 Pittsburgh, Pennsylvania, 
June 20-June 23, 2010, American Society of Agricultural and Biological Engineers: 2010; p 1. 
 
182.  Nicolai, R.;  Ollerich, J.; Kelley, J. In Screw auger power and throughput analysis, 2004 
ASAE Annual Meeting, American Society of Agricultural and Biological Engineers: 2004; p 1. 
 
183.  Srivastava, A. K., Engineering Principles of Agricultural Machines. American Society of 
Agricultural Engineers: 2006. 
 
184.  Perry, R. H.; Green, D. W., Perry's chemical engineers' handbook. McGraw-Hill 
Professional: 1999. 
 

 

190 

185.  Groundwater Temperature Map. http://www.enoscientific.com/groundwater-temp-
map.htm. 
 
186.  Carmo, M.;  Fritz, D. L.;  Mergel, J.; Stolten, D., A comprehensive review on PEM water 
electrolysis. Int. J. Hydrogen Energy 2013, 38 (12), 4901-4934. 
 
187.  Zhou, Y.;  Klinger, G. E.;  Hegg, E. L.;  Saffron, C. M.; Jackson, J. E., Multiple 
Mechanisms Mapped in Aryl Alkyl Ether Cleavage via Aqueous Electrocatalytic Hydrogenation 
over Skeletal Nickel. Journal of the American Chemical Society 2020. 
 
188.  Lam, C. H.;  Lowe, C. B.;  Li, Z.;  Longe, K. N.;  Rayburn, J. T.;  Caldwell, M. A.;  
Houdek, C. E.;  Maguire, J. B.;  Saffron, C. M.; Miller, D. J., Electrocatalytic upgrading of 
model lignin monomers with earth abundant metal electrodes. Green Chemistry 2015, 17 (1), 
601-609. 
 
189.  DOE Technical Targets for Hydrogen Production from Electrolysis. 
https://www.energy.gov/eere/fuelcells/doe-technical-targets-hydrogen-production-
electrolysis#:~:text=The%202020%20target%20is%20based,year%20is%20set%20to%202025. 
 
190.  Ouammi, A.;  Bersani, C.;  Sacile, R.; Dagdougui, H., Hydrogen Infrastructure for 
Energy Applications: Production, Storage, Distribution and Safety. Elsevier Academic Press: 
2018. 
 
191.  Campbell, R. M.;  Anderson, N. M.;  Daugaard, D. E.; Naughton, H. T., Financial 
viability of biofuel and biochar production from forest biomass in the face of market price 
volatility and uncertainty. Applied Energy 2018, 230, 330-343. 
 
192.  Kocoloski, M.;  Michael Griffin, W.; Scott Matthews, H., Impacts of facility size and 
location decisions on ethanol production cost. Energy Policy 2011, 39 (1), 47-56. 
 
193.  Thompson, J. L.; Tyner, W. E., Corn stover for bioenergy production: Cost estimates and 
farmer supply response. Biomass and Bioenergy 2014, 62, 166-173. 
 
194.  Wakeley, H. L.;  Hendrickson, C. T.;  Griffin, W. M.; Matthews, H. S., Economic and 
Environmental Transportation Effects of Large-Scale Ethanol Production and Distribution in the 
United States. Environmental Science & Technology 2009, 43 (7), 2228-2233. 
 
195.  Hooper, A.; Murray, D. An Analysis of the Operational Costs of Trucking: 2018 Update; 
2018. 
 
196.  Orella, M. J.;  Román-Leshkov, Y.; Brushett, F. R., Emerging opportunities for 
electrochemical processing to enable sustainable chemical manufacturing. Current Opinion in 
Chemical Engineering 2018, 20, 159-167. 
 
197.  Levelized Cost and Levelized Avoided Cost of New Generation Resources in the Annual 
Energy Outlook 2019; United States Energy Information Administration: 2019. 

 

191 

 
198.  Pintauro, P. N.;  Gil, M. P.;  Warner, K.;  List, G.; Neff, W., Electrochemical 
Hydrogenation of Soybean Oil with Hydrogen Gas. Industrial & Engineering Chemistry 
Research 2005, 44 (16), 6188-6195. 
 
