COST PREDICTION AND LIFE CYCLE ASSESSMENT OF WOODY BIOMASS SUPPLY-CHAIN IN MICHIGAN By Yingqian Lin A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Biosystems Engineering Ð Doctor of Philosophy 2018 ABSTRACT COST PREDICTION AND LIFE CYCLE ASSESSMENT OF WOODY BIOMASS SUPPLY-CHAIN IN MICHIGAN By Yingqian Lin As a state with rich forest resources and a good transportation system, Michigan is in a position to promote the use of woody biomass for bioenergy production. To achieve sustainable development in MichiganÕs woody biomass supply chain, the goals this rese arch were to: 1) to develop a cost prediction model in Excel using Visual Basic for Application (VBA) programming language; 2) to perform cradle -to-grave Life Cycle Assessment (LCA) to account for the GHG emissions, energy return on investment, and nutrien t removal; and 3) to design an eco -efficient (Define) woody biomass supply chain with minimal logistic cost and GHG emissions in Michigan. Five woody biomass production systems were monitored to develop predictive regression equations for different harves ting machines and to predict the total production cost of woody biomass in Michigan. Based on the predictive machine productivity equations and machine hourly cost obtained from each studied system, a spreadsheet model was developed in Excel 2016 using VBA programming language. In order to better understand field storage of woody biomass, 5 studies were conducted to monitor the biomass quality (biomass Higher Heating Value (HHV) and biomass moisture content) change under different storage forms (wood logging residues piles and wo od chips piles). The results indicated that storing woody biomass in logging residue pile could effectively reduce the biomass moisture content and maintain the HHV at a stable level. On the contrary, increases in moisture content were observed in all wood chips piles. Based on the above findings, an improved operations system structured with linear programming was developed for minimizing the total cost of woody biomass preprocessing, storage, and transportation. The operation details suggested by the impr oved operations system can be used as a guideline of real operations to achieve the lowest possible operations cost. To evaluate the total GHG emissions, energy return on investment and nutrient removal in each studied biomass production system, five crad le-to-grave LCAs were performed. Results suggested that over 90% of GHG emissions were from the combustion stage, which can be effectively reduced by increasing biomass HHV and decreasing biomass moisture content. Including soil carbon sequestration in LCA can largely offset the total global warming effect caused by woody biomass production and utilization. However, a better approach is needed to estimate soil carbon sequestration to avoid uncertainties caused by vegetation types, considered soil depth, and time duration. A multi -criteria optimization framework was developed to design a woody biomass supply chain with minimal GHG emissions and production cost in Michigan. The trade -off between rising cost and reducing GHG emissions was that b y increasing the cost by 1.46 ¢/kWh, the total GHG emissions could be reduced by 0.66 kg CO2 -eq/kWh. The sensitivity analysis indicated that biomass HHV and biomass moisture content had a larger impact on the optimized solutions and the trade -offs, as comp ared to the transportation distance. This again, confirmed that in order to improve the efficiency and sustainability of the woody biomass supply chain, future research efforts should be spent on improving the HHV and decreasing the moisture content of woo dy biomass. iv ACKNOWLEDGMENTS I would like to offer my sincerest appreciation to Dr. Fei Pan for giving me th e opportunity to start my Ph.D. program . Without his invaluable support and guidance during the 5 years , it would never be possible for me to complet e this degree. My deep est gratitude goes to Dr. Christopher Saffron for being my co -advisor. He has given me great patience, a lot of encouragement, support and suggestions, especially in the most difficult time of my research study. I would like to give s pecial thank s to Dr. Ajit Srivastava for being my committee member and for giving me so many support, inspiring advices and great opportunities to learn how to teach and how to be come an independent researcher. My thanks also go to Dr. David MacFarlane for serving on my committee board and for his insightful criticism and valuable suggestions on my research topic modification s. I am grateful to have so many amazing professors and colleagues in my Ph.D. journey. Special thanks to Dr. Daniel Guyer and Dr. Pouyan Nejadhashemi, for their professional advices on my Ph.D. program development . Thanks to my amazing colleagues and friends in BAE: Zachary Cater, Melissa Rojas -Downing, Mahlet Garedew, Yuan Zhong, Juan Sebas tian Hernandez Suarez , Alexandre Chabrelie, Matthew Herman, and Rachael Sak, for providing personal support and encouragement , sharing great knowledge , and offering generous help in my research work. I would like to thank my dearest friends: Jing Yang, Yi ng Qin, Huei Wang, Keyin Wang, Weiyue Chen, Yiyuan Zhu, Shutian Yan, for constantly supporting me by the ir loves, thoughts and prayers. Finally, I want to thank my parents and my husband, for their unconditional love, support and trust for me. God has been so merciful and faithful to me by putting you in my life. v TABLE OF CONTENTS LIST OF TABLES ............................................................................................................ x LIST OF FIGURES ........................................................................................................ xii KEY TO ABBREVIATIONS ........................................................................................ xv CHAPTER 1 !INTRODUCTION .................................................................................. 1!1.1!Woody biomass as a renewable energy source .......................................................... 1!1.2!Key challenges in woody biomass supply chain ........................................................ 5!1.2.1!Prediction of production costs ........................................................................ 5!1.2.2!Fuel quality change during storage ................................................................. 7!1.2.3!Life -cycle impacts assessment ........................................................................ 8!1.2.3.1!Greenhouse gas (GHG) emissions ........................................................... 8!1.2.3.2!Soil carbon sequestration .......................................................................... 9!1.2.3.3!Nutrient removal ..................................................................................... 10!1.2.4!Cost minimi zation, trade -off between GHG emissions and cost .................. 11!1.3!Hypothesis ................................................................................................................ 12!1.4!Goal and objectives .................................................................................................. 13 CHAPTER 2 !LITERATURE REVIEW .................................................................... 15!2.1!Cost analysis and models in woody biomass harvesting, processing and transportation .................................................................................................................... 15!2.2!Exi sting forestry -based biomass economic models ................................................. 19!2.2.1!Fuel Reduction Cost Simulation (FRCS) ...................................................... 19!2.2.2!Harvest Cost -Revenue (HCR) Estimator ...................................................... 20!2.2.3!My Fuel Treatment Planner (MyFTP) .......................................................... 20!2.2.4!BioSum 3.0 ................................................................................................... 20!2.2.5!Auburn Harvest Analyzer (AHA) ................................................................. 21!2.2.6!USDA Forest Service Region 6 Forest Products Web Page: Logging Systems and Economic Programs ........................................................................................... 21!2.2.7!Biomass Site Assessment Tool 3.0 (BioSAT 3.0) ........................................ 21!2.2.8!Forest Residue Transportation Costing Model (FoRTSv5) .......................... 22!2.3!Woody biomass storage ........................................................................................... 23!2.3.1!Wood chips storage ....................................................................................... 23!2.3.2!Logging residue storage ................................................................................ 25!2.4!Life Cycle Assessment (LCA) in forestry ............................................................... 26!2.4.1!Categories and key steps of LCA .................................................................. 26!2.4.2!LCA in forest products .................................................................................. 27!2.4.3!LCA in Short Rotation Woody Crop (SRWC) systems ................................ 28!2.4.4!LCA in other forest production systems ....................................................... 30!2.4.5!Soil carbon sequestration and sources of uncertainties ................................. 34!2.5!Nutrient removal caused by forest harvesting systems ............................................ 37! vi 2.5.1!Intra -specific variability of N , P, K in different parts of trees ...................... 37!2.5.2!Established aboveground tree biomass estimation equations ....................... 38!2.5.3!Sources of uncertainties in tree biomass equations ....................................... 39!2.5.4!Quantifying the uncertainties using Monte Carlo analysis ........................... 40!2.6!Optimization in woody biomass supply chain ......................................................... 41!2.6.1!Single objective optimization models ........................................................... 43!2.6.1.1!Strategic planning ................................................................................... 43!2.6.1.2!Tactical planning .................................................................................... 44!2.6.2!Bi-objective optimization models ................................................................. 45!2.6.3!Tri -objective optimization models ................................................................ 47 CHAPTER 3 !WOODY BIOMASS PRODUCTION COST PREDICTION .......... 48!3.1!Introduction .............................................................................................................. 48!3.2!Methodology ............................................................................................................ 50!3.2.1!Study sites and harvesting systems ............................................................... 50!3.2.2!Cycle time estimation and productivity calculation ...................................... 54!3.2.3!Harvesting and pre -processing c osts ............................................................. 55!3.2.4!Transportation costs ...................................................................................... 56!3.2.5!Biomass weight estimation ........................................................................... 59!3.3!Results ...................................................................................................................... 60!3.3.1!Site 1 ............................................................................................................. 60!3.3.1.1!Regression equations and operation cycle time prediction .................... 60!3.3.1.2!Cycle stem wood weight ........................................................................ 60!3.3.1.3!Machine hourly rate and production cost ............................................... 61!3.3.2!Site 2 ............................................................................................................. 63!3.3.2.1!Regression equations and operation cycle time prediction .................... 63!3.3.2.2!Cycle stem wood weight ........................................................................ 63!3.3.2.3!Machine hourly rate and production cost ............................................... 64!3.3.3!Site 3 ............................................................................................................. 66!3.3.3.1!Regression equations and operation cycle time prediction .................... 66!3.3.3.2!Cycle stem wood weight ........................................................................ 66!3.3.3.3!Machine hourly rate and production cost ............................................... 66!3.3.4!Site 4 ............................................................................................................. 69!3.3.4.1!Productivity ............................................................................................ 69!3.3.4.2!Machine hourly rate and the production cost ......................................... 69!3.3.5!Site 5 ............................................................................................................. 70!3.3.5.1!Regression equations and operation cycle time prediction .................... 70!3.3.5.2!Productivities of forwarder and truck/van .............................................. 70!3.3.5.3!Machi ne hourly rate and production cost ............................................... 70!3.4!Discussions .............................................................................................................. 72!3.4.1!Cycle t ime predictions for harvesting machines and woody biomass transportation ............................................................................................................ 72!3.4.2!Woody biomass harvesting production cost ................................................. 74!3.5!Conclusion ............................................................................................................... 75 vii CHAPTER 4 !BIOMASS STORAGE AND FUEL QUALITY CHANGE ............. 76!4.1!Introduction .............................................................................................................. 76!4.2!Methodology ............................................................................................................ 78!4.2.1!Site conditions ............................................................................................... 78!4.2.1.1!Logging residues storage study sites ...................................................... 78!4.2.1.2!Wood chips storage study sites .............................................................. 79!4.2.2!Biomass moisture content and HHV ............................................................. 79!4.3!Results ...................................................................................................................... 80!4.3.1.1!Biomass moisture content change of wood residue piles ....................... 80!4.3.1.2!Biomass moisture content change of wood chips piles .......................... 82!4.3.1.3!Biomass HHV change in logging residue piles ...................................... 83!4.3.1.4!Biomass HHV change in wood chips piles ............................................ 84!4.4!Discussions .............................................................................................................. 85!4.4.1!Wood logging residues piles ......................................................................... 85!4.4.2!Wood chips piles storage .............................................................................. 86!4.5!Conclusion ............................................................................................................... 88 CHAPTER 5 !COST MINIMIZATION OF WOODY BIOMASS LOGISTICS INTEGRATING INFIELD DRYING AS A COST -SAVING PREPROCESS IN MICHIGAN 89!5.1!Introduction .............................................................................................................. 89!5.2!Problem Description ................................................................................................ 91!5.2.1!Feedstock storage and transportation operations .......................................... 91!5.2.2!Feedstock end user and demand ................................................................... 91!5.2.3!Traditional operations system and improved operations system .................. 92!5.2.4!Biomass storage ............................................................................................ 93!5.2.5!Transportation costs ...................................................................................... 94!5.2.6!Holding cost and additional p rofit ................................................................ 94!5.2.7!Mathematical model ...................................................................................... 96!5.2.7.1!Indices .................................................................................................... 96!5.2.7.2!Parameters .............................................................................................. 97!5.2.7.3!Constraints .............................................................................................. 98!5.2.7.4!Objective function .................................................................................. 99!5.2.8!Results and Discussion ............................................................................... 101!5.2.8.1!The improved operations system A ...................................................... 101!5.2.8.2!Comparison between improved operations system A and the traditional operations system ............................................................................................. 103!5.2.8.3!Improved operations system with two feedstock end users (system B) 104!5.2.8.4!Sensitivity analysis ÑEffects of transportation distance on the total cost and the improved operations system ................................................................ 107!5.2.8.5!Sensitivity analysis ÑEffect of biomass MC on the total cost and the improved operations system ............................................................................. 108!5.2.8.6!Model limitations ................................................................................. 109!5.2.8.7!Conclusion ............................................................................................ 110 viii CHAPTER 6 !LIFE CYCLE ASSESSMENT OF MIXED -SPECIES FOREST AND SHORT ROTATION WOODY CROP PLANTATION IN MICHIGAN ..... 111!6.1!Introduction ............................................................................................................ 111!6.2!Goal, Scope and Functional Unit for Life Cycle Assessment ............................... 114!6.3!Life cycle inventory (LCI) ..................................................................................... 115!6.4!Life Cycle Impact Assessment (LCIA) .................................................................. 122!6.4.1!Soil carbon sequestration estimation .......................................................... 122!6.4.2!Nutrient removal ......................................................................................... 125!6.4.2.1!Biomass weight estimation ................................................................... 125!6.4.2.2!Macronutrient (N, P, K) concentrations ............................................... 126!6.4.3!Abiotic Depletion Potential (ADP) ............................................................. 129!6.4.4!Energy Return On Investment (EROI) ....................................................... 130!6.4.5!Sensitivity analysis ...................................................................................... 131!6.5!Life Cycle Assessment Results and Discussion ..................................................... 131!6.5.1!Global Warming Potential (GWP) .............................................................. 131!6.5.2!Soil C sequestration and its uncertainty ...................................................... 136!6.5.3!Sensitivity analysis: impacts of transportation distance, biomass moisture content and biomass HHV on the GHG emissions. ................................................ 137!6.5.4!Energy Return on Investment (EROI) ........................................................ 141!6.5.5!Nutrient Removal and Abiotic Depletion Potentia l (ADP) ........................ 145!6.6!Conclusion ............................................................................................................. 146 CHAPTER 7 !MULTI -CRITERIA OPTIMIZATION FOR WOODY BIOMASS SUPPLY CHAIN IN MICHIGAN .............................................................................. 148!7.1!Introduction ............................................................................................................ 148!7.2!Problem description ............................................................................................... 150!7.3!Mathematical model ............................................................................................... 153!7.3.1!Indices ......................................................................................................... 153!7.3.2!Parameters ................................................................................................... 153!7.3.3!Decision Variables ...................................................................................... 158!7.3.4!Objective functions ..................................................................................... 159!7.3.5!Constraints .................................................................................................. 160!7.4!Case scenarios ........................................................................................................ 160!7.5!Algorithm ............................................................................................................... 161!7.6!Results .................................................................................................................... 161!7.6.1!Pareto -optimal sets analysis ........................................................................ 161!7.6.2!Sensitivity analysis ...................................................................................... 171!7.6.2.1!The impact of the transportation distance between torrefaction depot and power plant on the pareto -optimal sets ............................................................. 171!7.6.2.2!The impact of the torrefaction price on the pareto -optimal sets ........... 173!7.7!Discussions ............................................................................................................ 174!7.8!Conclusion ............................................................................................................. 177 CHAPTER 8 !CONCLUSION ................................................................................... 178 ix APPENDICES ............................................................................................................... 181!Appendix 1. User guide for the cost prediction spreadsheet model ............................... 182!Appendix 2. VBA codes for the cost prediction spreadsheet model .............................. 197! REFERENCES .............................................................................................................. 247! x LIST OF TABLES Table 1. LCA studies in short rotation woody crop plantations ....................................... 30!Table 2. LCA studies in forest production systems (non -SRWC) .................................... 33!Table 3. Machine hourly rate ($/SMH) calculated based on data obtained from equipment owners ............................................................................................................................... 58!Table 4. Delay -free average cycle time equations for harvesting machines at Site 1. ..... 62!Table 5. Productivity and production cost at Site 1. ......................................................... 62!Table 6. Delay -free average cycle time equations for harvesting machines at Site 2. ..... 65!Table 7. Productivity and production cost at Site 2. ......................................................... 65!Table 8. Delay -free average cycle time equations for harvesting machines at S ite 3. .... 68!Table 9. Productivity and production cost at Site 3. ......................................................... 68!Table 10. Delay -free average cycle time equations for harvesting machines at Site 5. ... 71!Table 11. August -to-November woody biomass moisture content in Michigan (Lin and Pan, 2013; Lin and Pan, 2015) .......................................................................................... 94!Table 12. Transportation costs at different transportatio n distances in Michigan ............ 94!Table 13. Calculated feedstock purchased prices based on the LHV and the energy cost of $2.49/MM Btus. a ............................................................................................................... 96!Table 14. Parameter values used in the case simulation ................................................. 101!Table 15. The costs of using an improved ope rations system to sell biomass feedstock to the end user in improved system A a ................................................................................ 102!Table 16. The costs of using a traditional operation system to sell biomass feedstock to the end user a .................................................................................................................... 104! xi Table 17. The costs of using an improved operations system to sell biomass feedstock to the two end users in improved syste m B a ....................................................................... 106!Table 18. Information of studied woody biomass production systems .......................... 115!Table 19. Life cycle inventroy of site 1 .......................................................................... 117!Table 20. Life cycle inventory of site 2 .......................................................................... 118!Table 21. Life cycle inventory of site 3 .......................................................................... 119!Table 22. Life cycle inventory of site 4 .......................................................................... 120!Table 23. Life cycle inventory of site 5 .......................................................................... 121!Table 24. Referenced studies in s oil carbon sequestration rates estimation ................... 124!Table 25. Nitrogen concentration allocation in stem, foliage, branch, and bark ............ 127!Table 26. Phosphorus concentration allocation in stem, foliage, branch, and bark ........ 128!Table 27. Potassium concentration allocation in stem, foliage, branch, and bark .......... 129!Table 28. Nutrient removal in each studied site ............................................................. 145!Table 29. Parameters used in the multi -criteria optimization model .............................. 156! xii LIST OF FIGURES Figure 1. U.S. energy consumption by energy source (EIA, 2017) .................................... 2!Figure 2. Components of terrestrial carbon stock (Lal, 2005) .......................................... 35!Figure 3. Classification of reviewed optimization studies in biomass supply chain ........ 43!Figure 4 a and b. Plots layout at Site 4 and tree layout in each plot ................................. 53!Figure 5. Biomass moisture content (wet basis, %) changes in logging re sidues piles in site 1 throughout field storage period. .............................................................................. 81!Figure 6. Wood moisture content changes affected by pile sizes in logging residue piles in Site 2. ............................................................................................................................ 82!Figure 7. Biomass moisture content (wet basis, %) changes in wood chips piles throughout field storage period ......................................................................................... 83!Figure 8. Biomass HHV changes in logging residues piles throughout field storage period. ............................................................................................................................... 84!Figure 9. Biomass HHV changes in wood chips piles throughout field storage period. .. 85!Figure 10. An illustration of the improved operations system includes biomass chipping, storage, and transportation ................................................................................................ 93!Figure 11. The weight of woody biomass feedstock delivered to the end -user ................ 97!Figure 12. Total cost after deducting AP associated with differ ent one -way transportation distances .......................................................................................................................... 107!Figure 13. Total cost after deducting AP associated with different biomass MC .......... 108!Figure 14. System boundary of woody biomass production systems in this LCA study 115!Figure 15 a -e. GHG emis sions contribution of Site 1 to Site 5 ...................................... 132!Figure 16. GHG emissions of cultivation, harvesting, collecting, pre -processing (loading + grinding/chipping), and transportation in Site 1 to 5 ................................................... 134! xiii Figure 17 a-e. Sensitivity analysis of Site 1 to 5: impacts of transportation distance, biomass moisture content and biomass HHV on the GWP ............................................ 138!Figure 18 a -e. Energy input and out of Site 1 to 5 .......................................................... 141!Figure 19. Decision nodes in the multi -criteria optimization model .............................. 151!Figure 20. Defined possible routes in the multi -criteria optimization model ................. 152!Figure 21a and b. Pareto -optimal sets and decision variable variations in case 1 .......... 163!Figure 22a and b. Pareto -optimal sets and decision variable variations in case 2 .......... 165!Figure 23a and b. Pareto -optimal sets and decision variable variations in case 3 .......... 166!Figure 24a an d b. Pareto -optimal sets and decision variable variations in case 4 .......... 167!Figure 25a and b. Pareto -optimal sets and decision variable variation in case 5 ............ 168!Figure 26. Pareto -optimal sets of the five studied cases ................................................. 169!Figure 27. Cost contribution of each life stage to the final cost in soultion B, D, F, H and J in base scenario ............................................................................................................ 170!Figure 28. GHG emissions contribution of each life stage to t he final cost in soultion B, D, F, H and J in base scenario ......................................................................................... 171!Figure 29. Impacts of distance between the torrefaction depot and the biomass power plant on the pa reto -optimal sets in base case .................................................................. 172!Figure 30. Impacts of torrefaction prices on the pareto -optimal sets in base case ......... 173! Figure A - 1. Model input page of the cost prediction spreadsheet model ...................... 184!Figure A - 2. Step1: Biomass source selection ................................................................ 185!Figure A - 3. Step 1: Tree species selection ..................................................................... 185!Figure A - 4. Step 2: Herbicide and fertilizer application ................................................ 186!Figure A - 5. Step 3: Choosing harvesting prescription ................................................... 187! xiv Figure A - 6. Step 3: Whole tree harvesting system information window ...................... 187!Figure A - 7. Step 3: Harvesting system machinery cost estimation window (feller -bunch er) .......................................................................................................................... 189!Figure A - 8. Step 3: CTL harvesting system information window ................................. 190!Figure A - 9. Step 3: Reconfigured harvesting system information window .................. 191!Figure A - 10. Step 3: Include pre -processing in the harvesting system or not ............... 192!Figure A - 11. Step 3: Grinder and loader information window ...................................... 193!Figure A - 12. Step 4: Transportation system information .............................................. 193!Figure A - 13. Step 5: Storage form an d moisture contents during storage ..................... 194!Figure A - 14. Step 6: Combustion system information .................................................. 194!Figure A - 15. Outputs table for the spreadsheet model .................................................. 196! xv KEY TO ABBREVIATIONS ADP: abiotic depletion potential BTU: British Thermal Unit CTL: cut -to-length harvesting system DBH: diameter at breast height EROI: renewable energy return on investment FS: natural forest stand FBIC: Forest Biomass Innovation Center GHG: greenhouse gas GT: green ton GWP: global warming potential HHV: higher heating value ISO: International Standards Organization KBS: Kellogg Biological Station LCA: Life Cycle Assessment LCI: Life Cycle Inventory MC: moisture content MSU: Michigan State University ODT: oven dry ton PMH: prod uctive machine hour Reconfigured system: tractor -pulled cut -and -chip reconfigured forage -harvesting system RHV: recoverable heating value SMH: scheduled machine hour xvi SRP: short rotation poplar SRWC: short rotation woody crop SRW: short rotation willow TRC: Tree Research Center U.S.: United States WT: whole tree harvesting system Yr: year 1 CHAPTER 1 INTRODUCTION 1.1 Woody biomass as a renewable energy source Woody biomass was defined as Òthe trees and woody plants, including limbs, tops, needles, leaves, and other woody parts, grown in a forest, woodland, or rangeland environment, that are the b yproducts of forest management Ó (USDA Forest Service, 2008). It can be produces from various forest management and production processes such as non -timber tree removal, timber harvesting, landfill diversion and dedicated plantations (Shelly, 2011). Annually, United States produces about 14 million dry tons of wood debris and waste, 87 million dry tons of woody milling residues, and 64 million d ry tons of harvesting residues (White, 2010). Woody biomass can be used to produce a wide range of wood products like lumbers, paper and pulp, furniture, and building materials. Historically , it has been used to produce heat and electrical energy in combined heat and power (CHP) plants . There are about 178 biomass power plants in the U.S., with a total capacity of 20,156 MW (Biomass Magazine, 2017). O verall combustion efficiency of CHP plants varies from 65 -85% and highly depends on wood character istics such as ash content, moisture content and the higher heating value of the wood fuel (Clarke et al., 2012). In the past decades, due to the growing need of reducing the dependence on fossil -based fuels, renewable energy has become the world's fastes t-growing energy sources (Zanchi et al., 2012; REN 21, 2012; EIA, 2015). From 2008 to 2017, the total annual electric power produced from renewable energy sources (excluding hydroelectric and solar) has risen from 1.25E+08 MW to 3.34E+08 MW (EIA, 2018a). As one of the most important renewable energy sources, wood and wood -derived biomass supplied about 19% 2 of total U.S. renewable energy consumption in 2016 ( Figure 1). About 69% of the bioenergy produced from woody biomass was consumed to produce power and heat for industrial applications such as wood and paper production (EIA, 2018b). Figure 1. U.S. energy consumption by energy source (EIA, 201 7) Besides heat and electricity, woody biomass can also be utilized to produce biofuels such as bioethanol and bio -methanol ( Galbe and Zachhi, 2002). Compared to petroleum -based fuels, biofuels have many environmental advantages: 1) high availability; 2) reduction of greenhouse gases (GHG) emissions; 3) biodegradability; 4) carbon sequestration (Balat, 2011). From 2010 to 2016 , the annual volume requirement suggested by EPA for cellulosic biofuel production has grown from 6.5 million gallons to 206 million gallons ( Schnepf and Yacobucci and , 201 0). There are several techniques to covert woody biomass to biofuels, e.g. fermentation, digestion, gasification, liquefaction, enzymatic conversion and pyrolysis (McKendry, 2002; Papari and Hawboldt, 2015). Compared to enzyme conversion, pyrolysis requires shorter time and less pretreatment like stem explosion and hydrolysis (Mettler et al., 2012). Pyrolysis is a thermo decomposition process that can convert green woody biomass into bio -oil under inert atmospheric co nditions (Sinha et al., 2000). Based on different heating rates, temperature 3 and processing time, pyrolysis is categorized as slow pyrolysis, moderate pyrolysis and fast pyrolysis (McKendry, 2002; Papari and Hawboldt, 2015). The energy recovery rate of fas t pyrolysis with hybrid poplar (29.4 -34.2%) has been concluded to be higher than other biochemical conversion techniques (Dou et al., 2017). Biofuel yield from fast pyrolysis can be up to 80%, but it is highly depends on feedstock type, chemical compositio n and reaction conditions (Mettler et al., 2012; Papari and Hawboldt, 2015). Besides conversion techniques, t he long -term potential for using woody biomass as bioenergy resource also largely relies on its availability (Demirba ! , 2003). Based on the 2016 B illion -Ton report, around 8 2 million to 8 8 million dry tons of woody biomass are available annually in U.S. at the price of $60 per dry ton (USDOE, 2016). One dry ton of woody biomass can produce about 70 gallons of biofuels ( Bracmort et al., 2011 ). This indicates a good potential of woody biomass to be a primary biofuel feedstock. In order to ensure the year -round operation of biofuel processing plants, it is critical to have reliable and consistent supply of woody biomass. In the past two decades, SRWC such as hybrid poplar and hybrid willow have been largely grown for cellulosic biofuel production because of their high growth rates , high quality (high carbohydrate and low ash content), and flexible harvest period (Hinchee et al., 2011; Emerson et al., 201 8). For instance, hybrid poplar production increased from 211 acres to 2,554 acres from August to November 2014 (US Department of Energy , 2016). To increase the biomass yield and improve the biomass chemical composition of SRWC, many research studies have been done to develop new breeds (Volk et al., 2011; Shield et al., 2014 ). Another key obstacle to maintain the year -round supply of SRWC is the leaf removal. Leaves that are included in the harvested biomass can affect both the initial and final fuel qualities after storage 4 (Eisenbies et al., 201 5). In addition, Dou et al. (2017) has reported that bio -oil produced from whole tree coppice wood chips had 25.3% lower C content compared to those produced from no -leaf coppice. In order to remove the low qua lity parts such as leaves and bark, a low cost technique, air classification has been tested by Emerson et al . (2018) for hybrid poplar and shrub willow samples. Their results indicated that with majority of leaves removed, the ash content could be reduced by 28.6% for hybrid poplar and 17.7% for shrub willow. Michigan has rich forest resources and t he forest cover is over 53 percent. Extensive studies have been done to estimate and assess the woody biomass availability in MI (Jakes and Smith, 1983; MacFarl ane, 2009; Mueller et al., 2010; Zhang et al., 2018). With a broad base of 18.6 million acres of timberland , about 20 million dry tons of forest biomass can be produced as bioenergy feedstock annually (Cook, 2010). The se rich forest resources bring signifi cant economic benefits to MichiganÕs economy , especially the Upper Peninsula . There are more than 1,400 forest products manufacturing facilities, 1,700 units of forest products manufacturing business, and about 200,000 jobs are supported by these forest -based industries (Michigan Department of Natural Resources, 2003). The annual electricity produced from woody biomass ranks fourth nationally in 2015 (Michigan Biomass, 2017). Presently there are 10 biomass -based power facilities in MI, producing 209 MW powe r annually with 1.86 million dry tons of woody biomass (Auch, 2016). With the woody biomass availability of 20 million dry tons , MI has a good opportunity to increase use of woody biomass . Hence, the total logistic cost of producing woody biomass in MI bec omes a key question. In addition, the environmental impacts 5 caused by different kinds of forest harvesting operations in MI such as CO 2 emissions and soil nutrient depletion should be studied more . 1.2 Key challenges in woody biomass supply chain The potentia l of using woody biomass as an alternative bioenergy source highly depends on the combined cost of logistics and the quality of the produced biomass. There are five important components in woody biomass supply chain, including biomass harvesting, collectio n, pre -processing, storage and transportation. The supply chain planning of woody biomass can be very challenging due to several factors such as the biomass availability, the consumers demand and all kinds of regulations and policies (Soylu et al., 2006). To achieve the eco -efficiency in MichiganÕs woody biomass supply chain, which is defined as Ômaintaining or increasing the value of economic output while simultaneously decreasing the impact of economic activity upon ecological systemsÕ (Braungart et al., 2007), there are several k ey challenges to consider about: 1) production costs estimation; 2) fuel qualities monitor during storage; 3) life -cycle impacts; 4) t otal cost minimization, trade -off between GHG emissions and total logistics cost . 