199.  Ruthenium Price Chart. http://www.platinum.matthey.com/prices/price-charts#. 
 
200.  Peterson, D.;  Vickers, J.; DeSantis, D. Hydrogen Production Cost From PEM 
Electrolysis - 2019; February 03, 2020. 
 
201.  Peters, M. S.;  Timmerhaus, K. D.; West, R. E. Equipment Costs-Plant Design and 
Economics for Chemical Engineers. http://www.mhhe.com/engcs/chemical/peters/data/. 
 
202.  Peters, M. S.;  Timmerhaus, K. D.; West, R. E., Plant Design and Economics for 
Chemical Engineers. 5th Edition ed.; McGraw Hill. 
 
203.  Heat Exchanger Heat Transfer Coefficients. The Engineering Toolbox. 
 
204.  Xu, H.;  Sieverding, H.;  Kwon, H.;  Clay, D.;  Stewart, C.;  Johnson, J. M. F.;  Qin, Z.;  
Karlen, D. L.; Wang, M., A global meta-analysis of soil organic carbon response to corn stover 
removal. GCB Bioenergy 2019, 11 (10), 1215-1233. 
 
205.  An, W.;  Hong, J. K.; Pintauro, P., Current efficiency for soybean oil hydrogenation in a 
solid polymer electrolyte reactor. Journal of Applied Electrochemistry 1998, 28, 947-954. 
 
206.  Genovese, J.;  Harg, K.;  Paster, M.; Turner, J. Current (2009) State-of-the-Art Hydrogen 
Production Cost Estimate Using Water Electrolysis; NREL/BK-6A1-46676; NREL: 2009. 
 
207.  Colella, W. G.;  James, B. D.;  Moton, J. M.;  Saur, G.; Ramsden, T. Techno-economic 
Analysis of PEM Electrolysis for Hydrogen Production; NREL, Strategic Analysis Inc.: Golden, 
Colorado, 2013. 
 
208.  Amos, W. A. Costs of Storing and Transporting Hydrogen; NREL/TP-570-25106; 
National Renewable Energy Laboratory (NREL): Golden, CO (US), 1999; p 6574. 
 
209.  Keiser, J.;  Howell, M.;  Lewis, S.; Connatser, R., Corrosion Studies Of Raw And Treated 
Biomass-Derived Pyrolysis Oils. 2012. 
 
210.  Lu, Q.;  Zhang, J.; Zhu, X., Corrosion properties of bio-oil and its emulsions with diesel. 
Chinese Science Bulletin 2008, 53 (23), 3726-3734. 
 
211.  Loh, H. P.;  Lyons, J.; White, C. W. Process Equipment Cost Estimation Final Report; 
DOE/NETL-2002/1169; National Energy Technology Laboratory (NETL): Morgantown, WV 
(United States), 2002; p 797810. 
 

 

192 

212.  Saba, S. M.;  Müller, M.;  Robinius, M.; Stolten, D., The investment costs of electrolysis 
– A comparison of cost studies from the past 30 years. International Journal of Hydrogen 
Energy 2018, 43 (3), 1209-1223. 
 
213.  Dutta, A.;  Sahir, A.;  Tan, E.;  Humbird, D.;  Snowden-Swan, L. J.;  Meyer, P.;  Ross, J.;  
Sexton, D.;  Yap, R.; Lukas, J. Process Design and Economics for the Conversion of 
Lignocellulosic Biomass to Hydrocarbon Fuels; NREL/TP-5100-62455, PNNL-23823; NREL, 
PNNL: 2015. 
 
214.  Natural Gas Prices. U.S. Energy Information and Administration. 
 
215.  Campbell, R. M.;  Anderson, N. M.;  Daugaard, D. E.; Naughton, H. T., Technoeconomic 
and Policy Drivers of Project Performance for Bioenergy Alternatives Using Biomass from 
Beetle-Killed Trees. Energies 2018, 11 (2), 293. 
 
216.  Shabangu, S.;  Woolf, D.;  Fisher, E. M.;  Angenent, L. T.; Lehmann, J., Techno-
economic assessment of biomass slow pyrolysis into different biochar and methanol concepts. 
Fuel 2014, 117, 742-748. 
 