1.2.1 Prediction of production costs Due to the traditional harvesting strategy and the undeveloped biomass supply chain, the costs of using woody biomass are mainly higher or close to fossil fuels and coals (Dunnett et al., 2007; Perlack et al., 2011; Caputo, 2014;). In U.S. , compared to $2.37/MMBTU for coals, the average price of using woody biomass for electric power purpose is at $2.69/MMBTU ( EIA , 2014). There are many reasons can lead to high production cost , such as high capital investment on harvesting machines, inappropriate harvesting season, difficult harvesting site conditions , low travel distance and lack of 6 operation management (Ghaffariyan et al., 2012a; Harril and Han, 2012; Strandgard, 2014). Cost prediction in woody biomass supply chain is important to determine the economic feasibility of using woody biomass for energy purpose (Alam et al., 2012). Several cost analysis models such as BioSum 3.0, Auburn Harvest Analyzer (AHA) and FRCS -North have been developed to provide logistic co st predictions. Since transportation costs have always been accounted for an essential part of the total costs, linear programming models and GIS -based forest biomass data are used to reduce transportation costs in European Countries and Canada (Ranta, 200 2; Ranta, 2005; Panichelli and Gnansounou, 2008). Besides cost prediction, determination of the effective factors in biomass supply chain and developing their corresponding time prediction models are also important to improve the production efficiency (Al am et al., 2012). For instance, harvester productivity can be affected by many factors such as tree size, spacing, ground slope and roughness (Ghaffariyan et al., 2012b; Wright et al., 2010). Harril and Han (2012) found a noticeable relationship between tr ansportation cost and road type that every 50 m increase of spur road can increase the total transportation cost in $0.08/ODT. Many research trials monitored that the overall productivity decreased as the slope increased (Bolding and Lanford, 2002; Acuna a nd Kellogg, 2009; Spinelli et al., 2010). Due to the differences in tree species, stand density, site and terrain conditions, productivities of woody biomass supply chain in Lake States such as Illinois, Indiana, Michigan and Ohio are expected to be diffe rent from Western U.S. For instance, ground slope is smaller in Lake States compared to Western States, which might reduce the 7 harvesting and biomass collecting time and cost. Also, the lower ground elevation in Lake States makes biomass hauling and transp ortation easier than in Western States. Consider about the impacts of terrain condition and forest transportation road types on the production cost, there is a need to develop a cost prediction model based on Michigan collected data. 1.2.2 Fuel quality change du ring storage Storage is a key component within the woody biomass supply chain, especially when year -round harvesting is impossible. Storage is complicated because of the changing seasonal availability of woody biomass and the varied demand of energy plants throughout the year (Sokhansanj et al., 2006; Lin and Pan, 2013). Meanwhile, different storage methods will produce biomass at various quality levels, which can significantly affect the transportation and energy conversion efficiency (Jirjis, 2001; Casal et al., 2010). The most common way in northern United States to store green biomass is to directly process wood into chips and store these in piles before being utilization (Lin and Pan, 2013). This storage method poses several problems such as dry matter loss, moisture content (MC) increase, and energy content reduction (Fredholm and Jirjis, 1988; Hornqvist and Jirjis, 1999; Jirjis 2001; FRL, 2002; Afzal et al, 2010). Store wood residues in bundles, as the second option, can produce high quality biomass feedstock with lo w biomass MC, higher energy content, and low ash content (Lehtikangas, 2001; Pettersson and Nordfjell, 2007; Afzal et al., 2010). Yet, the bundling technology is associated with several problems such as high capital investment and low productivity caused b y saw binding, materials handling, twine spool collapse, and slow movement at the harvesting site (Rummer et al. 2004; Leinonen, 2004; Harrill, 2010). In order to 8 ensure a year -round supply of high -quality biomass feedstock, it is necessary to further stud y the fuel quality during the storage period. 1.2.3 Life -cycle impacts assessment 1.2.3.1 Greenhouse gas (GHG) emissions Another key question in woody biomass supply chain is how to correctly assess and minimize the GHG emissions. Historically, it was assumed that the carbon emitted into the air from biomass production and combustion can be offset by tree carbon sequestration. Therefore, woody biomass has been recognized as Òcarbon neutralÓ and Òenvironmentally friendlyÓ and has been recommended to produce in a large sc ale to replace fossil fuel (Lippke et al., 2004; Puettmann and Wilson, 2007; Solomon et al., 2007). However, it takes a long term for the immediately emitted carbon dioxide and other greenhouse gas to be sequestered by forest again; the global warming effe ct caused by the carbon flux during this long term has normally been underestimated (Searchinger et al., 2009; Cherubini et al., 2011; Sedjo, 2011 and 2013). Under In 2010, 90 scientists has expressed their concerns on carbon neutrality of woody biomass a nd stated that using woody biomass might not necessarily stop global warming (Sedjo, 2013). UK Department of Environment Climate Change has reported that although utilizing wood residues could result in a low net GHG emission, however harvesting round wood from natural forest or plantation could lead to very high emissions (Stephenson and MacKay, 2014). In short rotation woody crops plantation, N 2O emissions occur at cultivation stage from N fertilizer application, leaf litter decomposition and other mainte nance practices (Crutzen et al., 2016; Bouwman et al., 9 2010). N 2O emissions were reported to be 4.6 and 5.9 Mg ha -1 of CO 2 equivalents in willow and poplar plantation (Niki‘ma et al., 2012). 1.2.3.2 Soil carbon sequestration Soil organic carbon is the one of the most important carbon pool on earth. It majorly forms from decomposition of detritus such as leafs, dead roots and leachates from living roots. The formed soil organic carbon will be stored in the topsoil (0 -25 cm) and gradually transport to subsoil (25 -100 cm). Over 50% of the carbon is stored in soil organic matter, which is more stable compared to debris a nd litter (Brand‰o et al., 2013 ; Helin et al., 2013 ). Qin et al. (2016) suggested that including soil organic carbon (SOC) sequestration in LCA could s ignificantly influence the total GHG footprints of bioenergy production systems. However, SOC changes are highly impacted by regional climate conditions, management practices, vegetation type, soil depth , and time period considered. For instance, Freibauer et al. (2004) have reported that reduce tillage and increase surface residue return could increase soil C by 0.4 -0.6 t C ha -1yr-1. Surface litter cover has been demonstrated to increase microbial activit ies, thus to increase SOC accumulation rates (Tolbert et al., 2002). Besides management practice, change of vegetation type can also impact SOC change by directly change the microbial communities (Freibauer et al., 2004; Xue et al., 2016; Qin et al., 2016) . Previously, most studies have assumed that majority of accumulated SOC is in top soil (0 -30cm), yet, to fully assess the soil C inventory, SOC in deeper profile (0 -100cm) should also be included (Knops and Bradley, 2009; Follett et al., 2012; Qin et al., 2016). Another key factor is the time horizon with the SOC because SOC change tend s to decrease with time until the soil C reach the equilibrium level (Guo and Gifford, 2002; Stewart et al., 2007). As suggested by Qin et al. (2016), there are high 10 uncerta inties with SOC changes rates in different perennial crops production systems. Due to these challenges and uncertainties in accounting soil carbon, majority LCA studies have not included soil C sequestration in their inventory data (Koerber et al., 2009; P etersen et al., 2013). 1.2.3.3 Nutrient removal Other environmental concerns for woody biomass supply chain include increased removal of forest debris, threats to wildlife and biodiversity, nutrients loss and soil compaction (Evans and Pershel, 2009). Among all t he concerns, the nutrient removal or nutrients loss caused by the forest harvesting projects was widely expressed from the prospect of the environment sustainability (Staaf and Olsson, 1991; Vanguelova et al., 2010; Hall and Richardson, 2001) . For instance , whole tree harvesting system has been reported to remove 44% of K and have a long -term negative impact on soil nutrition of K and P ( Duchesne and Houle, 2008;Vanguelova et al., 2010 ). Soil nutrient and fertility loss were shown to cause 3 -7% reduction in future tree growth up to 33 years after harvesting (Achat et al., 2015). The negative impact caused by whole tree harvesting system is site sensitive, in some regions that originally have low soil organic matter content, the reduction of soil C and cation exchange capacity is more obvious (Thiffault et al., 2006). Several states in U.S. have developed biomass harvesting guidelines since 2007 and offered suggestions for how to avoid the impacts of intensive forestry harvesting operations in Minnesota (MFRC, 2007), Wisconsin (Herrick et al., 2009) and Maine (Benjamin, 2010). A better understanding of the nutrients removal caused by forest harvesting system in Michigan 11 has become an impotency to limit the nutrient depletion caused by utilization of woody bioma ss. Life -cycle assessment (LCA) has been largely adopted to count and study the environment impacts including carbon emission and nutrients removal in many bioenergy production systems (Di Nassi o Nasso et al., 2010; Bracomrt, 2015; Vasquez Sandoval, 2015). GHG emissions in woody biomass production system in Michigan and Lake States have rarely been studied. A correct and comprehensive LCA that includes all life stages of woody biomass supply chain can provide a clear picture of the energy and mass flow i n a woody biomass bioenergy production system. With the help of LCA, better decisions can be made in promoting woody biomass utilization, and in improving the system performance in terms of minimizing GHG emissions (Puettmann, 2006). 1.2.4 Cost minimization, trade -off between GHG emissions and cost In addition to the cost analysis and the LCA, many mathematical models for optimization were widely used to implement cost -effective bioenergy production (Gunasekaran et al., 2004; Bredstro !m et al., 2004; Parker e t al., 2010; Shabani and Sowlati, 2013a). As woody biomass transportation cost accounts for the largest part of the total cost and energy consumption (Eriksson and Bjo¬rheden, 1989; Allen et al., 1998; Alam et al., 2012), the developed model mainly focus o n location selection and woody biomass collection to minimize the logistic cost. However, reducing the cost and reducing the carbon emissions could be two conflicting objectives. Only focus on cost minimization might lead to a production system with high c arbon footprints. Therefore, it is critical to include both the economic and environmental objectives when optimizing 12 the woody biomass supply chain, more importantly, to understand the trade -off between the total production cost and total carbon emissions . Multi -criteria optimization has been commonly used to support decision making with multiple competing criteria in renewable energy production systems. Afgan and Carvalho (2008) have evaluated the economic, environmental and social performance of five dif ferent renewable energy production systems using multi -criteria optimization method. The method examined the sustainability index of the five renewable energy production systems in scenario cases with different weight assigned to indicators such as electri city cost, efficiency, CO 2 emissions and NO x emissions. Ayoub et al. (2009) proposed a multi -level optimization model to determine the bioenergy generation plan using different energy sources, with optimized energy efficiency, minimized total cost and CO 2 emissions for Japan. However, only a few multi -criteria optimization studies have been published in woody biomass supply chain. Kanzian et al. (2013) has formulated a multi -criteria optimization problem to minimize the CO 2 emissions and to maximize the pro fit for large -scale forest energy supply networks in Austria. The optimized biomass production strategy suggested by the model can double the profit with only 4.5% increase in CO 2 emissions. In order to sustainably utilize woody biomass as a bioenergy sour ce in Michigan and U.S., there is a great need to combine environmental and economic demands and develop a multi -criteria optimization framework. 1.3 Hypothesis 1) The productivity and production cost of woody biomass supply chain can be effectively predicted by multi -linear regression equations and machine price survey collected from loggers in MI; 13 2) Life cycle assessment can reveal the environmental impacts such as total global warming potential and nutrient removal caused by different woody biomass supply chain in MI; 3) A multi -criteria optimization model can be applied to provide an optimized woody biomass production strategy with minimal cost and minimal total global warming potential, and to examine the trade -off between the total global warming potential reduct ion and cost increase in the woody biomass supply chain in MI. 1.4 Goal and objectives As a state with high availability of woody biomass, Michigan has a high potential to replace fossil fuels. To use the forest resources sustainably , the goal of this Ph.D. pr oject is to develop a spreadsheets cost prediction model in Excel using VBA programming language, to perform LCA to account for the GHG emissions and nutrient removal from biomass cultivation to combustion, and to design an eco -effecient woody biomass supp ly chain with minimal logistic cost and GHG emissions in Michigan. The specific objectives of this Ph.D. project were: 1) Predicting the cost of the entire biomass supply chain consists of harvesting, biomass collecting, handling and transportation, based on the cost analysis of different harvesting systems (Whole -tree harvesting system, Cut -to-length harvesting system and Single -pass cut -and -chip harvesting system) in Michigan; 2) Use LCA to project the total GHG emissions and to illustrate nutrients flow (N, P, K) in harvesting, biomass collecting, biomass handling (grinding/chipping), transportation and combustion phases in Whole -tree harvesting system, Cut -to-length harvesting system and Single -pass cut -and -chip harvesting system in Michigan; 14 3) Provide an optimized logistic strategy which can minimize the total logistic cost and GHG emissions using multi -criteria optimization method, and to examine the impacts of transportation distance and carbon tax policy on the biomass production strategy. 15 CHAPTER 2 LITERATURE REVIEW 2.1 Cost analysis and models in woody biomass harvesting, processing and transportation Harvesting systems contain four stages include timber harvesting, primary transportation, loading, and secondary transportation (Conwa y, 1982). Determination of the effective factors in each stage and developing their corresponding time prediction models can help to improve the efficiency of managing the process of harvesting operations (Mohammad et al., 2012). Timber harvesting includes felling (severing the standing tree from the stump) and processing (often called bucking, limbing, or topping) (Johnson et al., 2012). Feller -buncher is a harvesting machine normally used in a whole -tree harvesting system, with a cutting or felling head t hat can gather and cut one or several trees at a time (Hakkila, 1989; Adebayo et al., 2007; Spinelli et al., 2002). The selection of different types fell -buncher depends on the site condition. For instance, in mountainous Western U.S., track mounted feller -buncher with felling head and short boom are more commonly used. In the south U.S., where the sites are more flat with slope less than 25%, 4 -wheel rubber tired drive -to-tree feller -buncher is appropriate to use (Jaffe and Obrien, 2009; Mitchell, 2008). The other commonly used felling machine is harvester. They are utilized in cut -to-length harvesting systems. For steep ground with slope ranging from 35 -45%, track -mounted harvester is normally selected, and rubber tired harvester will be selected for flatt er ground with slope less than 25% (Jaffe and Obrien, 2009). Harvesters have lighter cutting head and longer booms compared to feller -buncher and they can move vertically and horizontally (Jaffe and Obrien, 2009). 16 Productivity of harvesting machines has wi dely been studied using different methods in North America (Tufts and Brinker, 1993; Kellogg and Bettinger, 1994; MCNeel and Rutherford, 1994; Landford and Stokes, 1995; Tufts, 1997). In these studies, time and motion studies are conducted in order to inve stigate the main contributing factors that affect work productivity and to establish a base for cost calculation (Nurminen et al. 2006). For instance, hourly productivity and hourly cost for the feller -buncher in a Central Appalachian hardwood forest were reported to be about 428.9 to 2267.7 ft3 per productive machine hour (PMH) and $99.68/PMH (Long et al., 2002). In Michigan, the productivity and hourly cost of feller -buncher were estimated to be 5.95 -28 BDT/PMH and $51.52 -$51.64/PMH (Pan and Srivastava, 2 013). The most influencing factors for the productivity of harvesting machines were summarized to be: 1) environmental conditions such as forest stand and harvesting site and 2) operational factors such as ground conditions, operators motivation and skill, operational layout, etc. (Jirou "ek et al. 2007; Mizaras et al., 2008). Skidding and forwarding operation, indicating the transportation of unprocessed forest residues from harvesting site to the centralized locations. In the skidding process, tracked skidder or wheeled are used based on specific purpose. Tracked skidder is normally used when there is a big load of materials and the ground slope is steep. Wheeled skidder has smaller size and higher speed, and is suitable to be used in less steeper groun d. Skidder can also be categorized as grapple skidder and cable skidder depends on their grapple mechanism. The grapple skidder can pick up more than one tree at a time; while the cable skidder has a skid line with chokers attached. Skidders generally come with either a grapple or a cable drum, now many modern skidders come 17 with both (Forest and Rangelands). The productivity and hourly cost of skidder have been estimated by many previous studies in different regions (Wang et al., 2008; Goychuk et al., 2011; Borz et al., 2013; Lotfalian et al., 2011). It has been indicated that the most influencing factors on the productivity of skidding or forwarding process are: skidding distance, piece size, load volume, winching distance and slope of the trail (Egan and B aumgras, 2003; Sabo and Porsinsky, 2005). There is also a loading process involves in the movement of logs or residue in the first stage transportation (Johnson et al., 2012). The loader cost has been projected to range from 125 to 150 $/SMH and the transp ortation cost ranged from 70 to 115 $/SMH (Perez et al., 2012). In field processing, there are different machines that can be used to reduce size and homogenize the forest residues such as biomass bundler, baler, and chipper. The biomass bundler is commonl y used in cut -to-length harvesting systems to process slashes into a slash bundle or a compressed residue log (Gallagher, 2010, Martin, 2008). The major problems in producing biomass bundles with a slash bundler is the low productivity and the high hourly cost due to delays caused by saw binding, materials handling, twine spool collapse, and slow movement at the site (Patterson et al., 2008; Harrill, 2010; Rummer et al. 2004; Leinonen, 2004). Another machine developed to handle forestry logging residues is Bio -baler. The benefits of using Bio -baler include reduction in fire hazard, decrease in herbicide application, and wildlife enhancement (Klepac and Rummer, 2009). However, the cost per unit for Bio -baler ranges from $17.60/green ton to 36.75$/green ton, which will largely decrease its economic feasibility (Klepac and Rummer, 2009). VTo reduce the size of woody biomass, chipper or grinder is utilized in the harvesting site. One of the 18 most commonly used chipper is the disk chipper, which equipped with a rev olving heavy disk with straight knives (Naimi et al., 2006). The other type of commonly used chipper is the drum chipper, which has a large drum where the knives are mounted (Van Loo and Koppejan, 2007). The productivity of the chippers will be decreased a s knives become dull, meanwhile, the fuel consumption will be increased (Zamora Cristales, 2013). Compared to chippers, grinders are less sensitive to the cleanness of the material. The main factor that drives the in -feed speed is the size of the biomass piece (Zamora Cristales, 2013). The most popular grinders are horizontal grinder and tub grinder. The two types grinders differ in their in -feed system, residues are fed into the grinder horizontally in horizontal grinder while in tub grinder residues are fed in a rotating tub (Zamora Cristales, 2013). Grinders can also be classified into stationary grinder and mobile grinder depends on where they are operated (Hummel et. al. 1988). The average productivities of grinders have been estimated to range from 32 .5 Gt/PMH to 70 Gt/PMH (Zamora Cristales, 2013; Rawlings et al., 2004; Aman et al., 2010). Results obtained from Harrill and Han (2010) and the related thesis by Harrill (2010) had also been used to model the cost and production of the grinders. The bioma ss transportation involves highway truck -tractors and chip vans, which has been commonly used in the United States to ship chips for the pulp and paper industry (Zamora Cristales, 2013; Johnson et al., 2012; Rawlings 2004). However, because of the adverse road condition, smaller trucks such as Hook -lift truck are favorable to transport the biomass (Harrill et al., 2009). To eliminate the high cost associated with slash collection and transportation, a roll -off trucking system has been 19 evaluated in northern California, which was recommended for short hauling distance and loose material (Han et al., 2010). For distance larger than 50 km, a truck -and -trailer unit is preferred (Wolfsmayr and Rauch, 2014; Spinelli and Hartsough, 2001). The transportation cost of transportation from forest to mill has been studied by many researchers, and the most effective variables have been decided to be the transport distance, load volume and load weight. (Asikainen, 1995 and 1998; Moll and Copstead, 1996; Sikanen et al., 2005; Nurminen and Heinonen, 2007; Mo !ller and Nielsen, 2007). The transportation cost of forestry harvesting system is normally calculated based on the hauling distances in different types of roads (Mo !ller and Nielsen, 2007). The classification of the road type is based on the transportation speed in each road type in average mph. For instance, 25 mph in paved road, 55 mph in gravel road, 45 mph in dirt road , and 55 mph in state highway (Michigan State Police). 2.2 Existing forestry -based biomass economic model s 2.2.1 Fuel Reduction Cost Simulation (FRCS) The FRCS is a spreadsheet application developed by Pacific Northwest (PNW) Research Station, with Microsoft¨ Excel¨ from 2002 to 2007. It can be used to estimate the costs of fuel reduction treatments if given the site condition and the harvest system configuration. This model uses the cost estimating approach developed by Miyata (1980) and combine machines into systems following the approach introduced by Hartsough et al (2001). As described above, this model can s imulate the total cost by the information given; however, it cannot decide what system will be best fit for the harvesting site, which is one of its key limitations. Another limitation is the equipment production rates used in the model were from studies i n Pacific Northwest, further modifications is 20 necessary to improve the accuracy for lake states such as Michigan, Wisconsin or Minnesota. 2.2.2 Harvest Cost -Revenue (HCR) Estimator This software is developed to provide cost of harvesting small -diameter ponderosa pine in Southwest United States. It can simulate stand -level producing cost and revenue for logging contractors and forest planners to design the fuel reduction plan and final financial profits. 2.2.3 My Fuel Treatment Planner (MyFTP) This model is another spr eadsheets application developed by P acific Northwest Research Station at 2002 with Microsoft¨ Excel¨. It is designed for providing the harvesting cost, revenue, economic impacts and total biomass production of fuel reduction project in national forest dist rict or similar size unit. This application can only be valid when the volume of the estimated trees volume is smaller than 50 ft 3. The sample data files and cited scenarios in this model were all from Western U.S. model, therefore this spreadsheets is sug gested to be used for the dry -forest types in Western United States. 2.2.4 BioSum 3.0 The BioSum 3.0 is software developed and tested by PNW Research Station. This software is designed to explore alternative landscape -scale treatment scenarios that achieve a c ertain management objective. It combined forest inventory data, treatment cost model, fuel treatment model and raw material hauling cost model (Fried et al. 2005). As an effective tool to optimize the woody biomass supply chain and achieve the 21 largest bene fits, it has been successfully used in Oregon, California and New Mexico with a wide range of forest bioenergy -facilities. It is still under modification and not currently available online. More information will be updated later after it is officially published. 2.2.5 Auburn Harvest Analyzer (AHA) Auburn Harvest Analyzer (AHA) is designed by Shawn Baker and Dale Greene from the Center for Forest Business, Warnell School of Forestry and Natural Resources at the University of Georgia. AHA is a spreadsheet template that can only be used to estimate the whole -tree harvesting costs of merchantable tree or round wood. It was developed based on a whole -tree harvesting system included a Hydro -Ax 511 feller -buncher, a CAT 518 skidder and a 210 Prentice loader. 2.2.6 USDA Forest Service Region 6 Forest Products Web Page: Logging Systems and Economic Programs This website contains spreadsheets models that can calculate logging and hauling cost, evaluate residual value and project economics for 19 National Forests in Oregon and Washington. The biomass harvesting cost estimation model can handle logging systems and equipment includ e skyline, mechanized, tractor, shovel and helicopter systems. The hauling cost estimation model only handle tran sportation cost of log truck. 2.2.7 Biomass Site Assessment Tool 3.0 (BioSAT 3.0) BioSAT model is developed t o estimate marginal, average and total cost of producing and delivering agricultural residues, logging residues and mill residues within 80 miles haulin g distance. The model is based on data collected from 33 Eastern Unite States. In this model, stumpage and delivered wood cost on Michigan, Minnesota and 22 Wisconsin are cited from loggersÕ surveys conducted by Timber Mart North. Harvesting cost for merchant able trees is determined by AHA cost model; and logging residues cost is estimated using FRCS model. Trucking cost is calculated using the Trucking Cost Model for Transportation Managers developed by North Dakota State University at 2003, which did not cla ssify different transportation road types (e.g. dirt road, paved road or highway) . 2.2.8 Forest Residue Transportation Costing Model (FoRTSv5 ) FoRTSv5 is a spreadsheet calculator designed by U.S. Forest Service Southern Research Station to decide loading cost a nd transportation cost of moving woody biomass from the harvesting site to the feedstock buyer. There are two stages transportation considered in the model : the biomass hauling process from the forest to landing site , and from the landing site to the end -user. Although it was developed at Southern U.S., the intermediate values such as the machine cost, production rate and fuel consumption rate can be modified by users to better suit their own condition s. In conclusion, there were many cost analysis models d eveloped to estimate costs of different harvesting systems (Whole tree or Cut -to-length) with different harvesting purposes (forest thinning or clear cut). Yet, most of the developed models were produced and can only be applied at Southern or Western U.S., which cause a concern for users in Lake States. In Lake States, terrain conditions and major tree species are different compared to Southern and Western U.S., which could result in different biomass volume and weight harvested during certain machine harve sting hours. Furthermore, none of above -discussed models have combined harvesting with transportation stage to provide a complete cost prediction for the whole woody biomass 23 supply chain. This indicates a research need to develop a comprehensive cost predi ction tool for Lake States, which considers all stages in woody biomass supply chain, use the same cost calculation method as previous discussed models (Miyata 1980), but with machine model information and productivity collected from Michigan based field s tudy. 2.3 Woody biomass storage Owing to increasing energy demands and the need to reduce greenhouse emissions, there is a strong necessity to decrease dependence on fossil -based fuels (REN21 2012, Zanchi et al. 2012). B iomass materials such as trees, grasses, and agricultural cr ops have thus become imperative alternative energy res ources (US Department of Energy [USDOE] 2004). Amon g all of these materials, woody biomass is one of the most feasible c hoices because of its relatively low cost and high ava ilability (USDOE 2004). Approximately 87 million dry tons (short tons) of wood residues and 64 million dry tons of forest harvest residues are produced in the United States every year, which accounts for approximatel y 2 percent of the total energy consumed (USDOE 2004, White 2010). In Michigan, there are over 1,400 forest produc ts manufacturing facilities and 1,700 units that are working in the forest products manufacturing business, which implies a high availability of forest residues that can b e used for bioenergy generation (Michigan Forest Products Council 2010). 2.3.1 Wood chips storage Green biomass is usually directly processed into wood chips by mills or other wood -using facilities and is stored on-site before being transporte d to a power plant or a biofue l refinery (Lin and Pan 2013). This introduces many problems and concerns, such as a risk of self -ignition, health issues caused by the release of hi gh concentrations of 24 allergenic microspores, and most impo rtant, dry matter loss followed by the decrease o f the bioma ss quality (Fredholm and Jirjis 1988, Jirjis 1995). Previous stu dies showed that the dry matter loss in a large green chip pile was approximately 12 percent during a 7 -mont h storage period (Hornqvist and Jirjis 1999). A dry matter lo ss of 26 per cent was also found in large bark piles throughout a 6 -month storage period (Fredholm and Jirjis 1988). I n addition, the dry matter loss in the large bark pile result ed in a 20 percent reduction in energy content (Fredholm and Jirjis 1988), thus lowering t he energy yield and the value of woody biomass feedstock . Wood chip quality control during storage is a key consideration because woody biomass with a high, consta nt, and uniform fuel quality is always desired (Lehtikangas and Jirjis 1998, Afzal et al. 2010). Wood chip piles, compared with bundled and unchipped logging residu es, pose more challenge such as dry matter loss, incremen t in moisture content (MC), and reduction in energy con tent (Fredholm and Jirjis 1988; Tho¬rnqvist and Jirjis 1990; Ji rjis 1995, 2001; Garstang et al. 2002; Afzal et al. 2010). Because of these concerns, the duration of wood chip stor age is normally suggested to be less than 6 months. Kofman a nd Spinelli (1997) suggest that willow from short rotation coppice should be del ivered immediately to heatin g plants after harvest to avoid difficulty in storage. In Michi gan, the typical storage period of wood chips is aroun d 60 to 70 days (Scott Robbins, Director of SFI and Public Affairs in Michigan Forest Products Council, pers onal communication, October 15, 2013). However, no study is available to validate these suggestions for wood chip st orage in Michigan. In addition, a key yet unresolved issu e is how to predict the MC in a biomass pile without fre quent measurement (Erber et al. 2012). This is also the pro blem for biomass higher heating 25 value (HHV) estimates, because all existing models to predict the HHV o f woody biomass use independent variables such as fixed carbon content, volatile matter content, and ash content, which all r equire additional testing (Iyer et al. 2002, Channiwala and Parikh 20 02, Parikh et al. 2005). Several factors exist that can affect woody biomass MC. They are air movement in the p ile, relative air humidity, and monthly cumulative rainfall (Jirjis 1995, 20 01; Garstang et al. 2002; Afzal et al. 2010). Using these weather factors can be a possible solution to predict woody biomass MC and HHV. However, to our knowledge, no previous research has been documented regarding this type of predicting model. 2.3.2 Logging r esidue storage Several studies highlighted that the storage of logging residue in bundles c an produce high -quality biomass feedstock with low moisture content (MC), increased higher heating value (HHV), a nd low ash content (Lehtikangas 2001, Pettersson and Nordfjell 2007, Afzal et al. 2010). Patterson et al. (2008) reported that the MC of biomass bundles decreased by 10 to 25 percent within 1 month after piling. Meanwhile, Karha and Vartiamaki (2006) found that the reduction of MCs resulted in a 12 to 28 percent incr ease in energy content per uni t volume. The major problems in producing biomass bundles with a slash bundler is the low productivity and the high h ourly cost due to delays caused by saw binding, materials handling, twine spool collapse, and slow movement at the site (Leinonen 2004, Rummer etal. 2004, Patterson et al. 2008 , Harrill 2010). This makes the economic feasibility of usin g the savings from transporting low MC and highly compac ted biomass to pay for the cost of bundling operations question able. 26 Previous studies fou nd that woody biomass is better stored in a loose form, allowing more air movement to take place within the pile (Whit e et al. 1983, Thornqvist 1985, Sampson and McBeath 1987, Jirjis 2005, Afzal et al. 2010). With the increased interests in developing wood -based renewable energy production in Michigan, ensuring a year -round supply of high -quali ty woody biomass feedstock with consistently low MC and h igh HHV without incurring extra processing cost becomes imperative. 2.4 Life Cycle Ass essment (LCA) in forestry 2.4.1 Categories and key steps of LCA LCA is a standard approach developed in the late 1960s to account the environmental impacts or potential in all life stages of producing a product or operating a process (Jensen et al. 1997; Guin”e et al. 2011; Rebitzer et al. 2004; Finnveden et al. 2009; Sedjo, 2013; Klein et al., 2015). The primary goal of LCA is to prove and improve the environmental soundness of any product or production process (ISO, 2006; Heinimann, 2012). Since renewable ener gy sources are promising alternatives to reduce our dependence on fossil fuels, thousands of LCA have been performed globally in the past 20 years (Amponsah et al., 2014). There are typically two types of LCA: ÒattributionalÓ LCA (ALCA) and Òconsequential Ó LCA (CLCA) (Sedjo, 2013; Vasquez Sandoval, 2015). ALCA is performed to understand Òthe environmentally relevant physical flows of a past, current or potential future product systemÓ (Currant et al., 2005; Sedjo, 2013). CLCA is developed to decide the con sequence of possible decisions that would have been made (Poeschl et al., 2012a; Vasquez Sandoval, 2015). The two types of LCA provide different information, an ALCA can report total carbon emissions from woody biomass 27 supply chain while a CLCA can show th e possible consequences of mainly using woody biomass for energy production in Michigan. Based on the methodology provided by Curran (2006) and framework described in ISO 14040 (2006), key steps of LCA can be summarized as below: 1) Define the goal(s) an d scope of the project: In this step, the primary goal of the project, major assumptions, data quality assurance procedures, system boundary and function units of the LCA will be decided. 2) Life -cycle inventory (LCI): All input and output data will be col lected in this phase to show the energy and material flow, and greenhouse gas (GHG) emissions throughout the life cycle of the evaluated system. The inventory data will be presented in units related to function units defined. 3) Life -cycle impact assessmen t (LCIA): The impacts caused by previously identified resources or GHG emissions will be assessed to review their contribution to impact categories such as global warming, acidification and eutrophication. 4) LCA interpretation: The results will be interpr eted in terms of project objectives and functional units. Comparison with other production systems can be made in this phase, and conclusions will be drawn. 2.4.2 LCA in forest products In the past 20 years, several LCAs of wood products have been performed, attempting to evaluate their resulted environmental impacts, especially carbon footprints. The accessed wood products processing systems include wood boards (Rivela et al., 2007; Gonz⁄lez -Garc™a et al., 2009a), paper pulp (Gonz⁄lez -Garc™a et al., 2009b), 28 writing paper (Dias et al., 2007), floor coverings (Petersen and Solberg, 2003; Nebel et al., 2006) and furniture (Gonz⁄lez -Garc™a et al., 2011). In U.S., the Consortium for Research on Renewable Industrial Material (CORRIM) started a large project to as sess the material flows and the environmental consequences for kiln -dried softwood lumber (Puettmann et al., 2010), plywood (Wilson and Sakimoto, 2005), laminated veneer lumber (Wilson and Dancer, 2005), oriented -strand board (Kline, 2005), particleboard ( Wilson, 2008a), and medium -density fiberboard (Wilson, 2008b). CORRIM concluded that wood used in long -term products such as construction material could provide a great reduction in fossil fuel use and emissions. Later on, Puettmann and Wilson (2005) publi shed the first cradle -to-gate scope LCA for wood products and made a comparison between their report and CORRIM 2004 report. The comparison indicates that wood products used 1/3 of their energy consumption from renewable resources and the rest from fossil fuels when the system boundary includes all cradle -to-gate stages (Puettmann and Wilson, 2005). 2.4.3 LCA in Short Rotation Woody Crop (SRWC) systems Short rotation woody crops such as Populus, Salix and Eucalyptus are gaining interests because their high produ ctivity, and flexibility in harvest time (Hinchee et al., 2009). Since they are largely promoted to offset GHG emissions, many LCA studies are carried out globally to quantify the environment impacts and to chase the energy flow in their production systems . Table 1 summarizes part of the documented LCA studies focus on SRWC systems in recent 13 years. Although most of them reach the same conclusion that SRWC could significantly reduce GHG emissions, large differences in reported energy ratio and GHG 29 emissions level was found. For example, the energy ratio and total CO 2 emission in short rotation willow plantation reported by Goglio and Owende (2009) are 19.3 and 134 kg CO2 Gj-1 electricity produced, whereas the values reported by Keoleian and Volk are 55.3 and 10.5 Mg CO2 ha-1 (equivalent to 19.48 kg CO 2 Gj-1). Djomo et al. (2011) have published a literature review, in which they compiled 26 LCA studies on energy ratio and GHG emissions of short rotation woody crops (poplar and willow). They highlighted that GHG emissions varied from 0.6 to 10.6 g CO2Eq MJ -1biomass (or 39 to 132 g CO2Eq kWh -1); a nd energy ratios for willow and poplar production systems varied from 16 to 79 and 13 to 55. This difference is due to different project goals, raw materials and research locations in the two studies . It indicates that LCA is very case specific and relevan t for the geographic location where the life cycle inventory data are collected. Therefore, to evaluate the environmental impacts of SRWC production systems in Michigan and lake States, it is necessary to develop a LCA model that covers from cultivation to combustion stage. 30 Table 1. LCA studies in short rotation woody crop plantations 2.4.4 LCA in other forest production systems Besides SRWC, there are other forest production systems such as spruce, pine and eucalypt stands that are originally planted for pulp production purpose or other industrial uses. In these forest production systems, trees certainly did not receive management practices like fertilization or herbicide application as intensively as in SRWC production Year of study Author(s) Energy crops Region Research focus Scope Environm ental Impact Energy 2005 Keoleian and Volk Willow U.S. ! ! Cultivation to energy generation 2007 Adler et al. Poplar U.S. ! Cultivation to energy generation 2009 Goglio and Owende Willow Italy ! ! Cultivation to energy generation 2009 Gasol et al. Poplar Spain ! ! Cultivation to energy generation 2010 Nassi o di Nasso et al. Poplar Italy ! Cultivation and harvesting 2011 Djomo et al. Poplar and willow Global ! ! Literature review 2011 Cherubini and Str¿mman Poplar and willow Global ! ! Literature review 2012 Gonz⁄lez -Garc™a et al. Willow Swede n ! ! Cultivation to energy generation 2012 Nikiema et al. Poplar and willow U.S. ! Cultivation 2013 Dillen et al. Poplar Belgiu m ! Cultivation to transportation 2014 Caputo et al. Willow U.S. ! ! Cultivation to transportation 2015 Vasquez Sandoval Poplar U.S. ! ! Cultivation and harvesting 31 system. The environment impacts caused by these not intensively manag ed forest production systems were not well studied, especially in U.S. (Kilpel−inen et al., 2011; Gonz⁄lez -Garc™a et al., 2014; Cherubini and Str¿mman, 2011). In a literature review completed by Cherubini and Str¿mman (2011), there are 13 out of 94 studi es done in evaluating environmental impacts of forest wood production, compared to 27 studies done in SRWC production systems. Among the 13 studies in forest production system, only two studies: Pimentel and Patzek (2005) and William et al. (2009) are from U.S. Pimentel and Patzek (2005) compared the ethanol production and energy used in using corn, switch grass and wood. William et al. (2009) reviewed environmental impacts include greenhouse emissions, soil health and quality, water use and water quality i n agricultural residues and forest residues production systems. Walker et al. (2010) carried out an LCA study called Manomet that aimed to evaluate GHG emissions and potential impacts caused by utilizing forest biomass in Massachusetts. However, this study only involved one single harvest site with one single plot that allows no room for any change in future markets or prices. The approach used in Manomet was adopted in a Canadian study conducted by McKechnie et al. (2011), which involved a 5.3 million ha m ature natural forest as a carbon sink. They combined LCA analysis with forest carbon modeling to provide a more accurate picture of forest carbon dynamic. Their major finding was that it might take up to 100 years to see the carbon mitigation benefit of th e forest. Later on, Sedjo and Tian (2012) published the conceptual Timber Supply Model (TSM) analysis using a well -known dynamic optimization forest management model to examine the effect of changing wood biomass demand on the existing forest and the 32 amou nt of carbon captured by the forest system (Daigneault et al. 2012; Sohngen et al. 1999; Sedjo and Sohngen 2013). The TSM model used an unconstrained approach to maximized the profit in wood biomass market and to foresees the change in forest harvest plan corresponding to widespread demand increases for wood. Handler et al. (2014) published an LCA that analyzed GHG emissions and energy input in harvesting and transportation stages (truck and railway transport) in roundwood supply chain in Michigan. This st udy is based on previous published peer -reviewed literature, national database, and a Michigan loggersÕ survey. Handler et al. (2014) claimed that Michigan roundwood supply chain has smaller environment burden compare to similar production systems from oth er regions. Table 2 summarized some other LCA studies that have been done in U.S. It is noticeable none of them covers the whole woody biomass supply chain start from cultivation, end at biomass utilization stage . Besides the incomplete life scope, the results reported are widely different. Even though all published studies follow similar LCA procedure, critical issues are raised regarding about how the LCA procedure is exactly adopted in each study. As discussed by Heinimann (2012) and Klein et al. (2015), there were large differences found in scope definition, system boundaries, and the function units in many previous published studies. In 22 different peer -review ed LCA reports, the Global Warming Potential (GWP) reported were at a range from 6.3 -67.1 kg CO 2 equiv. m -3 ob (median = 17.0; n= 36) from site preparation to plant gate (Klein et al., 2015). These large variations not only made those studies very site -spe cific, but also created difficulties in cross -comparing these results to standard LCA studies (Cherubini, 2010). 33 Table 2. LCA studies in forest production systems (non -SRWC) Year of study Author(s) Region of study Goal and scope 2005 White et al. Wisconsin LCA of forestry harvesting 2006 Sonne Northwest Pacific, West side of the Cascade Mountains in Washington and Oregon Greenhouse gas emissions from forestry harvesting operation 2010 Oneil et al. Minnesota, Maine, Missouri, West Virginia, Pennsylvania; Idaho, Montana, Washington Carbon emissions and environmental toxicity from establishment to harvesting 2011 Neupane et al. Maine LCA of wood chips used for biofuel production 2012 Johnson et al. Western and Southern U.S. LCA of biomass collection and processing 2013 Saud el al. West Virginia Carbon emissions of harvesting Another concern is that for woody biomass from mature forests and non -SRWC stand, there is no available LCA model developed to estimate environmental impacts caused by all life stages in a woody biomass supply chain. By 2015, there are over 20 millions acr es of forested land in Michigan, and 19.3 millions acres of them are timberland (Pugh et al., 2016). Similarly for other Lake States, the land areas of non -SRWC forest stands are much larger than SRWC plantations. This indicates a great need of developing a LCA framework to assess the life impacts in the complete woody biomass supply chain, including cultivation, harvesting, biomass collection, biomass handling, transportation and combustion stages, especially in forest stands . T his LCA 34 framework should be applied to access the environmental impacts in the wood biomass supply chain on Michigan and other regions. 2.4.5 Soil carbon sequestration and sources of uncertainties Carbon pool in forest biomass production systems include many co mponents such as the aboveground biomass, belowground biomass, soil organic carbon, dead wood residues and litter (Figure 2). Based on EPAÕs report (EPA, 2008), 59% of the net CO2 in US are sequestrated in the forest ecosystems, while 16% of the net CO 2 are stored in soil. Soil organic carbon majorly forms from decomposition of detritus such as leafs, dead roots and leachates from living roots. It it sequestrated into the soil by two major ways: humification and microphotosynthesis (Sedjo and Sohngen, 2012) . The formed soil organic carbon will be stored in the topsoil (0 -25 cm) and gradually transport to subsoil (25 -100 cm). Over half of the carbon is stored in soil organic matter, which is more stable compared to debris a nd litter (Brand‰o et al., 2013 ; Hel in et al., 2013). Qin et al. (2016) suggested that including SOC sequestration in LCA could significantly influence the total GHG footprints of bioenergy production systems. However, it is challenging to include SOC because there are high uncertainties in accounting SOC changes. Therefore, majority LCA studies have not included soil C sequestration in their inventory data (Koerber et al., 2009; Petersen et al., 2013). The large uncertainty in SOC changes rate is because SOC are highly impacted by many fac tors such as regional climate conditions, soil management practice, vegetation type, soil depth and time period considered , C: N ratio of plant debris and so on (Lal, 2005). Soil C storage ability various from different biomes and forest zones. Boreal/taig a can store 471 Mg C/ha of soil, while temperate can only store 100 Mg C/ha 35 of soil (Lal, 2005). In tropical forests, temperate forests, and boreal forest, Soil C stock density varies from 122 to 296 Mg C/ha (Prentice, 2001). Figure 2. Components of terrestrial carbon stock (Lal, 2005) Extensive studies have been conducted to evaluate the impact of forest ecosystems and land use change on the soil carbon storage and soil carbon dynamics (Hansen et al., 1993; Johnston et al., 1996; Zan et al., 2001; Rothstein et al., 2004; Schulp et al., 2008; Arevalo et al., 2011; Lockwell et al., 2012; Rytter et al., 2015; Winans et al., 2015). Freibauer et al. (2004) reported that reduce tillage and increase surface residue return could incr ease soil C by 0.4 -0.6 t C ha -1yr-1. Surface litter cover has been demonstrated to affect the soil nitrogen balance and increase microbial activities, thus to increase SOC accumulation rates (Tolbert et al., 2002; Helmisaari et al., 2011). On the other han d, Lippke et al. (2011) concluded that soil C accumulation is not significantly impacted by management practice, but only driven by soil moisture, soil nitrogen content and climatic conditions. As different vegetation can result in different organic matte r input s, root systems, and microbial communities, the impact of land use change on soil C stock has been 36 largely studied. Guo and Gifford (2002) did a mega analysis of soil C stock changes following different land use changes based on 74 publications, wh ich suggested that land use change caused significant increases or decreases in SOC stocks . Xue et al. (2016) studied the effects of land conversion from hayfields -to-willow and hayfields -to-hybrid poplar on soil microbial communities and soil properties a fter 3 years in Michigan and Wisconsin. The results indicated that total soil C decreased in one of the two study sites, and did not change in the other site. Qin et al. (2016) reported an overall 6-14% SOC gain in land that was converted from cropland to energy crop plantation, which 9 -35% SOC losses were observed in lands that were converted from grassland and forest to corn field. They have also pointed out that SOC change rates slow down after 10 years of land conversion, which indicated time duration c onsidered for SOC change is also important. As discussed above, SOC stock can be largely impacted by the accounting method (e.g. depth, time duration considered) and many environmental factors, which all contribute to the large uncertainties and differences found in the reported soil C values. Todd -Brown et al. (2013 and 2014) reported a large variance range of 510 to 3040 pg C in predicted soil C stocks by 11 Earth system models. Tian et al. (2015) found the SOC estimations by 10 terrestrial biosphere models vary from 425 to 2111 Pg C (1 Pg = 10 15 g). To effect ively estimate and reduce the uncertainties in SOC stocks, many soil C modelling studies have utilized Markov Chain Monte Carlo (MCMC) technique (Heath and Smith, 2002; Verbeeck et al., 2006; Shi et al., 2018). 37 2.5 Nutrient removal caused by forest harvesti ng systems Carbon neutrality, GHG emissions and energy balance in woody biomass production systems are always the key focus in about 90% LCA study. Nutrients loss in forest production system has rarely been quantified in LCA studies. Yet, the removal of fine woody materials can cause significant negative impacts to nutrients cycles and soil productivity (Martin et al. 2000, Watmough and Dillon 2003; Walker et al., 2010). Nutrients removal assessment is critical for soil nutrients protection and forest productivity maintenance, it should be considered as an important part of LCA study. 2.5.1 Intra -specific variability of N, P, K in different parts of trees Intra -specific variability between different parts of the same tree has been widely studied. Tharakan et al. (2003) reported that nutrients (N, P, K) concentrations in wood and bark parts varied significantly among 7 hybrid poplar clones (P < 0.05). Leaf nitrogen concentrations were analyzed to be significantly different among the four clones (Kara # i $ and Weih, 2 006). Tharakan et al. (2003) reported that nutrients (N, P, K) concentrations in wood and bark parts varied significantly among 30 hybrid willow clones (P < 0.05). The average N and P content in leaf among 6 willow varieties were reported to be statistical ly the same (P > 0.05) and the concentration of K was found to increase significantly with the leaf biomass production (P < 0.05) (Hangs, 2013). Tree foliar chemistry database have been established in both U.S. and Canada. Pardo et al. (2004) has built a report database of foliar nutrients data from 218 articles and publications in the Northeastern United States. In Canada, Par” et al. (2013) has compiled 12,800 nutrient concentration values for different components of 30 most common Canadian tree species . From their database, noticeable difference can be found 38 in reported values, which might be differed by species, studied regions, treatments and nutrients measurement techniques. 2.5.2 Established aboveground tree biomass estimation equations Given the nutrien ts allocations in each species, the estimated aboveground tree weight becomes a key component to know the total nutrients contents in a tree (Lambert et al., 2005; Pacala et al., 2001; Jenkins et al., 2003). ÒAboveground tree biomassÓ, as defined by Jenkin s et al. (2003), refers to Òthe weight of that portion of the tree found above the ground surface, when oven -dried until a constant weight is reachedÓ. The total aboveground biomass in each plot can be estimated by summing the biomass values of each tree i ncluding all tree components in a plot, and expressed in a per -unit -area basis such as Mg ha Ð1 or kg m Ð2. ÒTree componentsÓ refer to the different parts of a tree such as foliage, merchantable stem, roots, or branches (Jenkins et al., 2003). Pearson et al. (1983) has estimated the aboveground and belowground tree biomass including bole, branch, foliage, root crown and lateral root biomass of Lodgepole pine (Pinus contorta) using a combination of dimension analysis and sampling in southeastern Wyoming. Tritt on and Hornbeck (1982) have estimated biomass above ground weight for major tree species in the Northeast of U.S. based on 178 sets of published equations. Similarly, TerMikaelian and Korzukhin (1997) have developed compilations of equations to estimated 6 5 tree species in U.S. In Michigan, 32 hardwood tree species included American basswood ( Tilia Americana L .), American beech ( Fagus grandiolia Ehrh. ), black cherry ( Prunus serotina Ehrh. ), slippery elm (Ulmus rubra Muhl. ) and sugar maple ( Acer saccharum Ma rsh. ) were studied to 39 illustrate the impact of wood density and whole -tree factors on biomass equations (MacFarlane and Ver Planck, 2012). Jenkins et al. (2003) compiled all available diameter -based allometric regression equations for estimating total abov eground and component biomass, defined in dry weight terms, for trees in the United States. Also, they applied a modified meta -analysis based on the published equations to develop a set of consistent, national -scale aboveground biomass regression equations for U.S. species. As concluded, the equations developed in this study were analyzed to generally agree with the biomass (±30%) estimated by the U.S. forest inventory data for eastern U.S. species. This study is the first one that compiled and analyzed all available biomass literature in a consistent national -scale framework. The traditional way of predicting tree biomass is to fell, measure and weight the target trees, which is also called destructive sampling. On the other hand, functional branch analysi s (FBA) has been reported to be a promising, non -destructive method to evaluate tree biomass (MacFarlane et al., 2014). It enables users to compute tree biomass by using fractal branching rules, woody density of tree volume components (Van Noordwijk and Mu lia, 2002; MacFarlane et al., 2014). Besides whole tree biomass, FBA has also been applied to estimate tree component biomass based on standing tree measurements (Salas et al., 2004; Smith, 2001; Santos -Martin et al., 2010). 2.5.3 Sources of uncertainties in tre e biomass equations Estimating aboveground biomass (AGB) is very species -specific (Ketterings et al., 2001). There are four major steps for developing AGB estimation based on allometric equations: (1) choosing a suitable model to estimate the AGB (e.g. 40 polynomial function or power function); (2) choosing suitable values for any adjustable parameters in the equation; (3) measuring the input variables by field experiment (e.g. tree height and tree diameter); and (4) using the allometric equation to calculate the AGB of individual trees and to get the summation estimates (Brown, 1997; Ketterings et al., 2001; Chave et al., 2004). Each of the four steps can bring uncertainties in different levels and lead to error propagation when estimating the AGB. Many studi es are conducted to evaluate the uncertainties listed above (Laurance et al., 1999; Clark and Clark, 2000; Phillips et al., 2002; Shettles et al., 2016). The uncertainty due to measurement error and allometric model selection error were about 16% and 31% ( Chave et al., 2004). The difference caused by allometric equations will be larger through the tree diameter increase (Laurance et al., 1999). 16% difference in AGB was found due to landscape -scale environmental variability, and there was no effect of soil type on AGB detected (Clark and Clark, 2000). Relative contributions for measurement, model and sampling error were 5%, 70% and 25%, respectively when using terrestrial laser scanner, and 11%, 66% and 23%, respectively using the traditional inventory measu rements as inputs into the models (Shettles et al., 2016). 2.5.4 Quantifying the uncertainties using Monte Carlo analysis Nutrients removal can be estimated by multiplying nutrients (N, P and K) concentrations in removed biomass with weight of removed biomass. However, as discussed above, there are many uncertainties in AGB estimations and tree nutrients concentrations results, due to the biological variations such as intra -specific variability 41 in different parts of trees. Therefore, it is necessary to quantify the uncertainty in nutrients removal estimations. Monte Carlo analysis has been widely used to understand the impact of risk and uncertainties in LCA studies, forest measurements and models and nutrients budge estimations (Yanai et al., 2012; Caputo et a l., 2013; Zamar et al., 2015; Eyvindson and Kangas, 2016). It consists of randomly sampling values from a given probability density distributions of each input parameters to the model. These randomly sampled values are fed into the mathematical model and u sed to calculate a value for the output (risk) and to develop a discrete approximation to the output distribution ( Burmaster and Anderson, 1994). Monte Carlo analysis is expected to provide results such as: (1) the uncertainty in estimated nutrients remova l caused by each nutrient concentration estimate; (2) sensitivity analysis of estimated results to each nutrients concentration estimation, (3) graphical results and probabilistic results, which can illustrate the distribution of results based on nutrients concentrations estimation. 2.6 Optimization in woody biomass supply chain Unlike the steady state operation of fossil fuel, the supply chain of woody biomass can be highly complex because its reliance on several activities such as the biomass availability; the consumers Õ demand ; the logistic costs of biomass harvesting, storage and delivery; the landowner objectives; and also different laws, all kinds of regulations and policies (Soylu, 2006). For over 3 decades, many optimization models and methods have bee n used to solve the p lanning problems in woody biomass supply chain, such as strategic, tactical and operational planning. Strategic planning is a systematic process that defines the future strategy of using or allocating the forest resource in a long peri od 42 such as 50 to 100 years. Tactical planning can provide a detail plan to decide the harvesting area, harvesting system, transportation distance and so on for a shorter time of several months to one year. Operational planning is the process that defines t he harvesting system or plan for a specific harvesting area to reduce the operation cost and increase the product value. The typically used models in woody biomass supply chain include dynamic programming, integer programming, linear programming and non -linear programming. Forty -nine studies were reviewed to summarize the optimization models developed in woody biomass supply chain since 1997. The reviewed studies were categorized based on the aspects they focus on, including economic, environmental and soc ial. The main indicators for economic optimization model are the capital cost and production cost. For environmental optimization models, the most used indication is the total GHG emissions. The jobs created by the bioenergy production system are the main considered social criterion. In the 63 reviewed studies, 47 of them are single objective optimization models with the objective of cost minimization (Figure 3). The other 12 studies are bi -objective optimization models, aiming to minimize both cost and GHG emissions. Three out of the 12 studies were purely focused on woody biomass supply chain, the remaining nine studies considered agricultural crop as well. There are four studies conducted to optimize cost, environmental and social aspects in bioenergy pro duction systems using agricultural crops and woody biomass as feedstock. 43 Figure 3. Classification of reviewed optimization studies in biomass supply chain 2.6.1 Single objective optimization models 2.6.1.1 Strategic planning Strategic optimization models generally aim to provide information for long -term decision -making. Many studies have been published to analyze the economic feasibility of producing bioenergy using woody biomass in different regions (Nagel, 2000; Kaylen et a l., 2000; Tittmann et al., 2010; Wetterlund and Sıderstrım, 2010; Difs et al., 2010; Bırjesson and Ahlgren, 2010; Huang et al., 2009; Yagi and Nakata, 2011; Schmidt et al., 2010a; Upadhyay et al., 2012; Keirstead et al., 2012; Kong et al., 2012; Fern⁄ndez et al., 2015; Cambero et al., 2015; De Meyer et al., 2016 ). Results showed that with the advanced biomass conversional techniques, the biofuel price could be competitive with gasoline (Nagel, 2000; Tittmann et al., 2010). Scenario analysis and sensitivity analysis were applied in these studies to reveal the impacts of many factors 44 such as biomass availability, energy price, market and policy change, and production scales on the bioenergy production cost. As it is necessary for stakeholders to make decision s to allocate their investment and biomass flow strategies, many studies have focused on the design of the biomass supply chain networks (Chineses et al., 2005; Frombo et al., 2009 a and b; Feng et al., 2010; Parker et al., 2010; Schmidt et al., 2010 a and b; Elia et al., 2011; Zhang et al., 2016; Lim and Lam et al., 2016). The developed models plan the size and the optimal location of facilities, the feedstock combinations, the conversion processes, the harvested area, the transportation methods, and the bes t transportation routes with minimal cost or maximum profit. With careful supply chain management, the supply cost of biofuel can be reduced to $15.68 to $22.06/GJ, comparable to the cost of coal ($16/GJ) and compressed natural gas ($16.95/GJ) (Elia et al. , 2011). Facility locating has gained many interests because it can largely affect the transportation cost and the efficiency of the whole bioenergy production system. Thus, several optimization models have been developed to identify the optimal location of bio -refinery or power plant in Italy (Freppaz et al., 2004), Austria (Schmidt et al., 2009), Greece (Rentizelas and Tatsiopoulos, 2010), Sweden (Leduc at al., 2010 a and b), Finland (Natarajan et al., 2014), Colombia (Duarte et al., 2014), and U.S. (Kim et al., 2011 a and b). Factors such as plant capital investment, conversion efficiency, feedstock cost and industrial competition are summarized to be the most influential in the bioenergy production cost. 2.6.1.2 Tactical planning Tactical supply chain models pr ovide detail plan at the operational level to 45 realize the strategies, such as harvesting schedule, distribution network and transportation method at certain region (Gunnarsson et al., 2004; Kanzian et al., 2009; Rauch and Gronalt, 2010; Shabani et al., 201 2 and 2014) . As woody biomass transportation cost accounts for the largest part of the total cost and energy consumption (Eriksson and Bjo¬rheden, 1989; Allen et al., 1998; Alam et al., 2012), the developed tactical optimization tools primarily focus on tw o categories: location selection and woody biomass collection. The location selection models mainly emphasized on finding the best location for single or multiple processing facilities over large -scale biomass supply chain (Zhang et al., 2011). The optimiz ation models for woody biomass collection aimed to estimate the feedstock availability and to reduce cost of biomass procurement (Ranta, 2002; Ranta, 2005; Panichelli and Gnansounou, 2008). For instance, Lautala et al. (2012) have published a cost minimiza tion model to minimize the cost for woody biomass transportation using railroads in Michigan and Wisconsin. 2.6.2 Bi-objective optimization models While economic aspects of woody biomass supply chain have always been the major focus, recently t he environmental impacts of renewable energy production systems are getting more concerns . Thereby, numerous supply chain models have been developed to minimize the environmental burden and analyze the trade -off between cost and GHG emissions . Nagurney et al. (2006) prese nted a framework for decision makers to determine the optimal carbon taxes should be applied to electric power plants that use renewable energy resources in different scenarios. To minimize the environmental impacts of the bioenergy supply chain, LCA was largely used to provide the environmental indicators in many multi -objective 46 optimization models (Steubing et al., 2012; Sacchelli et al., 2014; P”rez -Fortes et al., 2014). Zamboni et al., (2009) have developed a multi -objective environmental optimization model to apply in a corn -based bioethanol system in Italy. You and Wang (2011) presented a county -level optimization model to determine the optimal biomass (agricultural, energy crops, and wood residues) supply chain design under economic and environmental criteria in Iowa. SantibaŒ ez et al. (2011, 2015 and 2016) developed three different multi -objective optimization models to address different needs in the bio -refinery supply chain in Mexico using multi -feedstock . Multi -objective optimization framework h as also been used to determine the conversion technologies, facility size, location, and raw material in a global scale (Giarola et al. 2011 and 2013). There are only a few studies focus on multi -criteria optimization in woody biomass supply chain. For in stance, Steubing et al. (2012) developed a strategy to determine the optimal size and location of a bioenergy plant that converts wood to synthetic natural gas in Switzerland. The minimal cost can be achieved when the plant size is around 100 -200 MW, howev er if the environmental performance is considered, the optimal plant size is determined to be 90 MW. Kanzian et al. (2013) have formulated a model to maximize the profit and minimize the CO 2 emissions in a wood -based supply chain in Austria, with decision variables of pre -processing location, transportation mode, volume and terminal. The results suggested that with 4.5% increase in CO 2 emissions, the profit can be increased to more than twice. A strategic multi -objective optimization framework was developed to design the optimal supply chain networks with maximum Net Present Value (NPV) and minimal GHG emissions, for woody biomass supply chain in 20 years horizon (Cambero et al., 2016). This model was applied in a case study in 47 British Columbia, Canada. The results indicated that converting woody biomass to pellet and bio -oil and export to Europe could achieve the highest NPV and lowest GHG emissions. However, optimization model that considers both economic and environmental impacts of woody biomass supply chain in U.S. lacks (Shabani et al., 2013b), which indicates a great need for future study. 2.6.3 Tri -objective optimization models Besides economic and en vironmental aspects, social aspect of woody biomass supply chain is also important due to its effects on society such as household income and job creations (McKay, 2006). The social criterion considered in the study published by %u# ek et al. (2012) is ÒSoc ial footprintÓ , whic h is defined as the risk of using farmlands to the bioenergy production instead of food production. In the model developed by Sacchelli et al. (2014), social indicators include risk of negative profit for sawmills, negative profit for l ogging companies, and negative profit for energy plants. You et al. (2012) and Yue et al. (2014) presented two multi -criteria optimization models to minimize the economic (annualized cost), environmental (GHG emissions), and social objectives (the number o f the accrued local jobs) in multi -feedstock biomass supply chain in Illinoi. However, in these two models, the harvesting, collection and pre -processing cost of woody biomass were combined into the biomass acquisition cost , which was a simulated value cit ed from previous study. 48 CHAPTER 3 WOODY BIOMASS PRODUC TION COST PREDICTION 3.1 Introduction In the past decades, due to the growing need to reduc e dependence on fossil -based fuels, renewable energy has become the world's fastest -growing energy s ector (Zanchi et al., 2012; REN Renewables, 2012; EIA, 2015). From 2008 to 2017, the total annual electric power produced from renewable energy sources (excluding hydroelectric and solar) has risen from 1.25E+08 MW to 3.34E+08 MW (EIA, 2018a). As one of the most important renew able energy sources, wood and wood -derived biomass supplied about 19% of total U.S. renewable energy consumption in 2016 ( Figure 1). About 69% of the bioenergy produc ed from woody biomass was consumed to produce power and heat for industrial applications such as wood and paper production (EIA, 2018b). In order to promote the use of woody biomass as a renewable energy resource, it is critical to evaluate its production cost. Due to the traditional harvesting strategy and the undeveloped biomass supply chain, the costs of using woody biomass are tend to be higher than those of fossil fuels like coal (Dunnett et al., 2007; Perlack et al., 2011; Caputo, 2014). In U.S., comp ared to $2.37/MMBTU for coal, the average price of using woody biomass for electric power is $2.69/MMBTU ( EIA , 2014). There are many reasons for high production cost s, such as high capital investment o f harvesting machines, inappropriate harvesting season, difficult harvesting site conditions , long travel distance, and lack of operation management (Ghaffariyan et al., 2012a; Harril l and Han, 2012; Strandgard, 2014). Cost prediction s for woody biomass supply chain s are important to determine the economic feasibility of using woody biomass for bio energy (Alam et al., 2012). 49 Extensive cost analysis and models such as BioSum 3.0, Auburn Harvest Analyzer (AHA) and FRCS -North have been developed to provide logistic cost predictions estimate woody biomass produc tion cost . Transportation cost is a major factor influencing final production cost. Galik et al. (2009) have claimed that transportation cost could vary from $10 -$30 per dry ton, even under 50 miles distance. Linear programming models and GIS -based forest biomass data were used to reduce transportation costs in the woody biomass supply chain (Ranta, 2002; Ranta, 2005; Panichelli and Gnansounou, 2008). Besides cost prediction, determin ing the limiting factors in the biomass supply chain and developing their corresponding time prediction models are also important to improve the production efficiency (Alam et al., 2012). For instance, harvester productivity can be affected by many factors such as tree size, spacing, ground slope and roughness (Bolding and Lanf ord, 2002 ; Acuna and Kellogg, 2009 ; Wright et al., 2010 ; Ghaffariyan et al., 2012b). Bolding and Lanford (2002) reported that travelling distance and hauling weight affected the forwarder productivity in CTL harvesting system. Harril l and Han (2012) found a noticeable relationship between transportation cost and road type that every 50 m increase in transportation distance can raise the total transportation cost by $0.08/ODT. Due to the differences in tree species, stand density, site and terrain condition s, productivit y of woody biomass supply chain s in the Lake States , including Illinois, Indiana, Michigan and Ohio , might be different from the Western U.S. For instance, the ground slope is gentler in the Lake States compared to the Western States, which may contribute to reduc ing the harvesting, and biomass collecting time and cost. Because of 50 the impacts of terrain condition s and forest transportation road types on the production cost, there is a need to develop a cost prediction model based on the data collected in Michigan. In this study, five forest harvesting projects in Michigan were monitored to estimate the productivity and production cost of producing woody biomass. Based on data collected from time and motion studies, multiple linear reg ression equations were developed for each machine at each studied site. 3.2 Methodo logy 3.2.1 Study sites and harvesting systems Five woody biomass harvest projects were monitored using time and motion technique s at different sites in Michigan from 2012 to 2014. Wh ole tree (WT) harvesting systems were investigated at four sites: Delton, East Lansing, Escanaba and Albion. Delton (Site 1) was located in the Kellogg Biological Station, Lux Arbor reserve, Michigan. The stand was dominantly stocked with Douglas -fir tree s, mixed with Norway spruce , white spruce and other hardwood species . The t otal harvesting area was 33.33 acres. The average tree DBH at Site 1 is 5.36 inches. East Lansing (Site 2) was at the Sandhill Research Area, south of the Michigan State University Tree Research Center, East Lansing, Michigan. The harvesting site was a 28.55 -acre mixed -species forest stand with hardwoods such as poplar, oak, black locust ( Robinia pseudoacacia ) and softwoods like spruce, larch , and Engelmann spruce (Picea engelmannii ). The average tree DBH at Site 2 was around 8.5 inches. Site 1 and Site 2 were harvested by a WT harvesting system: a wheeled hot-saw feller -buncher (Hydro Ax 511EX ) that felled and bunched trees, and a rubber -tired grapple skidder (John Deere 648G) that s kids the trees from the stump to the landing. 51 Harvesting prescription s were 70% thinning for Site 1 and clear -cut for Site 2. The harvested wood was pre -processed to wood chips by a whole -tree chipper with an attached loader (Trelan 21L chipper). Site 3 wa s a 7.8 -acre, 7-year -old hybrid poplar plantation at Michigan State UniversityÕs Forest Biomass Innovation Center (FBIC), in Escanaba, MI. The average tree DBH was 4 inches. A WT harvesting system , including a feller -buncher (John Deere¨ 653G feller -buncher ), grapple skidder (John Deere 740A), log loader (Hood S -182), and grinder (Peterson 4700B) was used to harvest the entire stand . Site 4 was located at Albion, MI. A single -pass, cut and chip harvesting system was operated to evaluate the tot al biomass yield in a 1.1 acres (site length L 1=112.5 ft.; site width W 1=416 ft.), 3-year old hybrid willow plantation (stand density = 5,808 trees/acre) . The average tree DBH at Site 4 was about 0. 75 inch . There were 80 plots (plot length L 2 = 22.5 ft.; p lot width W 2 = 26 ft.) in this plantation in total and 16 plots in each line ( Fig. 4a). In each individual plot, there are 3 pair -rows with 13 trees in each row ( Fig. 4b). Pair rows were planted 2.5 -ft apart (SR 1=2.5 ft.) and the distance between two pair -rows was 5 ft (SR 2=5 ft.). This harvesting system consisted of three equipment items: a John Deere 7330 tractor to power the willow harvester, a Ny Vraa JF192 willow harvester and a Komatsu CK35 -1 bobcat to hold the wood chips . In this harvesting project, only 32 of the 80 plots were harvested, in a direction from plot 17 through plot 32 and from plot 49 to plot 64 ( marked red in Fig. 4a). In harvesting each plot, only the 18 trees in the center pair -rows were selected to record the biomass yield ( circ led trees in Fig. 4b). A Cut-to-Length (CT L) harvesting system was studied at Site 5, Gwinn , Michigan. 52 The harvest site was a 56-acre, 29 years old Jack pine ( Pinus banksiana ) plantation. Since this study focused on using the by -product of the CTL harvest ing system such as limbs and tops , t his study only estimate d the logging residues collecting cost. The forwarder used at Site 5 was a Ponsse Buffalo forwarder with 15 tons of loading capacity. 53 Figure 4 a and b. Plots layout at Site 4 and tree layout in each plot 54 3.2.2 Cycle time estimation and productivity calculation Multiple linear r egression equations were developed for the feller -buncher, skidder, and forwarder to predict cycle time in centi -minutes (1 centi -minute = 0.01 minute) , using the independent variables such as the DBH of trees, travel distance and loaded swing degree , etc. The collected time -motion study data w ere tested for normality and outliers, and then used to develop predictive equati ons by multiple regressions with ordinary least squares estimators in Stata 12.0 . 33% of all collected data were reserved and the remaining 67% of the collected data were used to develop the predictive equations. To validate the developed equations , the re served data were used to generate predicted values and a two-sample t -test ( & =0.05) was used to compare the predicted values with collected data . A validation p-value larger than 0.05 indicated the difference between the predicted value and observed data was insignificant, and the developed equation capably predict ed harvesting productivity . To estimate the cycle time of feller -buncher, skidder, and forwarder the a verage observed values for each independent variable were used in the regression equation s. For the feller -buncher, a complete cycle start ed from movement to the first tree, subsequent cuttings to make a full bunch , and ended with the placement of that tree bunch on the ground (Pan et al., 2008) . Move -to-tree distance, the number of cuts per cycle, and move -to-bunch distance were defined as variables for these elemental activities within a cycle. Regarding the skidder, a complete cycle start ed from moving from the landing site to a tree bunch and subsequent grappling to make a full skid load, return to the landing site, and ended with the placement of loaded trees on the landing site (Pan et al., 55 2008). Elemental activities within a skidding cycle were defined to be: travel empty distance, positioning distance, number of trees per cycle, and travelin g loaded distance. For the forwarder, a complete cycle start ed from the movement from the landing site to logging residue piles and subsequent grappling to make a full load, travel back to the landing site and unload ing biomass on the ground near the load er (Pan et al., 2008) . The included variables in predicting forwarding cycle time were : travel empty distance, intermediate travel distance in feet, number of grapples per cycle, travel loaded distance , and number of unloading grapples. For the loader -grin der /loader -chipper unit, a complete cycle was defined as the time required for the loader -grinder/loader -chipper to process woody biomass to fill a full truckload. For Site 1, Site 2, Site 3 and Site 5, an average cycle time (mins) was presented based on a ll recorded cycle time values. Site 4 was a 3 -year -old hybrid -poplar plantation and was harvested by a reconfigured harvesting system consisting of a Ny Vraa JF 192 harvester, a John Deere 7330 tractor and a Komatsu CK 35 -1 to collect chips. The productivi ty of the harvesting system was estimated by dividing the total harvested biomass dry weight by the total recorded work time, which was a method also adopted in other studies (Schweier and Becker, 2012; Berhongaray et al., 2013). 3.2.3 Harvesting and pre-proces sing costs The machine hourly cost in dollars per schedule d machine hour ($/SMH) was estimated using the standard machine rate calculation methods reported by Miyata (1980). The information of machine make and model, purchas e price, horsepower, scheduled m achine hours (hr/yr) and labor cost has been collected from equipment 56 owner (Table 3). The economic life span of each machine was assumed to be 5 years; the salvage value was set to be 20% of the original purchase price; the utilization rate of feller -buncher, skidder, forwar der was assumed to be 80%; the utilization rate of loader, and grinder was assumed to be 80% (Carter et al., 2017), the utilization rate of truck/van was assumed to be 95%. The machine hourly cost in dollars per productive machine hour ($/PMH) was converte d to production cost in $/PMH using the method in Mitaya (1980). The production rate (ODT/PMH) of each machine was determined by dividing the total produced dry weight of biomass ( ODT) by the each predicted cycle time (PMH). Production cost in $/ ODT was computed by dividing the machine hourly cost ($/ PMH) by the productivity (ODT/PMH). 3.2.4 Transportation costs Multiple regression analyses using ordinary least squares estimators were used to develop the prediction models that estimate the travel times based on all data collected at Site 1, Site 2, and Site 5 . For truck/van, a complete cycle started from loading wood chips to the empty truck, followed by hauling a full tank of woody biomass to the end user and ended at the end userÕs gate. The prediction model for travel time was based on travel distance in miles for each road type, including forest/dirt road, gravel road, pave d road and highway . In this study, forest/dirt road refers to an unpaved road with single lane and hard dirt surface; a gravel road refer s to unpaved road covered with gravel and single lane ; a paved road was defined as the road with durable surface pavement and double lane ; and highway referred to the main road that connected towns or cities. The travel speed on forest/dirt road, gravel road, paved road and highway were set to be less 57 than 10 mph, 10 -30 mph, 30 -45 mph and 45 -65 mph. Roads in all study sites were properly planned and constructed to assume smooth woody biomass transportation. The multiple linear regression equation can be written as: !"#$% !!"#$%& !!"#$ !!!!"!!!!!!!!!"#$!!"#$ !!"#$%&'( !!!!"#$%&!!"#$ !!"#$%&'( !!!!"#$% !!"#$ !!"#$%&'( !!!!!"!!"#!!"#$%&'( Where !! = intercept; !! = coefficient of the variable "dirt road distance" ; !! = coefficient of the variable " gravel road distance" ; !! = coefficient of the variable " paved road distance" ; !! = coefficient of the variable " paved road distance" . 58 Table 3. Machine hourly rate ($/SMH) calculated based on data obtained from equipment owners 59 3.2.5 Biomass weight estimation For study sites (Site 1, Site 2, and Site 3) that were harvested by the WT harvesting system, whole tree dry weight and stem wood dry weight were estimated using equations developed by Jenkins (2003): !"!!"# !!!!!!!"!"!! Where bm = total aboveground biomass (kg) for trees 2.5 cm and larger in dbh; dbh = diameter at breast height (cm); Exp = exponential function; ln = natural log base ÒeÓ (2.718282); For Site 1: !! = -2.2304 and !! = 2.4435; For Site 2 and Site 3: !!= -2.2094 and !! = 2.3867. !"#$% !!"# !!!!!!!!"!! Where ratio = ratio of component to total aboveground biomass for trees 2.5 cm and larger in dbh; dbh = diameter at breast height (cm); Exp = exponential function; ln = natural log base ÒeÓ (2.718282); For Site 1: !! = -0.3737 and !! = -1.8055; For Site 2 and Site 3: !! = -0.3065 and !! = -5.4240. 60 3.3 Results 3.3.1 Site 1 3.3.1.1 Regression equations and operation cycle time prediction The multiple linear regression equations were developed for the feller -buncher, skidder and truck/van used in Site 1 (Table 4). All included variables have a p -value smaller than 0.05, indicating that they were signifi cant in predicting cycle times. In the equation s developed for the feller -buncher and skidder, the validation p-value larger than 0.05 suggested that there was no significant difference existed between predicted values and the observed values. Based on the mean value of each independent variable, the average predicted cycle time for the feller -buncher and skidder at Site 1 were calculated to be 89.80 centi -minutes (0.90 minute) and 233.74 centi -minutes (2.33 minutes). The total cycle time for loading and hauling biomass to the end user was estimated to be 2.36 hrs (based on 0.30 miles of dirt road, 0 miles of gravel road, 1.75 miles of paved road and 73.20 miles of highway). For the chipper and loader, the average monitored delay -free cycle time was 4,069 centi -minutes (40.69 minutes). 3.3.1.2 Cycle stem wood weight The feller Ðbuncher cuts, on average, 3 trees per operation cycle. Based on an average observed tree DBH of 5.36 inches, the stem wood weight per tree was estimated to be 38.23 kg (about 0.04 ODT) using the equations developed by Jenkins et al. (2003), and the harvested biomass weight per cycle was about 0.13 ODT. For the skidder, the number of trees collected in each complete cycle was averaged to be 12 (number of trees) , which was calculated as 0.5 1 ODT. For the loader -chipper unit , the average 61 biomass processed per cycle was about 17.87 ODTs. The average biomass weight transported per cycle for truck/ van was also estimated to be 17.87 ODTs. 3.3.1.3 Machine hourly rate and production cost The productivity and production cost of each machine were presented in Table 5. Based on the machine cycle time and cycle stem wood w eight produced, the productivity of the feller -buncher, skidder, loader -chipper, and truck van were determined to be 8.45 ODT/PMH, 13.13 ODT/PMH, 26.35 ODT/PMH, and 7.57 ODT/PMH, respectively . The machine hourly rate for the feller -buncher, skidder, chippe r/loader and truck/van were 51.52 $/PMH, 54.88 $/PMH, 113.72 $/PMH, and 70.57 $/PMH (Pan and Srivastava, 2013). The production cost of biomass harvesting, collecting, pre -processing and transportation were determined to be 6.10 $/ODT, 4.18 $/ODT, 4.32 $/OD T, and 9.32 $/ODT. 62 Table 4. Delay -free average cycle time equations for harvesting machines at Site 1. Table 5. Productivity and production cost at Site 1. 63 3.3.2 Site 2 3.3.2.1 Regression equations and operation cycle time prediction The multiple linear regression equations developed for the feller -buncher, skidder and truck/van at Site 2 are presented in Table 6. Al l the included variables have a p-value smaller than 0.05, indicating that they were significant in predicting cycle times. In the equation developed for feller -buncher and skidder, the validation p-value larger than 0.05 suggests that there was no signifi cant difference existing between the predicted values and the observed values. The average predicted cycle times for the feller -buncher and skidder at Site 2 were 44.40 centi -minutes (0.44 minute), 307.65 centi -minutes (3.07 minutes). The loading and trans portation time was estimated to be 1.20 hours based on the transportation distance (0.40 miles of dirt road, 0 mile of gravel road, 4.00 miles of paved road and 0 mile of highway). For chipper and loader, the average monitored delay -free cycle time was 4,5 81 centi -minutes (45.81 minutes). 