217.  Galinato, S. P.;  Yoder, J. K.; Granatstein, D., The economic value of biochar in crop 
production and carbon sequestration. Energy Policy 2011, 39 (10), 6344-6350. 
 
218.  Kung, C.-C.;  McCarl, B. A.; Cao, X., Economics of pyrolysis-based energy production 
and biochar utilization: A case study in Taiwan. Energy Policy 2013, 60, 317-323. 
 
219.  Yoder, J.;  Galinato, S.;  Granatstein, D.; Garcia-Pérez, M., Economic tradeoff between 
biochar and bio-oil production via pyrolysis. Biomass and Bioenergy 2011, 35 (5), 1851-1862. 
 
220.  Dickinson, D.;  Balduccio, L.;  Buysse, J.;  Ronsse, F.;  van Huylenbroeck, G.; Prins, W., 
Cost-benefit analysis of using biochar to improve cereals agriculture. GCB Bioenergy 2015, 7 
(4), 850-864. 
 
221.  Granatstein, C.;  Kruger, C.;  Collins, H.;  Galinato, S.;  Garcia-perez, M.; Yoder, J. Use 
of biochar from the pyrolysis of waste organic material as a soil amendment; 2009. 
 
222.  Kim, D.;  Anderson, N. M.; Chung, W., Financial Performance of a Mobile Pyrolysis 
System Used to Produce Biochar from Sawmill Residues. Forest Products Journal 2015, 65 (5-
6), 189-197. 
 
223.  Shackley, S.;  Hammond, J.;  Gaunt, J.; Ibarrola, R., The feasibility and costs of biochar 
deployment in the UK. Carbon Management 2011, 2 (3), 335-356. 
 
224.  Draper, K.;  Groot, H.;  Miles, T.; Twer, M. Survey and Analysis of the US Biochar 
Industry: Preliminary Report Draft  (August 16, 2018); 2018. 
 
225.  Biomass Multi-Year Program Plan; U.S. Department of Energy: 2011. 

 

193 

 
226.  Kreuter, W.; Hofmann, H., Electrolysis: The important energy transformer in a world of 
sustainable energy. International Journal of Hydrogen Energy 1998, 23 (8), 661-666. 
 
227.  Liu, L.;  Chen, W.; Li, Y., An overview of the proton conductivity of nafion membranes 
through a statistical analysis. Journal of Membrane Science 2016, 504, 1-9. 
 
228.  Singh, A.;  Pant, D.;  Korres, N. E.;  Nizami, A.-S.;  Prasad, S.; Murphy, J. D., Key issues 
in life cycle assessment of ethanol production from lignocellulosic biomass: Challenges and 
perspectives. Bioresource Technology 2010, 101 (13), 5003-5012. 
 
229.  Fu, G. Z.;  Chan, A. W.; Minns, D. E., Life Cycle assessment of bio-ethanol derived from 
cellulose. The International Journal of Life Cycle Assessment 2003, 8 (3), 137-141. 
 
230.  Mu, D.;  Seager, T.;  Rao, P. S.; Zhao, F., Comparative Life Cycle Assessment of 
Lignocellulosic Ethanol Production: Biochemical Versus Thermochemical Conversion. 
Environmental Management 2010, 46 (4), 565-78. 
 
231.  Bai, Y.;  Luo, L.; van der Voet, E., Life cycle assessment of switchgrass-derived ethanol 
as transport fuel. The International Journal of Life Cycle Assessment 2010, 15 (5), 468-477. 
 
232.  Dunn, J. B.;  Mueller, S.;  Kwon, H.-y.; Wang, M. Q., Land-use change and greenhouse 
gas emissions from corn and cellulosic ethanol. Biotechnology for Biofuels 2013, 6, 51-51. 
 
233.  Wang, M.;  Han, J.;  Dunn, J. B.;  Cai, H.; Elgowainy, A., Well-to-wheels energy use and 
greenhouse gas emissions of ethanol from corn, sugarcane and cellulosic biomass for US use. 
Environmental Research Letters 2012, 7 (4), 045905. 
 
234.  Kim, S.;  Dale, B. E.; Jenkins, R., Life cycle assessment of corn grain and corn stover in 
the United States. The International Journal of Life Cycle Assessment 2009, 14 (2), 160-174. 
 
235.  Spatari, S.;  Bagley, D. M.; MacLean, H. L., Life cycle evaluation of emerging 
lignocellulosic ethanol conversion technologies. Bioresource Technology 2010, 101 (2), 654-
667. 
 
236.  Han, J.;  Elgowainy, A.;  Dunn, J. B.; Wang, M. Q., Life cycle analysis of fuel production 
from fast pyrolysis of biomass. Bioresource Technology 2013, 133, 421-428. 
 
237.  Kim, S.;  Zhang, X.;  Reddy, A. D.;  Dale, B. E.;  Thelen, K. D.;  Jones, C. D.;  
Izaurralde, R. C.;  Runge, T.; Maravelias, C., Carbon-Negative Biofuel Production. 
Environmental Science & Technology 2020, 54 (17), 10797-10807. 
 
238.  Wang, M. The Greenhouse Gases, Regulated Emissions, and Energy Use in 
Transportation Model; Argonne National Laboratory: Argonne, Illinois, 1999. 
 

 

194 

239.  Dunn, J. B.;  Qin, Z.;  Mueller, S.;  Kwon, H. Y.;  Wander, M.; Wang, M. Carbon 
Calculator for Land Use Change from Biofuels Production (CCLUB); ANL/ESD/12-5 Rev. 2; 
Argonne National Laboratory: 2014. 
 
240.  Bare, J., TRACI 2.0: the tool for the reduction and assessment of chemical and other 
environmental impacts 2.0. Clean Technologies and Environmental Policy 2011, 13 (5), 687-
696. 
 
241.  Bare, J. C. Tools for the Reduction and Assessment of Chemical and Other 
Environmental Impacts (TRACI), Version 2.1-User's Manual; United States Environment 
Protection Agency: 2012. 
 
242.  Bayart, J.-B.;  Bulle, C.;  Deschênes, L.;  Margni, M.;  Pfister, S.;  Vince, F.; Koehler, A., 
A framework for assessing off-stream freshwater use in LCA. The International Journal of Life 
Cycle Assessment 2010, 15 (5), 439-453. 
 
243.  Boulay, A.-M.;  Bare, J.;  Benini, L.;  Berger, M.;  Lathuillière, M. J.;  Manzardo, A.;  
Margni, M.;  Motoshita, M.;  Núñez, M.;  Pastor, A. V.;  Ridoutt, B.;  Oki, T.;  Worbe, S.; 
Pfister, S., The WULCA consensus characterization model for water scarcity footprints: 
assessing impacts of water consumption based on available water remaining (AWARE). The 
International Journal of Life Cycle Assessment 2018, 23 (2), 368-378. 
 
244.  Morelli, B.;  Hawkins, T. R.;  Niblick, B.;  Henderson, A. D.;  Golden, H. E.;  Compton, 
J. E.;  Cooter, E. J.; Bare, J. C., Critical Review of Eutrophication Models for Life Cycle 
Assessment. Environmental Science & Technology 2018, 52 (17), 9562-9578. 
 
245.  Norris, G. A., Impact Characterization in the Tool for the Reduction and Assessment of 
Chemical and Other Environmental Impacts: Methods for Acidification, Eutrophication, and 
Ozone Formation. Journal of Industrial Ecology 2002, 6 (3/4), 79. 
 
246.  Weidema, B. P.; Wesnæs, M. S., Data quality management for life cycle inventories—an 
example of using data quality indicators. Journal of Cleaner Production 1996, 4 (3), 167-174. 
 
247.  Toffel, M. W.; Horvath, A., Environmental Implications of Wireless Technologies:  
News Delivery and Business Meetings. Environmental Science & Technology 2004, 38 (11), 
2961-2970. 
 
248.  Couillard, S.;  Bage, G.; Trudel, J.-S. Comparative Life Cycle Assessment (LCA) of 
Artificial vs Natural Christmas Tree; 1043-RF3-09; 2009. 
 