3.3.2.2 Cycle stem wood weight In each production cycle, the feller -buncher can harvest 2 trees on average, which was estimated to be 0.22 ODT based on an average DBH of 8.5 inches. The skidder can collect about 10 trees per cyc le, which was equal to about 1.12 ODT per cycle. For the chipper attached with loader, the average biomass processed per cycle was about 19.70 ODT. The average biomass weight transported per cycle for truck/van was also estimated to be 19.70 ODT. 64 3.3.2.3 Machine h ourly rate and production cost The productivity and production cost of each machine are presented in Table 7 . The productivit ies of the feller -buncher, skidder, loader -chipper unit and truck/van were determined to be 29.72 ODT/PMH, 21.89 ODT/PMH, 25.80 ODT/PMH, and 16.42 ODT/PMH, respectively. Because Site 2 shared the same harvesting system with Site 1, they have the same machine hourly rate. The machine hourly rate of the feller -buncher, skidder, chipper/loader and truck/van were 51.52 $/PMH, 54.88 $/P MH, 113.72 $/PMH, and 70.57 $/PMH (Pan and Srivastava, 2013). Therefore, biomass harvesting, collecting, pre-processing and transportation costs were estimated to be 1.73 $/ODT, 2.51 $/ODT, 4.41 $/ODT, and 6.92 $/ODT, respectively. 65 Table 6. Delay -free average cycle time equations for harvesting machines at Site 2 . Table 7. Productivity and production cost at Site 2. 66 3.3.3 Site 3 3.3.3.1 Regression equations and operation cycle time prediction The multiple linear regression equations developed for all machines employed at Site 3 are presented in Table 8. All the included variables have a p-value smaller than 0.05, indicating that they were significant in predicting cycle times. In the equations developed, the validation p-value larger than 0 .05 suggest ed that there was no significant difference between predicted values and the observed values. The average predicted cycle time for the feller -buncher and skidder in Site 3 were 74.02 centi -minutes (0.74 minute) and 143.85 centi -minutes (1.44 min utes ). The average observed cycle time for the loader -grinder was 36.67 minutes. Based on the travel distance (1 mile of dirt road, 0 miles of gravel road, 4 miles of paved road, and 45 miles of highway), the loading and transportation time was estimated t o be 2.79 hours. 3.3.3.2 Cycle stem wood weight The whole tree dry weight at Site 3 was estimated to be 0.03 ODT, using average DBH = 4 inches and equations developed by Jenkins et al. (2003). Therefore, cycle stem wood weights produced by the feller -buncher and skidder were 0.18 ODT (6 trees per cycle) and 0.86 ODT (28 trees per cycle). In each work cycle for loader -grinder, 17.56 ODT of woody biomass was processed. For the truck/van, the cycle stem wood weight was 17.56 ODT and the productivity was 6.29 ODT/PMH. 3.3.3.3 Machine hourly rate and production cost The productivity and production cost of each machine at Site 3 were presented in Table 8. Biomass harvesting, collecting, pr e-processing and transportation productivities 67 were calculated to be 14.59 ODT/PMH, 35.83 ODT/PMH, 28.73 ODT/PMH, and 6.29 ODT/PMH. The m achine hourly rate of the feller -buncher, skidder, chipper/loader and truck/van were estimated to be 70.92 $/SMH (or 86 .10 $/PMH assuming utilization rate = 80%), 79.92 $/SMH (or 95.92 $/PMH assuming utilization rate = 80%), 203.14 $/SMH (or 244.46 $/PMH assuming utilization rate = 80%), and 100.24 $/SMH (or 101.66 $/PMH assuming utilization rate = 95%), respectively. Therefore, biomass harvesting, collecting, pre -processing and transportation costs were 5.90 $/ODT, 2.68 $/ODT, 8.51 $/ODT, and 16.16 $/ODT. 68 Table 8. Delay -free average cycle time equations for harvesting machines at Site 3 . Table 9. Productivity and production cost at Site 3. 69 3.3.4 Site 4 3.3.4.1 Productivity The total working time for the reconfigured harvesting system to harvest the 1.1 acre 3 -years old hybrid willow plantation was 4.13 hours, with 4.02 hours of actual harvesting time and 0.11 hours travel time between lanes in the plantation. A total of 7.18 ODT of biomass was harvested and processed into wood c hips. The overall productivity of this configured harvesting system was estimated at 1.74 ODT/PMH. Based on a hauling distance of 50 miles (0.5 mile of forest/dirt road and 49.5 miles of highway), total loading and transportation time was calculated to be 1.96 hrs. A full truckload of wood chips was about 18.72 ODT. Therefore, the transportation productivity was estimated to be 9.55 ODT/PMH. 3.3.4.2 Machine hourly rate and the production cost Machine hourly rate of the reconfigured system was determined to be 132. 69 $/SMH, with a cost of 75.75 $/SMH from the harvesting unit and 56.94 $/SMH from the chip collector. Assuming a utilization rate of 80%, the total production cost was estimated to be 85.27 $/ODT ( 88.89% of the total production cost). The machine hourly rate truck/van was 100.24 $/SMH (or 101.66 $/PMH assuming utilization rate = 95%) and the production cost of biomass transportation was 10.65 $/ODT ( 11.11% of the total production cost). 70 3.3.5 Site 5 3.3.5.1 Regression equations and operation cycle time prediction The developed multiple linear regression equations for the forwarder and the transportation stage are presented in Table 10. The predicted cycle time of the forwarder was determined to be 1,569.13 centi -minutes (15.69 minutes). For the transportation stage, the average cycle time was calculated to be 2.04 hours based on a total transportation distance of 50.33 miles (0.35 mile of forest/dirt road, 0.5 mile of gravel road, 3.31 mile of pave road and 46.17 miles of highway). 3.3.5.2 Productivities of forwarder and truck/van The overall production rate of forwarder was calculated to be 5.49 ODT/PMH based on total recorded delay free work time of 15.98 hrs and total recorded collec ted biomass weight of 87.73 ODT. The average biomass dry weight in each truck/van work cycle was determined to 17.55 ODT. Thus the productivity of the truck/van was estimated to be 8.60 ODT/PMH. 3.3.5.3 Machine hourly rate and production cost The machine hourly rate calculated for the forwarder and the truck/van was 71.33 $/SMH and 100.24 $/SMH. Assuming a utilization of 80% for the forwarder and 95% for the truck/van, the production costs were 85.19 $/PMH and 101.66 $/PMH. Therefore the total production cost of this biomass production system was 27.34 $/ODT, with 15.52 $/ODT (56.77% of the total cost) from forwarding process and 11.82 $/ODT (43.23% of the total cost) from transportation process. 71 Table 10. Delay -free average cycle time equations for harvesting machines at Site 5. 72 3.4 Discussions 3.4.1 Cycle time predictions for harvesting machines and woody biomass transportation In the linear equations developed for the feller -buncher, skidder, and forwarder, all dependent variables have validation p-values smaller than 0.05, which means they can effectively produce estimates. However, as suggested by Kozak and Kozak (2003), the significant p-values of the indepent variab les might not necessarily demonstrate that these equations are the best predicting equations. To validate these developed equations, 33% of the collected field data were used to make predicted data. The validation outcomes suggested that there was no signi ficant difference ( p-value <0.05) between the observed and predicted data, and the developed equations can be used in harvesting projects with similar site conditions . For different machine s, significant cycle time estimators could be different . For instan ce, Bolding et al. (2009) used the non -merchantable pieces and merchantable pieces per bunch as the predictors for feller -buncher productivity; skidding distance and trees per skidding cycle as the predictors for estimating the skidderÕs working cycle time . Li et al. (2006) used tree DBH and travel distance between harvested trees to predict the hourly production rates for a feller -buncher in West Virginia, and average extraction distance and payload size for estimating the production rates for the skidder and forwarder. In this study, for the feller -buncher cycle time prediction, move -to-tree distance, number of trees per cycle, and move -to-bunch distance were found to be significant at sites 1, 2 and 3. For estimating the skidder cycle time, travel empty distance and loaded distance were determined to be significant predictors at Site 1 and 73 Site 2; while at Site 3, position distance and tree s collected per cycle were also tested to be significant. At Site 5, the significant cycle time estimators for the for warder included the travel empty distance, intermediate travel distance, number of grapples per cycle, travel loaded distance and number of unloading grapples. The cycle time predicting equations in this study were developed and validated using field -base data collected from harvesting projects in Michigan. These equations cannot be representative of all biomass -harvesting projects in Michigan and can only be applied in harvesting projects that have similar site conditions (tree DBH, ground slope and etc.). The model user should also be cautioned that these predicting equations were developed based on independent variables with limited data range, whereas out-of-range input could lead to prediction errors . In future if similar regression models are reported, users are supposed to determine which assumptions of the models are more feasible to their study. Also, they will need to understand the improvements or differences of the new equations compared to our models, and decide wisely if the changes are applicab le to their study. The hauling time of woody biomass largely affected by the combination of the delivery distance and road types (Pan et al., 2007; Harrill and Han, 2012; Keefe, 2014; Anderson and Mitchell, 2016), which was confirmed by this study. In thi s study, the multiple linear regression equation developed for truck/van indicated that every 0.1 mile increase in forest/dirt road distance could raise about 0.11 hr (or 6.78 minutes) in transportation time, while 0.1 mile increase in highway distance onl y caused 0.001 hr (or 0.12 mins). This suggested that the hauling distance in forest/dirt road had larger influence in total traveling time compared to distance on the highway. In addition, to 74 reduce hauling time and transportation cost of woody biomass, m ore effort should be spent on reducing the off -highway transportation distance. 3.4.2 Woody biomass harvesting production cost The estimated total production costs of producing woody biomass at the 5 sites were 23.92, 15.57, 33.25, 95.92 and 39.16 $/ODT. The hig hest production cost at Site 4 was mainly due to its lowest biomass productivity and additional cost from the front -end loader. The production cost at Site 4 was also higher than the costs (49.66 -$57.30 $/ODT) reported in other short rotation woody crop p roduction systems (Walsh et al., 1996; Tharakan et al., 2005; Volk et al., 2006; Schweier and Becker, 2012). This was mainly because of the additional cost from the front -end loader which functioned as a chip collector, if it was substituted with an attach ed trailer to collect chips, the production cost can be lowered to 43.82 $/ODT. WT harvesting systems generally achieved lower production cost ( 23.92 $/ODT at Site 1 and 15.57 $/ODT at Site 2 ) than CTL harvesting systems ( 39.16 $/ODT at Site 5). WT harvesting system at Site 3 had a slightly higher biomass production cost (39.84 $/ODT) than CTL harvesting systems because of its higher transportation cost. The reported production costs in this study were generally lower than the values reported by Zhang et al. (2016). The main reason for this difference was that the machine hourly production rates in Zhang et al. (2016) was cited from a Michigan loggersÕ survey. Based on a validation study done by Pan and Srivastava (2013), the machine hourly production rate in the Michigan loggerÕ survey deviated from the real field -based production rates. In particular, the production rate in clearcut hardwood stand case was underestimated, and the production rate in 70% cut softwood stand was overestimated. 75 The re ason accounting for this phenomenon was the fact that the loggerÕs survey did not consider tree size as one of the factors, while it was significant in predicting cycle time for feller -buncher, skidder and forwarder (Pan and Srivastava, 2013). 3.5 Conclusio n In this study, five woody biomass production systems were monitored to predict the productivity and production cost of utilizing woody biomass in the State of Michigan. Effective cycle time estimators were illustrated in predictive regression equations f or the studied machines. The predicted cycle time for each machine was used with the machine hourly rate to calculate the machine productivity and hourly production cost. The obtained results suggested that due to the higher hourly productivity, WT harvest ing systems generally achieved lower production cost ( 15.57 $/ODT ~ 33.25 $/ODT) than CTL harvesting systems ( 39.16 $/ODT). Among the five evaluated studied sites, Site 5, the 3 -year -old hybrid willow plantation harvested by a reconfigured harvesting syste m resulted in the highest production cost of 95.92 $/ODT, owing to the low biomass yield per acre and the additional hourly cost from the chips collector. Overall, it can be concluded that producing woody biomass from natural forest stands with the WT harv esting system was the most economical way of utilizing woody biomass as it has the highest productivity and lowest cost . 76 CHAPTER 4 BIOMASS STORAGE AND FUEL QUALITY CHANGE 4.1 Introduction Due to the increase of energy demand and the need of reducing greenhouse emissions, there is a strong necessity to decrease the dependence on fossil -based fuels (Zanchi et al., 2011; REN21, 2012). Biomass materials such as trees, grasses and agricultural crops have thus become imperative alternative energy resources (DOE, 2004). Among all these materials, woody biomass is one of the most feasible choices because of its relatively low cost and high availability (DOE, 2004). In most regions in North America, green biomass is directly processed into small chips by mills or other wood -usi ng facilities. This introduce s many problem s and concerns , such as a the risk of self -ignition , health issues caused by the release of high concentrations of allergenic microspores , and most important , dry matter loss followed by the decrease of the biomass quality ( Fredholm and Jirjis 1988, Jirjis 1995 ). Previous studies showed that the dry matter loss in a large green chip pile was approximately 12 percent during a 7-month storage period (Hornqvist and Jirjis 1999). A dry matter loss of 26 percent was also found in large bark piles throughout a 6-month storage period (Fredholm and Jirjis 1988). In addition, the dry matter loss in the large bark pile resulted in a 20 percent reduction in energy content (Fredholm and Jirjis 1988), thus lowering the ene rgy yield and the value of woody biomass feedstock. Due to the above concerns, the duration of the wood chips storage is normally suggested to be less than 6 months. Kofman and Spinelli (1997) suggested that willow from short rotation coppice (SRC) be pre ferred to deliver immediately to heating plants after harvested to avoid difficulty in storage; if the biomass has to be stored as wood 77 chips, the storage time should not exceed 2 months. In Michigan, according to experienced forest products manager, the t ypical storage period of biomass chip pile is around 60 -70 days (Scott Robbins, personal contact, October 15th, 2013). However, there has been no study available to prove the validity of these suggestions for wood chip storage in Michigan. In addition, a k ey yet unresolved issue is how to predict the moisture content in a biomass pile without frequently measurement (Erber et al., 2012). This is also the problem for biomass HHV estimate, because all existing models to predict the HHV of woody biomass use ind ependent variables such as fixed carbon content, volatile matter content and ash content, which all require additional testing (Parikh et al, 2005; Grover et al, 2002; Channiwala and Parikh, 2002). Several studies highlighted that the storage of logging r esidue in bundles can produce high -quality biomass feedstock with low moisture content (MC), increased higher heating value (HHV), and low ash content (Lehtikangas 2001, Pettersson and Nordfjell 2007 , Afzal et al. 2010). Patterson et al. (2008) reported th at the MC of biomass bundles decreased by 10 to 25 percent within 1 month after piling . Meanwhile, Karha and Vartiamaki (2006) found that the reduction of MCs resulted in a 12 to 28 percent increase in energy content per unit volume. T he major problems in producing biomass bundles with a slash bundler is the low productivity and the high hourly cost due to delays caused by saw binding, materials handling, twine spool collapse, and slow movement at the site ( Leinonen 2004, Rummer et al. 2004, Patterson et al . 2008 , Harrill 2010). This makes the economic feasibility of using the savings from transporting low MC and highly compacted biomass to pay for the cost of bundling operations questionable. 78 This study was designed and conducted in five different regions in Michigan. The objectives of this study are: 1) to monitor the change of moisture content (MC) and higher heating value (HHV) in logging residue pile and wood chips pile during a 4 months field storage; 2) to examine the effect of stor age locations, positions within a biomass pile on wood MC and HHVs in logging residue pile and wood chips pile . 4.2 Methodology 4.2.1 Site conditions 4.2.1.1 Logging residues storage study sites The two study sites were both located in Escanaba, Upper Peninsula of Michigan. The first study site (Site 1) was a mixed -species forest stand containing various hardwood and softwood species, including sugar maple ( Acer saccharum ), red maple (Acer rubrum ), and white spruce ( Picea glauca ). The second study site (Site 2) was located i n a red pine ( Pinus resinosa ) stand, 45.4 miles away from Site 1. The monthly precipitation at both sites averaged from 0.5 to 4.1 inches from June to November 2011 , and the monthly maximum temperatur e varied from 53.4¡F to 84.2¡F . A mechanized cut -to-leng th harvesting system was used to harvest the trees at the two study sites from June 1 to 25, 2011. At Site 1, logging residues, including limbs, tops , and broken log s, were shuttled to the roadside and piled by a Ponsse Buffalo forwarder with 15 tons of lo ading capacity. The resulting residue piles averaged 86 feet long, about 34 feet wid e, and 15 feet high (sample size , n = 18). At Site 2, the forwarder piled the logging residues into two different sizes. Small, cone -shaped piles were made with a geometric dimension of 9 to 10 feet in diameter and 2 to 3 feet in height , while the large piles were piled into a shape and size similar with those at Site 1. A total of 60 79 small piles were established in three rows under partial shade of the residual stands after the thinning treatment, while three large piles were piled without touching any of the residual stand crown shade . 4.2.1.2 Wood chips storage study site s The first study site (Site 1) was located at Michigan State University (MSU) Forest Biomass Innovation Center , Escanaba, Michigan (45.75¡ N, 87.06¡ W). The wood chips pile at Site 1 was established on July 14 th, 2013 using 33.14 green tons of Jack Pine ( Pinus banksiana ) hog fuel generated from a Cut -to-length (CTL) harvesting and grinding operation. The dimension of the pile was measured to be around 15 feet in length and 7 feet in height. The second study site (Site 2) was at MSU Tree Research Center in East Lansing, Michigan (42.74¡ N, 84.48¡ W ). The wood chips pile at Site 2 was set up on July 19 th 2013, consi sting of 25.87 green tons of Pitch pine ( Pinus rigida ) wood chips resulted from a Whole -tree (WT) harvesting and chipping operation. The pile had a dimension of 12 feet in length and 6 feet in height . The third study site (Site 3) was at MSU Kellogg Biolog ical Station in Augusta, Michigan (42.34¡ N, 85.35¡ W), and the wood chips pile was established on July 31 st, 2013, with around 20 green tons of Larch ( Larix decidua ) chips harvested using a WT harvesting plus chipping operation. The size of the pile was 1 0 feet in length and 6 feet in height . 4.2.2 Biomass moisture content and HHV The MC of the s ample wood chips was measured at the Michigan State University (MSU) lab following ASTM E 871 -82 (ASTM International 2003a). The 80 HHV of the sampled wood chips was tested at MSU using an oxygen bomb calorimeter according to the standard described by ASTM E 711 -87 (ASTM International 2003b). 4.3 Results 4.3.1.1 Biomass moisture content change of wood residue piles The initial wet -basis bioma ss MCs at Site 1 ranged from 25.0 to 47.0 percent and averaged 34.0 percent (n = 78). ANOVA indicated that after 5 months of field storage, the average biomass MCs were significantly decreased to around 26.3 percent (P < 0.05). During field storage, biomas s MC continuously decreased from late June to September. The monthly MC ( Figure 5) decreased 9.6 percent from June to July, 2.1 percent from July to August , and 5.1 percent from August to September. The lowest average biomass MC of 17.2 percent was recorded in September , and a statistically insignificant regain of biomass MC ( P = 0.27) was detected from September to October. The increase of biomass MC became signific ant from October to November ( P < 0.05), when the field -stored biomass MC returned to 26.3 percent . The overall pattern of biomass MC change implied that field storage and air -drying of unprocessed logging residues can effectively reduce the biomass MC, es pecially if residues are collected before October. 81 Figure 5. Biomass moisture content (wet basis, %) changes in logging residues piles in site 1 throughout field storage period. At Site 2, MC change followed the same pattern as Site 1 ( Figure 6) because of the similar harvesting time and weather conditions. The Wilcoxon signed -rank test (Higgins 2004) indicated a significant difference in biomass MC change between the large and smal l biomass piles (P < 0.05) . The initial average MC of small piles was tested to be 48.2 percent, and the large pilesÕ average MC was lower at 42.8 percent . However, MCs of small piles decreased noticeably faster compared with larger piles from June to the end of August. In September, the average MCs of both large and small piles reach ed the lowest point at around 16.0 percent . Start ing in September, the biomass MCs in both large and small piles began to rise, with a faster increase shown in small piles ( Fig. 6 ). At the end of the field storage, the small piles resulted in a higher average MC of 43.8 percent compared with large piles at 38.7 percent . This result suggested that a smaller pile could be used for short -term storage of woody biomass, while larger piles are more suitable for long -term storage because biomass MC in larger piles is less sensitive to weather conditions. 34.0% 24.4% 22.3% 17.2% 18.8% 26.3% 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% June July August September October November Wet basis moisture content: (%) 82 Figure 6. Wood moisture content changes affected by pile sizes in logging residue piles in Site 2. 4.3.1.2 Biomass moisture content change of wood chips piles The initial wet basis biomass MCs were 29.4% at Site 1, 27.8% at Site 2, and 52.9% at Site 3 (sample size n= 6 at each study site). After 4 months of field storage, the biomass MC at Site 1, Site 2, and Site 3 increased to 39.3%, 28.2% and 63.6%, respectively. Compared with the initial values, MC increases percentage at the three study sites were 33.4%, 1.6% and 20.2%, respectively. The biomass MC change at the three study sites during storage was illustrated i n Figure 7. At Site 1, the biomass MC increased from 29.4% to 31.6% on September 9 th, and then decreased to the lowest value of 19.0% on October 21 st. The highest MC of 39.3% was reached on December 2 nd. Biomass MC at Site 2 has the minimum fluctuation range compared with the other two study sites. The lowest biomass MC of 25.9% was reached around the middle of the August and the highest MC of 31.8% appeared in the mid dle of November. The biomass MC at Site 3 first climbed up to 56.4% 42.8% 45.1% 39.9% 15.7% 22.9% 38.7% 48.2% 37.9% 31.8% 16.8% 28.6% 43.8% 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% June July August September October November Wet basis moisture content: % Big piles Small piles 83 on August 12 th and followed by a decline to its lowest point of 51.0% on October 7 th. The highest biomass MC of 63.6% was reached on December 2 nd. During storage, the overall patterns of biomass MC change at the three study sites did not show a clear declining trend, but kept stable within a certain range and even increased towards the winter. This indicated that storing woody biomass in the form of chips pile might not effectively reduce the biomass MC. Figure 7. Biomass moisture content (wet basis, %) changes in wood chips piles throughout field storage period 4.3.1.3 Biomass HHV change in logging residue piles In wood logging residues piles storage study, the average HHV for each randomly selected residue pile ranged from 7,610 to 8,344 BTUs per dry pound from June to November ( Figure 8). A statistically higher average HHV w as detected by multiple comparisons in July compared to the other five months ( p-value < 0.05) ; while the HHVs in the other five months were statisticall y similar ( p-value = 0.26). Since t he 84 biomass HHV in July (8,344 BTUs/lb) and the average HHV of the other five months (7,811 BTUs/lb) only bears a difference of about 6.4%, the HHV s throughout the storage period were considered to generally be the same. Over the time course of the study, piling unprocessed, loose logging residue did not significantly alter biomass HHV on a dry basis. Figure 8. Biomass HHV changes in logging residues piles throughout field storage period. 4.3.1.4 Biomass HHV change in wood chips piles Compared to logging residues piles, w ood chip HHVs at the three study sites all decreased from the initial values during field storage ( Figure 9). At Site 1, wood chip HHVs decreased from 8,355.5 ± 352.1 to 7,404.6 ± 340.2 BTUs per dry pound, with a continuous declining trend. At Site 2, the wood chip HHVs constantly deceased from the ir original value of 8,422.0 ± 438.2 BTUs per dry pound to the lowest point of 7,618.2 ± 699.9 BTUs per dry pound in late September, and then slightly increased to 8,001.9 ± 132.0 BTUs per dry pound in early November. At Site 3, wood chip HHVs started at 8 ,579.5 ± 189.4 BTUs per dry pound and ended at 8,300.9 ± 436.7 BTUs per dry pound accompanied by the larger variations. The highest value of Site 3 wood chip 7932 8344 7610 7888 7727 7898 6800 7200 7600 8000 8400 8800 9200 June July August September October November Higher heating value (BTUs/lb) 85 HHVs of 8,634.2 ± 157.6 BTUs per dry pound was found in the middle of August, and the lowest value of 8,039.3 ± 272.3 BTUs per dry pound was detected at the end of August ( Figure 9). The decrease in biomass HHV caused by the field storage method in this study sug gested that storing woody biomass in chip form could not necessarily ensure a high energy content of biomass. Figure 9. Biomass HHV changes in wood chips piles throughout field storage period. 4.4 Discussions 4.4.1 Wood logging residues pil es The air -dried method tested in this study resulted in a significant reduction in piled biomass MC during a 5 -month storage time. The MCs of piled biomass rapidly decrease d after harvesting with the lowest MC detected around the end of Aug ust at both study sites, due to the relatively low air humidity and rainfall. Biomass MC started to increase in September and kept rising until November when low temperature limited water transfer between wood and air (Gigler et al., 2000). The resulting biomass MC 6400.0 6800.0 7200.0 7600.0 8000.0 8400.0 8800.0 8/2/13 8/13/13 8/26/13 9/9/13 9/23/13 10/7/13 10/21/13 11/4/13 HHV: Btu's/lb Site 1 Site 2 Site 3 86 change during the 5 -month field storage is comparable with the findings from other studies (Nellist, 1997; Gigler et al., 2000; Nurmi, 1995). Millet (1953) and Gautam et al. (2012) also found that the MC of field -stored biomass would further dec rease with longer storage time and become significantly lower in the second storage season. Because of the shorter field storage time and limited data quantity, data from this study cannot support their findings. Evaluating biomass MC change over a longer field storage period is necessary to determine whether biomass MC cumulatively decreases on an annual basis. Many studies found that HHV of biomass can only be maintained during the first four months of field storage and will decrease after 18 months, due to the changes in the chemical composition resulting from biodegradation processes (Nurmi, 1995; Brand et al., 2011). Although data from this study is limited in determining the pattern of piled biomass HHV over a longer storage period, it is sufficient to ascertain that maintaining biomass HHVs stable over 5 months in the field is feasible. The fast decrease of biomass MC towards winter can effectively restrict microbial activities and decay in wood can be minimized (Gautam et al., 2012; Hudson, 1992). 4.4.2 Wood chips piles storage Based on the research results, the chipped biomass MC increased after 4 months of field storage at all the study sites. This finding was consistent with the results provided by Afzal et al. (2010), who observed a biomass MC increase of around double the initial value. This observed wood chip MC change pattern is different from the findings in the previous study using unchipped biomass, which showed a continuously declining trend in biomass MC during a similar storage period (Lin and P an 2013). The main reasons for 87 the difference are the smaller particle size and higher degree of compaction in the wood chip pile, compared with an unchipped biomass pile (Afzal et al. 2010). The small chip size and high compaction resulted in less space f or air movement and therefore lower drying rate within the chip piles, thus causing irregular and increasing MC during the field storage (Jirjis 1995, 2001; Garstang et al. 2002). In this study, decreases in biomass HHVs were observed at all study sites, w hich was consistent with many other studies (Jirjis and Theander 1990, Jirjis et al. 2005 Afzal et al. 2010). The reason for biomass HHV decline was the high MC remaining in the piles, which enhanced the microbial activity and resulted in lower HHVs (Hudso n 1992, Gautam et al. 2012). Microbes, such as mold fungi, wood -decaying fungi, and blue stain fungi, will start to consume the wood biomass by aerobic degradation and then produce heat, carbon dioxide, and water (Eriksson 2011). Most of the time the micro bes attack cellulose and hemicellulose; they degrade lignin as well (Eriksson 2011). Noticeable variations in biomass HHVs were observed at Sites 2 and 3 ( Figure 9). T he wood chips at these two study sites were all produced from whole trees and consisted of branches, barks, and chunk wood. In this mixture of wood chips produced from different parts, the content and types of lignin and the extractives are expected to be substantially diverse, which can directly lead to the diversity in biomass HHV (White 1987, Melin 2008, Telmo and Lousada 2011, Burkhardt et al. 2013). Meanwhile, the decomposition rates are also found to be faster in the branches and barks compared with t he chunk wood part (Slaven et al. 2011). The variations found in biomass HHV decline, therefore, can be committed to the different chemical compound contents and varying decomposition 88 rates of the wood chips. This finding implies that the biomass HHV s of w hole tree wood chips are more erratic and difficult to predict during the field storage. 4.5 Conclusion Storage is a key component within the woody biomass supply chain, especially when year -round harvesting is impossible. In order to better understand the fi eld storage of woody biomass, 5 wood storage studies have been conducted to monitor the biomass quality (biomass MC and biomass HHV) change under different storage forms, wood logging residues piles and wood chips piles. In wood logging residues piles, bio mass MCs were significantly reduced during the storage period. As an important fuel quality property, biomass HHV was determined to be generally stable during the 5 -month storage period. In comparison, because of the small particle size and high degree of compaction in the wood chip pile, increases in biomass MCs were observed at all wood chips piles. In addition, decreasing trends of biomass HHV were detected during the storage time, at all wood chips piles, as a result of energy loss caused by the high MC and microbial activity in the wood chip pile. Further monitoring of biomass MC and HHV over a longer field storage period will reveal whether biomass MC will cumulatively decrease on an annual basis and whether HHV of field -stored biomass is stable for m ore than 5 months. In addition, future research will be conducted to develop a biomass field drying model to quantify the relationship between biomass MC change and weather factors and thus to better understand the mass and energy flow in field -stored biom ass pile. 89 CHAPTER 5 COST MINIMIZATION OF WOODY BIOMASS LOGIST ICS INTEG RATING INFIELD DRYIN G AS A COST -SAVING PREPROCESS IN MICHIGAN 5.1 Introduction With the development of computational tools, mathematical models for optimization have been widely used to implement cost -effective bioenergy production (Macmillan 2001 , Mentzer 2001 , Rınnqvist 2003 , Gunnarsson et al. 2004 , Bredstr ım et al. 2004). As woody biomass transportation cost accounts for the largest part of the total cost and energy con sumption (Eriksson and Bj ırheden 1989 , Allen et al. 1998 , Alam et al. 2012), the developed optimization tools focus primarily on two categories: location selection and woody biomass collection. The location selection models have emphasized mainly on findin g the best location for single or multiple processing facilities over the large -scale biomass supply chain (Zhang et al. 2011). The optimization models for woody biomass collection have aimed generally to estimate the feedstock availability and reduce cost for biomass procurement (Ranta 2002 , 2005; Panichelli and Gnansounou 2008) . For instance, Lautala et al. (2012 a) have published a cost minimization model to minimize the cost of woody biomass transportation using railroads in Michigan and Wisconsin. Howev er, as a critical phase in woody biomass supply chain logistics, optimization of woody biomass storage has rarely been studied (Rentizelas et al. 2009). Storage is complicated because of the changing seasonal availability of woody biomass and the varied de mand of energy plants throughout the year (Sokhansanj et al. 2006, Lin and Pan 2013). Meanwhile, different storage methods will produce biomass at 90 various quality levels, which can significantly affect the transportation and energy conversion efficiency (J irjis 2005 , Casal et al. 201 0). The most common way in the northern United States to store green biomass is to directly process wood into chips and store these in piles before being used (Lin and Pan 2013). This storage method poses several problems , such as dry matter loss, moisture content (MC) increase, and energy content reduction (Fredholm and Jirjis 1988 , Thırnqvist and Jirjis 1999 , Jirjis 2001 , Garstang et al. 2002, Afzal et al . 2010). Stor ing forest harvesting residues in bundles, as the second opti on, can produce high -quality biomass feedstock with low biomass MC, higher energy content, and low ash content (Lehtikangas and Jiris 1998, Pettersson and Nordfjell 2007 , Afzal et al. 2010). Yet the bundling technology is associated with several problems , such as high capital investment and low productivity caused by saw binding, materials handling, twine spool collapse, and slow movement at the harvesting site (Leinonen 2004 , Rummer et al. 2004 , Harrill 2010). Compared with piling wood chips and bundling residues, leaving unchipped or unbundled harvesting residues on -site in piles can avoid high processing costs and effectively reduce biomass MC, thus increasing transportation and conversion efficiency (Amos 1998 , Lin and Pan 2015). In Michigan, there are 11 biomass -based power plants with a total of 210 MW of energy generated annually, which is about 2.8 percent of the total production in the United States (Biomass Power Association 2014 , Biomass Magazine 2015). During the winter in M ichigan, from October to March, the average high temperature is about 41¡F , and the average low temperature is about 26¡F. With more than 51 inches of annual snowfall, forest harvesting operations are not always possible in the winter months. To ensure a cost -effective and rel iable supply of high -quality biomass feedstock to the 91 power plants, a computer -aided improved operations system was developed. The objectives of this research were (1) to develop an improved operations system that can increase biomass feedstock quality and minimize the total cost , including processing, storage , and transportation , and (2) to test the effects of transportation distance and biomass MC on the total cost of processing, storage , and transportation. In this article , the objective function is set to be the total cost ( in dollars ). The unit cost of the biomass feedstock ( dollars per green ton ; short ton) is also reported. 5.2 Problem Description 5.2.1 Feedstock storage and transportation operations Because the quantitative relationships between local weather factors and biomass MC during storage are developed based on two previous studies conducted in Michigan from August to November, the woody biomass is assumed to be harvested at the beginning of August and stored in the field from August to November (Lin an d Pan 2013, 2015). The selected harvest site is a natural forest stand with mixed hardwood and softwood species 40 miles away from the feedstock end user , Cadillac Renewable Energy LLC. A part of the logging residues are in -woods chipped and then transport ed to and stored in the end userÕs facility to meet its first -month demand. The remaining unprocessed residues are allowed to be piled at the harvest site for a certain time. The unprocessed residues are chipped and hauled to the end user based on its cont inued monthly demand. 5.2.2 Feedstock end user and demand The feedstock end user, Cadillac Renewable Energy LLC , is located in Cadillac, Michigan. It is one of the largest biomass -based power plant s in Michigan and is 92 exclusively designed to use recycled wood waste as its primary fuel source. This power plant has a 38 -MW energy production capability, and the average monthly use of woody material (~45% MC) is about 35,000 green tons in the wintertime and about 25,000 green tons in other months. These feedstocks are constantly supplied by 40 logging companies delivering around 1 ,000 green tons (equivalent to 550 dry tons if assuming 45% MC) per month. Although the hauling distance varies for each loggin g company, the average feedstock supply radius is around 20 to 40 miles. 5.2.3 Traditional operation s system and improved operation s system In this case simulation, a traditional operations system refers to the traditional way of handling harvested biomass, where logging residues are directly processed into wood chips and immediately delivered to the feedstock end user and then stored in the facility as a wood chip pile . In an improved operations system ( Figure 10), a linear programming model is used to determine the weight of the logging residue that will be chipped right away and delivered to the feedstock end user to meet its immediate demand ; the remaining unprocessed portion will be stored as a large logging residues pile at the roadside. After several months of air -drying, unprocessed logging residues will be chipped by a mobile chipper and delivered to the feedstock end user monthly. 93 Figure 10. An illustration of the improved operations system includes biomass chipping, storage, and transportation 5.2.4 Biomass storage In the improved operations system, chipping, storage, and transportation costs are closely related to the woody biomass MC, which is significantly affected by storage form. Two previous studies (Lin and Pan 2013 , 2015) showed that piling unprocessed logging residues can effectively reduce biomass MC but that piling wood chips cannot. Several predictive equations were developed in these two previous studies to reveal the quantitative relationship between certain weather conditions and the MC of the logging residues pile ( Table 11). Because biomass MC is critical for deciding the selling price of woody biomass in this model, we use the previously developed predictive model and the local weather conditions from August to November 2013 to predict the monthly MC of piled logging residues. The bio mass MCs of the wood chip pile are cited values from the study published by Lin and Pan (2015). 94 Table 11. August -to-November woody biomass moisture content in Michigan (Lin and Pan, 2013; Lin and Pan, 2015) Biomass MC (%) Wood Chi ps Pile (WCP) Logging Residues Pile (LCP) August 40.3% 23.8% September 39.3% 18.1% October 40.7% 26.1% November 45.5% 25.9% 5.2.5 Transportation costs The one -way transportation distance is set to be 40 miles in this case simulation. The transportation cost in dollars per green ton is estimated based on the equation developed for the Lower Peninsula of Michigan (Lautala et al. 2012 b; Table 12). Table 12. Transportation costs at different transportation distances in Michigan One -way transportation distance (mi) Transportation cost ($/green ton) 20 5.88 30 6.42 40 6.97 50 7.52 60 8.07 5.2.6 Holding cost and additional profit In the improved operations system, part of the fresh biomass will be stored as residue piles at the harvest site for air -drying. This will delay the feedstock suppliersÕ cash flow and lea d to future operation costs , such as those for machine mobilization . The delay in cash flow in this model is defined as the holding cost, which accounts for the interest lost from the revenue of all the piled logging residues. The equation used to calculat e the holding cost (HC) is ()()2HC11 1 jRRrtrtrt !"=# + # ++ # +++ #$%! (1) 95 0RnRWRPR =! (2) where j is the total time of biomass storage ( j = 3 mo nths ); RR is the total revenue for selling all the biomass stored in logging residue pile form; WRn is the green weight of biomass harvested in Aug ust , stored as piled logging residue for n months; PR0 is the purchasing price ($/green ton) of logging residues that were harvested in August; r is the yearly interest rate (0.03); and t = 1 month (0.08 yr). It is beneficial for feedstock end users to use fe edstock with higher energy content and lower MC to increase energy conversion efficiency. To offer an incentive and profit for feedstock suppliers to store biomass in residue piles, it is assumed that the feedstock purchase price is based on their lower heating value (LHV), which will increase with the decrease of biomass MC. Therefore, drier biomass has higher purchasing prices (Roise et al. 2013). The Michigan -based prevailing purchase price for wood chips is assumed to be $23.00 per green ton for biomass with 45 percent MC (L . Heibel, personal communication, June 11 , 2014; N . Verhanovitz, personal communication, June 25 , 2014). The LHV of the 45 percent MC wood chips can be estimated using the following equation (Maker 2004): LHVHHV(1MC/100) =!" (3) where HHV is the higher heating value of the oven dried biomass and MC is the wet -basis MC of the received biomass. For instance, if the HHV of the woody biomass is assumed to be 8 ,400 BTUs/lb (Maker 2004), the LHV of 45 percent MC woody biomass is calculated to be 4 ,620.00 BTUs /lb. When the Michigan -based prevailing purchase price is $23.00/green ton, then the energy cost is determined to be $2.49 per million BTUs ($2.49/MM BTUs). The 96 calculated feedstock purchase prices for woody biomass at various MC levels are summarized in Table 13. Table 13. Calculated feedstock purchased prices based on the LHV and the energy cost of $2.49/MM Btus. a Biomass MC (%) HHV (BTUs/lb) LHV (BTUs/lb) Calculated purchase price ($/green ton) 60 8,400 3,360.00 16.73 55 8,400 3,780.00 18.82 50 8,400 4,200.00 20.91 45 8,400 4,620.00 23.00 40 8,400 5,040.00 25.09 35 8,400 5,460.00 27.18 30 8,400 5,880.00 29.27 25 8,400 6,300.00 31.36 20 8,400 6,720.00 33.45 a LHV = lower heating value; MC = moisture content; HHV = higher heating value . 5.2.7 Mathematical model 5.2.7.1 Indices n: biomass storage time ( n = 0, 1, 2, 3 mo nths). Variables - WCn: g reen weight of biomass harvested in Aug ust , stored as piled wood chips for n months ( Figure 11); WRn: g reen weight of biomass harvested in Aug ust , stored as piled logging residue for n months ( Figure 11). 97 Figure 11. The weight of woody biomass feedstock delivered to the end -user 5.2.7.2 Parameters db: monthly feedstock demand of 1 ,000 green tons (45% MC in wet basis ; equivalent to 550 dry tons) for the energy plant ; Z: total cost of preprocessing, storing, and delivering feedstock ; KC: total chipping cost ($) for processing all the biomass ; KP: total piling cost ($) for shaping the logging residues into biomass piles ; KMG: machine mobilization cost ($) of moving a mobile grinder to the harvest site; KML: machine mobilization cost ($) of moving a loader to the harvest site ; KT: transportation cost ($) of delivering chipped biomass to the end user ; AP: additional profit ($) earned by selling higher -quality biomass ; mcCn: MC of biomass harvested in August and stored as wood chips for n month(s) ; 98 mcRn: MC of biomass harvested in August and stored as logging residue for n month(s) ; hhv: higher heating value (BTUs/lb) ; LHVCn: net energy content of wood chips (BTUs/lb); LHVRn: net energy content of logging residue (BTUs/lb); PCn: purchasing price ($/green ton) of wood chips that were harvested in August and store d for n month(s) ; PRn: purchasin g price ($/green ton) of logging residues that were harvested in August and store d for n month(s) ; ps: standard purchasing price ( $23/green ton) for energy plant to purchase biomass (45% wet basis) ; ecs: e nergy cost ($/BTU) ; HC: costs ($) incurred while holding the biomass stored as logging residues piles ; r: yearly interest rate. 5.2.7.3 Constraints Satisfy the monthly demand (dry weight) of the energy plant : ()()[11](145%)0 CnnRnn mcWCmcWRdb !" + !"!!# where n = 0, 1, 2, 3 months; mcCn = MC of biomass harvested in August and stored as wood chips for n month(s); mcRn = MC of biomass harvested in August and stored as logging residue for n month(s); 99 WCn = the green weight of biomass harvested in Aug ust and stored as wood chip pile for n month(s); WRn = the green weight of biomass harvested in Aug ust and stored as piled logging residue for n month(s); db = 1,000 green tons (MC = 45%, or 550 dry tons). 5.2.7.4 Objective function The total cost can be expressed as ZKCKPKMGKMLKTAPHC =++++ ! + (4) where 305.00 () nnnKCWCWR ==+! (5) 304.59nnKPWR ==! (6) 302.52() nnnKMGWCWR ==+! (7) 302.52 nnKMLWR ==! (8) 306.97() nnnKTWCWR ==+! (9) ()33000()nnnnnn nnAPPCWCPRWRPCWCWR ===! + !"! +## (10) 2,000 lb green ton nCns PCLHVec =!! (11) ()301 CnCn nLHVhhvmc ==!" (12) 100 2,000 lb green ton nRns PRLHVec =!! (13) ()301 RnRn nLHVhhvmc ==!" (14) ()()()230HC1111 nWRPRrtrtrtrt !"=#### ++ # ++ # ++ #$% (15) where n = 0, 1, 2, 3 months; r = 0.03 (yearly interest rate); t = 1 month (0.08 yr); WCn = green weight of biomass harvested in Aug ust and stored as wood chips for n month(s); WRn = green weight of biomass harvested i n Aug ust and stored as piled logging residue for n month(s); PCn = purchasing price ($/green ton) of wood chips that were harvested in August and store d for n month(s); PRn = purchasing price ($/green ton) of logging residues that were harvested in August and store d for n month(s); hhv = higher heating value (BTUs/lb); LHVCn = net energy content of wood chips (BTUs/lb); LHVRn = net energy content of logging residue (BTUs/lb); ecs = energy cost ($/BTU); mcCn = MC of biomass harvested in August a nd stored as wood chips for n month(s); and mcRn = MC of biomass harvested in August and stored in logging residue for for n month(s) . Other parameter values are listed in Table 14. This linear programming optimization model is analyzed by Solver (Frontline Systems, Inc. 1990 Ð2009) and can also be solved by other software , such as the General 101 Algebraic Modeling System ( GAMS Development Corporation 2013), LINDO API 9.0 (LINDO Systems, Inc. 2015a), or LINGO 15.0 (LINDO Systems, Inc. 2015b) . Table 14. Parameter values used in the case simulation Site conditions Feedstock user Cadillac Renewable Energy Transportation distance (miles) 40 Parameters Wood Chips Pile (WCP) Logging Residues Pile (LCP) Chipping cost ($/green ton) 5.00 (1) 5.00 (1) Piling cost ($/green ton) (4) 0 4.59 (2) Machine mobilization cost ($/green ton) (5) 2.52 (3) 2.52 (3) Total Processing cost ($/green ton) 7.52 12.11 Transportation cost ($/ green ton) (6) 6.97 (1) 6.97 (1) (1) Lautala et al., 2012 (2) Harrial, 201 0 (3) Zamora, 2013 (4) Assume the chipper has an attached loader; the wood chips are blown to a chip van and hauled away immediately after the chipping operations. (5) The mobilization cost includes cost for moving chipper and loader. (6) The transportation cost is 9.75 $/green ton with additional $0.15/green ton per mile after 20 miles (Barnes, 2010). 5.2.8 Results and Discussion 5.2.8.1 The improved operation s system A The details of using the improved operations system to continuously supply the end user with high -quality biomass feedstock for 4 months is summarized in Table 15. The improved operations system favors a shift from piling wood chips toward piling logging residues for achieving lower MC. 102 Table 15. The costs of using an improved ope rations system to sell biomass feedstock to the end user in improved system A a a Definitions of the abbreviations used in the equations are provided in the text. b Total weight of green biomass stored as wood chip pile. c Total weight of green biomass stored as logging residues pile. At the beginning of August, a total of 3 ,079.68 green tons of biomass will be harvested to meet the end userÕs demand until November. An amount of 921.69 green tons of biomass will be immediately processed into wood chips and delivered to the end user to meet its August demand. The remaining 2,157.99 green tons of biomass will be stored as piled logging residues at the harvest site. At the end of August, a mobile grinder needs to be moved to the harvest site to process 671.51 green tons of 1 -month air-dried logging residue into wood chips. The wood chips will then be delivered to the end user to meet its September demand. During October and November , a similar process will take place. The chipper will produce 744.49 green tons of 2 -month field -Month Storage form Split cost ($) Holdi ng cost ($) Total cost ($) Wood chip pile Logging residues pile Chippin g Piling Machine mobilizati on Transp ortation Aug. 30n=!WCn = 921.69 b 30n=!WRn = 2,157.99 c 4,608.45 9,905. 16 7,760.78 6,424.1 8 164.9 6 28,863 .54 Sep. WC1 = 0.00 WR1 = 671.51 3,357.53 0.00 1,692.19 4,680.3 9 165.3 6 9,895. 48 Oct. WC2 = 0.00 WR2 = 744.49 3,722.45 0.00 1,876.12 5,189.1 0 165.7 6 10,953 .44 Nov. WC3 = 0.00 WR3 = 741.99 3,709.95 0.00 1,869.81 5,171.6 7 166.1 5 10,917 .60 Total 921.69 2,157.99 15,398.3 8 9,905. 16 13,198.91 21,465. 34 662.2 4 60,630 .06 Total biomass harvested (green tons) 3,079. 68 Additional profit ($) 22,204 .90 Total cost after additional profit ($) 38,425 .16 Production cost ($/green ton) 12.48 103 stored bioma ss and 741.99 green tons of 3 -month field -stored biomass to meet the end userÕs October and November demands, respectively. The highest total operations cost of $28,863.54 occurs in August. The chipping cost, piling cost, machine mobilization cost, and tra nsportation cost account for 15.96 , 34.31, 26.89, and 22.26 percent of the total cost, respectively. The lowest total cost of $9,895.48 is in September. This includes the chipping cost, machine mobilization cost, and transportation cost for selling 671.51 green tons of biomass stored as logging residue pile. The monthly total cost s for October and November are $10,953.44 and $10,917.60, which depend mainly on the weight of biomass processed and delivered in each month. The total cost for the 4 months of operations sums up to $60,630.19 , and the unit production cost is $19.68/green ton. The largest component of the total cost is the transportation cost, which represents 35.40 percent of the total cost. The holding cost of $662.24 accounts for only 1.09 percent of the total cost owing to the relatively small amount of held biomass. 5.2.8.2 Comparison between improved operation s system A and the traditional operation s system The total cost of the improved operations system A is $60,630.19, which costs the feedstock supplier $6,089.70 more compared with the traditional operations system because of the extra machine mobilization cost and the piling cost associated with establishing logging residue piles ( Table 16). However, the higher cost of the improved operations system can be offset by the additional profit of $22,204.90 from selling higher -quality feedstock ( Table 15). As a result, the feedstock suppliers can expect a net 104 cost (total cost minus the additional profit) of $38,425.29 by adopting the improved operation s system. Table 16. The costs of using a traditional operation system to sell biomass feedstock to the end user a Month Storage form Split cost ($) Holdin g cost ($) Total cost ($) Wood chip pile Logging residues pile Chippin g Pilin g Machine mobiliza tion Transp ortatio n Aug. 30n=!WCn = 3764.00 b 30n=!WRn = 0.00 c 18,820. 02 0.00 9,485.29 26,235. 08 54,540. 36 Sep. WC1 = 906.64 WR1 = 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Oct. WC2 = 927.32 WR2 = 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Nov. WC3 = 1,008.35 WR3 = 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Total 3,764.00 0.00 18,820. 02 0.00 9,485.29 26,235. 08 0.00 54,540. 36 Total biomass harvested (green tons) 3,764.0 0 Additional profit ($) 0.00 Total cost after additional profit ($) 54,540. 36 Production cost ($/green ton) 14.49 a Definitions of the abbreviations used in the equations are provided in the text. b Total weight of green biomass stored as wood chip pile. c Total weight of green biomass stored as logging residues pile. In improved operations system A, the total amount of biomass required to meet the end userÕs 4-month demand is 3 ,079.68 green tons , while in the traditional operation s system, a total of 3 ,764.00 green tons of biomass is required to meet the 4-month demand. The 684.32 green tons of reduction in green biomass delivered to the end user is cau sed by the drier biomass using the logging residues pile as the storage method suggested by the improved operations system. 5.2.8.3 Improved operation s system with two feedstock end users ( system B) To further test the model, a second feedstock end user ( end user 2) is introduced to the operation s system. End user 2 is assumed to be located 20 miles away from the 105 harvest site, with a monthly demand of 2 ,000 green tons of woody biomass (1 ,100 dry tons assuming 45% wet -basis MC). All the parameters remain the same as those in the improved operation s system A with one feedstock end user. The corresponding objective function becomes the summed net cost of supplying woody biomass to two end users. The constraints for the improved operation s system B are to meet the 1 ,000 green tons of monthly demand for end user 1 (550 dry tons assuming 45% wet -basis MC) and 2 ,000 green tons of monthly demand for end user 2 (1 ,100 dry tons assuming 45% wet -basis MC). The decision variables are the monthly delivered biomass weight in green tons to the two end users. Table 17 presents the optimized solution with the monthly delivered biomass weight for end user 1 and end user 2. Since the unit cost ( dollars per green ton) for chipping, piling, and machine mobilization is the same for the two end users, the total chipping, piling, and machine mobilization costs for end user 2 are doubled compared with end user 1 owing to its doubled biomass monthly demand . For the transportation cost, the unit transportation cost for end user 2 is reduced because of the shorter transportation distance ; therefore , the transportation cost will not increase proportionally to the biomass weight increase ( Figure 12). The simulation showed that the total cost to supply biomass feedstock to the two end users is $123,100.57 with a unit cost of $13.32/green ton. The results indicate that the log ging residue pile is the recommended storage form in operation s system B because it can produce drier woody biomass. However, there is a small chance that the biomass from one harvest site can support two feedstock 106 end users at the same time because 1 MW of electrical production requires 3,987 acres of typical pine plantation (National Association of Conservation District s 2015 ). Table 17. The costs of using an improved operations system to sell biomass feedstock to the two end users in improved system B a Mont h End user 1 b End user 2 c Split cost ($) Holdi ng cost ($) Tot al cost ($) Wood chip pile Loggi ng residu es pile Wood chip pile Loggi ng residu es pile Chip ping Pilin g Machin e mobiliz ation Transpor tation Aug. 30n=! WCn = 921.69d 30n=! WRn = 2,157. 99e 30n=! WCn = 1,84 3.38 f 30n=! WRn = 4,315. 97g 13,82 5.35 29,7 15.4 7 23,282. 35 31,802.1 494.89 99,1 20.1 6 Sep. 0 671.5 1 0 1,343. 01 10,07 2.58 0 5,076.5 8 12,577.3 496.08 28,2 22.5 4 Oct. 0 744.4 9 0 1,488. 98 11,16 7.36 0 5,628.3 5 13,944.31 497.27 31,2 37.2 9 Nov. 0 741.9 9 0 1,483. 98 11,12 9.85 0 5,609.4 4 13,897.47 498.46 31,1 35.2 3 Total 921.69 2,157. 99 1,84 3.38 4,315. 97 46,19 5.14 29,7 15.4 7 39,596. 73 72,221.18 1,986. 7 189,715.23 Total biomass harvested (green tons) 9,23 9.03 Additional profit ($) 66,6 14.6 5 Total cost after additional profit ($) 123,100.57 Production cost ($/green ton) 13.3 2 a Definitions of the abbreviations used in the equations are provided in the text. b End user 1 refers to Cadillac Renewable Energy. c End user 2 refers to the simulated second feedstock end user, which is added to test the model. d Total weight of green biomass stored as wood chip pile for end user 1. e Total weight of green biomass stored as logging residues pile for end user 1. f Total weight of green biomass stored as wood chip pile for end user 2. g Total weight of green biomass stored as logging residues pile for end user 2. 107 5.2.8.4 Sensitivity analysis ÑEffects of transportation distance on the total cost and the improved operations system In this simulation, the transportation distance from the harvest site to the end user was set at 40 miles. In the sensitivity analysis, the range of the transportation distance considered is from 20 to 60 miles. The transportation distance has no im pact on the biomass storage and transportation strategy but affects the total cost through changing the transportation cost. When the distance increases from 20 to 60 miles, the total cost after deducting the additional profit rises linearly from $35,056.0 8 to $41,806.73 ( Figure 12). The sensitivity analysis indicates that every 1 mile of transportation distance increase will raise the total cost by $168.77. The additi onal profit earned from selling higher -quality feedstock is $22,204.90. This additional profit can cover the increased transportation cost caused by a one-way distance increase for up of 171 miles. This result suggests that the negative impact of longer tr ansportation distance in the woody biomass supply chain can be mitigated by the higher feedstock quality. Figure 12. Total cost after deducting AP associated with different one -way transportation distances 35056.08 36743.80 38425.24 40119.07 41806.73 30000.00 32000.00 34000.00 36000.00 38000.00 40000.00 42000.00 44000.00 20 30 40 50 60 Total cost after deducting AP ($) One-way Transportation distance (miles) 108 5.2.8.5 Sensitivity analysis ÑEffect of biomass MC on the total cost and the improved operation s system The effect of biomass MC on the total cost was determined by changing the MC at a 5 percent increment ( Figure 13). On average, every 1 percent increase in biomass MC can result in a $760.68 increase in the total cost after deducting additional profit. With every 5 percent decrease in biomass MC, the total cost after deducting additional profit is reduced by $2,976.29. On the other hand, when the biomass MC increases by 5 , 10, and 15 percent , respectively, the total cost after deducting additional profit will increase to $3,439.78, $4,023.11, and $4,772.51, respectively. In addition, this cost increase owing to feedstock MC increment presents an ascending curve instead of being linear, indicating that a large increase in MC will have a more significant impact on the total co st. The harvesting operations, therefore, are suggested to take place in the late spring or summer , when initial biomass MC tends to be lower , to reduce the total cost. Figure 13. Total cost after deducting AP associated with dif ferent biomass MC 35448.95 38425.24 41865.13 45888.33 50660.91 28000.00 33000.00 38000.00 43000.00 48000.00 53000.00 -5% 0% +5% +10% +15% Total cost after deducting AP ($) Moisture content increments (%) 109 Although the effect of biomass MC on the total cost is nonlinear ( Figure 13), the sensitivity analysis indicates that every 1 percent decrease in b iomass MC will reduce the total delivered biomass green weight by 52.10 green tons on average. This means that using the improved operations system can prevent 52.10 tons of water from being transported to the end user, thus increasing the transportation efficiency. For different biomass MC, piling unprocessed logging residue is always the preferred way to store biomass mainly because this storage method can produce drier biomass feedstock through air -drying. 5.2.8.6 Model limitations The improved operations syste m simulated by the linear programming model has many limitations in real operations. For example, real operations cannot process field -stored biomass at the accurate amount as the computer -aided improved operations system suggests. However, this improved o perations system can serve as a guideline for real -world operations. The scheduling of real -world operations can be adjusted toward what the improved operations system indicates ; thus , improved feedstock supply chain cost -effectiveness can be realized. The calculated feedstock purchase price in this simulation is based on a feedstock purchase price provided by a personal research contact (L . Heibel, personal communication , June 11 , 2014) because currently the increased economic value of higher -quality feeds tock is justifiable only by increased recoverable energy content. In reality, a feedstock conversion and upgrading facility may only partially return its profit from using higher -quality feedstock to the feedstock suppliers. The c urrent US market does not have any mechanism for pricing the higher -quality woody biomass or 110 allocat ing the increased profit between feedstock suppliers and end users. Using drier biomass is a more profitable way for both feedstock suppliers and end users , as transporting drier bio mass (having higher energy content) results in lower energy cost and using drier biomass increases boiler efficiency. The additional profit from selling higher -quality feedstock discussed in this article is based on using direct combustion as the conversio n option. The additional profits from the increased efficiency using different biomass conversion and upgrading options will vary. In addition to direct combustion, other conversion and upgrading options include palletization, fast pyrolysis, torrefaction, and fermentation. In future research, more conversion and upgrading scenarios will be considered. 5.2.8.7 Conclusion An improved operations system for biomass storage and transportation is proposed using a computer -based linear programming technique. The case sim ulation results indicate that when using logging residue pile as the major storage form, the extra cost of $6,089.70 owing to piling operations and machine mobilizations can be offset by the additional profits of $22,204.90 from selling higher -quality feed stock. In addition, because of the drier biomass achieved in the improved operation s system, the delivered biomass green weight to satisf y the 4-month energy demand is reduced by 684.32 green tons compared with the traditional operation s system. By introdu cing end user 2 in the additional testing of the operation s system, the simulation results confirm that logging residue pile is still the preferred storage method. 111 CHAPTER 6 LIFE CYCLE ASSESSMEN T OF MIXED -SPECIES FOREST AND SHORT ROTATION WOODY CROP PLANTATION IN MICHIGAN 6.1 Introduction Woody biomass is the third largest renewable energy source and contributes about 10% of the global primary energy consumption (EIA, 2017; Lauri et al., 2014). Historically, it was assumed that the carbon emitted into the air from biomass combustion is sequestrated by future tree growth . Unde r this regeneration assumption , woody biomass has been recognized as Òcarbon neutralÓ and Òenvironmentally friendlyÓ and re commended as a carbon sources to replace fossil fuel s (Lippke et al., 20 11). However, immediate carbon dioxide emissions can cause near -term increases in atmospheric carbon, which may require a long t ime to be resequestered (Cherubini et al., 2011; Sedjo , 2011; Sedjo, 2013) . The total annual greenhouse gas ( GHG) emissions from the global woody biomass supply chain are estimated to be 890 million tonnes, with more than half of the carbon released f rom energy consumption (FAO, 2007; Asikainen et al., 2010). The global warming effect caused by th is large carbon flux should not be underestimated. To completely assess the environmental performance of woody biomass, it is necessary to study cradle -to-grave GHG emissions in the woody biomass supply chain (Sedjo, 20 13; England et al., 2013). Many life cycle assessment (LCA) studies have been carried out globally to quantify the environment al impacts of short rotation woody crop (SRWC) production systems (Mann and Spath , 1997; Keoleian and Volk, 2005; Adler et al. , 2007; Gonz⁄lez -Garcfia et al. , 2010; Di Nassi o Nasso et al., 2010 ; Gabrielle et al., 2013; Vasquez Sandoval , 2015). These LCAs have suggest ed crucial areas need to be improved to 112 further reduce GHG emissions and improve sustainability . However, these LCA s rarely include combustion or biomass utilization, which normally contributes the largest amount of GHG emissions in the woody biomass supply chain. Klein et al. (2015) reported that only 14% of the 28 reviewed LCAs of forest production systems used a cra dle -to-grave scope, and over 54% did not mention any methodical approach besides the Intergovernmental Panel on Climate Change ( IPCC ) (2006) guidelines. The energy ratios reported by previous cradle -to-farm gate LCAs vary from 13 to 79, and from 3 to 16 in the cradle -to-grave LCAs. The GHG emissions reported ranged from 39 to 2,953 g CO2-eq/kWh. These large varia tions were due to different scope definition, project goals, system boundaries, raw materials , and research locations in each study. A similar problem is also found in the LCAs for forest production systems that are not SRWC. In 29 reviewed LCAs conducted from 2000 to 2017, there are 18 LCAs that define the scope from forest to plant gate, 7 from forest to forest road, and 4 from cradle-to-grave. In addition, 12 different functional units such 1 m 3 over bark , 1 acre, 1 year, 1 ove n dry ton, and 1 MJ were used in these LCAs. These differences in the previous studies have made comparison s between studies difficult and bias ed (Klein et al., 2015). Besides the incomplete scope and the different functional units chosen , a critical but often -neglected component in previous LCAs is the soil organic carbon (C) sequestration, which determines the net environmental benefit of biomass energy production (And erson -Teixeira et al., 2009; Russell et al., 2015). Soil organic C stock is one of the major benefits of growing woody biomass and can play an important role in 113 offsetting GHG emissions (Liski et al., 2006; Goglio et al., 2015). However, soil organic carbo n has been excluded from previous LCA approaches (Helin et al., 2013). Another important environmental aspect , typically ignored in woody biomass supply chain analyses, is the nutrient pool. Excessive removal of forest debris from harvesting sites can caus e significant negative impacts to nutrients cycles, soil , and site productivity (Martin et al., 2000; Watmough and Dillon, 2003; Walker et al., 2010). For instance, Achat et al. (2015) concluded that whole -tree harvesting (WTH) cause d a 3 -7% reduction in t ree growth up to 33 years after harvesting. Mason et al. (2011) reported that WTH caused 5 -9% height and diameter reduction at two medium fertility sites, with 9% height reduction and 19% diameter reduction at the poor fertility site. In order to maintain a nutrient balance and utilize forest resources sustainably, it is important to estimate the nutrient loss caused by forest harvesting. GHG emissions in woody biomass production system s in Michigan and the Lake States have rarely been studied. So far, only Handler et al. (2014) published an LCA that analyzed GHG emissions and energy input s in the harvesting and transportation stages (truck and railway transport) for the round wood supply chain in M I. This study was based on previous published peer -reviewed literature, national database s, and a Michigan loggersÕ survey. By 2015, there are over 20 million acres of forested land in Michigan, and 19.3 million acres are timberland (Pugh et al., 2016). In order to provid e a clear er picture of the energy and mass flow in woody biomass production system s, a comprehensive LCA that includes all life cycle stages is necessary. With the help of LCA, better decisions 114 can be made in promoting woody biomass utilization, and in imp roving the system performance in terms of minimizing environmental impacts (Puettmann, 2006). 6.2 Goal, Scope and Functional Unit for Life Cycle Assessment This LCA followed the standard approach suggested by ISO 14040 series, Environmental Management -Life C ycle Assessment. The goal of this LCA study is to analyze and compare the environmental impacts, including soil carbon sequestration, GHG emissions , nutrient depletion and Energy Return on Investment (EROI) in five woody biomass production systems in Michi gan. The site location, stand type, harvesting system, and cut type for the five studied systems are summarized in Table 18. Life cycle stages considered are: forest cultivation, management (fertilization and herbicide application), harvesting, forest residues extraction , pre-processing, transportation , storage, and combustion (Figure 14). In the hybrid poplar (Site 3) and hybrid willow (Site 4) plantations, the harvesting, extraction, and pre -processing stage were combined into one stage because a reconfigured harvester was employed. The functional u nit (FU), forming the basis of comparison between systems for this LCA , was selected to be 1 kWh of electricity produced by co -firing wood pellets. 115 Table 18. Information of studied woody biomass production systems Site Location Stand type Tree species Harvesting system Area (acres) 1 Augusta, MI Mixed -species (average age 10 -year) 85% Douglas -fir, 5% Norway spruce, 5% white spruce and 5% hardwoods Whole -tree harvesting system 23.31 2 East Lansing, MI Mixed -species (average age 15 -year) 55% poplar, 25% Engelmann spruce, 10% larch and 5% pine.) Whole -tree harvesting system 28.55 3 Escanaba, MI Hybrid poplar plantation (7 -year old) Hybrid poplar Whole -tree harvesting system 7.80 4 Albion, MI Hybrid willow plantation (3 -year old) Hybrid willow Harvest -and -chip harvesting system 1.10 5 Gwinn, MI 29-year -old Jack pine plantation Jack pine Cut -to-length harvesting system 66.00 Figure 14. System boundary of woody biomass production systems in this LCA study 6.3 Life cycle inventory (LCI) A l ife cycle inventory was tabulated in MS Excel for each woody biomass production system regarding fertilizer, herbicide, energy , and fossil fuel use during the whole life cycle . The life cycle inventory for each site is summarized in Table s 19-23. In the harvesting, extraction, and pre -processing stages, fossil fuel consumption for each machine was calculated by multiplying their total operation time (h ours) to the diesel 116 consum ption rates (gals/h our), which were based on their horsepower provided by the manufacturing company. Total operation time (h ours) was calculated by dividing the harvested biomass dry weight (BDT) by the predicted productivity (BDT/P roductive Machine Hour ( PMH)) obtained upon multiple linear regression of time and motion study date for each site . The total diesel consumption of each machine , in gallons , was converted into equivalent GHG emissions . For combustion, an overall boiler capacity of 21 MWh and a combustion efficiency of 35% were assumed based on conventional power plant practice in the U.S. 117 Table 19. Life cycle inventroy of site 1 Site 1 Process/activity Value Cultivation Herbicide application (Glyphosate) 16.6 lbs Herbicide application (Simazine) 32.12 lbs Fertilizer (Ammonium Sulfate) 0 lb Harvesting operation Feller -buncher (Diesel) 80.37 gal Skidder (Diesel) 50.23 gal Loader (Diesel) 18.42 gal Chipper (Diesel) 125.23 gal Transportation (Chip -van) 318.64 gal One way distance (miles) 74.3 miles Total green weight 223.56 green tons Combustion (T.B. Simon Power Plant) Higher heating values 9173 Btus /lb (21.34 MJ/kg) Recoverable Heating Value 2970 Btus /lb (6.91 MJ /kg) Boiler capacity 21 MWh Plant efficiency 35% CO2 emissions 232.21 metric tons CH4 emissions 0.08 metric tons NO2 emissions 0.01 metric tons 118 Table 20. Life cycle inventory of site 2 Site 2 Process/activity Value Cultivation Herbicide application (Glyphosate) 16.6 lbs Herbicide application (Simazine) 32.12 lbs Fertilizer (Ammonium Sulfate) 0 lb Harvesting operation Feller -buncher (Diesel) 45.88 gal Skidder (Diesel) 41.40 gal Loader (Diesel) 22.32 gal Chipper (Diesel) 151.73 gal Transportation (Chip -van) 163.53 gal One way distance 4.4 miles Total green weight 344.29 green tons Combustion (T.B. Simon Power Plant) Higher heating values 7179 Btus/lb ( 16.70 MJ /kg) Recoverable Heating Value 2025 B tus/lb (4.71 MJ /kg ) Boiler capacity 21 MWh Plant efficiency 35% CO2 emissions 238.82 metric tons CH4 emissions 0.08 metric tons NO2 emissions 0.01 metric tons 119 Table 21. Life cycle inventory of site 3 Site 3 Process/activity Value Cultivation Tilling/spading 15.4 gal diesel Herbicide application: 6.16 gal diesel -Scepter 70 DG (tractor) 0.34 gal -Pendulum (Tractor) 5.85 gal -Glyphosate (manual) 0.25 gal Sapling Planting 2.94 gal diesel Coppice Cut 5.46 gal diesel Insecticide application: -BT Insecticide (manual) 0.26 gal Herbicide application: -Glyphosate spot app (manual) 0.26 gal Harvesting operation Feller -buncher (Diesel) 103.9 gal Skidder (Diesel) 23.8 gal Loader (Diesel) 20.8 gal Grinder (Diesel) 82.3 gal Transportation (Chip -van) 318.23 gal One way distance 50 miles Total green weight 265 green tons Combustion (T.B. Simon Power Plant) Higher heating values 8518 Btus/lb (19.81 MJ/kg) Recoverable Heating Value 2902 Btus/lb (6.75 MJ/kg) Boiler capacity 21 MWh Plant efficiency 35% CO2 emissions 224.48 metric tons CH4 emissions 0.08 metric tons NO2 emissions 0.01 metric tons 120 Table 22. Life cycle inventory of site 4 Site 4 Process/activity Value Cultivation Tilling/spading 5.6 gal diesel Herbicide application 2.31 gal -Simazine 0.91 lb -Goal 0.46 gal -Glyphosate 1.82 lb Sapling Planting 4.52 gal diesel Coppice Cut 0.77 gal diesel Herbicide application (glyphosate, manual) 1.82 lb Harvesting operation Harvesting Unit 3.89 gal diesel Chip Collector 3.89 gal diesel Chip Loader 0.57 gal diesel Transportation (Chip -van) 7.40 gal diesel One way distance 50 miles Total green weight 12.71 green tons Combustion (T.B. Simon Power Plant) Higher heating values 8014 Btus/lb (18.64 MJ/kg) Recoverable Heating Value 2903 B tus/lb (6.75 MJ/kg) Boiler capacity 21 MWh Plant efficiency 35% CO2 emissions 10.80 metric tons CH4 emissions 0.004 metric tons NO2 emissions 0.0005 metric tons 121 Table 23. Life cycle inventory of site 5 Site 5 Process/activity Value Cultivation Herbicide application (Glyphosate) 16.6 lbs Herbicide application (Simazine) 32.12 lbs Fertilizer (Ammonium Sulfate) 0 lb Harvesting operation Forwarder (Diesel) 1 90.53 gal Loader (Diesel) 8.38 gal Chipper (Diesel) 56.98 gal Transportation (Chip -van) 175.09 gal One way distance (miles) 49 miles Total green weight 146.21 green tons Combustion (T.B. Simon Power Plant) Higher heating values 8689 Btus /lb (20.21 MJ/kg) Recoverable Heating Value 3548 Btus/lb ( 8.25 MJ/kg ) Boiler capacity 21 MWh Plant efficiency 35% CO2 emissions 142.99 metric tons CH4 emissions 0.05 metric tons NO2 emissions 0.01 metric tons 122 6.4 Life Cycle Impact Assessment (LCIA) In this LCA, three life cycle impact categories were considered: 1) net GWP, which is the total GWP minus soil carbon sequestration; 2) macro nutrient (N, P, K) removal, which is presented as Abiotic Depletion Potential; 3) energy return on investment (EROI) , defined as the renewable energy returned on fossil energy invested. GHG emissions are converted into Global Warming Potential (GWP) using the Tool for Reduct ion and Assessment of Chemicals and Other Environmental Impacts (TRACI ) 2.1 database. Detailed approaches to account for soil carbon sequestration, nutrient removal, and EROI are described in subsequent sections. 6.4.1 Soil carbon sequestration estimation Soil carbon sequestration rates (kg C/acre/yr) are compiled from previous published studies that have similar soil properties or forest types with the studied sites (Table 24). To avoid using a single averaged value from previous studies, Crystal Ball software was used to perform Monte -Carlo simulation for the generation of the probability distributions at each st udy site. Uniform probability distributions were selected with range from R to S, where R was the lowest value and S was the highest value referenced from previous published studies ( Table 24). The 95 -percentile values for each distribution from Site 1 to 5 were 509.10, 667.35, 777.64, 155.63 and 582.65 kg C/acre/yr, respectively. These values were used to estimate the total soil carbon sequestration (kg C) during the c ultivation period. The soil C sequestration value for sites 1, 2, and 5 are similar in magnitude , while the value of site 3 is higher and the value at site 4 is much lower. The highest soil C sequestration value for site 3 was included as per Arevalo et al . (2011), which was a study conducted at Alberta, CA , to investigate 123 soil C change s in 2 -4 and 9 -11 years after land conversion from agricultural land to hybrid poplar plantation s. In t heir study, site preparation (tillage operations and mechanical activit ies) and negligible soil biomass due to saplings being small, c ontribut e to soil C loss during the first two years, then, after four years the soil C starts to increase, and in year seven, the soil C content reaches the pre -plantation level . This explains the lower soil C changes found in newly established hybrid willow plantation sites, and why the soil C value at site 4 (a 3 -year old hybrid willow plantation) is much lower compared to other study sites. 124 Table 24. Referenced studies in soil carbon sequestration rates estimation Site Referenced study Wood species/Forest type Region, Country Soil series Sampling depth Soil organic C sequestration rat (kg of C/acre/yr) 1 Vesterdal et al, 2006. Norway Spruce Tsjh, SE Sandy Forest floor + 0-25 cm 299.47 Norway Spruce Gejlvang, DK Poor sandy Forest floor + 0-25 cm 411.11 Oak and Norway Spruce Sellingen, NL Poor sandy Forest floor + 0-25 cm 598.94 Gough et al., 2008. Aspen -dominated mixed northern hardwood forest Michigan, US Sandy 0-20 cm 619.17 Schulp et al., 2008. Douglas Fir Veluwe, NL Loamy cover sands Forest floor + Humus layer + 0 -20 cm 598.94 Rytter. 2012. Poplar plantation Uppsala, SE Clay 0-30 cm 210.44 2 Rothstein et al., 2004. Jack pine Michigan, US Sandy 0-100 cm 647.50 Vesterdal et al, 2006. Norway Spruce Tsjh, SE Sandy Forest floor + 0-25 cm 299.47 Oak and Norway Spruce Sellingen, NL Poor sandy Forest floor + 0-25 cm 598.94 Schulp et al., 2008. Scots pine Veluwe, NL Loamy cover sands Forest floor + Humus layer + 0 -20 cm 649.93 Arevalo et al., 2011. Poplar plantation Alberta, CA Silty clay loam 0-30 cm 809.37 Wang et al., 2012. Jack pine Saskatchewan, CA Sandy 0-40 cm 726.05 Rytter. 2012. Poplar plantation Uppsala, SE Clay 0-30 cm 210.44 3 Arevalo et al., 2011. Poplar plantation Alberta, CA Silty clay loam 0-30 cm 809.37 Rytter. 2012. Poplar plantation Uppsala, SE Clay 0-30 cm 210.44 4 Lockwell et al., 2012. Willow Plantation Southern Quebec, CA Clay - loam 0-40 cm 0.00 Rytter. 2012. Willow Plantation Nykıping, SE Clay - clay loam 0-30 cm 165.92 5 Rothstein et al., 2004. Jack pine Michigan, US Sandy 0-100 cm 647.50 Wang et al., 2012. Jack pine Saskatchewan, CA Sandy 0-40 cm 726.05 125 6.4.1.1 Nutrient removal Nutrient removal in each site was estimated by multiplying nutrient (N, P and K) concentrations in removed biomass with the removed biomass weight. The detail methodology of estimating biomass weight and nutrient concentration are presented in the following section s: 6.4.1.2 Biomass weight estimation Sites 1 -4 are harvested by WT harvesting system s, the total removed biomass weight was estimated based on the recorded tree diameter at breast height ( DBH ) and the equation provide d by Jenkins et al. (2003): !"!!"# !!!!!!!!"! [1] where bm = total aboveground biomass (kg dry weight) for trees 2.5 cm dbh and larger dbh = diameter at breast height (cm) Exp = exponential function ln = log base e (2.718282) Parameters !!!!"#!!!were cited from Jenkins et al. (2003) and depend on tree species. Site 5 was harvested by a CTL system and the pulpwood was shipped to a paper mill. T he branch biomass was milled on site and sent to a n end -user for co-firing combustion . The total weight of harvested branch biomass is the total recorded biomass weight sen t to the feedstock end -user. 126 6.4.1.3 Macronutrient (N, P, K) concentrations Monte -Carlo simulations were performed to provide the 95 -percentile value of the nutrient concentration probability di stribution. The maximum and minimum nutrient concentration ( !"!!! values w ere cited from two database s: one compiled by Pardo et al. (2004), which includes foliar nutrient data from 218 articles and publications in the Northeastern United States , and another compiled by Par” et al. (2013) in Canada that has 12,800 nutrient concentration values for different components of the 30 most common Canadian tree species. 127 Table 25. Nitrogen concentration allocation in stem, foliage, br anch, and bark Common name Scientific name Component Stem (%) SE Foliage (%) SE Branch (%) SE Bark (%) SE Balsam fir Abies balsamea 0.089 0.041 1.351 0.186 0.370 0.138 0.449 0.176 Paper birch Betula papyrifera 0.096 0.028 2.063 0.346 0.394 0.107 0.364 0.124 White spruce Picea glauca 0.058 0.011 1.175 0.169 0.363 0.127 0.303 0.066 Black spruce Picea mariana 0.076 0.041 0.951 0.186 0.284 0.072 0.240 0.036 Jack pine Pinus banksiana 0.067 0.019 1.176 0.180 0.296 0.074 0.246 0.052 Lodgepole pine Pinus contorta 0.045 0.021 1.070 0.168 - - 0.273 0.033 Quaking aspen Populus tremuloides 0.130 0.058 2.179 0.558 0.498 0.144 0.450 0.263 Douglas fir Pseudotsuga menziesii 0.133 0.020 2.197 0.387 0.370 0.066 0.396 0.060 Softwoods 0.075 0.003 1.289 0.012 0.318 0.011 0.314 0.009 Hardwoods 0.129 0.007 2.124 0.093 0.379 0.013 0.436 0.014 128 Table 26. Phosphorus concentration allocation in stem, foliage, branch, and bark Common name Scientific name Component Stem (%) SE Foliage (%) SE Branch (%) SE Bark(%) SE Balsam fir Abies balsamea 0.008 0.005 0.155 0.035 0.085 0.049 0.052 0.014 Paper birch Betula papyrifera 0.013 0.009 0.211 0.073 0.053 0.021 0.034 0.010 White spruce Picea glauca 0.005 0.002 0.169 0.038 0.050 0.024 0.042 0.014 Black spruce Picea mariana 0.007 0.007 0.130 0.033 0.040 0.013 0.047 0.041 Jack pine Pinus banksiana 0.005 0.002 0.119 0.023 0.029 0.015 0.026 0.008 Lodgepole pine Pinus contorta 0.008 0 0.127 0.019 0.000 0.000 0.049 0.005 Quaking aspen Populus tremuloides 0.014 0.009 0.213 0.07 0.074 0.031 0.054 0.025 Douglas fir Pseudotsuga menziesii 0.007 0.004 0.171 0.034 0.063 0.013 0.023 0.006 Softwoods 0.007 0.001 0.156 0.002 0.055 0.004 0.046 0.002 Hardwoods 0.011 0.001 0.175 0.005 0.069 0.013 0.042 0.003 129 Table 27. Potassium concentration allocation in stem, foliage, branch, and bark 6.4.2 Abiotic Depletion Potential (ADP) This impact category quantifies the extraction of minerals and fossil fuels needed by each system. In this study, ADP represents biomass nutrient removal that occurs during forest harvesting. The ADP of each study site is calculated following the method Common name Scientific name Component Stem (%) SE Foliage (%) SE Branch (%) SE Bark (%) SE Balsam fir Abies balsamea 0.090 0.036 0.558 0.119 0.241 0.089 0.250 0.109 Paper birch Betula papyrifera 0.053 0.017 0.889 0.219 0.159 0.052 0.120 0.035 White spruce Picea glauca 0.044 0.011 0.579 0.156 0.260 0.184 0.164 0.075 Black spruce Picea mariana 0.043 0.022 0.552 0.117 0.134 0.031 0.154 0.013 Jack pine Pinus banksiana 0.045 0.019 0.409 0.106 0.155 0.052 0.115 0.024 Lodgepole pine Pinus contorta 0.041 0 0.551 0.135 0.000 0.000 0.171 0.052 Quaking aspen Populus tremuloides 0.112 0.045 0.962 0.423 0.277 0.111 0.263 0.062 Douglas fir Pseudotsuga menziesii 0.100 0.029 0.874 0.244 0.252 0.037 0.119 0.033 Softwoods 0.060 0.003 0.653 0.017 0.177 0.012 0.173 0.013 Hardwoods 0.077 0.003 0.854 0.030 0.348 0.061 0.420 0.178 130 provided by Guin ”e (2001). Based on the ADP extraction rate suggested by Guin ”e (2001), every 1 kg of N, P and K removal results in 18.70 g, 0.08 g and 0.03 g of antimony equivalent removal. 6.4.3 Energy Return On Investment (EROI) To evaluate the energy use eff iciency of the biomass production system in each studied cite, the total renewable energy gained and the fossil fuel energy input were calculated to determine the ratio of the amount of usable energy delivered from the amount of energy supplied. The total renewable energy gained was determined by the total recoverable heating values for woody biomass, which were calculated with the formula described by Ince (1979): RHV= HHV ! (1-MCwb) - HL [2] Where: RHV= recoverable heating value, BTUs/pound HHV= higher heating value, BTUs/pound MCwb= wet -basis moisture content, percent HL= heat loss, BT Us/pound At each study site, random wood samples were collected from the harvesting operations. The moisture contents of the wood chips were measured following ASTM E 871-82 (ASTM International 2003a). The biomass HHV was tested using an oxygen bomb calorimeter at MSU based on the standard described by ASTM E 711 -87 (ASTM International 2003b). 