249.  Kim, S.;  Zhang, X.;  Dale, B. E.;  Reddy, A. D.;  Jones, C. D.;  Cronin, K.;  Izaurralde, 
R. C.;  Runge, T.; Sharara, M., Corn stover cannot simultaneously meet both the volume and 
GHG reduction requirements of the renewable fuel standard. Biofuels, Bioproducts & 
Biorefining 2018, 12, 203-212. 
 

 

195 

250.  Rees, J.;  Schmer, M.; Wortmann, C. Corn Stover Removal: Nutrient Value of Stover and 
Impacts on Soil Properties. https://cropwatch.unl.edu/2017/corn-stover-removal-nutrient-value-
stover-and-impacts-soil-properties. 
 
251.  Alvarez, R., A review of nitrogen fertilizer and conservation tillage effects on soil 
organic carbon storage. Soil Use and Management 2005, 21 (1), 38-52. 
 
252.  Baker, J. M.;  Ochsner, T. E.;  Venterea, R. T.; Griffis, T. J., Tillage and soil carbon 
sequestration—What do we really know? Agriculture, Ecosystems & Environment 2007, 118 (1), 
1-5. 
 
253.  Minasny, B.;  McBratney, A. B.;  Bellon-Maurel, V.;  Roger, J.-M.;  Gobrecht, A.;  
Ferrand, L.; Joalland, S., Removing the effect of soil moisture from NIR diffuse reflectance 
spectra for the prediction of soil organic carbon. Geoderma 2011, 167-168, 118-124. 
 
254.  West, T. O.; Post, W. M., Soil Organic Carbon Sequestration Rates by Tillage and Crop 
Rotation. Soil Science Society of America Journal 2002, 66 (6), 1930-1946. 
 
255.  Angers, D. A.; Eriksen-Hamel, N. S., Full-Inversion Tillage and Organic Carbon 
Distribution in Soil Profiles: A Meta-Analysis. Soil Science Society of America Journal 2008, 72 
(5), 1370-1374. 
 
256.  Virto, I.;  Barré, P.;  Burlot, A.; Chenu, C., Carbon input differences as the main factor 
explaining the variability in soil organic C storage in no-tilled compared to inversion tilled 
agrosystems. Biogeochemistry 2012, 108 (1), 17-26. 
 
257.  Zhang, W.;  Liu, K.;  Wang, J.;  Shao, X.;  Xu, M.;  Li, J.;  Wang, X.; Murphy, D. V., 
Relative contribution of maize and external manure amendment to soil carbon sequestration in a 
long-term intensive maize cropping system. Scientific Reports 2015, 5 (1), 10791. 
 
258.  Lal, R.;  Negassa, W.; Lorenz, K., Carbon sequestration in soil. Current Opinion in 
Environmental Sustainability 2015, 15, 79-86. 
 
259.  Hoekstra, A. Y.;  Chapagain, A. K.;  Aldaya, M. M.; Mekonnen, M. M., The Water 
Footprint Assessment Manual: Setting the Global Standard. Earthscan: 2011. 
 
260.  Lampert, D. J.;  Cai, H.; Elgowainy, A., Wells to wheels: water consumption for 
transportation fuels in the United States. Energy & Environmental Science 2016, 9 (3), 787-802. 
 
261.  Pfister, S.;  Koehler, A.; Hellweg, S., Assessing the Environmental Impacts of Freshwater 
Consumption in LCA. Environmental Science & Technology 2009, 43 (11), 4098-4104. 
 
262.  Ridoutt, B. G.; Pfister, S., A revised approach to water footprinting to make transparent 
the impacts of consumption and production on global freshwater scarcity. Global Environmental 
Change 2010, 20 (1), 113-120. 
 

 

196 

IPCC Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories; 1996. 

263.  Pfister, S.; Hellweg, S., The water “shoesize” vs. footprint of bioenergy. Proceedings of 
the National Academy of Sciences 2009, 106 (35), E93-E94. 
 
264.  Hoekstra, A. Y., A critique on the water-scarcity weighted water footprint in LCA. 
Ecological Indicators 2016, 66, 564-573. 
 
265. 
 
266.  Powers, S. E. Quantifying Cradle-to-Farm Gate Life-Cycle Impacts Associated with 
Fertilizer Used for Corn, Soybean, and Stover Production NREL: Golden, Colorado, 2005. 
 
267.  Smil, V., PHOSPHORUS IN THE ENVIRONMENT: Natural Flows and Human 
Interferences. Annual Review of Energy and the Environment 2000, 25 (1), 53-88. 
 
268.  Edwards, W. Economics of Harvesting and Transporting Corn Stover Iowa State 
University: 2014. 
 
269.  Hess, J. R.;  Kenney, K. L.;  Wright, C. T.;  Perlack, R.; Turhollow, A., Corn stover 
availability for biomass conversion: situation analysis. Cellulose 2009, 16 (4), 599-619. 
 
270.  Sokhansanj, S.;  Turhollow, A.; Wilkerson, E., Integrated Biomass Supply and Logistics: 
A Modeling Environment for Designing Feedstock Supply Systems for Biofuel Production. 
Resource Magazine 2008, 15 (6), 15-18. 
 
271.  Emery, I.; Mosier, N., Direct emission of methane and nitrous oxide from switchgrass 
and corn stover: implications for large-scale biomass storage. GCB Bioenergy 2015, 7 (4), 865-
876. 
 
272.  Emery, I.;  Dunn, J. B.;  Han, J.; Wang, M., Biomass Storage Options Influence Net 
Energy and Emissions of Cellulosic Ethanol. BioEnergy Research 2015, 8 (2), 590-604. 
 
273.  Crombie, K.;  Mašek, O.;  Cross, A.; Sohi, S., Biochar – synergies and trade-offs between 
soil enhancing properties and C sequestration potential. GCB Bioenergy 2015, 7 (5), 1161-1175. 
 
274.  Crombie, K.; Mašek, O., Investigating the potential for a self-sustaining slow pyrolysis 
system under varying operating conditions. Bioresource Technology 2014, 162, 148-156. 
 
275.  Sun, K.;  Jin, J.;  Keiluweit, M.;  Kleber, M.;  Wang, Z.;  Pan, Z.; Xing, B., Polar and 
aliphatic domains regulate sorption of phthalic acid esters (PAEs) to biochars. Bioresource 
Technology 2012, 118, 120-127. 
 
276.  Panagopoulos, A.;  Haralambous, K.-J.; Loizidou, M., Desalination brine disposal 
methods and treatment technologies - A review. Science of The Total Environment 2019, 693, 
133545. 
 

 

197 

277.  McGill,  J.;  Darr,  M.  Transporting  Biomass  on  Iowa  Roadways  Iowa  State  University: 
2014. 
 
278.  Kumar, D.; Murthy, G., Life cycle assessment of energy and GHG emissions during 
ethanol production from grass straws using various pretreatment processes. The International 
Journal of Life Cycle Assessment 2012, 17, 388-401. 
 
279.  Dias Mayer, F.;  Brondani, M.;  Carrillo, M.;  Hoffmann, R.; Lora, E., Revisiting Energy 
Efficiency, Renewability, And Sustainability Indicators In Biofuels Life Cycle: Analysis And 
Standardization Proposal. Journal of Cleaner Production 2019, 252, 119850. 
 
280.  Luo, L.;  van der Voet, E.;  Huppes, G.; Udo de Haes, H. A., Allocation issues in LCA 
methodology: a case study of corn stover-based fuel ethanol. The International Journal of Life 
Cycle Assessment 2009, 14 (6), 529-539. 
 
281.  Sheehan, J.;  Aden, A.;  Paustian, K.;  Killian, K.;  Brenner, J.;  Walsh, M.; Nelson, R., 
Energy and Environmental Aspects of Using Corn Stover for Fuel Ethanol. Journal of Industrial 
Ecology 2003, 7 (3‐4), 117-146. 
 
282.  Sokhansanj, S.;  Turhollow, A.;  Cushman, J.; Cundiff, J., Engineering aspects of 
collecting corn stover for bioenergy. Biomass and Bioenergy 2002, 23 (5), 347-355. 
 