131 The fossil fuel consumption of ea ch machine was calculated by multiplying their total operation time (h ours) to the diesel consumption rates (gals/h our), which are based on their horsepower provided by the manufacturing company and consumption rates provided by Plummer and Stokes (1983). Fuel consumption was converted to an equivalent heating value so that comparisons could be made with energy output values in the form of British Thermal Units (BTU). As described by Adams (1983), it was assumed that one gallon of diesel fuel was approximat ely equal to 137,000 BTUs. 6.4.4 Sensitivity analysis Sensitivity analys es were performed to study the impacts of moisture content, energy demand of the power plant, and transportation distance s on the GWP generated by producing 1 kWh of electricity . These thr ee factors are increased or decreased by 10% to reveal their impacts on the GWP. 6.5 Life Cycle Assessment Results and Discussion 6.5.1 Global Warming Potential (GWP) Fig. 15a-e present the GWP for Site 1 -5 itemized for each system component, including biomass cultivation, harvesting, pre -processing, transportation, combustion, and soil C sequestration. Figure 16 presents GWP contributions from all life cycle stages, excluding combustion and soil carbon sequestration. At Site 1, GHG emissions from biomass harvesting (feller -buncher) and collecting (sk idder) are 1,320 kg of CO 2-eq (or 9.77 kg CO 2-eq per dry ton ). Sonne (2006) reported that the GHG emissions from the harvesting and collecting were 5.9 Mg CO 2-eq per 700 m 3 timber harvested in Douglas -fir plantations located along the coastal regions of t he Pacific Northwest, which is equivalent to 15.90 kg CO 2-eq per dry ton assuming a dry wood density of 530 kg per 132 m3 (Engineering Toolbox, 2004). The lower emissions found in this study can be attributed to MichiganÕs flatter terrain when compared to the coastal Pacific Northwest, which increases the productivity and decreases diesel fuel consumption. The total GHG emissions generated in Site 2 were 2.48E+05 kg of CO 2-eq (or 1.94 kg of CO 2-eq/kWh). The largest contributor was biomass combustion, and the second largest contributor was biomass pre -processing (loading and chipping). Many studies have concluded that transportation was the largest contributor to the total GWP in the woody biomass supply chain, starting from cultivation (Berg and Lindholm, 2005; Michelson et al., 2008; Neupane et al., 2011). However for this studied site, while the transportation distance was only five miles, biomass pre -processing was the largest GWP contributor compared to the other system components. This confirms that the impact of transportation highly depends on the hauling distance, which is also concluded by Zhang et al. (2015). Figure 15 a-e. GHG emissions contribution of Site 1 to Site 5 a. Site 1 b. Site 2 133 Figure 15a -e (contÕd) c. Site 3 d. Site 4 e. Site 5 134 Figure 16. GHG emissions of cultivation, harvesting, collecting, pre -processing (loading + grinding/chipping), and transportation in Site 1 to 5 Fiala and Bacenetti (2011) reported GHG emissions from harvesting 5 -6 years old poplar plantations ranging from 15.7 to 18.2 kg CO 2-eq/ODT. These values are comparable with the results we obtained from Site 3, which was 16.57 kg CO 2-eq/ODT. The total GHG emissions from cultivation, harvestin g and transportation stages in the Site 3 case were found to be 41.17 kg CO 2-eq/ODT. This was lower than the number reported by Vasquez "#$%&'#( (2015) of 93.1 kg CO 2-eq/ODT. This large difference is mainly due to the higher emissions (34 kg CO 2-eq/ODT ) in their cultivation stage, and 13.9 kg CO 2-eq/ODT emissions from a land restoration activity, which was not considered in our study. Caputo et al. (2014) conducted a LCA to quantify GWP in different short rotation willow biomass production scenarios. In one of the scenarios, the total GWP was about 61.22 kg CO 2-eq per dry ton of woody biomass produced, including cultivation, harvesting (with a Case New Holland forage harvester), and transportation 135 (transportation distance = 44 miles (or 71 km)). The GHG emis sions in Site 4 (54.12 kg CO2-eq/ODT) were slightly lower but comparable with the number reported by Caputo et al. (2014). The difference may be caused by the higher GHG emissions in the cultivation stage in Caputo et al. (2014). Krzy ' aniak et al. (2016) h ave reported average GHG emissions of 44.54 kg CO 2-eq/ODT and 61.67 CO 2-eq/ODT in hybrid willow plantations when transportation distances were 31 miles (or 50 km) and 62 miles (or 100 km). They also highlighted that N, P, K fertiliz er application contribut ed 74% to the total global warming potential, therefore , the cultivation stage was the largest GHG emissions contributor and should be carefully managed. Overall, the GHG emissions in each study site were in a comparable range with previous studies. In al l study sites, biomass combustion was consistently the largest contributor to the GHG emission ( Fig. 1 5a-e). This is consistent with the emission factors reported by EPA (2014). As discussed above, although these emissions could be sequestrated by future b iomass growth, the global warming effect caused by this remaining carbon flux should not just be neglected (Cherubini et al., 2011; Helin et al., 2013). As such, it is very important to optimize biomass combustion efficiency and to minimize the GHG emissions in this stage. The second largest GWP contributor was transportation, which contributed 52%, 55%, 43%, and 53% to the total GWP in Site 1, 3, 4, and 5. In Site 2, transportation only contributed 38% because the transportation distance was less than 5 miles. This finding suggested that transportation played an important role in total GWP in addition to combustion, a result confirmed by other cradle -to-gate LCA studies of woody biomass supply chain (Michelsen et al., 2008; Neupane et al., 2011; Handler et al., 2014; Chen et al., 2017) . 136 6.5.2 Soil C sequestration and its uncertainty Forests sequester CO 2 from the atmosphere by photosynthesis; the sequestered C is either converted into biomass or fixed into soil organic matter pool through litter decomposition ( Gru¬ neberg et al., 2014). As the largest carbon pools (about 80%) in EarthÕs terrestrial ecosystems, soil organic carbon plays an important role in offsetting the carbon emissions in bioenergy production systems (Lal, 2008; Helin et al., 2013; Qin et al., 2016). Qin et al. (2016) suggested that including soil organic carbon (SOC) sequestration in LCA could significantly influence the total GHG footprints of bioenergy production systems, which is corroborated by o ur findings. In Site 1, 2 and 5, soil C sequ estration fully offsets all GHG emissions generated in biomass production and combustion stages ( Fig 15 a, b and e ). However, soil C changes can be highly impacted by vegetation type, stand age, and the time period considered. For instance, soil C loss is observed in sites that are newly converted from forestland or agricultural land to SRWC plantation (Grigal and Berguson, 1998; Arevalo et al., 2011). This is because during site preparation, tillage disrupts soil structure and causes soil organic matter ox idation. In addition, in the first two years, the litter contribution from young trees is lower than agricultural land, which leads to decreased soil microbial activity and soil organic matter input (Arevalo et al., 2011). The total soil C increases after two years of plantation establishment, soil C formation rates were faster after year 4, and the total soil C slowly reached the pre -plantation level at year 7 (Arevalo et al., 2011). Similarly, Qin et al. (2016) reported that soil C level decreased during 0-5 years in cropland to willow, forestland to willow, and grassland to willow sites. Owing to this fact, the soil C sequestration in Site 3 was much lower, and the net GWP at these sites was higher than 137 Sites 1, 2, and 5. Although soil C increase rate at Site 4 is higher that other study sites, due to the shorter time period of Site 4 (seven years), the total soil C increase is still lower than Site 1, 2, and 5. In conclusion, a fter subtra cting the CO 2-eq sequestration from the total GHG emissions , the ne t GWP caused by woody biomass production systems could be very low, even negative. However, with such high uncertainties found in soil C sequestration values, future work is required to develop a consistent approach to include soil C in LCA. In addition, m ore effort is needed to develop a soil C model that includes different soil C sequestration rates under different vegetation types, time horizons, and soil depths. 6.5.3 Sensitivity analysis: impacts of transportation distance, biomass moisture content and biom ass HHV on the GHG emissions . To reveal how changes in transportation distance, biomass moisture content, and biomass HHV affect the GHG emissions in each site, sensitivity analyses were conducted ( Fig. 17a-e). The biomass moisture content and biomass HHV both have a non-linear effect on the GWP, while the transportation distance has a linear effect. Compared to moisture content and transportation distance, changes in biomass HHV have the largest impact on GHG emissions . The major cause of this observation is the non-linear relationship between biomass HHV and biomass recoverable heating value. Based on Equation [2], a 50% increase in biomass HHV can increase the total recoverable heat by over 50%, reducing the biomass feed rate and combustion emissions by m ore than 50%. Due to the nonlinearity in HHVÕs sensitivity, reducing HHV is more problematic than raising HHV is beneficial. This suggests that combusting wood pellets with low HHV raises more concerns in causing high GHG emissions. 138 1.00 2.20 3.40 4.60 5.80 7.00 -50% -40% -30% -20% -10% 0 10% 20% 30% 40% 50% Total GWP (kg CO 2-eq/kWh) Change in percent Transportation Distance (miles) Moisture Content (% WB) HHV (BTUs/lb) b. Site 2 Figure 17 a-e. Sensitivity analysis of Site 1 to 5: impacts of transportation distance, biomass moisture content and biomass HHV on the GWP 1.00 1.40 1.80 2.20 2.60 3.00 -50% -40% -30% -20% -10% 0 10% 20% 30% 40% 50% Total GWP (kg CO2-eq/kWh) Change in percent a. Site 1 Transportation distance (miles) Moisture Content (% WB) HHV (BTUs/lb) 139 Figure 17 a -e (contÕd) 1.00 1.60 2.20 2.80 3.40 4.00 -50% -40% -30% -20% -10% 0 10% 20% 30% 40% 50% Total GWP (kg CO2-eq/kWh) Change in percent d. Site 4 Transportation Distance (miles) Moisture content (% WB) HHV (BTUs/lb) 1.00 1.60 2.20 2.80 3.40 4.00 -50% -40% -30% -20% -10% 0 10% 20% 30% 40% 50% Total GWP (kg CO2-eq/kWh) Change in percent c. Site 3 Transportation Distance (miles) Moisture Content (% WB) HHV (BTUs/lb) 140 Figure 17 a -e (contÕd) Variation of biomass moisture can non-linearly affect emissions, as impacts are larger when biomass is wetter. Assuming that green woody biomass has a HHV of 9,173 Btus/lb and a moisture content of 35%, the recoverable heating value is calculated to be 4,250 Btus/lb; if the moisture conten t rises from 35% to 70%, the recoverable heating value decreases to 1,290 Btus/lb (about 70% decrease). To satisfy the same energy demand, more than twice the amount green biomass will be required. Because wetter biomass has a lower recoverable heating val ue, more biomass is required for energy generation, resulting in increased emissions. Woody biomass transportation has been largely studied, with key focuses on location selection and transportation distance minimization ( Zhang et al., 2011 ; Alam et al., 2 012). However in this study, transportation distance has small and linear impact on GHG emissions. In all study sites, each 10% decrease or increase in transportation 1.00 1.50 2.00 2.50 3.00 3.50 -50% -40% -30% -20% -10% 0 10% 20% 30% 40% 50% Total GWP (kg CO2-eq/kWh) Change in percent e. Site 5 Transportation Distance (miles) Moisture content (% WB) HHV (BTUs/lb) 141 distance only causes 0.04% to 0.11% decrease or increase in the total GWP produced. There fore, transport distances under 75 miles do not practically contribute to GWP. 6.5.4 Energy Return on Investment (EROI) To evaluate the energy use efficiency of the biomass production system, the fossil fuel energy input and the total renewable energy gained we re calculated and presented in kWh ( Fig. 18a-e). Figure 18 a-e. Energy input and out of Site 1 to 5 142 Figure 18 a -e (contÕd) 143 Figure 18 a -e (contÕd) In Site 1, 2 and 5, the fossil fuel energy inputs in the cultivation stage were 0 because these sites were hand planted and managed manually. The overall EROI in Site 1 to 5 were determined to be 6.63, 7.60, 6.16, 5.97 and 6.57, respectively. The highest EROI was found in Site 2 because the much shorter tran sportation distance (4.5 miles) and lower fossil fuel consumptions. The lowest EROI of 5.67 observed in Site 4 was mainly due to the relatively larger fossil fuel inputs in the cultivation stage. Statistically, it is not fair to compare the EROI of the fiv e study sites because there is no replication for each production system. In addition, fossil fuel input in each study site heavily depends on the specific management practices (site preparation, fertilization application and herbicide application), harves ting operation productivities, and transportation distances. However, we can still reach some general conclusions. First, in Site 1, 2, 3 and 5, transportation is consistently the largest contributor to the total fossil fuel 144 consumption (54%, 38%, 54% and 53%). The second largest contributor is the biomass loading plus grinding/chipping stage, in Site 1, 2, 3 and 5, it consumed 24%, 41%, 18% and 20% of the total fuel consumption, respectively. These results suggest that in order to improve the EROI of a woo dy biomass supply chain that is harvested by traditional harvesting system, transportation and biomass handling stages should be the key focuses. Second, in SRWC plantations (Site 3 and Site 4) management practices in the cultivation stage should be carefu lly planned. In Site 3, fossil fuel input during cultivation only accounts for 7% of the total value because the insecticide and herbicide were manually applied. In Site 4, all management practices were done using machines, and cultivation accounts for 27% of the total fossil fuel consumption . The high capital inputs (machinery use and fertilization) caused by intensive management practices in energy crops production systems have also been reported by Liska et al. (2009), Grassini et al. (2012) and Djomo et al. (2015). EROI values of bioethanol production systems were reported to be around 3 to 5 based on 31 previous LCAs (Hall et al., 2013). Zaimes et al. (2015) reported EROIs ranged from 1.52 to 2.56 in fast pyrolysis systems using perennial grasses. They concluded that the major reasons for the low EROI were high fossil hydrogen consumption and a high process electricity requirement. Later in 2017, after about 40% reduction in hydrogen use, Zaimes et al. reported EROIs from 1.32 to 3.76 using SRWC in sever al multistage torrefaction and pyrolysis systems. Compared to liquid biofuels , wood torrefaction , and pyrolysis , producing wood pellets for electricity generation from these five sites appears to achieve a higher EROI. 145 6.5.5 Nutrient Removal and Abiotic Depletion Potential (ADP) The 95th percentiles in the N, P, and K simulated distributions were used to quantify the nutrient removal in each study site ( Table 28). N utrient removal caused by forest harvesting has been largely studied due to concerns of nutrient depletion in forest soils (Stark, 1979; Par” and Thiffault, 2016). Among the five studied sites, the least nutrient removal occurred in Site 5 because only the logging residues were collected for energy production. Saunders et al. (2011) have conducted a study to analyze nutrient concentration of logging residues in mixed -wood forest s in Maine, U .S. They reported that lower nutrient (N, P, K) concentration s were found in softwood debris compared to hardwood , which is also confirmed by the analysis of tree nutrient content published by Par” et al. (2013). This result suggests that using wood chips generated from softwood species could reduce nutrient removal compa re to hardwood species. Table 28. Nutrient removal in each studied site Site 1 Site 2 Site 3 Site 4 Site 5 Biomass harvested (ODT) 134.94 177.31 140.48 7.18 87.73 N concentration (%) 3.26 2.94 3.84 3.15 1.65 P concentration (%) 0.29 0.35 0.43 0.31 0.17 K concentration (%) 1.52 1.56 2.02 1.87 0.68 N removed (kg) 3,990.77 4,729.06 4,893.81 205.24 1,313.13 P removed (kg) 355.01 562.98 548.00 20.20 135.29 K removed (kg) 1,860.73 2,509.30 2,574.35 121.84 541.17 The total ADPs in Site 1 to 5 were calculated as 4.93E -04 kg antimony/kWh (or 0.55 kg antimony/ODT), 6.89E -04 kg antimony/kWh (or 0.50 kg antimony/ODT), 6.77E-04 kg antimony/kWh (or 0.65 kg antimony/ODT), 5.92E -04 kg antimony/kWh 146 (or 0.54 kg antimony/ODT) and 2.69E -04 kg antimony/kWh (or 0.28 kg antimony/ODT), respectively. There are only a few studies that estimated ADP in bioenergy production systems. Krzy ' aniak et al. (2015) reported a range of ADP from 0.31 to 0.74 kg antimony/ODT in hybrid willow production systems with different biomass production yield. Luo et al. (2009) and Bai et al. (2010) have reported ADP of producing bioethanol using sugarcane and switc hgrass. However, the functional unit in these two studies is defined as power to wheels for 1 km driving of a midsize car, which is very difficult to compare with our functional unit. 6.6 Conclusion This LCA estimated the environmental impacts generated in five woody biomass production systems in Michigan, U.S., including cultivation, biomass harvesting, collecting, pre -processing, transportation , and combustion stages. The estimated GWP in all study sites were in a comparable range with previous published L CAs. It is important to note that, among all studied life cycle stages, biomass combustion consistently contributes about 95% to 97% of the final GWP. Biomass HHV and biomass moisture content are the two most important factors in biomass combustion efficie ncy; they highly affect total energy production and resulting GWP emissions. In our cradle -to-plant gate scope, transportation contributed from 35% to 55% of total GWP and was determined to be the largest source of GWP . This finding confirmed with other pr evious studies and suggested that biomass transportation was a key link to minimize environmental burdens in biomass value chains. Soil C sequestration played a significant role in reducing the net GWP of woody biomass production system, and when included, results in all five sites becoming carbon negative . However, due to the high uncertainties 147 in accounting soil C stocks, future research is required to quantify soil C changes under different scenarios and to develop a consistent and comprehensive approach to include soil C in LCA. In terms of EROI, using woody biomass for electricity production may be a better option compared to other bio -products such as liquid biofuels and wood pellets. The EROI of producing wood chips greatly depends on the transportati on efficiency and biomass pre -processing (loading and grinding/chipping) productivities. Also, in SRWC plantation, intensive machinery management should be reduced to improve the EROI. The nutrient removal and ADP were lower when using softwood pellets pro duced from logging residues from the CTL harvesting system vs. using hardwood pellets produced from WT harvesting system. To reduce the nutrient depletion in forest ecosystems, careful selection should be made in choosing tree species. In conclusion, cons idering the large amount of GHG emissions and the long time required to sequester these emissions, biomass combustion should be included in future LCA s of woody biomass supply chain s. In order to reduce the GHG emissions from the combustion stage, future r esearch efforts should be aimed to provide biomass with lower moisture content and higher HHV. Utilizing woody biomass for bioenergy production can be carbon neutral while including soil C increase during biomass growth, however future work is needed to re duce the uncertainties of including soil C in LCAs. 148 CHAPTER 7 MULTI -CRITERIA OPTIMIZATIO N FOR WOODY BIOMASS SUPPLY CHAIN IN MICH IGAN 7.1 Introduction Historically, woody biomass has been recognized as a Òcarbon neutralÓ energy resource, and has been recommended fo r large -scale production to mitigate CO2 emissions and to replace established fossil fuels. Unlike the steady state operation of fossil fuel, the supply chain of woody biomass can be highly complex because of its reliance on several activities such as biomass availability, the demand of consumers, the logistical cost, and various regulations and policies (Soylu, 2006) . In the past decades, many studies has been published to predict and to reduce the logistics cost in the woody biomass. Woody biomass tra nsportation cost accounts for the largest part of the total cost and energy consumption (Eriksson and Bjo¬rheden, 1989; Allen et al., 1998; Alam et al., 2012) . Thus, optimization models have been developed to identify the optimal location of bio -refinery o r power plant in Italy (Freppaz et al., 2004), Austria (Schmidt et al., 2009), Greece (Rentizelas and Tatsiopoulos, 2010), Sweden (Leduc at al., 2010 aand b), Finland (Natarajan et al., 2014), Colombia (Duarte et al., 2014), and U.S. (Kim et al., 2011 a and b). Lautala et al. (2012) have published a cost minimization model to minimize the cost for woody biomass transportation using railroads in Michigan and Wisconsin. However, a woody biomass production system with minimized cost might have high carbon footpri nts. Therefore, it is critical to include both the economic and environmental criteria when optimizing the biomass production system and understand the trade -off between the two objectives. Multi -criteria optimization has been commonly used to support deci sion making 149 with multiple competing criteria in renewable energy systems. Ayoub et al. (2009) proposed a multi -level optimization model to determine the bioenergy generation plan using different energy sources, with optimized energy efficiency, total cost and CO 2 emissions for Japan. Zamboni et al., (2009) have developed a multi -objective environmental optimization model to apply in a corn -based bioethanol system in Italy. You and Wang (2011) presented a county -level optimization model to determine the opti mal biomass (agricultural, energy crops, and wood residues) supply chain design under economic and environmental criteria in Iowa. Multi -objective optimization framework has also been used to determine the conversion technologies, facility size, location, and raw material in a global scale (Giarola et al. 2011 and 2013). SantibaŒ ez et al. (2011, 2015 and 2016) developed three different multi -objective optimization models to address different needs in the bio -refinery supply chain in Mexico using multi -fee dstock . There are only a few studies focus on multi -criteria optimization in woody biomass supply chain. Steubing et al. (2012) developed a strategy to determine the optimal size and location of a bioenergy plant that converts wood to synthetic natural gas in Switzerland. The minimal cost can be achieved when the plant size is around 100 -200 MW, however if the environmental performance is considered, the optimal plant size is determined to be 90 MW. Kanzian et al. (2013) have formulated a model to maximize the profit and minimize the CO 2 emissions in a wood -based supply chain in Austria, with decision variables of pre -processing location, transportation mode, volume and terminal. The results suggested that with 4.5% increase in CO 2 emissions, the profit can be increased to more than twice. A strategic multi -objective optimization framework 150 was developed to design the optimal supply chain networks with maximum Net Present Value (NPV) and minimal GHG emissions, for woody biomass supply chain in 20 years horizo n (Cambero et al., 2016). This model was applied in a case study in British Columbia, Canada. The results indicated that converting woody biomass to pellet and bio -oil and export to Europe could achieve the highest NPV and lowest GHG emissions. However, optimization model that considers both economic and environmental impacts of woody biomass supply chain in U.S. lacks (Shabani et al., 2013b), which indicates a great need for future study. In order to sustainably utilize woody biomass as a bioene rgy source in Michigan and U.S., there is a great need to combine environmental and economic demands and develop a multi -criteria optimization framework. This study aims to 1) p rovide an optimized logistic strategy, which can minimize the total logistic cost and GHG emissions using multi -criteria optimization method , 2) and to examine the impacts of transportation distance and carbon tax policy on the biomass production strategy. 7.2 Problem description Figure 19 presents the four decision nodes considered in this optimization model framework: biomass sources (B), harvesting system (H), pre -processing (P), and biomass conversion and utilization (C). In this model, the consi dered biomass sources include natural forest stand (FS), short rotation hybrid poplar ( Populus spp .) (SRP) and short rotation hybrid willow (SRW). The natural forest stand is defined as a contiguous community of mixed hardwood and softwood species trees with uniform age and average DBH . The short rotation hybrid poplar is defined as a hybrid poplar plantation 151 with the average tree DBH equal to 5 -6 inches; while the short rotation hybrid willow is defined as a hybrid willow plantation with the average tree D BH equal to 2 -3 inches. Figure 19. Decision nodes in the multi -criteria optimization model Harvesting systems included in this model are Whole -Tree (WT) harvesting system, Cut -To-Length (CTL) harvesting system and reconfigured (RCF) harvesting system. It is assumed that biomass from natural forest stand can be harvested by both WT and CTL harvesting system. The selection of harvesting system for different biomass sources are based on below assumptions: 1) FS can be harvested by either WT or CTL harvesting system; 2) SRP can only be harvested by WT harvesting system; 3) SRW can only be harvested by RCF harvesting system. Also assumed is that all of the woody biomass will be harvested under the scenario of WT or RCF harvesting syst em, but with CTL harvesting system, only logging residues will be collected. 152 In WT and CTL harvesting system, the harvested biomass is directly pre -processed by a loader and a grinder. In RCF harvesting, biomass is harvested and chipped by the reconfigure d harvesting machine, so there is no additional pre -processing associated. There are two options in the biomass conversion and utilization stage: 1) the woody biomass can be directly delivered from the harvesting cite to the biomass power plant, and be co -fired with coal, or 2) the woody biomass can first be transported to a torrefaction plant, torrefied, and then delivered to the biomass power plant. Based on the above assumptions, a total of 8 routes were defined ( Figure 20). The multi -criteria optimization model determines the weight (short green ton) of woody biomass assigned to each route, to meet the yearly total energy demand of a wood -based power plant, with minimal total logistic cost and GHG emissions. Figure 20. Defined possible routes in the multi -criteria optimization model 153 7.3 Mathematical model 7.3.1 Indices a: decision variable ( a = 1,2,É,8) i: biomass sources ( i: 1 = FS; 2 = SRP; 3 = SRW) j: Harvesting systems ( j: 1 = Cut -To-Length harvesting system; 2 = Whole -Tree harvesting system; 3 = reconfigured harvesting system) k: Pre -processing ( k: 1 = grinding; 2 = no pre -processing) l: Biomass conversion and utilization ( l: 1 = direct combustion; 2 = combustion after torrefaction) m: transportation routes ( m: 1 = from the harvesting site to biomass power plant; 2 = from the harvesting site to torrefaction depot; 3 = from the torrefaction depot to biomass power plant) 7.3.2 Parameters ECi: site establishment cost (pesticide and herbicide cost) of different biomass sources ($/gt), GCijk : biomass generation cost, the total cost biomass harvesting, collection and preprocessing ($/gt) of different biomass sources, harvested by different harvesting systems, and pre -processed by different way, TCm: biomass transportation cost ($/ shor t ton/ mile) of different transportation routes, calculated using (1) !"!"!!"#$% !!!"#$%!!"#$ !!!!! 154 !"#$%&'#$ !!"#$%& !!"#$ !!!!!!!"#$!!!"#$%&'!!"#$ !!"#$%&&!!"#$!!!!"# !!"#$% !!"#$!!!!!"#!!"#! (1) The truck hourly cost of 70.07 $/hr is cited from previous study, the predicted travel time is calculated using previous developed equation based on MichiganÕs road conditions: !"#$% !!"#$%&'#$ !!"#$ !!!!"!!!!!!!"#!!"!!!"#$% !!!!!!!"!!!"!!!"#$% !!!!!!!"#!!"!!!"#$% !!!!!!!"#!!"# !!!"#$% !!!!!!!"#, the truck loading time of 0.67 hr is cite from previous study, when m = 1 and 2, the biomass weight per truck load is 33 short tons (for 30 -50% moisture content biomass), when m = 3, the biomass weight per truck load is calculated as 90 short tons (truck volume = 145 yard 3; wood pellets density = 725 kg/m 3 (Adams et a l., 2015)) TRC: biomass torrefaction cost ($/ gt), EEi: GHG emissions from site establishment of different biomass sources (kg CO 2 Ðeq/gt), GEijk : the total GHG emissions from biomass harvesting, collection and pre -processing stages (kg CO 2 Ðeq/gt) of different biomass sources, harvested by different harvesting systems, and pre -processed by different way TEl: GHG emissions from biomass transportation (kg CO 2 Ðeq/ short ton/mile) of green harvested biomass or wood pellets, calculated using (2 ) !"!!"#$%&'#$ !!"#$%& !!"#$ !!!!!!!"#$% !!"#$%&'!!"#$ ! 155 !"#$% !!"#$!!"#$%&'()"# !!"#$ !!!!"! !!"#$$#%&$!!"# !!"#!!"!!"##$%!!"#$%!!!"!!"!!!!"!"# !!!"#$%&&!!"#$!!!!"# !!"#$% !!"#$!!!!!"#!!"#! (2) The predicted travel time is calculated using previous developed equation based on MichiganÕs road conditions: !"#$% !!"#$%&'#$ !!"#$ !!!"!!!!!"#!!"#$ !!"#$ !!"#$% !!!!!"!!!!"#$% !!"#$ !!"#$% !!!!!"#!!"#$%!!"#$ !!!"#$% !!!!!!!"#!!"# !!"#!!!"#$% !!!!!!!"#, the truck loading time of 0.67 hr is cite from previous study, the truck fuel consumption of 12 gals/hr is cited from the Carter (2017), GHG emissions from consuming one gallon of fossil fuel is 10.08 kg CO 2 Ðeq, the biomass weight per truck load is 33 short tons for 30 -50% moisture content biomass, and for wood pellets, the biomass weight per truck load is calculated as 90 short tons (truck volume = 145 yard 3; wood pellets density = 725 kg/m 3 (Adams et al., 2015)) TRE: GHG emissions from biomass torrefaction (kg CO 2 Ðeq/gt), CBE a: GHG emissions from the biomass combustion (kg CO 2 Ðeq/gt) using biomass from different sources, harvested and pre -processed by different methods, utilized and converted by different ways, calculated by the Electronic Code of Federal Regulations 98.33 (e)(1), BYi: biomass yield (odt/acre) of different biomass sources is cited from previous studies, MCl: wet-basis moisture content (%) of biomass utilized and converted by different methods, 156 RHVa: recoverable heating value (kWh/gt) of biomass from different sources, harvested and pre -processed by different methods, utilized and converted by different ways, calculated by the method provided by Ince (1979), Dm: transportation distance between different transportation routes, DB: annual energy demand of the biomass power plant (kWh), C: daily processing capacity of the torrefaction depot (ODT/day), N: number o f torrefaction depots, CSi: carbon sequestration of different biomass sources (MG C/acre), The values of all the parameters used in this model are presented in Table 29. Table 29. Parameters used in the multi -criteria optimization model Parameters Value Unit Remarks Cost parameters Establishment cost EC1 0.79 $/gt Chapter 3 EC2 0.28 $/gt Chapter 3 EC3 0.8 $/gt Chapter 3 Generation cost GC11 17.26 $/gt Chapter 3 GC12 8.4 $/gt Pan and Srivastava, 2013 GC22 11.27 $/gt Carter et al., 2016 GC33 40.5 $/gt Carter et al., 2016 Transportation cost TC1 0.19 $/short ton/mile Calculated TC2 0.19 $/short ton/mile Calculated TC3 0.07 $/short ton/mile Calculated Torrefaction cost TRC 69.97 $/gt Cited GHG emissions parameters Establishment emissions EE1 0 kg CO2 Ðeq/gt Chapter 3 - TRC site EE2 0.93 kg CO2 Ðeq/gt Chapter 3 - FBIC site EE3 10.47 kg CO2 Ðeq/gt Chapter 3 - KBS site 157 Table 29 (contÕd) Generation emissions GE11 10.75 kg CO2 Ðeq/gt Chapter 3 - Gwinn site GE12 8.2 kg CO2 Ðeq/gt Chapter 3 - TRC site GE22 8.78 kg CO2 Ðeq/gt Carter et al., 2016 GE33 6.63 kg CO2 Ðeq/gt Carter et al., 2016 Combustion emissions CBE1 937.24 kg CO2 Ðeq/gt Calculated CBE2 463.4 kg CO2 Ðeq/gt Calculated CBE3 942.19 kg CO2 Ðeq/gt Calculated CBE4 467.25 kg CO2 Ðeq/gt Calculated CBE5 978.96 kg CO2 Ðeq/gt Calculated CBE6 477.87 kg CO2 Ðeq/gt Calculated CBE7 998.62 kg CO2 Ðeq/gt Calculated CBE8 483.18 kg CO2 Ðeq/gt Calculated Transportation emissions TRE1 0.33 kg CO2 Ðeq/short ton/mile Calculated TRE2 0.33 kg CO2 Ðeq/short ton/mile Calculated TRE3 0.12 kg CO2 Ðeq/short ton/mile Calculated Torrefaction emissions TRE 21.76 kg CO2 Ðeq/gt Cited Biomass properties Biomass yield BY1 6.21 ODT/acre Chapter 3 - TRC site BY2 18.01 ODT/acre Chapter 3 - FBIC site BY3 6.53 ODT/acre Chapter 3 - KBS site Biomass moisture content MC1 50 % (wet -basis) Chapter 3 MC2 50 % (wet -basis) Chapter 3 MC3 5 % (wet -basis) Chapter 3 158 7.3.3 Decision Variables xa: the green weight (short ton) of woody biomass from i biomass source, harvested by j harvesting system, pre -processed by k method, and converted and utilized using l method. The eight decision variables included in this model are described as below: x2: the green weight (short ton) of woody biomass from FS, harvested by CTL harvesting system, grinded, and directly combusted, x3: the green weight (short ton) of woody biomass from FS, harvested by CTL harvesting system, grinded, torrified, and combusted, Table 29 (contÕd) Biomass lower heating value RHV 1 689.46 kWh/short ton Chapter 3 RHV 2 1423.97 kWh/short ton Chapter 3 RHV 3 689.24 kWh/short ton Chapter 3 RHV 4 1423.36 kWh/short ton Chapter 3 RHV 5 687.60 kWh/short ton Chapter 3 RHV 6 1421.68 kWh/short ton Chapter 3 RHV 7 686.73 kWh/short ton Chapter 3 RHV 8 1420.83 kWh/short ton Chapter 3 Other parameters Transportation distance D1 20 miles Assumption D2 20 miles Assumption D3 20 miles Assumption Power demand DB 50 MWh Assumption Daily capacity of torrefaction depot C 100 ODT/day Assumption 159 x4: the green weight (short ton) of woody biomass from FS, harvested by WT harvesting system, grinded, and directly combusted, x5: the green weight (short ton) of woody biomass from FS, harvested by WT harvesting system, grinded, torrified, and combusted, x6: the green weight (short ton) of woody biomass from SRP, harvested by WT harvesting system, grinded, and directly combusted, x6: the green weight (short ton) of woody biomass from SRP, harvested by WT harvesting system, grinded, torrified, and combusted, x7: the green weight (short ton) of woody biomass from SRW, harvested by RCF harvesting system, and directly combusted, x8: the green weight (short ton) of woody biomass from SRW, harvested by RCF harvesting system, torrified, and combusted. 7.3.4 Objective func tions The first objective function of this model is to minimize the total biomass production cost (a sum of site establishment cost, biomass generation cost, transportation cost, conversion cost) per kWh electricity generated. The cost minimization function is specified as below: !"#$ !!!!!"#!!!!"!"!!!!!!!!!!!!!!"!"# !!!!!!!!!!!!!!!!!!!!!"!!!!!!!!!!!!!!!"#!!!!!!!!!!!!!!!!!!!!!!!!""!!!!"# !!!!!!!!" 160 The second objective function of this mode is to minimize the total GHG emissions ( kg CO 2 - eq) per kWh electricity generated. The GHG emissions minimization function is specified as below: !"! !!"!!"!!!"!!"!!!!!"!!!!!!!!!!!!!!!!"!"# !!!!!!!!!!!!!!!!!!!!!"!!!!!!!!!!!!!!!"#!!!!!!!!!!!!!!!!!!!!!!!!"!!!!!!!!!!!!!!!!"# !!!!!!!! 7.3.5 Constraint s There constraints in this model are specified as below: 1) The recoverable heating value of the biomass generated needs to satisfy the total annual power demand of the 50 MW biomas s power plant; 2) The total woody biomass send to the torrifaction plant(s) do not exceed the total plant(s) capacity. 3) The annual total biomass consumed is less than 650,000 green tons. 7.4 Case scenarios The developed multi -criteria optimization model is applie d for five case scenarios. In case 1 scenario (base scenario) , it is assumed that the wood basket has a radiu s of 20 miles and there is a torrefaction depot located in the center or the wood basket . The wood basket and the torrefaction depot unit are 20 mi les away from a 161 biomass power plant . In case 2 to 5 , the unit number of the combination of one wood basket with one centralized torrefaction depot is increase d from 2 to 5. 7.5 Algorithm This multi -criteria optimization model was solved in M atlab, using the Non -dominated Sorting Genetic Algorithm II (NSGA - II) method developed by Deb et al. (2002). For each case scenario, a total of 100,000 solutions were produced through 1,000 solutions generations. In the first generation, 100 solutions will be randomly generated. The generated solutions that meet the constraints were ranked based on their associated objective function values. The dominated solutions are eliminated and only the non -dominated solutions were selected to produce 100 solutions in the next gener ation. After 1000 generations, 100 non -dominated solutions were selected out of the total 100,000 solutions generated and be plotted in the pareto -optimal sets. 7.6 Results 7.6.1 Pareto -optimal sets analysis Fig. 21a - 25a present the pareto -optimal sets in the 5 ca se scenarios, which are the optimized solutions sets generated by the NSGA-! for each case study. The x - and y-axes are the production cost (¢/kWh) and GHG emissions (kg CO 2 - eq/kWh). In each pareto -optimal set figure, 100 points are plotted, and each poi nt represents one optimized solution ( Fig. 21a - Fig. 25a). The x - and the y -coordinates are the cost and the GHG emissions of that solution. The pareto -optimal sets of case 1 scenario are presented in Fig. 21a. Point A in Fig. 21a is the solution with the lowest cost (1.88 ¢/kWh) and the highest GHG emissions (1.38 kg CO 2 Ð eq/kWh). To achieve this solution, all of the woody biomass 162 will be allocated to route 3. A total of 586,617 green tons of woody biomass will be grown from FS, h arvested by WT harvesting system, grinded at the harvesting site into wood chips, and directly delivered to the biomass power plant. The cultivation cost, biomass generation cost, transportation cost and torrefaction cost are 0.115 ¢/kWh (6.08%), 1.219 ¢/kWh (64.67%), 0.551 ¢/kWh (29.25%), and 0.000 ¢/kWh (0.00%), respectively. The GHG emissions from the cultivation, biomass generation, transportation, torrefaction and combustion stages are 0.000 kg CO 2 Ð eq/kWh (0.00%), 0.012 kg CO 2 Ð eq/kWh (0.86%) , 0.010 kg CO 2 Ð eq/kWh (0.69%), 0.000 kg CO 2 Ð eq/kWh (0.00%), and 1.367 kg CO 2 Ð eq/kWh (98.45%), respectively. Point B in Fig. 21a is the solution with the lowest GHG emissions (0.71 kg CO 2 Ð eq/kWh) but the highest cost (3.36 ¢/kWh) in case scenario 1. Bioma ss weight allocated in each route are: 0.00 green tons (route 1), 0.00 green tons (route 2), 123,200.67 green tons (route 3), 52,400.23 green tons (route 4), 0.00 green tons (route 5), 12,965.17 green tons (route 6), 0.00 green tons (route 7), and 0.00 gre en tons (route 8), respectively. Compared to solution A, the biomass green weight in route 3 in solution B are largely reduced and spread to route 4 and route 6. The cultivation cost, biomass generation cost, transportation cost and torrefaction cost are 0 .068 ¢/kWh (2.04%), 0.780 ¢/kWh (23.28%), 0.364 ¢/kWh (10.85%), and 2.138 ¢/kWh (63.82%), respectively. The GHG emissions from the cultivation, biomass generation, transportation, torrefaction and combustion stages are 0.000 kg CO 2 Ð eq/kWh (0.00%), 0.007 kg CO 2 Ð eq/kWh (1.05%) , 0.006 kg CO 2 Ð eq/kWh (0.87%), 0.007 kg CO 2 Ð eq/kWh (0.99%), and 0.691 kg CO 2 Ð eq/kWh (97.03%), respectively. Switching from solution A to solution B, the GHG emissions can be reduced by 0.66 kg CO 2 Ð eq/ kWh, 163 yet the cost will climb up by 1.46 ¢/kWh. The trade -off between rising up cost and reducing GHG emissions in case 1 is 2.21 ¢/ kg CO2 Ð eq. Fig. 21b shows the variations among the eight decision variables in all pareto -optimal solutions, which is important for the decision makers to ex ecute the optimal solutions. The 8 route options considered in this model are shown in x -axis, and the biomass green weight allocated in each route in a solution is shown in the y -axis. Each line represents one optimal solution, and is colored based on its cost and GHG emissions. The solution colored in deep blue indicates the solution has a low cost but high GHG emission , and deep red indicates the solution has high cost but low GHG emissions. Based on Fig. 21b, to achieve the solution with the lowest cost , all the biomass green weight should be allocated to route 3. By shifting the biomass green weight allocated in route 3 to the routes with torrefaction options (route 2, 4 and 6), the GHG emissions can be largely reduced. a. The pareto -optimal set of case 1. b. Decision variable variations of optimal solutions in case 1. Figure 21a and b. Pareto -optimal sets and decision variable variations in case 1 164 In case 2, the number of torrefaction depot increased from 1 to 2, which means the torrefaction capacity is doubled compared to case 1. The optimal solution with the lowest cost and the highest GHG emissions, solution C, is the same solution with solution A in case 1. With all the biomass green weight allocated to route 3, the cost of 1.88 ¢/kWh and the GHG emissions of 1.39 kg CO 2 Ð eq/kWh can be achieved ( Fig. 22a). The cost and GHG emissions contributions of each stage in the biomass supply chain in solu tion C are also the same with solution A. The optimal solution with the lowest GHG emissions and the highest cost in case 2, solution D, has a lower GHG emissions of 0.28 kg CO 2 Ð eq/kWh and a higher cost of 4.36 ¢/kWh, compared to solution B ( !"! !!"#!!"#$!!!!!!!"!!"!!"!!!!!"!!"!!!"#$ !!!!!!!!!!!"!) in case 1. In solution D, Biomass weight allocated in each route are: 0.00 green tons (route 1), 25.64 green tons (route 2), 1,691.77 green tons (route 3), 28,329.38 green tons (route 4), 35.30 green tons (route 5), 82,512.60 green tons (route 6), 0.00 green tons (route 7), and 0.00 green tons (route 8), respectively. The cultivation cost, biomass generation cost, transportation cost and torrefaction cost are 0.022 ¢/kWh (0.51%), 0.557 ¢/kWh (12.77%), 0.233 ¢/kWh (5.34%), and 3.552 ¢/kWh (81.39%), respectively. The GHG emissions from the cultivation, biomass generation, transportation, torrefaction and combustion stages are 0.000 kg CO 2 Ð eq/kWh (0.13%), 0.005 kg CO 2 Ð eq/kWh (1.66%) , 0. 004 kg CO 2 Ð eq/kWh (1.46%), 0.011 kg CO 2 Ð eq/kWh (4.12%), and 0.256 kg CO 2 Ð eq/kWh (92.64%), respectively. By changing from the solution C to solution D, the GHG emissions can be reduced by 1.11 kg CO 2 Ð eq/kWh, while the cost will be increase by 2.48 ¢/kWh (2.21 ¢/kg CO 2 Ð eq). Fig. 2 2b shows the variable variations in different optimal solutions in case 2. Compare to case 1, when the torrefaction capacity 165 is doubled, more woody biomass is shifted to route 2, 4 and 6 from route 3, to produce a lower GHG emissions. In case 3, point E and point F (Fig. 2 3a and b ) are the solutions with the lowest cost/ the highest GHG emissions and the highest cost/ the lowest GHG emissions. Similar with case 2, solution E is the same solution with solution A and solution C. Solution F has the same cost (4.36 ¢/kWh) but a slightly lower GHG emissions (0.27 kg CO2 Ð eq/kWh) compared to solution D (0.28 kg CO 2 Ð eq/kWh). Biomass weight allocated in each route in solution F are: 0.00 green tons (route 1), 514.40 green tons (route 2), 0.00 green tons (route 3), 109,153.95 green tons (route 4), 30.03 green tons (route 5), 0.00 green tons (route 6), 0.00 green tons (route 7), and 0.00 green tons (route 8), respectively. The cultivation cost, biomass generation cost, transportation cost and torrefaction cost are 0.042 ¢/kWh (0.97%), 0.451 ¢/kWh (10.36%), 0.235 ¢/kWh (5.39%), and 3.630 ¢/kWh (83.30%), respectively. The GHG emissions from the cultivation, biomass generation, transportation, torrefaction and combustion stages are 0.000 kg CO 2 Ð eq/kWh (0.00%), 0.004 kg CO 2 Ð eq/kWh (1.63%), 0.004 kg CO 2 Ð a. The pareto -optimal set of case 2. b. Decision variable variation s of optimal solutions in case 2 . Figure 22a and b . Pareto -optimal sets and decision variable variations in case 2 166 eq/kWh (1.51%), 0.012 kg CO 2 Ð eq/kWh (4.31%), and 0.250 kg CO 2 Ð eq/kWh (92.56%), respectively. The trade -off of reducing the GHG emissions for 1.12 kg CO 2 Ð eq/kWh is increasing the cost by 2.47 ¢/kWh (2.21 ¢/kg CO 2 Ð eq). Assigning all the biomass weight to route 3 is still the way to get the lowest cost. With three torrefication depots available, majority of biomass weight will be allocated to route 4 to obtain the lowest GHG emissions ( Fig. 23b). a. The pareto -optimal set of case 3. b. Decision variable variations of optimal solutions in case 3. In case 4 and case 5, the solutions with the lowest cost and the highest GHG emissions are solution G and solution I ( Fig. 24a and Fig. 25a), which are the same with previous obtained solutions A, C, and E. The solutions with the lowest GHG emissions and the highest cost in case 4 and case 5 are solution H and solution J ( Fig. 24a and Fig. 25a). The economic and environmental performance of solution H and solution J are the same ( !"#$ !!!!!"!!!!"!!!"! !!"#$$#%&$!!!!!"!!"!!"!!!!!"!!"!). In solution H, the biomass weight allocated in each route are: 0.00 green tons (route 1), 0.00 green tons (route 2), 0.00 green tons (route 3), 114,422.73 green tons (route 4), 0.00 green tons Figure 23a and b. Pareto -optimal sets and decision variable variations in case 3 167 (route 5), 48.60 green tons (route 6), 0.00 green tons (route 7), and 189.76 green tons (route 8), respectively. In solution J, the biomass weight allocated in each route are: 0.00 green tons (route 1), 617.21 green tons (route 2), 0.00 green tons (route 3), 109,309.61 green tons (route 4), 0.00 green tons (route 5), 47.82 green tons (route 6), 0.00 green tons (route 7), and 56.75 green tons (route 8), respectively. When the torrefaction capacity increases from case 4 to case 5, more biomass is allocated to the route 4 with torrefaction option to decrease the GHG emissions ( Fig. 24b and Fig. 25b). a. The pareto -optimal set of case 4. b. Decision variable variations of optimal solutions in case 4. Figure 24a and b. Pareto -optimal sets and decision variable variati ons in case 4 168 Figure 26 summarizes all of the pareto -optimal sets in the five case scenarios. The solutions with the lowest cost and the highest GHG emissions are the same for the five case scenarios. Wh en the number of available torrefaction depots increases, the trade -off between cost increase and GHG emissions reduction stays at 2.21 ¢/kg CO 2 Ð eq, but the lowest GHG emissions in each case scenario can be further reduced, from 0.71 kg CO2 Ð eq/kWh in c ase 1 to 0.27 kg CO 2 Ð eq/kWh incase 5. a. The pareto -optimal set of case 5. b. Decision variable variations of optimal solutions in case 5. Figure 25a and b. Pareto -optimal sets and decision variable variation in case 5 169 Figure 26. Pareto -optimal sets of the five studied cases Figure 27 shows the cost contributions of each stage in solution B, D, F, H, and J, which are the solutions with the lowest GHG emissions but highest cost in case 1, 2, 3, 4, and 5. When the torrefacti on capacity increases, more biomass are allocated to the route with torrefaction option to further reduce GHG emissions, and the torrefaction cost contributes more to the final cost through case 1 to case 5, from 63.82% to 83.27%. In contrast, the contribu tion of biomass generation cost and transportation cost are reduced when more biomass are torrified. 170 Figure 27. Cost contribution of each life stage to the final cost in soultion B, D, F, H and J in base scenario In Figure 28, the GHG emissions contribution s of each stage are shown. In all solutions, GHG emissions from the combustion phase are constantly the largest contributor. However when torrefaction capacity raises, more biomass will be torrified, thus the contribution of combustion stage are lessened and the torrefaction stage becomes the second largest contributor. Solution B Solution D Solution F Solution H Solution J Torrefaction Cost 63.82% 81.39% 83.30% 83.29% 83.27% Transportation Cost 10.85% 5.34% 5.39% 5.39% 5.39% Generation Cost 23.28% 12.77% 10.34% 10.36% 10.37% Establishment Cost 2.04% 0.51% 0.97% 0.97% 0.97% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Cost contribution (%) 171 Figure 28. GHG emissions contribution of each life stage to the final cost in soultion B, D, F, H and J in base scenario 7.6.2 Sensitivity analysis 7.6.2.1 The impact of the transportation distance between torrefaction depot and power plant on the pareto -optimal sets To evaluate the impact of the transportation distance between the torrefaction depot and the biomass power plant on the pareto -optimal sets and the trade -off between cost and GHG emissions, a sensitivity analysis was conducted using the case 1 scenar io. The transportation distance was increased in 20 miles increments, from 10 miles to 90 miles. Figure 29 presents the sensitivity of the model to the transportation distance change . Results suggest that t he transportation distance change does not affect the solutions with the lowest cost and the highest GHG emissions ( !"#$ !!!!!!!!!!"!!!!"! !!"#$$#%&$!!!!!"!!"!!"!!!!!"!!"!) because in those so lutions there Solution B Solution D Solution F Solution H Solution J Combustion Emissions 97.03% 92.64% 92.56% 92.56% 92.56% Torrefaction Emissions 0.99% 4.12% 4.31% 4.31% 4.31% Transportation Emissions 0.87% 1.46% 1.51% 1.51% 1.51% Generation Emissions 1.05% 1.66% 1.63% 1.62% 1.63% Establishment Emissions 2.04% 0.51% 0.97% 0.97% 0.97% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Ghg emissitions contribution (%) 172 are no torrefaction options. However, in solution K, L, M, N and O, the solutions with the lowest GHG emissions and the highest cost, as the transportation distance increases from 10 miles to 90 miles, the cost increases from 3.20 ¢/kWh to 3.84 ¢/kWh (Figure 29). T he trade -off between cost and GHG emissions for the transportation distance of 10, 30, 50, 70, 90 miles are calculated as 2.14, 2.35, 2.56 , 2.75, and 2.97 ¢/kg CO2 Ð eq, respectively. Figure 29. Impacts of distance between the torrefaction depot and the biomass power plant on the pareto -optimal sets in base case 173 7.6.2.2 The impact of the torrefaction price on the pa reto-optimal sets To examine the impact of torrefaction price on the pareto -optimal sets, the torrefaction price was increased from 54.40 to 80.60 $/green ton in 6.80 $/green ton increments. The sensitivity of the pareto -optimal sets to the torrefaction pr ice is presented in Figure 30. Changing the torrefaction price does not affect the solutions with the lowest cost and the highest GHG emissions. Point P, Q, R, S, and T represent the solutions that have the lowest GHG emissions and highest cost under different torrefaction prices. When the torrefaction price is equal to 54.40, 61.20, 68.00, 74.80 and 80.6 $/green ton, the cost are 2.92, 3.18, 3.36, 3.62 and 3.80 ¢/kWh , and the GHG emissions are 0.72, 0.69, 0.71, 0.72, 0.70 kg CO2 Ð eq/ kWh, respectively. T he trade -off between cost increas e and GHG emissions reduction in solution P, Q, R, S, and T are estimated to be 1.57, 1.88, 2.21, 2.63, and 2.82 ¢/kg CO2 Ð eq. Figure 30. Impacts of torrefaction prices on the pareto -optimal sets in base case 174 7.7 Discussions In order to mitigate GHG emissions from burning fossil fuels, using renewable energy sources such as wind, solar and biomass are gaining popularity. However, it is quite challenging to supply woody biomass with low production cost and low GHG emissions at the same time (Basu et al., 2011). Therefore, it is critical to understand the trade -off between decreasing the GHG emissions and increasing the cost. A typical 50 MW biomass power plant can annually consume 500,000 to 650,000 green tons of woody biomass (Mayhead, 2010; PFPI, 2012). In case 1 scenario solution A, the model assigned a total of 586,617 green tons of woody biomass to route 3 (FS + WT + Grinded + Direct combustion) to achieve the lowest cost at 1.88 ¢/kWh. The biomass feedstock cost in Michiga n contributes about 3.50 to 4.60 ¢/kWh to the total electricity production cost of 7.00 to 10.00 ¢/kWh (Mayhead, 2010; EIA, 2015). Due to the low burning efficiency, producing electricity using woody biomass generally emits more GHG than coal and natural gas. The GHG emissions from burning woody biomass is around 1.36 to 1.42 kg CO 2-eq/kWh, while the GHG emissions from burning coal and natural gas are only around 0.95 kg CO 2-eq/kWh and 0.23 kg CO 2-eq/kWh (PFPI, 2011). In solution A, the total GHG emission is 1.38 kg CO 2-eq/kWh, which is comparable to the normal range. To reduce the GHG emissions by 0.66 kg CO2-eq/kWh, the model assigned a portion of biomass to torrefaction routes, and thus to reach to the lowest GHG emissions of 0.71 kg CO 2-eq/kWh in solut ion B. However, the cost of solution B is increased to 3.36 ¢/kWh, which is 1.46 ¢/kWh higher than the cost of solution A. In this case, the trade -off between GHG emissions reduction and cost increase is 2.21 ¢/kg CO 2-eq. EPA has estimated the social cost of carbon from 2015 to 175 2050, at discount rates of 5%, 3% and 2.5%, to value the social impacts of climate change (EPA, 2016). The social cost of CO 2 on 2020 at 5%, 3% and 2.5% discount rates are $12, $42 and $62 per metric ton CO 2, which are equivalent t o 1.2 ¢, 4.2 ¢ and 6.2 ¢/ kg CO 2. At present, the US discount rate is at 1.75%. Assuming on 2020 the discount rate will be at 2.5% and the carbon credit is set at 6.2 ¢/ kg CO 2, then there will be enough incentive for biomass supplie r and end -user to adopt solution B instead of solution A. In case 2 to case 5, while the torrefaction capacity increases, more biomass is assigned to torrefaction routes, and the GHG emissions can be further reduced. Woody biomass has been historically recognized as a renewable energy sources because it is sustainable and widely available. However, the cost and quality of woody biomass can be largely impacted by many factors such as harvesting season, transportation distance, storage form and handling method (Nunes et al., 2014). Torrefaction can be an efficient and a favorable biomass thermal conversion technique to avoid the issues discussed above (Uslu et al., 2008; Ciolkosz and Wallace, 2011 ). It has been claimed by several studies that torrefaction can increase the energy den sity of woody biomass by more than 3 times (Samy and Sunita, 2009; Chen and Kuo, 2010; Nunes et al., 2014). With higher energy density and lower moisture content, the transportation efficiency can be largely increased. More important, the moisture content of torrefied wood is around 7 -10%, which can largely improve the boiler efficiency compared to wood chips with moisture content of 30 -50%. Since over 95% of GHG emissions in woody biomass supply chain are generated in the biomass combustion stage, the impr ovement in boiler efficiency plays a very important role in GHG emissions reduction. 176 In woody biomass supply chain, transportation planning has always been a big research focus ( Gunnarsson et al., 2004; Gronalt and Rauch, 2007 ; Kanzian et al., 2009). To figure out the impact of the torrefaction depot location on the pareto -optimal sets, a sensitivity analysis was conducted. When the distance between the torrefaction depot and the biomass power plant increased from 10 miles to 90 miles, the GHG emissions stays in a narrow range, but the cost increased from 3.29 to 3.94 ¢/kWh, by 16.72%. The resulted raise in cost is mainly owing to the longer transportation cost with longer transportation distance. Nevertheless, because of the high energy density of torrifi ed wood pellets, the impact of the increases in transportation distance is relatively small. With 8 times longer transportation distance, the total cost will only be increased by 16.72%. The price of torrefcation process is an important factor to promote the usage of forest -derived fuels , which can be affected by many factors such as biomass availability, feedstock generation method, biomass quality, drying technology and torrefaction yield (Svanberg et al., 2013; Shah et al., 2012; Pirraglia et al., 2013) . Many studies have been conducted to model and minimize the torrefaction cost using different technologies (Svanberg et al., 2013; G„rdbro et al., 2014; Chai and Saffron, 2016). Our study also confirmed that torrefaction price can largely impact the total biomass production cost and the trade -off between GHG emissions reduction and cost increase. For instance, when the torrefaction price is increased in a 10% increment from 54.40 $/ton to 80.60 $/ton, the rises in the cost are 8.90%, 15.07%, 23.97% and 30. 14%. In addition, as the torrefaction price increases from 54.40 $/ton to 80.60 $/ton, to decrease 1 kg of kg CO 2-eq, the trade -off increases from 1.58 ¢ to 2.82 ¢, which is about 79.19% increase. 177 7.8 Conclusion A multi -criteria optimization model was develop ed to provide an optimized woody biomass utilization route and to analyze the trade -off between GWP and cost. The results suggested that in the base scenario, by increasing the cost by 1.46 ¢/kWh (55.54% increase), the total GHG emissions can be reduced by 0.66 kg CO 2-eq/kWh (52.17% decrease). In addition, as an efficient and a favorable biomass thermal conversion technique, torrefaction can be a way to further reduce the GHG emissions of utilizing woody biomass. The sensitivity analysis indicated that biom ass HHV and biomass moisture content have larger impacts on the optimized solutions and the trade -offs, compared to the transportation distance. This again, confirmed that in order to largely improve the efficiency and sustainability of the woody biomass s upply chain, future research efforts should be spent on improving the HHV and decreasing the moisture content of woody biomass. 178 CHAPTER 8 CONCLUSION In this Ph.D. study, five woody biomass production systems were monitored to predict the productivity and production cost of utilizing woody biomass in the State of Michigan. Effective cycle time estimators were illustrated in predictive regression equations for the studied ma chines. The predicted cycle time for each machine was used with the machine hourly rate to calculate the machine productivity and hourly production cost. The obtained results suggested that due to the higher hourly productivity, WT harvesting systems gener ally achieved lower production cost ( 15.57 $/ODT ~ 33.25 $/ODT) than CTL harvesting systems ( 39.16 $/ODT). Among the five evaluated studied sites, Site 5, the 3 -year -old hybrid willow plantation harvested by a reconfigured harvesting system resulted in the highest production cost of 95.92 $/ODT, owing to the low biomass yield per acre and the additional hourly cost from the chips collector. Overall, it can be concluded that producing woody biomass from natural forest stands with the WT harvesting system was the most economical way of utilizing woody biomass as it has the highest productivity and lowest cost . Storage is a key component within the woody biomass supply chain, especially when year -round harvesting is impossible. In order to better understand the field storage of woody biomass, two studies have been conducted to monitor the biomass quality (biomass MC and biomass HHV) change under different storage forms, wood logging residues piles and wood chips piles. In wood logging residues piles, biomass MCs were significantly reduced during the storage period. As an important fuel quality property, biomass HHVs were determined to be generally stable during the 5 -month storage period. In comparison, because of the small particle size and high degree of compac tion 179 in the wood chip pile, increases in biomass MCs were observed at all wood chips piles. In addition, decreasing trends of biomass HHV were detected during the storage time, at all wood chips piles, as a result of energy loss caused by the high MC and m icrobial activity in the wood chip pile. Five LCAs were conducted to estimate the environmental impacts generated in woody biomass production systems in Michigan, U.S., including cultivation, biomass harvesting, collecting, pre -processing, transportation , and combustion stages. The estimated GWP in all study sites were in a comparable range with previous published LCAs. It is important to note that, among all studied life cycle stages, biomass combustion consistently contributes about 95% to 97% of the fina l GWP. Considering the large amount of GHG emissions and the long time required to sequester these emissions, biomass combustion should be included in future LCA s of woody biomass supply chain s. In order to reduce the GHG emissions from the combustion stag e, future research efforts should be aimed to provide biomass with lower moisture content and higher HHV. Utilizing woody biomass for bioenergy production can be carbon neutral while including soil C increase during biomass growth, however future work is n eeded to reduce the uncertainties of including soil C in LCAs. Based on the estimated production cost and evaluated GWP, a multi -criteria optimization model was developed to suggest an optimized woody biomass utilization route and to analyze the trade -off between GWP and cost. The results suggested that by increasing the cost by 1.46 ¢/kWh (55.54% increase), the total GHG emissions can be reduced by 0.66 kg CO 2-eq/kWh (52.17% decrease). In addition, as an efficient and a favorable biomass thermal conversio n technique, torrefaction has been shown as a way 180 to further reduce the GHG emissions of utilizing woody biomass. The sensitivity analysis indicated that biomass HHV and biomass moisture content have much larger impacts on the optimized solutions and the t rade -offs, compared to the transportation distance. This again, confirmed that in order to largely improve the efficiency and sustainability of the woody biomass supply chain, more attention should be spent on improving the energy content and decreasing th e moisture content of woody biomass. 181 APPENDI CES 182 Appendix 1. User guide for the cost prediction spreadsheet model Introduction As a state with high availability of forest resources , Michigan has a high potential to develop woody biomass as renewable energy source . Previously, many cost analysis models such as BioSum 3.0, Auburn Harvest Analyzer (AHA) and FRCS -North have been developed to provide logistic cost predictions. The predic ting equations in these models were mostly obtained from harvesting operations studies conducted in Northwestern or Southeastern United States, which have the different site and terrain conditions, forest type, and tree size compare to MI. Since woody biom ass production cost can be heavily affected by factors such as tree size, spacing, ground slope and roughness , and road type (Wright et al., 2010 ; Ghaffariyan et al., 2012 ; Harril and Han , 2012), directly using these models to make cost prediction for MI f orest harvesting project may cause inaccurate estimation. Dalia et al. (2014) have published a survey analysis of MI wood -based production (include harvesting and transportation) based on loggerÕs survey , which reported the harvesting productivities of dif ferent harvesting systems in different forest types. Although this survey analysis provided a good understanding of logging industry in MI, a cost and productivity prediction model that is developed from MI -based timber harvesting operations is in great ne ed. In order t o provide a better tool to predict the entire supply chain cost (harvesting, biomass collecting, handling , and transportation ) of producing wood pellets for energy generation purpose in MI , this spreadsheet model was designed . This model was developed using VBA programming language in Excel, based on multiple regression cost prediction equations that were developed from 5 forest harvesting 183 projects in Michigan . This model can be used to estimate production cost of woody biomass resulting from harvesting operation including whole -tree (WT) harvesting, logging residue extraction in cut -to-length (CTL) harvesting and reconfigured forage harvesting. The target users of this spreadsheet model include contractors and logging companies, researchers an d nonprofit organizations. With this model, users can understand the critical cost factors , estimate the harvesting productivity and cost, and design the harvesting project. Getting started This spreadsheet model was developed on Excel 201 6. It provides es timates of machine hourly rate ($/hr.), harvesting productivity (ODT/hr.), production cost ($/ODT), total cost ($) and cost per acre ($/acre). To get started with this spreadsheet model, Microsoft Office needs to be installed and following information is r equired: ¥ Site information such as biomass source, tree species, harvested area (acres), average harvested tree DBH (inches) , age of the biomass source (yr.), stand density (trees/acre), and assumed soil carbon sequestration rate (Mg C/ha/yr). ¥ Herbicide and fertilization application rate during cultivation stage. ¥ Harves ting prescription, operation method, system type, and pre -processing technologies. ¥ Transportation method and one -way transportation distance (miles), including transportation distances in dirt road, gravel road, paved road and highway. ¥ Storage form, moisture content (% wet basis) before storage and after storage. ¥ Biomass higher heating value (Btus/lb) and boiler combustion efficiency (%). 184 Using the model Fig. A-1 shows the model input page of this spreadsheet model. The ÒModel resetÓ button in the right upper corner can be used to clear all inputs and output for the model. There are six main sections in the model input page for date entry, including site information, cultivation, harves ting system, transportation system, storage, and combustion system. Detail steps to set up this model will be discussed below. Figure A - 1. Model input page of the cost prediction spreadsheet model Site information In this step, users can provide site information inputs such as biomass source, tree species, harvested area, averaged harvested tree DBH and so on. Biomass source options include natural forest and energy wood plantation ( Fig. A-2). Users can change the selection by clicking ÒSelect optionÉÓ and then select another choice in the list. Natural forest in this model refers to secondary forest or timber land that is planted for 185 industrial purpose, while energy wood plantation refers to short rotation hybrid poplar or willow plantations that are grown to be harvested within 2 -9 years rotation cycle. Figure A - 2. Step1: Biomass source selection Tree species included in this model include hybrid willow, hybrid poplar, Jack pine ( Pinus banksi ana), and mixed hardwood and softwood ( Fig. A-3). After choosing tree species, users can enter the total number of acres to be harvested, estimated average tree DBH, age of biomass source, stand density and soil C sequestration rate, which should all be nu meric characters. User can also click the check -box ÒDefault valueÓ to use values provided by this model. The default values for different tree species are presented in Table A-1. Figure A - 3. Step 1: Tree species selection 186 Table A - 1. Step 1: Default site information for included tree species Harvested area (acres) Average DBH (inches) Age (yrs.) Soil C sequestration rate (Mg C/ha/yr.) Hybrid willow 1.1 1 3 0.00 Hybrid poplar 7.7 4 7 0.75 Jack pine 56.0 7 29 0.06 Mixed -species 33.0 8 10 0.53 Cultivation In this step, user can enter fertilization and herbicide application rates (only numeric characters) in biomass cultivation stage (Fig. A -4). At current stage, this model only handles one type of herbicide, which is glyphosate. There are many fertilizer choices such as ammonium nitrate, calcium ammonium nitrate and so on. The default value for herbicide rates is 16.6 gals/acre. Figure A - 4. Step 2: Herbicide and fertilizer application Harvesting system Harvesting prescription and operation method In this step, user can enter harvesting prescription, operation method, operation system type, and pre -processing technology (Fig. A -5). There are two options in harvesting prescription, clear -cut and 70% thinning. If Òclear cutÓ is chosen, it means all 187 trees in the stand will be harvested; if 70% thinning is chosen, it means 70% of the stand will be harvested. For th e operation method, this model can only handle cold decking operation at this stage. Figure A - 5. Step 3: Choosing harvesting prescription System type If user choose whole tree harvesting system, ÒWhole Tree Harvesting SystemÓ window will open for users to input detail information for the harvesting system ( Fig. A-6). So far this model can only handle medium size rotating saw feller buncher like John Deere 653 G and medium size grapple skidder like John Deere 740 A. The entered number of feller buncher and skidder employed can only be integer number larger than 0. Figure A - 6. Step 3: Whole tree harvesting system information window 188 In machine hourly rate section for feller buncher and skidder , user can choose to use default value provided by this model or use an estimated value based on userÕs input. If user s choose to input their own machine cost information, machinery cost estimation window ( Fig. A-7) will open. Machinery cost will be calcul ated by calculation method developed by Miyata (1980) based on userÕs input. In the machine cycle time prediction section, user s may use the model default value s or input value s based on their own projects . The values provided by user have to be non -negat ive values within the default range listed under each cell. For number of cuts per cycle, please only enter integer values. For feller -buncher, a complete cycle start from movement to the first tree, included subsequent cuts to make a full accumulation, and ended with the placement of that accumulation on the ground to make a bunch, where multiple accumulations often constituted a single bunch. Move to tree distance, the number of cuts per cycle, and move to bunch distance were defined as variables for thes e elemental activities within a cycle. For skidder, a complete cycle starts from moving from the landing site to a tree bunch and included subsequent grappling to make a full skid load, return to the landing site, and ended with the placement of loaded tre es on the ground near the log loader. Elemental activities within a skidding cycle were defined to be: travel empty distance, positioning distance, number of trees per cycle, and traveling loaded distance. 189 Figure A - 7. Step 3: Harvesting system machinery cost estimation window (feller -buncher) This model only provides cost and productivity estimates for logging residues collection in the CTL system. If user selected CTL as the desired harvesting system, a window ( Fig. 8 ) will open for user to select and enter the specific harvesting system information. This model temporarily can only handle forwarder model such as Ponsse Bison S15. The number of employed forwarder can only be integer that is larger than 0. For the machine hour cost, user can either accept the model default value or use machinery cost estimation tool to obtain a calculated value. In the cycle time prediction section, user can only provide values that are within the valid range of each parameter. A complete cycle for forward starts from the movement from the landing site to a logging residue piles and subsequent grappling to make a full load, travel back to the 190 landing site and unloaded the biomass on the ground near the loader. The included variables in pred icting forwarding cycle time are: travel empty distance, intermediate travel distance in feet, number of grapples per cycle, travel loaded distance an d number of unloading grapples. For the number of cuts per cycle and number of unloading grapples, please only input integer values. Figure A - 8. Step 3: CTL harvesting system information window In reconfigured harvesting system information window (Fig. A-9), user needs to choose a model for harvester employed and input the unit number of harvester(s) employed. Machinery hourly rate estimation for reconfigured harvesting system can be estimated by user input information or the model default value. In the cycle time prediction section, harvesting speed refers to the driving speed w hen the reconfigured 191 forage harvester is harvesting the stand, while travel speed refers to the driving speed when the reconfigured forage harvester is not harvesting. Figure A - 9. Step 3: Reconfigured harvesting system inform ation window Pre-processing Pre -processing in this model refers to loading and grinding/chipping operation to process harvested woody biomass into pellets. User can chose if pre -processing will be included in the harvesting system in ÒInclude pre-processing costÓ cell ( Fig. A-10). This model temporary cannot handle chipping operation. 192 Figure A - 10. Step 3: Include pre -processing in the harvesting system or not If user chooses to include grinding and loading in the harvesting system, a window ( Fig. A-11) will show for user to use the model default value or calculate machine hourly rate, and to predict working cycle time prediction in the unit of centi -minute. For the loader -grinder unit, complete cycle was defined a s the time required for the loader to supply the grinder with a grapple of trees and process them into wood chips. DBH measurements of the collected biomass being grappled by the log loader were documented during the time motion study through visual estima tion of the loaderÕs extended grapple diameter and average poplar stem DBH. The loader -grinder average cycle time estimator utilized the product of tree DBH and the number of trees per cycle as the independent variable in the linear regression model. The i nput value for number of trees per loading cycle can only be integer that is within the default range of this model. 193 Figure A - 11. Step 3: Grinder and loader information window Transportation system In this section, user ca n set up the transportation system for delivering the biomass (Fig. A -12). At this stage, this model can only handle chip van with volume of 148 yd 3. User can enter the travel distance in miles in each road type, which is categorized based on driving speed. Figure A - 12. Step 4: Transportation system information 194 Storage As an important part of the woody biomass supply chain, storage is included in this model ( Fig. A-13) to estimate the fuel value ($/ton) change caused by storage. The detail method of calculating fuel value can be found in the ÒStorage Ó sheet. User can select the storage form of wood chips pile or loggin g residue pile. For each storage form, different default values of original and resulted moisture contents will be provided by model. Users can also use their own estimates. The input numbers for moisture content at the time of chipping and after storage s hould be larger than 0 and less than 100. If users do not want to include storage, they may click ÒNone Ó in the ÒStorage form Ó choice list. Figure A - 13. Step 5: Storage form and moisture contents during storage Combustion sys tem In this section, user can enter the biomass higher heating value (Btus/lb), biomass moisture content (% wet basis ) and overall efficiency of c ombustion system (%) ( Fig. A-14). This information can be used to estimate the total recoverable heating value of the woody biomass and greenhouse gas emissions produced in combustion stage. Figure A - 14. Step 6: Combustion system information Running case and output page 195 Here we provide an example case for user to get started with the model. The case is describe below: ¥ The harvested plantation is a 7.7 -ac stand of 7 -year -old poplar hybrids in Michigan. ¥ The average DBH of the trees is 4 inches. ¥ The stand d ensity is 1100 trees/acre. ¥ It is assumed that the plantation sequesters 2 Mg soil C per ha annually. ¥ Glyphosate is applied in a rate of 16.6 gallons/acre to remove the weeds. ¥ No fertilizer is used. ¥ A whole -tree harvesting system is employed to harvest the site, which includes one unit of feller -buncher (John Deere 653 G), one unit of skidder (John Deere 740A), one unit of grinder (Peterson 4700B) and one unit of loader (Hood S -182). ¥ The operation method is cold decking operation. ¥ The harvesting prescriptio n is clear -cut. ¥ The processed wood chips are delivered by a chip van with volume of 148 yd 3. ¥ The transportation distance includes 0.2 miles of dirt road, 0.2 miles of gravel road, 0.2 miles of paved road and 60 miles of highway. ¥ It is assumed that the wood y biomass is stored at the biomass power plant, in the wood chips pile. The initial moisture content is assumed to be 29.4% and the resulted moisture content is assumed to be 39.3%. ¥ The biomass higher heating value of the wood pellets is 8518 Btus/lb. The biomass moisture content is 39.3%. The overall combustion efficiency is 35%. 196 Once all information about the biomass production system is entered, user can go the ÒOutputÓ sheet, click the ÒCalculateÓ button and all results will be displayed in the ÒBiomas s producedÓ table and the ÒCost predictionÓ table ( Fig. A-15). In the ÒBiomass producedÓ table, user can read the dry weight and green weight of the total produced biomass. In the ÒCost predictionÓ table, estimated results include machine hourly rate, machine productivity, cost per ODT, cost per green ton, total cost, and cost per acre are listed for each stage of the biomass production system. Figure A - 15. Outputs table for the spreadsheet model 197 Appendix 2. VBA codes for the cost prediction spreadsheet model Sheet 1. Model input Private Sub A_Change() Sheet3.Range("B4").Value = A.ListIndex + 1 End Sub Private Sub B_Change() Sheet3.Range("B5").Value = B.ListIndex + 1 Dim BS BS = Sheet3.Range("B4").Value Dim ts ts = Sheet3.Range("B5").Value If (BS = 1 And ts = 1) Then MsgBox "Invalid combination! Hybrid willow can only be defined as energy woods plantation.", , "Error Msg" If (BS = 1 And ts = 2) Then MsgBox "Invalid combination! Hybrid poplar can only be defined as e nergy woods plantation.", , "Error Msg" If (BS = 2 And ts = 3) Then MsgBox "Invalid combination! Jack pine can not be defined as energy woods plantation.", , "Error Msg" If (BS = 2 And ts = 4) Then MsgBox "Invalid combination! Mixed -species woods can not b e defined as energy woods plantation.", , "Error Msg" End Sub Private Sub G_Change() Sheet3.Range("B15").Value = G.ListIndex + 1 End Sub Private Sub E_Change() Sheet3.Range("B17").Value = E.ListIndex + 1 Dim hs hs = Sheet3.Range("B17").Value Dim ts ts = Sheet3.Range("B5").Value Dim PS PS = Sheet3.Range("B15").Value If hs = 1 And ts = 2 Then WT4_window.Show Sheet1.Range("f30").Value = "Whole tree harvesting system" Sheet1.Range("b31").Value = "Feller buncher" Sheet1.Range("c31").Value = "Unit number: " 198 Sheet1.Range("c32").Value = "Machine hourly cost ($/SMH):" Sheet1.Range("c34").Value = "Model:" Sheet1.Range("E31").Value = "Machine productivity (ODT/hr):" Sheet1.Range("e32").Value = WT4_window.Label12 Sheet1.Range("e33").Value = WT4_window.Label13 She et1.Range("e34").Value = WT4_window.Label14 Sheet1.Range("b36").Value = "Skidder" Sheet1.Range("c36").Value = "Unit number:" Sheet1.Range("c37").Value = "Machine hourly cost ($/SMH):" Sheet1.Range("c39").Value = "Model:" Sheet1.Range("e36").Value = "Machin e productivity (ODT/hr):" Sheet1.Range("e37").Value = WT4_window.Label16 Sheet1.Range("e38").Value = WT4_window.Label17 Sheet1.Range("e39").Value = WT4_window.Label18 Sheet1.Range("e40").Value = WT4_window.Label18 End If If hs = 1 And ts = 4 And PS = 1 Then WT2_window.Show Sheet1.Range("f30").Value = "Whole tree harvesting system" Sheet1.Range("b31").Value = "Feller buncher" Sheet1.Range("c31").Value = "Unit number:" Sheet1.Range("c32").Value = "Machine hourly cost ($/SMH):" Sheet1.Range("c34").Value = " Model:" Sheet1.Range("E31").Value = "Machine productivity (ODT/hr):" Sheet1.Range("e32").Value = WT2_window.Label12 Sheet1.Range("e33").Value = WT2_window.Label13 Sheet1.Range("e34").Value = WT2_window.Label14 Sheet1.Range("b36").Value = "Skidder" Sheet1.R ange("c36").Value = "Unit number" Sheet1.Range("c37").Value = "Machine hourly cost ($/SMH):" Sheet1.Range("c39").Value = "Model:" Sheet1.Range("e36").Value = "Machine productivity (ODT/hr):" Sheet1.Range("e37").Value = WT2_window.Label15 Sheet1.Range("e38" ).Value = WT2_window.Label16 End If If hs = 1 And ts = 4 And PS = 2 Then WT3_window.Show Sheet1.Range("f30").Value = "Whole tree harvesting system" Sheet1.Range("b31").Value = "Feller buncher" Sheet1.Range("c31").Value = "Unit number:" Sheet1.Range("c32") .Value = "Machine hourly cost ($/SMH):" Sheet1.Range("c34").Value = "Model:" Sheet1.Range("E31").Value = "Machine productivity (ODT/hr):" Sheet1.Range("e32").Value = WT3_window.Label12 199 Sheet1.Range("e33").Value = WT3_window.Label13 Sheet1.Range("e34").Value = WT3_window.Label14 Sheet1.Range("b36").Value = "Skidder" Sheet1.Range("c36").Value = "Unit number" Sheet1.Range("c37").Value = "Machine hourly cost ($/SMH):" Sheet1.Range("c39").Value = "Model:" Sheet1.Range("e36").Value = "Mach ine productivity (ODT/hr):" Sheet1.Range("e37").Value = WT3_window.Label15 Sheet1.Range("e38").Value = WT3_window.Label16 End If If hs = 2 Then CTL_WINDOW.Show Sheet1.Range("f30").Value = "Cut -to-length harvesting system" Sheet1.Range("b31").Value = "Fowa rder" Sheet1.Range("c31").Value = "Unit number:" Sheet1.Range("c32").Value = "Machine hourly cost ($/SMH):" Sheet1.Range("C35").Value = "Machine productivity (ODT/hr):" Sheet1.Range("c34").Value = "Model:" Sheet1.Range("e31").Value = CTL_WINDOW.Label14 She et1.Range("e32").Value = CTL_WINDOW.Label15 Sheet1.Range("e33").Value = CTL_WINDOW.Label16 Sheet1.Range("e34").Value = CTL_WINDOW.Label22 Sheet1.Range("e35").Value = CTL_WINDOW.Label23 End If If hs = 3 Then RCF.Show Sheet1.Range("f30").Value = "Reconfigured harvesting system" Sheet3.H.ListIndex = 2 Sheet1.Range("b36:f56").Value = "" Sheet1.Range("e32:f35").Value = "" Sheet1.Range("b31").Value = " " End If If hs = 4 Then Sheet6.Range("a65:i84").Interior.ColorIndex = 0 Sheet6.Range("i28:i36").Val ue = "" 'fb = 0 Sheet6.Range("i42:i52").Value = "" 'sk = 0 Sheet6.Range("i55").Value = "" 'fwd = 0 Sheet6.Range("i66, i74").Value = "" 'rcf = 0 End If If (ts = 1 And hs = 1) Then MsgBox "Invalid combination! Hybrid willow can only be harvested by reconfig ured harvesting system.", , "Error Msg" 200 If (ts = 1 And hs = 2) Then MsgBox "Invalid combination! Hybrid willow can only be harvested by reconfigured harvesting system.", , "Error Msg" If (ts = 2 And hs = 2) Then MsgBox "Invalid combination! Hybrid poplar c annot be harvested by CTL harvesting system. Please choose other harvesting system.", , "Error Msg" If (ts = 3 And hs = 3) Then MsgBox "Invalid combination! Jack pine cannot be harvested by reconfigured harvesting system. Please choose other harvesting sys tem.", , "Error Msg" If (ts = 4 And hs = 3) Then MsgBox "Invalid combination! Mixed -species forest stand cannot be harvested by reconfigured harvesting system. Please choose other harvesting system.", , "Error Msg" End Sub Private Sub F_Change() Sheet3. Range("B16").Value = F.ListIndex + 1 Dim OPM OPM = Sheet3.Range("B16").Value If OPM = 1 Then Sheet3.J.ListIndex = 2 INMC_Df.Value = False REMC_df.Value = False Sheet3.Range("e14").Value = 0 Sheet3.Range("e15").Value = 0 End If 'If OPM = 1 Then MsgBox "Invalid Choice! This model temporarily can only handle cold decking operation.", , "Error Msg - Operation method" End Sub Private Sub H_Change() Sheet3.Range("B18").Value = H.ListIndex + 1 Dim PPC_Include PPC_Include = Sheet3.Range("b18").Value If She et3.Range("b17").Value = 3 And PPC_Include = 1 Then MsgBox "The reconfigured harvesting system can directly process the woody biomass. It is not necessrary to add a chipper.", , "Error Msg" End If 201 If Sheet3.Range("b17").Value = 3 And PPC_Include = 2 Then MsgBox "The reconfigured harvesting system can directly process the woody biomass. It is not necessrary to add a grinder.", , "Error Msg" If PPC_Include = 1 And Sheet3.Range("b17").Value = 1 Then MsgBox "This model can not predict the productivity of chipp ing operation.", , "Error Msg - chipping" H.ListIndex = 2 'Sheet3.Range("b19").Value = "Chipping" 'Sheet1.Range("d42").Value = "Chipping" 'CPLD_W.Show End If If PPC_Include = 2 And Sheet3.Range("b17").Value = 1 Then Sheet3.Range("b19").Value = "Grinding" Sheet1.Range("d42").Value = "Grinding" Sheet1.Range("b42").Value = "Loader" Sheet1.Range("c42").Value = "Unit number:" Sheet1.Range("c43").Value = "Machine hourly cost ($/SMH):" Sheet1.Range("c44").Value = "Model:" Sheet1.Range("c44").Value = "Machine prod uctivity (ODT/PMH):" Sheet1.Range("b46").Value = "Grinder" Sheet1.Range("c46").Value = "Unit number:" Sheet1.Range("c47").Value = "Machine hourly cost ($/SMH):" Sheet1.Range("c48").Value = "Model:" Sheet1.Range("c49").Value = "Machine productivity (ODT/PMH ):" GRLD_W.Show End If If PPC_Include = 2 And Sheet3.Range("b17").Value = 2 Then Sheet3.Range("b19").Value = "Grinding" Sheet1.Range("d42").Value = "Grinding" GRLD_W.Show End If If PPC_Include = 3 Then Sheet3.Range("b19").Value = "None" Sheet1.Range("d42,d46").Value = "None" Sheet6.Range("a46:h47,a45,i46").Interior.ColorIndex = 0 'cell no color Sheet6.Range("i63").Value = "" End If If PPC_Include = 4 Then Sheet3.Range("b19").Value = " -" Sheet1.Range("d42").Value = " -" Sheet6.Range("a46:h 47,a45,i46").Interior.ColorIndex = 0 'cell no color End If 202 End Sub Private Sub I_Change() Sheet3.Range("E5").Value = I.ListIndex + 1 Dim TC_MD TC_MD = Sheet3.Range("E5").Value If TC_MD = 2 Then MsgBox "This model temporarily can only handle truck volume of 148 Yard3.", , "Error Msg - Truck Volume" End Sub Private Sub J_Change() Sheet2.Range("B14").Value = J.ListIndex + 1 End Sub Private Sub HBSIDE_Click() If HBSIDE.Value = True Then Sheet3.Range("B12").Value = 16.6 If HBSIDE.Value = False Then Sheet3.Range("B12").Value = 0 End Sub Private Sub DBH_Click() Dim ts ts = Sheet3.Range("B5").Value If ts = 1 And DBH.Value = True Then Range("B7").Value = 1 If ts = 1 And DBH.Value = False Then Range("B7").Value = 0 If ts = 2 And DBH.Value = True Then Range("B7").Value = 4 If ts = 2 And DBH.Value = False Then Range("B7").Value = 0 If ts = 3 And DBH.Value = True Then Range("B7").Value = 7 If ts = 3 And DBH.Value = False Then Range("B7").Value = 0 If ts = 4 And DBH.Value = True Then Range("B7").Value = 8 If ts = 4 And DBH.Value = False Then Range("B7").Value = 0 End Sub Private Sub HA_Click() Dim ts ts = Sheet3.Range("B5").Value If ts = 1 And ha.Value = True Then Range("B6").Value = 1.1 If ts = 1 And ha.Value = False Then Range("B6").Value = 0 203 If ts = 2 And ha.Value = True Then Range("B6").Value = 7.7 If ts = 2 And ha.Value = False Then Range("B6").Value = 0 If ts = 3 And ha.Value = True Then Range("B6").Value = 56 If ts = 3 And ha.Value = False Then Range("B6").Value = 0 If ts = 4 And ha.Value = True Th en Range("B6").Value = 33 If ts = 4 And ha.Value = False Then Range("B6").Value = 0 End Sub Private Sub AGEde_Click() Dim ts ts = Sheet3.Range("B5").Value If ts = 1 And AGEde.Value = True Then Range("B8").Value = 3 If ts = 1 And AGEde.Value = False The n Range("B8").Value = 0 If ts = 2 And AGEde.Value = True Then Range("B8").Value = 7 If ts = 2 And AGEde.Value = False Then Range("B8").Value = 0 If ts = 3 And AGEde.Value = True Then Range("B8").Value = 29 If ts = 3 And AGEde.Value = False Then Range("B8") .Value = 0 If ts = 4 And AGEde.Value = True Then Range("B8").Value = 10 If ts = 4 And AGEde.Value = False Then Range("B8").Value = 0 End Sub Private Sub CSQ_Click() Dim ts ts = Sheet3.Range("B5").Value If ts = 1 And CSQ.Value = True Then Range("B10").Value = 0.3 If ts = 1 And CSQ.Value = False Then Range("B10").Value = 0 If ts = 2 And CSQ.Value = True Then Range("B10").Value = 0.75 If ts = 2 And CSQ.Value = False Then Range("B10").Value = 0 If ts = 3 And CSQ.Value = True Then Range("B10"). Value = 0.06 If ts = 3 And CSQ.Value = False Then Range("B10").Value = 0 If ts = 4 And CSQ.Value = True Then Range("B10").Value = 0.53 If ts = 4 And CSQ.Value = False Then Range("B10").Value = 0 End Sub Private Sub INMC_Df_Click() If INMC_Df.Value = True And Sheet2.Range("B14").Value = 1 Then Sheet3.Range("e14").Value = 29.4 'use the initial mc from the woody chips storage study If INMC_Df.Value = True And Sheet2.Range("B14").Value = 2 Then Sheet3.Range("e14").Value = 34 'use the initial mc from the log ging residue study 204 If INMC_Df.Value = False Then Sheet3.Range("E14").Value = 0 End Sub Private Sub REMC_df_Click() If REMC_df.Value = True And Sheet2.Range("B14").Value = 1 Then Sheet3.Range("e15").Value = 39.3 'use the resulted mc from the woody chips storage study If REMC_df.Value = True And Sheet2.Range("B14").Value = 2 Then Sheet3.Range("e15").Value = 26.3 'use the resulted mc from the logging residue study If REMC_df.Value = False Then Sheet3.Range("E15").Value = 0 End Sub Private Sub Worksheet_Ch ange(ByVal Target As Range) Dim AGE AGE = Sheet3.Range("B8").Value Dim ts ts = Sheet3.Range("B5").Value 'DBH range Dim DBH DBH = Sheet3.Range("B7").Value If DBH > 10 Then MsgBox "Invalid input, please type in a value lower than 10 inches", , "Error Msg" If (ts = 1 And AGE > 9) Then MsgBox "Invalid age! The valid age range for hybrid willow is 1~9 year(s).", , "Error Msg" If (ts = 2 And AGE > 9) Then MsgBox "Invalid age! The valid age range for hybrid poplar is 1~9 year(s).", , "Error Msg" If (ts = 3 And AGE > 50) Then MsgBox "Invalid age! The valid age range for jack pine is 1~50 year(s).", , "Error Msg" If (ts = 4 And AGE > 50) Then MsgBox "Invalid age! The valid age range for mixed -species wood is 1~50 year(s).", , "Error Msg" 'Transportation range Dim DR DR = Sheet3.Range("E7").Value If DR > 10 Then MsgBox "Invalid input, please type in a dirt road distance lower than 10 miles.", , "Error Msg" If DR > 10 Then Sheet3.Range("E7").Value = 0 Dim GR GR = Sheet3.Range("E8").Value If GR > 45 Then MsgBox "Inva lid input, please type in a gravel road distance lower than 45 miles.", , "Error Msg" If GR > 45 Then Sheet3.Range("E8").Value = 0 205 Dim PR PR = Sheet3.Range("E9").Value If PR > 60 Then MsgBox "Invalid input, please type in a paved road distance lower than 60 miles.", , "Error Msg" If PR > 60 Then Sheet3.Range("E9").Value = 0 Dim Hwy Hwy = Sheet3.Range("E10").Value If Hwy > 200 Then MsgBox "Invalid input, please type in a highway distance lower than 200 miles.", , "Error Msg" If Hwy > 200 Then Sheet3.Range("E10").Value = 0 'Moisture content range Dim OMC OMC = Sheet3.Range("E14").Value 'If OMC > 65 Then MsgBox "Invalid input, please type in a moisture content between 25% ~ 65%.", , "Error Msg - Moisture Content." 