283.  Follett, R. F.;  Vogel, K. P.;  Varvel, G. E.;  Mitchell, R. B.; Kimble, J., Soil Carbon 
Sequestration by Switchgrass and no-Till Maize Grown for Bioenergy. Bioenergy Research 4 
(3), 866-875. 
 
284.  Burnham, A.;  Han, J.;  Clark, C. E.;  Wang, M.;  Dunn, J. B.; Palou-Rivera, I., Life-
Cycle Greenhouse Gas Emissions of Shale Gas, Natural Gas, Coal, and Petroleum. 
Environmental Science & Technology 2012, 46 (2), 619-627. 
 
285.  Bai, M.;  Wilske, B.;  Buegger, F.;  Esperschütz, J.;  Kammann, C. I.;  Eckhardt, C.;  
Koestler, M.;  Kraft, P.;  Bach, M.;  Frede, H.-G.; Breuer, L., Degradation kinetics of biochar 
from pyrolysis and hydrothermal carbonization in temperate soils. Plant and Soil 2013, 372 (1), 
375-387. 
 
286.  Kuzyakov, Y.;  Subbotina, I.;  Chen, H.;  Bogomolova, I.; Xu, X., Black carbon 
decomposition and incorporation into soil microbial biomass estimated by 14C labeling. Soil 
Biology and Biochemistry 2009, 41 (2), 210-219. 
 
287.  Brown, S.;  Sathaye, J.;  Cannell, M.; Kauppi, P. E., Management of Forests for 
Mitigation of Greenhous Gas Emissions. 1995. 
 
288.  Sundqvist, T.;  Oasmaa, A.; Koskinen, A., Upgrading Fast Pyrolysis Bio-Oil Quality by 
Esterification and Azeotropic Water Removal. Energy & Fuels 2015, 29 (4), 2527-2534. 
 

 

198 

289.  Oshchepkov, A. G.;  Bonnefont, A.;  Saveleva, V. A.;  Papaefthimiou, V.;  Zafeiratos, S.;  
Pronkin, S. N.;  Parmon, V. N.; Savinova, E. R., Exploring the Influence of the Nickel Oxide 
Species on the Kinetics of Hydrogen Electrode Reactions in Alkaline Media. Topics in Catalysis 
2016, 59 (15), 1319-1331. 
 
290.  Oshchepkov, A. G.;  Bonnefont, A.;  Parmon, V. N.; Savinova, E. R., On the effect of 
temperature and surface oxidation on the kinetics of hydrogen electrode reactions on nickel in 
alkaline media. Electrochimica Acta 2018, 269, 111-118. 
 
291.  Tourwé, E.;  Pintelon, R.; Hubin, A., Extraction of a quantitative reaction mechanism 
from linear sweep voltammograms obtained on a rotating disk electrode. Part I: Theory and 
validation. Journal of Electroanalytical Chemistry 2006, 594 (1), 50-58. 
 
292.  Singh, N.;  Sanyal, U.;  Ruehl, G.;  Stoerzinger, K. A.;  Gutiérrez, O. Y.;  Camaioni, D. 
M.;  Fulton, J. L.;  Lercher, J. A.; Campbell, C. T., Aqueous phase catalytic and electrocatalytic 
hydrogenation of phenol and benzaldehyde over platinum group metals. Journal of Catalysis 
2020, 382, 372-384. 
 
293.  Barbir, F., PEM Fuel Cells. Imprint Academic Press: 2013. 
 
294.  Rolando, G.;  Richard, G. C.;  Juan, M. F.;  Eliezer, G.;  Jacek, L.;  Wolfgang, S.; Sergio, 
T., Defining the transfer coefficient in electrochemistry: An assessment (IUPAC Technical 
Report). Pure and Applied Chemistry 2014, 86 (2), 245-258. 
 
295.  Prévoteau, A.;  Geirnaert, A.;  Arends, J. B. A.;  Lannebère, S.;  Van de Wiele, T.; 
Rabaey, K., Hydrodynamic chronoamperometry for probing kinetics of anaerobic microbial 
metabolism--case study of Faecalibacterium prausnitzii. Scientific reports 2015, 5, 11484-11484. 
 
 

 

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