'If OMC < 25 Then MsgBox "Inva lid input, please type in a moisture content between 25% ~ 65%.", , "Error Msg - Moisture Content." 'If OMC > 65 Then Sheet3.Range("E14").Value = 45 'If OMC < 25 Then Sheet3.Range("E14").Value = 45 Dim AMC AMC = Sheet3.Range("E15").Value 'If AMC > 65 Then MsgBox "Invalid input, please type in a moisture content between 15% ~ 65%.", , "Error Msg - Moisture Content." 'If AMC < 15 Then MsgBox "Invalid input, please type in a moisture content between 15% ~ 65%.", , "Error Msg - Moisture Content." 'If AMC > 65 Then Sheet3.Range("E15").Value = 45 'If AMC < 15 Then Sheet3.Range("E15").Value = 45 End Sub Private Sub Railway_Click() If Railway.Value = True Then MsgBox "This model temporarily can not handle railway transportation.", , "Error Msg - Railway" If Railwa y.Value = True Then Railway.Value = False End Sub 206 Sheet2. Model output Private Sub CommandButton1_Click() 'clear the harvesting system info Sheet1.Range("b31:f56, e30").Value = "" End Sub Private Sub SPre_Click() If SPre.Value = True Then Sheet1.Range("b13") = 1 End If If SPre.Value = False Then Sheet1.Range("b13") = 0 End If End Sub 207 Forms Ð WT2 WINDOW 'default value click Private Sub DFFBCOST_Click() If DFFBCOST.Value = True Then FBCOST.Text = Sheet6.Range("D20").Value FBUSDF = " --" FBDF.Value = False End If If DFFBCOST.Value = False Then FBCOST.Text = " --" End If End Sub Private Sub FBDF_Click() If FBDF.Value = True Then FBCOST_W.Show DFFBCOST.Value = False End If If FBDF.Value = False Then FBUSDF = "" FBCOST.Text = " --" End If End Sub Private Sub DFSKCOST_Click() If DFSKCOST.Value = True Then SKCOST = Format(Sheet6.Range("G20").Value, "0.00") SKUSDF = " --" SKDF.Value = False End If If DFSKCOST.Value = False Then SKCOST.Text = " --" End If End Sub Private Sub SKDF_Click() If SKDF.Value = True Then SKCOST_W.Show DFSKCOST.Value = False 208 End If If SKDF.Value = False Then SKUSDF = "" SKCOST.Text = " --" End If End Sub Private Sub DFMTD_CLICK() If DFMTD.Value = True Then FBMTD.Text = Format(Sheet6.Range("E33").Value, "0") If DFMT D.Value = False Then FBMTD.Text = "0" End Sub Private Sub DFCUTS_Click() If DFCUTS.Value = True Then FBCUT.Text = Format(Sheet6.Range("E34").Value, "0") If DFCUTS.Value = False Then FBCUT.Text = "0" End Sub Private Sub DFMBD_Click() If DFMBD.Value = True Then FBMBD.Text = Format(Sheet6.Range("E35").Value, "0") If DFMBD.Value = False Then FBMBD.Text = "0" End Sub Private Sub DFx1_Click() If DFx1.Value = True Then SKX1.Text = Format(Sheet6.Range("E48").Value, "0") If DFx1.Value = False Then SKX1.Text = "0" End Sub Private Sub DFX2_Click() If DFX2.Value = True Then SKX2.Text = Format(Sheet6.Range("E49").Value, "0") If DFX2.Value = False Then SKX2.Text = "0" End Sub 'Value of textbox Private Sub NofFB_Change() Dim numberofFB 209 numberofFB = Val(NofFB.Tex t) If numberofFB <= 0 Then MsgBox "Please input a string value equal or larger than 1 .", , "Error Msg - Number of feller buncher(s)" NofFB.Text = "1" End If End Sub 'model choice Private Sub SS_FB_Change() If MsgBox("This model temporarily can only handle feller buncher with rotating saw.", , "Error Msg - Shear saw feller buncher") = vbOK Then WT2_window.SS_FB.ListIndex = -1 End Sub Private Sub BS_FB_Change() If MsgBox("This model temporarily can only handle feller buncher with rotating saw.", , "Error Msg - Bar saw feller buncher") = vbOK Then WT2_window.BS_FB.ListIndex = -1 End Sub Private Sub LINE_SK_Change() If MsgBox("This model temporarily can only handle grapple skidder.", , "Error Msg - Line Skidder") = vbOK Then WT2_window.LINE_SK.ListIn dex = -1 End Sub Private Sub CB_SK_Change() If MsgBox("This model temporarily can only handle grapple skidder.", , "Error Msg - Clam Bunk Skidder") = vbOK Then WT2_window.CB_SK.ListIndex = -1 End Sub Private Sub NofSK_Change() Dim numberofSK numberofSK = Val(NofSK.Text) If numberofSK <= 0 Then MsgBox "Please input a string value equal or larger than 1 .", , "Error Msg - Number of skidder(s)" NofSK.Text = "1" End If 210 End Sub Private Sub WheelFB_BOX_Change() Dim FB_VL FB_VL = WT2_window.WheelFB_BOX.Val ue If FB_VL = "Large (with swing -boom and self leveling cab)" Then MsgBox "This model temporarily can only handle medium size feller buncher such as John Deere 653G.", , "Error Msg - Feller buncher" If FB_VL = "Small (drive -to-tree)" Then MsgBox "This mode l temporarily can only handle medium size feller buncher such as John Deere 653G.", , "Error Msg - Feller buncher" End Sub Private Sub WheelSK_BOX_Change() Dim SK_VL SK_VL = WT2_window.WheelSK_BOX.Value If SK_VL = "Large (300 HP)" Then MsgBox "This mode l temporarily can only handle medium size skidder such as John Deere 740A.", , "Error Msg - Skidder" If SK_VL = "Small (237 HP)" Then MsgBox "This model temporarily can only handle medium size skidder such as John Deere 740A.", , "Error Msg - Skidder" End Sub Private Sub WT_CANCEL_Click() Unload WT2_window 'Nofill the productivity estimation equation of the selected machine Sheet6.Range("a26:I99").Interior.ColorIndex = 0 'every cell End Sub Private Sub WT_OK_Click() Dim FB_VL FB_VL = WT2_window.WheelFB_BOX.Value If FB_VL = "Large (with swing -boom and self leveling cab)" Then MsgBox "This model temporarily can only handle medium size feller buncher such as John Deere 653G.Please change your choice.", , "Error Msg - Feller buncher" If FB _VL = "Small (drive -to-tree)" Then MsgBox "This model temporarily can only handle medium size feller buncher such as John Deere 653G.Please change your choice.", , "Error Msg - Feller buncher" Dim SK_VL SK_VL = WT2_window.WheelSK_BOX.Value 211 If SK_VL = "Lar ge (300 HP)" Then MsgBox "This model temporarily can only handle medium size skidder such as John Deere 740A.Please change your choice.", , "Error Msg - Skidder" If SK_VL = "Small (237 HP)" Then MsgBox "This model temporarily can only handle medium size sk idder such as John Deere 740A.Please change your choice.", , "Error Msg - Skidder" Sheet1.Range("d31").Value = NofFB Sheet2.Range("a10").Value = NofFB Sheet1.Range("d36").Value = NofSK Sheet2.Range("a11").Value = NofSK 'SK prediction Dim x1 x1 = Val(SKX1 .Text) Dim x2 x2 = Val(SKX2.Text) 'Highlight the productivity estimation equation of the selected machine Sheet6.Range("a26,a27:i27").Interior.ColorIndex = 36 ' TITLE Sheet6.Range("a32:h35").Interior.ColorIndex = 36 'FB3 Sheet6.Range("I32").Interior.ColorIndex = 37 'FB3 Sheet6.Range("a40,a41:i41").Interior.ColorIndex = 36 'SK TITLE Sheet6.Range("a47:h49").Interior.ColorIndex = 36 'SK3 Sheet6.Range("I47").Interior.ColorIndex = 37 'SK3 'harvesting system info summary Sheet1 .Range("e30").Value = "Whole tree harvesting system" 'system Sheet1.Range("b31").Value = "Feller buncher:" 'machine Sheet1.Range("b36").Value = "Skidder:" 'machine Sheet1.Range("d34").Value = WheelFB_BOX.Text 'model Sheet1.Range("d39").Value = WheelSK_BOX.Text 'model If DFFBCOST.Value = True Then Sheet1.Range("d32").Value = Val(FBCOST.Text) 'default hourly cost Sheet1.Range("c33").Value = "(Model default value)" End If If FBDF.Value = True Then Sheet1.Range("d32").Value = Val(FBUSDF.Text) 'us er defined hourly cost Sheet1.Range("c33").Value = "(User input value)" End If If DFSKCOST.Value = True Then Sheet1.Range("d37").Value = Val(SKCOST.Text) 'default hourly cost Sheet1.Range("c38").Value = "(Model default value)" End If If SKDF.Value = True Then 212 Sheet1.Range("d37").Value = Val(SKUSDF.Text) 'user defined hourly cost Sheet1.Range("c38").Value = "(User input value)" End If 'Sheet1.Range("e32").Value = "Move to tree distance (Feet):" 'Sheet1.Range("e33").Value = "Number of cuts per cycle:" 'Shee t1.Range("e34").Value = "Move to bunch distance (Feet):" Sheet1.Range("f32").Value = Val(FBMTD.Text) Sheet1.Range("f33").Value = Val(FBCUT.Text) Sheet1.Range("f34").Value = Val(FBMBD.Text) 'Sheet1.Range("e37").Value = "Travel empty distance (Feet):" 'She et1.Range("e38").Value = "Travel loaded distance (Feet):" Sheet1.Range("f37").Value = Val(SKX1.Text) Sheet1.Range("f38").Value = Val(SKX2.Text) 'FB prediction Dim MTD MTD = Val(FBMTD.Text) Dim MBD MBD = Val(FBMBD.Text) Dim CUT CUT = Val(FBCUT.Text) If MTD < 0 Or MTD > 90 Or MBD < 1 Or MBD > 82 Or CUT < 1 Or CUT > 12 Or x1 < 150 Or x1 > 950 Or x2 < 125 Or x2 > 1125 Then MsgBox "Note: The user input value is beyond the model training range. User should expect a larger predicting error.", , "Warning - User input is beyond the default range." End If Dim FB_PROD Dim biomasspertree biomasspertree = Exp( -2.2094 + 2.3867 * Log(2.54 * Sheet3.Range("b7").Value)) 'tree weight in kg BPT_ODT = biomasspertree * 2.20462 / 2000 FB_PROD = (CUT * BPT_ODT) / (((11.08 2 + 0.33 * MTD + 70.098 * CUT + 0.313 * MBD) * 0.01)) * 60 213 Sheet1.Range("f31").Value = FB_PROD Sheet6.Range("I32").Value = FB_PROD Dim SK_PROD SK_PROD = 1.6 * (60 / ((103.343 + 0.102 * x1 + 0.278 * x2) * 0.01)) Sheet1.Range("f36").Value = SK_PROD Sheet6 .Range("I47").Value = SK_PROD 'close the userform if all the condictions are satisfied If (FB_VL = "Medium (with swing -boom)") And (SK_VL = "Medium") And NofFB >= 1 And NofSK >= 1 Then Unload WT2_window End Sub 214 WT3 WINDOW 'default value click Private Sub DFFBCOST_Click() If DFFBCOST.Value = True Then FBCOST.Text = Sheet6.Range("D20").Value FBUSDF = " --" FBDF.Value = False End If If DFFBCOST.Value = False Then FBCOST.Text = " --" End If End Sub Private Sub FBDF_Click() If FBDF.Value = True Then FBCOST_W .Show DFFBCOST.Value = False End If If FBDF.Value = False Then FBUSDF = "" FBCOST.Text = " --" End If End Sub Private Sub DFSKCOST_Click() If DFSKCOST.Value = True Then SKCOST = Format(Sheet6.Range("G20").Value, "0.00") SKUSDF = " --" SKDF.Value = False End If If DFSKCOST.Value = False Then SKCOST.Text = " --" End If End Sub Private Sub SKDF_Click() If SKDF.Value = True Then SKCOST_W.Show DFSKCOST.Value = False End If If SKDF.Value = False Then 215 SKUSDF = "" SKCOST.Text = " --" End If End Sub Private Su b DFMTD_CLICK() If DFMTD.Value = True Then FBMTD.Text = Format(Sheet6.Range("E37").Value, "0") If DFMTD.Value = False Then FBMTD.Text = "0" End Sub Private Sub DFCUTS_Click() If DFCUTS.Value = True Then FBCUT.Text = Format(Sheet6.Range("E38").Value, "0") If DFCUTS.Value = False Then FBCUT.Text = "0" End Sub Private Sub DFMBD_Click() If DFMBD.Value = True Then FBMBD.Text = Format(Sheet6.Range("E39").Value, "0") If DFMBD.Value = False Then FBMBD.Text = "0" End Sub Private Sub DFx1_Click() If DFx1.Value = T rue Then SKX1.Text = Format(Sheet6.Range("E51").Value, "0") If DFx1.Value = False Then SKX1.Text = "0" End Sub Private Sub DFX2_Click() If DFX2.Value = True Then SKX2.Text = Format(Sheet6.Range("E52").Value, "0") If DFX2.Value = False Then SKX2.Text = "0" End Sub 'Value of textbox Private Sub NofFB_Change() Dim numberofFB numberofFB = Val(NofFB.Text) If numberofFB <= 0 Then MsgBox "Please input a string value equal or larger than 1 .", , "Error Msg - Number of feller buncher(s)" NofFB.Text = "1" End If End Sub 'model choice 216 Private Sub SS_FB_Change() If MsgBox("This model temporarily can only handle feller buncher with rotating saw.", , "Error Msg - Shear saw feller buncher") = vbOK Then WT3_window.SS_FB.ListIndex = -1 End Sub Private Sub BS_FB_Chang e() If MsgBox("This model temporarily can only handle feller buncher with rotating saw.", , "Error Msg - Bar saw feller buncher") = vbOK Then WT3_window.BS_FB.ListIndex = -1 End Sub Private Sub LINE_SK_Change() If MsgBox("This model temporarily can only h andle grapple skidder.", , "Error Msg - Line Skidder") = vbOK Then WT3_window.LINE_SK.ListIndex = -1 End Sub Private Sub CB_SK_Change() If MsgBox("This model temporarily can only handle grapple skidder.", , "Error Msg - Clam Bunk Skidder") = vbOK Then WT3 _window.CB_SK.ListIndex = -1 End Sub Private Sub NofSK_Change() Dim numberofSK numberofSK = Val(NofSK.Text) If numberofSK <= 0 Then MsgBox "Please input a string value equal or larger than 1 .", , "Error Msg - Number of skidder(s)" NofSK.Text = "1" End If End Sub Private Sub WheelFB_BOX_Change() Dim FB_VL FB_VL = WT3_window.WheelFB_BOX.Value If FB_VL = "Large (with swing -boom and self leveling cab)" Then MsgBox "This model temporarily can only handle medium size feller buncher such as John Deere 653G.", , "Error Msg - Feller buncher" If FB_VL = "Small (drive -to-tree)" Then MsgBox "This model temporarily can only handle medium size feller buncher such as John Deere 653G.", , "Error Msg - Feller buncher" End Sub Private Sub WheelSK_BOX_Change() 217 Dim SK_VL SK_VL = WT3_window.WheelSK_BOX.Value If SK_VL = "Large (300 HP)" Then MsgBox "This model temporarily can only handle medium size skidder such as John Deere 740A.", , "Error Msg - Skidder" If SK_VL = "Small (237 HP)" Then MsgBox "This model temporarily can on ly handle medium size skidder such as John Deere 740A.", , "Error Msg - Skidder" End Sub Private Sub WT_CANCEL_Click() Unload WT3_window 'Nofill the productivity estimation equation of the selected machine Sheet6.Range("a26:I99").Interior.ColorIndex = 0 ' every cell End Sub Private Sub WT_OK_Click() Dim FB_VL FB_VL = WT3_window.WheelFB_BOX.Value If FB_VL = "Large (with swing -boom and self leveling cab)" Then MsgBox "This model temporarily can only handle medium size feller buncher such as John Deere 653G.Please change your choice.", , "Error Msg - Feller buncher" If FB_VL = "Small (drive -to-tree)" Then MsgBox "This model temporarily can only handle medium size feller buncher such as John Deere 653G.Please change your choice.", , "Error Msg - Feller bu ncher" Dim SK_VL SK_VL = WT3_window.WheelSK_BOX.Value If SK_VL = "Large (300 HP)" Then MsgBox "This model temporarily can only handle medium size skidder such as John Deere 740A.Please change your choice.", , "Error Msg - Skidder" If SK_VL = "Small (237 H P)" Then MsgBox "This model temporarily can only handle medium size skidder such as John Deere 740A.Please change your choice.", , "Error Msg - Skidder" Sheet1.Range("d31").Value = NofFB Sheet2.Range("a10").Value = NofFB Sheet1.Range("d36").Value = NofSK Sheet2.Range("a11").Value = NofSK 'SK prediction Dim x1 x1 = Val(SKX1.Text) Dim x2 x2 = Val(SKX2.Text) 'If x1 < 15 Or x1 > 270 Or x2 <= 0 Or x2 > 3 Or x3 <= 15 Or x3 > 40 Or x4 <= 67.5 Or x4 > 322.5 Then 'MsgBox "Note: The user input value is beyond the mo del training range. User should expect a larger predicting error.", , "Warning - User input is beyond the default range." 218 'End If 'Highlight the productivity estimation equation of the selected machine Sheet6.Range("a26,a27:i27").Interior.ColorIndex = 36 ' TITLE Sheet6.Range("a36:h39").Interior.ColorIndex = 36 'FB3 Sheet6.Range("I36").Interior.ColorIndex = 37 'FB3 Sheet6.Range("a40,a41:i41").Interior.ColorIndex = 36 'SK TITLE Sheet6.Range("a50:h52").Interior.ColorIndex = 36 'SK3 Sheet6.Range("I50").Interior .ColorIndex = 37 'SK3 'harvesting system info summary Sheet1.Range("e30").Value = "Whole tree harvesting system" 'system Sheet1.Range("b31").Value = "Feller buncher:" 'machine Sheet1.Range("b36").Value = "Skidder:" 'machine Sheet1.Range("d34").Value = WheelFB_BOX.Text 'model Sheet1.Range("d39").Value = WheelSK_BOX.Text 'model If DFFBCOST.Value = True Then Sheet1.Range("d32").Value = Val(FBCOST.Text) 'default hourly cost Sheet1.Range("c33").Value = "(Model default value) " End If If FBDF.Value = True Then Sheet1.Range("d32").Value = Val(FBUSDF.Text) 'user defined hourly cost Sheet1.Range("c33").Value = "(User input value)" End If If DFSKCOST.Value = True Then Sheet1.Range("d37").Value = Val(SKCOST.Text) 'default hourly cos t Sheet1.Range("c38").Value = "(Model default value)" End If If SKDF.Value = True Then Sheet1.Range("d37").Value = Val(SKUSDF.Text) 'user defined hourly cost Sheet1.Range("c38").Value = "(User input value)" End If 'Sheet1.Range("e32").Value = "Move to tre e distance (Feet):" 'Sheet1.Range("e33").Value = "Number of cuts per cycle:" 'Sheet1.Range("e34").Value = "Move to bunch distance (Feet):" Sheet1.Range("f32").Value = Val(FBMTD.Text) Sheet1.Range("f33").Value = Val(FBCUT.Text) Sheet1.Range("f34").Value = Val(FBMBD.Text) 'Sheet1.Range("e37").Value = "Travel empty distance (Feet):" 'Sheet1.Range("e38").Value = "Positioning distance (Feet):" 'Sheet1.Range("e39").Value = "Number of trees per cycle:" 'Sheet1.Range("e40").Value = "Travel loaded distance (Feet): " Sheet1.Range("f37").Value = Val(SKX1.Text) 219 Sheet1.Range("f38").Value = Val(SKX2.Text) 'FB prediction Dim MTD MTD = Val(FBMTD.Text) Dim MBD MBD = Val(FBMBD.Text) Dim CUT CUT = Val(FBCUT.Text) If MTD < 12 Or MTD > 120 Or MBD < 12 Or MBD > 120 Or CUT < 1 Or CUT > 6 Or x1 < 75 Or x1 > 800 Or x2 <= 150 Or x2 > 1100 Then MsgBox "Note: The user input value is beyond the model training range. User should expect a larger predicting error.", , "Warning - User input is beyond the default range." End If Dim FB_ PROD Dim biomasspertree biomasspertree = Exp( -2.2094 + 2.3867 * Log(2.54 * Sheet3.Range("b7").Value)) 'tree weight in kg BPT_ODT = biomasspertree * 2.20462 / 2000 FB_PROD = (CUT * BPT_ODT) / (((25.32 + 0.356 * MTD + 7.179 * CUT + 0.584 * MBD) * 0.01)) * 60 Sheet1.Range("f31").Value = FB_PROD Sheet6.Range("I36").Value = FB_PROD Dim SK_PROD SK_PROD = 1.1 * (60 / ((103.343 + 0.102 * x1 + 0.278 * x2) * 0.01)) Sheet1.Range("f36").Value = SK_PROD Sheet6.Range("I50").Value = SK_PROD 'close the userform if all the conditions are satisfied If (FB_VL = "Medium (with swing -boom)") And (SK_VL = "Medium") And NofFB >= 1 And NofSK >= 1 Then Unload WT3_window End Sub 220 'default value click Private Sub DFFBCOST_Click() If DFFBCOST.Value = True Then FBCOST.Text = Sheet6 .Range("D20").Value FBUSDF = " --" FBDF.Value = False End If If DFFBCOST.Value = False Then FBCOST.Text = " --" End If End Sub Private Sub FBDF_Click() If FBDF.Value = True Then FBCOST_W.Show DFFBCOST.Value = False End If If FBDF.Value = False Then FBUSDF = "" FBCOST.Text = " --" End If End Sub Private Sub DFSKCOST_Click() If DFSKCOST.Value = True Then SKCOST = Format(Sheet6.Range("G20").Value, "0.00") SKUSDF = " --" SKDF.Value = False End If If DFSKCOST.Value = False Then SKCOST.Text = " --" End If End Su b Private Sub SKDF_Click() If SKDF.Value = True Then SKCOST_W.Show DFSKCOST.Value = False End If 221 If SKDF.Value = False Then SKUSDF = "" SKCOST.Text = " --" End If End Sub Private Sub DFMTD_CLICK() If DFMTD.Value = True Then FBMTD.Text = Format(Sheet6.Range("E29").Value, "0") If DFMTD.Value = False Then FBMTD.Text = "0" End Sub Private Sub DFCUTS_Click() If DFCUTS.Value = True Then FBCUT.Text = Format(Sheet6.Range("E30").Value, "0") If DFCUTS.Value = False Then FBCUT.Text = "0" End Sub Private Sub DFMBD_Click() If DFMBD.Value = True Then FBMBD.Text = Format(Sheet6.Range("E31").Value, "0") If DFMBD.Value = False Then FBMBD.Text = "0" End Sub Private Sub DFx1_Click() If DFx1.Value = True Then SKX1.Text = Format(Sheet6.Range("E43").Value, "0") If DFx1.Value = False Then SKX1.Text = "0" End Sub Private Sub DFX2_Click() If DFX2.Value = True Then SKX2.Text = Format(Sheet6.Range("E44").Value, "0") If DFX2.Value = False Then SKX2.Text = "0" End Sub Private Sub DFX3_Click() If DFX3.Value = True Then SKX3.Text = Format(Sheet6.Range("E45").Value, "0") If DFX3.Value = False Then SKX3.Text = "0" End Sub Private Sub DFX4_Click() If DFX4.Value = True Then SKX4.Text = Format(Sheet6.Range("E46").Value, "0") If DFX4.Value = False Then SKX4.Text = "0" End Sub 'Value of textbox 222 Private Sub NofFB_Change() Dim numberofFB numberofFB = Val(NofFB.Text) If numberofFB <= 0 Then MsgBox "Please input a string value equal or larger than 1 .", , "Error Msg - Number of feller buncher(s)" NofFB.Text = "1" End If End Sub 'model choice Private Sub SS_FB_Change() If MsgBox("This model temporarily can only handle feller buncher with rotating saw.", , "Error Msg - Shear saw feller buncher") = vbOK Then WT4_window.SS_FB.ListIndex = -1 End Sub Private Sub BS_FB_Change() If MsgBox("This model temporarily can only handle feller buncher with rotating saw.", , "Error Msg - Bar saw feller buncher") = vbOK Then WT4_window.BS_FB.ListIndex = -1 End Sub Private Sub LINE_SK_Change() If MsgBox("This model temporarily can only handle grapple skidder.", , "Error Msg - Line Skidder") = vbOK Then WT4_window.LINE_SK.ListIndex = -1 End Sub Private Sub CB_SK_Change() If MsgBox("This model temporarily can only handle grapple skidder.", , "Error Msg - Clam Bunk Skidder") = vbOK Then WT4_windo w.CB_SK.ListIndex = -1 End Sub Private Sub NofSK_Change() Dim numberofSK numberofSK = Val(NofSK.Text) If numberofSK <= 0 Then MsgBox "Please input a string value equal or larger than 1 .", , "Error Msg - Number of skidder(s)" NofSK.Text = "1" End If End Sub Private Sub WheelFB_BOX_Change() 223 Dim FB_VL FB_VL = WT4_window.WheelFB_BOX.Value If FB_VL = "Large (with swing -boom and self leveling cab)" Then MsgBox "This model temporarily can only handle medium size feller buncher such as John Deere 653G.", , "Err or Msg - Feller buncher" If FB_VL = "Small (drive -to-tree)" Then MsgBox "This model temporarily can only handle medium size feller buncher such as John Deere 653G.", , "Error Msg - Feller buncher" End Sub Private Sub WheelSK_BOX_Change() Dim SK_VL SK_VL = WT4_window.WheelSK_BOX.Value If SK_VL = "Large (300 HP)" Then MsgBox "This model temporarily can only handle medium size skidder such as John Deere 740A.", , "Error Msg - Skidder" If SK_VL = "Small (237 HP)" Then MsgBox "This model temporarily can only ha ndle medium size skidder such as John Deere 740A.", , "Error Msg - Skidder" End Sub Private Sub WT_CANCEL_Click() Unload WT4_window 'Nofill the productivity estimation equation of the selected machine Sheet6.Range("a26:I99").Interior.ColorIndex = 0 'ever y cell End Sub Private Sub WT_OK_Click() Dim FB_VL FB_VL = WT4_window.WheelFB_BOX.Value If FB_VL = "Large (with swing -boom and self leveling cab)" Then MsgBox "This model temporarily can only handle medium size feller buncher such as John Deere 653G.Pleas e change your choice.", , "Error Msg - Feller buncher" If FB_VL = "Small (drive -to-tree)" Then MsgBox "This model temporarily can only handle medium size feller buncher such as John Deere 653G.Please change your choice.", , "Error Msg - Feller buncher" Dim SK_VL SK_VL = WT4_window.WheelSK_BOX.Value If SK_VL = "Large (300 HP)" Then MsgBox "This model temporarily can only handle medium size skidder such as John Deere 740A.Please change your choice.", , "Error Msg - Skidder" If SK_VL = "Small (237 HP)" Then MsgBox "This model temporarily can only handle medium size skidder such as John Deere 740A.Please change your choice.", , "Error Msg - Skidder" Sheet1.Range("d31").Value = NofFB 224 Sheet2.Range("a10").Value = NofFB Sheet1.Range("d36").Value = NofSK Sheet2.R ange("a11").Value = NofSK 'SK prediction Dim x1 x1 = Val(SKX1.Text) Dim x2 x2 = Val(SKX2.Text) Dim x3 x3 = Val(SKX3.Text) Dim x4 x4 = Val(SKX4.Text) 'If x1 < 15 Or x1 > 270 Or x2 <= 0 Or x2 > 3 Or x3 <= 15 Or x3 > 40 Or x4 <= 67.5 Or x4 > 322.5 Then 'MsgBox "Note: The user input value is beyond the model training range. User should expect a larger predicting error.", , "Warning - User input is beyond the default range." 'End If 'Highlight the productivity estimation equation of the selected machine Sheet6.Range("a26,a27:i31").Interior.ColorIndex = 36 ' TITLE Sheet6.Range("a28:h31").Interior.ColorIndex = 36 'FB1 Sheet6.Range("I28").Interior.ColorIndex = 37 'FB1 Sheet6.Range("a40,a41:i41").Interior.ColorIndex = 36 'SK TITLE Sheet6.Range("a42:h46").Inte rior.ColorIndex = 36 'SK1 Sheet6.Range("I42").Interior.ColorIndex = 37 'SK1 'harvesting system info summary Sheet1.Range("e30").Value = "Whole tree harvesting system" 'system Sheet1.Range("b31").Value = "Feller buncher:" 'machine Sheet1.Range("b36").Value = "Skidder:" 'machine Sheet1.Range("d34").Value = WheelFB_BOX.Text 'model Sheet1.Range("d39").Value = WheelSK_BOX.Text 'model If DFFBCOST.Value = True Then Sheet1.Range("d32").Value = Val(FBCOST.Text) 'default hourly cost Sheet1.Range("c33" ).Value = "(Model default value)" End If If FBDF.Value = True Then Sheet1.Range("d32").Value = Val(FBUSDF.Text) 'user defined hourly cost Sheet1.Range("c33").Value = "(User input value)" End If If DFSKCOST.Value = True Then 225 Sheet1.Range("d37").Value = Val( SKCOST.Text) 'default hourly cost Sheet1.Range("c38").Value = "(Model default value)" End If If SKDF.Value = True Then Sheet1.Range("d37").Value = Val(SKUSDF.Text) 'user defined hourly cost Sheet1.Range("c38").Value = "(User input value)" End If 'Sheet1.Range("e32").Value = "Move to tree distance (Feet):" 'Sheet1.Range("e33").Value = "Number of cuts per cycle:" 'Sheet1.Range("e34").Value = "Move to bunch distance (Feet):" Sheet1.Range("f32").Value = Val(FBMTD.Text) Sheet1.Range("f33").Value = Val (FBCUT.Text) Sheet1.Range("f34").Value = Val(FBMBD.Text) 'Sheet1.Range("e37").Value = "Travel empty distance (Feet):" 'Sheet1.Range("e38").Value = "Positioning distance (Feet):" 'Sheet1.Range("e39").Value = "Number of trees per cycle:" 'Sheet1.Range("e40" ).Value = "Travel loaded distance (Feet):" Sheet1.Range("f37").Value = Val(SKX1.Text) Sheet1.Range("f38").Value = Val(SKX2.Text) Sheet1.Range("f39").Value = Val(SKX3.Text) Sheet1.Range("f40").Value = Val(SKX4.Text) 'FB prediction Dim MTD MTD = Val(FBMTD .Text) Dim MBD MBD = Val(FBMBD.Text) Dim CUT CUT = Val(FBCUT.Text) If MTD < 0 Or MTD > 90 Or MBD < 1 Or MBD > 82 Or CUT < 1 Or CUT > 12 Or x1 < 120 Or x1 > 432 Or x2 <= 0 Or x2 > 72 Or x3 < 15 Or x3 > 35 Or x4 < 144 Or x4 > 516 Then MsgBox "Note: The user input value is beyond the model training range. User should expect a larger predicting error.", , "Warning - User input is beyond the default range." End If Dim FB_PROD Dim biomasspertree 226 biomasspertree = Exp( -2.2094 + 2.3867 * Log(2.54 * Sheet3.Rang e("b7").Value)) 'tree weight in kg BPT_ODT = biomasspertree * 2.20462 / 2000 FB_PROD = (CUT * BPT_ODT) / (((8.766 + 0.565 * MTD + 8.816 * CUT + 0.325 * MBD) * 0.01)) * 60 Sheet1.Range("f31").Value = FB_PROD Sheet6.Range("I28").Value = FB_PROD Dim SK_PRO D SK_PROD = (x3 * BPT_ODT) / ((19.49 + 0.076 * x1 + 0.354 * x2 + 1.778 * x3 + 0.155 * x4) * 0.01) * 60 Sheet1.Range("f36").Value = SK_PROD Sheet6.Range("I42").Value = SK_PROD 'close the userform if all the condictions are satisfied If (FB_VL = "Medium (w ith swing -boom) (John Deere 653G)") And (SK_VL = "Medium (John Deere 740A)") And NofFB >= 1 And NofSK >= 1 Then Unload WT4_window End Sub 227 Form - CTL window Private Sub CTL_CANCEL_Click() Unload CTL_WINDOW Sheet6.Range("a39:h44,A38,I39").Interior.ColorIndex = 0 'CLEAR cell End Sub Private Sub DFFWDCOST_Click() If DFFWDCOST.Value = True Then FWDCOST.Text = Format(Sheet6.Range("M20").Value, "0.00") FWDUSDF = " --" FWDDF.Value = False End If If DFFWDCOST.Value = False Then FWDCOST.Text = "0" End If End Sub Private Sub FORWARDER_BOX_Change() End Sub Private Sub fwdDF_Click() If FWDDF.Value = True Then FWDCOST_W.Show DFFWDCOST.Value = False End If If FWDDF.Value = False Then FWDUSDF = "" End Sub Private Sub FW1_Click() If FW1.Value = True Then FWX1.Text = Format(Sheet6.Range("E56").Value, "0") If FW1.Value = False Then FWX1.Text = "0" End Sub Private Sub FW2_Click() If FW2.Value = True Then FWX2.Text = Format(Sheet6.Range("E57").Value, "0") If FW2.Value = Fal se Then FWX2.Text = "0" End Sub Private Sub FW3_Click() If FW3.Value = True Then FWX3.Text = Format(Sheet6.Range("E58").Value, "0") If FW3.Value = False Then FWX3.Text = "0" End Sub Private Sub FW4_Click() If FW4.Value = True Then FWX4.Text = Format(Sheet6 .Range("E59").Value, "0") If FW4.Value = False Then FWX4.Text = "0" End Sub Private Sub FW5_Click() 228 If FW5.Value = True Then FWX5.Text = Format(Sheet6.Range("E60").Value, "0") If FW5.Value = False Then FWX5.Text = "0" If FWX1 < 0 Or FWX1 > 52.75 Or FWX2 < 2.6 Or FWX2 > 27.1 Or FWX3 < 11 Or FWX3 > 40 Or FWX4 < 0.6 Or FWX4 > 53.5 Or FWX5 < 5 Or FWX5 > 17 Then MsgBox "Note: The user input value is beyond the model training range. User should expect a larger predicting error.", , "Warning - User input is beyon d the default range." End If End Sub 'Private Sub FORWARDER_BOX_Change() 'Dim FW_VL 'FW_VL = CTL_WINDOW.FORWARDER_BOX.Value 'If FW_VL = "Large (Load carring capacity: 15 tons)" Then MsgBox "This model temporarily can only handle forwarder model such as Ponsse Bison S15.", , "Error Msg - Forwarder" 'If FW_VL = "Medium (Load carring capacity: 13 tons)" Then MsgBox "This model temporarily can only handle forwarder model such as Ponsse Bison S15.", , "Error Msg - Forwarder" 'End Sub Private Sub NofFw_Change () Dim numberofFW As Integer numberofFW = Val(NofFw.Text) Sheet2.Range("A11") = numberofFW If numberofFW <= 0 Then MsgBox "Please input a integer value equal or larger than 1 .", , "Error Msg - Number of forwarder(s)" If numberofFW <= 0 Then NofFw.Text = " 1" End Sub Private Sub CTL_OK_Click() Dim x1 x1 = Val(FWX1.Text) Dim x2 x2 = Val(FWX2.Text) Dim x3 x3 = Val(FWX3.Text) Dim x4 x4 = Val(FWX4.Text) Dim x5 x5 = Val(FWX5.Text) Dim prod prod = 1.1776 * 60 / ((242.7 + 11.79 * x1 + 14.39 * x2 + 22.32 * x3 + 10.27 * x4 + 27.77 * x5) / 100) Sheet6.Range("I55").Value = prod 229 'Highlight the productivity estimation equation of the selected machine Sheet6.Range("a54:h60,A53,I54").Interior.ColorIndex = 36 'FW Sheet6.Range("I55").Interior.ColorIndex = 37 'FW Dim FW_V L FW_VL = CTL_WINDOW.FORWARDER_BOX.Value If FW_VL = "Large (Load carring capacity: 15 tons)" Then MsgBox "This model temporarily can only handle forwarder model such as Ponsse Bison S15. Please change your choice.", , "Error Msg - Forwarder" If FW_VL = "Me dium (Load carring capacity: 13 tons)" Then MsgBox "This model temporarily can only handle forwarder model such as Ponsse Bison S15.Please change your choice.", , "Error Msg - Forwarder" If FW_VL = "Small (Load carring capacity: 12 tons) (Ponsse Bison S15 )" Then Unload CTL_WINDOW Sheet2.Range("a12").Value = NofFw End Subwindow 230 Form - RCF window Private Sub RCF_B_Change() Dim RCF_VL RCF_VL = RCF.RCF_B.Value 'If RCF_VL = "Large (with swing -boom and self leveling cab)" Then MsgBox "This model temporarily ca n only handle medium size feller buncher such as John Deere 653G.", , "Error Msg - Feller buncher" 'If RCF_VL = "Small (drive -to-tree)" Then MsgBox "This model temporarily can only handle medium size feller buncher such as John Deere 653G.", , "Error Msg - Feller buncher" End Sub Private Sub Nofrcf_Change() Dim numberofRCF numberofRCF = Val(NofRCF.Text) If numberofRCF <= 0 Then MsgBox "Please input a string value equal or larger than 1 .", , "Error Msg - Number of recofigured harvester(s)" NofRCF.Text = "1" End If Sheet2.Range("a13").Value = NofRCF End Sub Private Sub rcf_CANCEL_Click() Unload RCF 'Nofill the productivity estimation equation of the selected machine Sheet6.Range("a26:I99").Interior.ColorIndex = 0 'every cell End Sub Private Sub rcf_OK_Cl ick() Dim nU, lE, w, BD, hsP, tsP nU = Val(n.Text) Sheet2.Range("a15") = nU lE = Val(l.Text) WD = Val(WDI.Text) BD = Val(B.Text) hsP = Val(hs.Text) tsP = Val(ts.Text) Dim biomasspertree biomasspertree = Exp( -2.2094 + 2.3867 * Log(2.54 * Sheet3.Range("b7") .Value)) 'tree weight in kg BPT_ODT = biomasspertree * 2.20462 / 2000 Dim PRO If nU Mod 2 = 0 Then 231 TT = nU * lE * hsP + (WD + n * BD) * (nU + 1) * tsP Sheet6.Range("a65:i65").Interior.ColorIndex = 36 ' TITLE Sheet6.Range("a66:h73").Interior.ColorIndex = 36 ' Sheet6.Range("I66").Interior.ColorIndex = 37 ' PRO = BPT_ODT * Sheet3.Range("B9").Value / ((TT / 100) / 60) Sheet6.Range("I66").Value = PRO Sheet1.Range("f31").Value = Sheet6.Range("i66").Value Else TT = nU * lE * hsP + (WD + n * BD) * (nU) * tsP Shee t6.Range("a65:i65").Interior.ColorIndex = 36 ' TITLE Sheet6.Range("a74:h81").Interior.ColorIndex = 36 ' Sheet6.Range("I74").Interior.ColorIndex = 37 ' PRO = BPT_ODT * Sheet3.Range("B9").Value / ((TT / 100) / 60) Sheet6.Range("I74").Value = PRO Sheet1.Range ("f31").Value = Sheet6.Range("i74").Value End If Sheet1.Range("e30").Value = "Reconfigured harvesting system" Sheet1.Range("d31").Value = "Harvester" Sheet1.Range("d31").Value = Sheet2.Range("a13").Value If DFRCFCOST.Value = True Then 'use the default in fo Sheet1.Range("d32").Value = Sheet6.Range("q20").Value Sheet1.Range("d34").Value = Sheet6.Range("q5").Value End If If DFRCFCOST.Value = False Then Sheet1.Range("d32").Value = RCFUSDF.Value Sheet1.Range("d34").Value = RCF_B.Text End If Unload RCF End Sub Private Sub RCFDF_Click() If RCFDF.Value = True Then RCFCOST_W.Show DFRCFCOST.Value = False End If If RCFDF.Value = False Then RCFUSDF = "" RCFCOST.Text = " --" End If End Sub Private Sub tsdf_Click() 232 If tsdf.Value = True Then ts.Text = Format(Sheet6.Ra nge("D73").Value, "0") If tsdf.Value = False Then ts.Text = "0" End Sub Private Sub wdf_Click() If wdf.Value = True Then WDI.Text = Format(Sheet6.Range("D69").Value, "0") If wdf.Value = False Then WDI.Text = "0" End Sub 233 Form Ð Preprocessing Private Sub pp_cancel_Click() Sheet3.Range("b19").Value = "None" Sheet1.Range("d41").Value = "None" Unload PP End Sub Private Sub PP_OK_Click() Dim PP_Choice PP_Choice = PP.PP_Cho.Value Sheet3.Range("b19").Value = PP_Choice If PP_Choice = "Chipping" Then Sheet1.Rang e("d41").Value = "Chipping" Sheet1.Range("b42").Value = "Chipper" End If If PP_Choice = "Grinding" Then Sheet1.Range("e41").Value = PP.PP_Cho.Text Sheet1.Range("b42").Value = "Loader" Sheet1.Range("D44").Value = "Hood S -182" Sheet1.Range("D43").Value = S heet6.Range("N18").Value Sheet1.Range("D42").Value = "1" Sheet1.Range("D45").Value = Sheet6.Range("i42").Value Sheet1.Range("b46").Value = "Grinder" Sheet1.Range("D48").Value = "Peterson 4700B" Sheet1.Range("D47").Value = Sheet6.Range("o18").Value Sheet1.Range("D46").Value = "1" Sheet1.Range("D49").Value = Sheet6.Range("i42").Value End If Unload PP End Sub 234 Form - grinder loader window Private Sub DFGDCOST_Click() If DFGDCOST.Value = True Then GDCOST.Text = Format(Sheet6.Range("O20").Value, "0.00") GDUSDF = " --" GDDF.Value = False End If If DFGDCOST.Value = False Then GDCOST.Text = " --" End If End Sub Private Sub DFLDCOST_Click() If DFLDCOST.Value = True Then LDCOST.Text = Format(Sheet6.Range("N20").Value, "0.00") LDUSDF = " --" LDDF.Value = False End If If DFLDCOST.Value = False Then LDCOST.Text = " --" End If End Sub Private Sub DFx1_Click() If DFx1.Value = True Then glx1.Text = Format(12, "0") If DFx1.Value = False Then glx1.Text = "0" End Sub Private Sub GDDF_Click() If GDDF.Value = True Then GDCOST_W.Show DFGDCOST.Value = False End If If GDDF.Value = False Then GDUSDF = "" GDCOST.Text = " --" End If End Sub Private Sub gl_CANCEL_Click() Unload GRLD_W Sheet6.Range("a25:i99").Interior.ColorIndex = 0 'CLEAR cell color 235 Sheet1.Range("d42:d49").Val ue = "" End Sub Private Sub gl_OK_Click() Dim x1 If Sheet3.Range("b5").Value = 4 Then x1 = 6 Else x1 = Val(glx1.Text) End If Dim prod Dim biomasspertree biomasspertree = Exp( -2.2094 + 2.3867 * Log(2.54 * Sheet3.Range("b7").Value)) 'tree weight in kg BPT_ODT = biomasspertree * 2.20462 / 2000 prod = x1 * BPT_ODT * 6000 / (0.155 * x1 * Sheet3.Range("b7").Value + 20.864) Sheet6.Range("i63").Value = prod If glx1 < 5 Or FWX1 > 21 Then MsgBox "Note: The user input value is beyond the model training range. U ser should expect a larger predicting error.", , "Warning - User input is beyond the default range." End If 'Highlight the productivity estimation equation of the selected machine Sheet6.Range("a62:h64,A61,I62").Interior.ColorIndex = 36 Sheet6.Range("I63" ).Interior.ColorIndex = 37 If DFGDCOST.Value = True Then Sheet1.Range("d47").Value = GDCOST.Value If GDDF.Value = True Then Sheet1.Range("d47").Value = GDUSDF.Value If DFLDCOST.Value = True Then Sheet1.Range("d43").Value = LDCOST.Value If LDDF.Value = True Then Sheet1.Range("d43").Value = LDUSDF.Value Sheet1.Range("d46").Value = 1 Sheet1.Range("d42").Value = 1 Sheet1.Range("d44").Value = Sheet6.Range("n5").Text Sheet1.Range("d45").Value = prod Sheet1.Range("d48").Value = Sheet6.Range("o5").Text Sheet1.R ange("d49").Value = prod If glx1 > 0 Then Unload GRLD_W End Sub Private Sub LDDF_Click() 236 If LDDF.Value = True Then LDCOST_W.Show DFLDCOST.Value = False End If If LDDF.Value = False Then LDUSDF = "" LDCOST.Text = " --" End If End Sub 237 Form ÐChipper loa der Private Sub DFGDCOST_Click() If DFGDCOST.Value = True Then GDCOST.Text = Format(86.59, "0.00") GDUSDF = " --" GDDF.Value = False End If If DFGDCOST.Value = False Then GDCOST.Text = " --" End If End Sub Private Sub DFLDCOST_Click() If DFLDCOST.Value = True Then LDCOST.Text = Format(59.21, "0.00") LDUSDF = " --" LDDF.Value = False End If If DFLDCOST.Value = False Then LDCOST.Text = " --" End If End Sub Private Sub DFx1_Click() If DFx1.Value = True Then glx1.Text = Format(Sheet6.Range("e68").Value, "0") If DFx1.Value = False Then glx1.Text = "0" End Sub Private Sub GDDF_Click() If GDDF.Value = True Then GDCOST_W.Show DFGDCOST.Value = False End If If GDDF.Value = False Then GDUSDF = "" GDCOST.Text = " --" End If End Sub Private Sub gl_CANCEL_Click() Unload CPLD_W Sheet6.Range("a66:h68,A65,I66").Interior.ColorIndex = 0 238 Sheet6.Range("I67").Interior.ColorIndex = 0 Sheet6.Range("I67").Value = "" Sheet1.Range("d42:d49").Value = "" End Sub Private Sub gl_OK_Click() Dim x1 x1 = Val(glx1.Text) Dim prod prod = 0.225 * x1 * Sheet3.Range("b7").Value + 20.864 Sheet6.Range("i67").Value = prod If glx1 < 2 Or FWX1 > 5 Then MsgBox "Note: The user input value is beyond the model training range. User should expect a larger predicting error.", , "Warning - User input is beyond the default range." End If 'Highlight the productivity estimation equation of the selected machine Sheet6.Range("a66:h68,A65,I66").Interior.ColorIndex = 36 Sheet6.Range("I67").Interior.ColorIndex = 37 If DFGDCOST.Value = True Then Sheet1.Range(" d47").Value = GDCOST.Value If GDDF.Value = True Then Sheet1.Range("d47").Value = GDUSDF.Value If DFLDCOST.Value = True Then Sheet1.Range("d43").Value = LDCOST.Value If LDDF.Value = True Then Sheet1.Range("d43").Value = LDUSDF.Value Sheet1.Range("d46").Valu e = 1 Sheet1.Range("d42").Value = 1 Sheet1.Range("d44").Value = Sheet6.Range("n5").Text Sheet1.Range("d45").Value = Sheet6.Range("i67").Value Sheet1.Range("d48").Value = "Trelan 21 L chipper" Sheet1.Range("d49").Value = Sheet6.Range("i67").Value If glx1 > 0 Then Unload CPLD_W End Sub Private Sub LDDF_Click() If LDDF.Value = True Then LDCOST_W.Show DFLDCOST.Value = False End If If LDDF.Value = False Then LDUSDF = "" LDCOST.Text = " --" End If End Sub 239 Form Ð Fbcost Private Sub FB_CAL_Click() Dim II II = Val(fbx1.Text) Dim EL EL = Val(fbx2.Text) Dim SV SV = Val(FBSV.Text) / 100 Dim SMH SMH = Val(fbx3.Text) Dim UR UR = Val(FBUR.Text) / 100 Dim Interest Interest = Val(fbx5.Text) Dim Insurance Insurance = Val(fbx6.Text) Dim tax tax = Val(fbx7.Text) Dim main main = Val(fbx8.Text) Dim fuel fuel = Val(fbx9.Text) Dim lube lube = Val(fbx10.Text) Dim lab lab = Val(fbx11.Text) 'Calculate AII, depreciation, and final cost Dim AAI AAI = II * (1 - SV) * (EL + 1) / (2 * EL) + 0.15 * II AAI_V.Value = Format(AAI, "0.0 0") Dim depre depre = II * (1 - SV) / EL fbx4.Value = Format(depre, "0.00") Dim finalc finalc = II * (1 - SV) / (EL * SMH) + (Interest / 100 + Insurance / 100 + tax / 100) * AAI / SMH + (main / 100) * depre / SMH + (fuel * UR) + (lube * UR) + (lab) fbcos t_usdf.Value = Format(finalc, "0.00") 240 End Sub Private Sub FB_CAN_Click() WT_window.FBUSDF.Text = "" WT_window.FBDF.Value = False Unload FBCOST_W End Sub Private Sub FB_OK_Click() Sheet2.Range("c12").Value = fbcost_usdf.Value WT_window.FBUSDF.Text = fbcost_usdf.Value WT_window.FBCOST.Text = " --" Unload FBCOST_W End Sub 241 Form Ð Skcost Dim lab lab = Val(skx11.Text) 'Calculate AII, depreciation, and final cost Dim AAI AAI = II * (1 - SV) * (EL + 1) / (2 * EL) + 0.15 * II AAI_V.Text = Format(AAI, "0.00") Dim depre depre = II * (1 - SV) / EL skx4.Value = Format(depre, "0.00") Dim finalc finalc = II * (1 - SV) / (EL * SMH) + (Interest / 100 + Insurance / 100 + tax / 100) * AAI / SMH + (main / 100) * depre / SMH + (fuel * UR) + (lube * UR) + (lab) skcost_usd f.Value = Format(finalc, "0.00") End Sub Private Sub SK_CAN_Click() WT_window.SKUSDF.Text = "" WT_window.SKDF.Value = False Unload SKCOST_W End Sub Private Sub SK_OK_Click() Sheet2.Range("c13").Value = skcost_usdf.Value WT_window.SKUSDF.Text = skcost_usdf.Value WT_window.SKCOST.Text = " --" Unload SKCOST_W End Sub Form Ð LD cost Private Sub ld_CAL_Click() Dim II II = Val(LDx1.Text) Dim EL EL = Val(LDx2.Text) Dim SV SV = Val(LDSV.Text) / 100 Dim SMH SMH = Val(LDx3.Text) Dim UR UR = Val(LDUR.Text) / 100 Dim Interest Interest = Val(LDx5.Text) Dim Insurance Insurance = Val(LDx6.Text) Dim tax 242 tax = Val(LDx7.Text) Dim main main = Val(LDx8.Text) Dim fuel fuel = Val(LDx9.Text) Dim lube lube = Val(LDx10.Text) Dim lab lab = Val(LDx11.Text) 'Calculate AII, depreciation, and final cost Dim AAI AAI = II * (1 - SV) * (EL + 1) / (2 * EL) + 0.15 * II AAI_V.Text = AAI Dim depre depre = II * (1 - SV) / EL LDx4.Value = depre Dim finalc finalc = II * (1 - SV) / (EL * SMH) + (Interest / 100 + Insurance / 100 + tax / 100) * AAI / SMH + (main / 100) * depre / SMH + (fuel * UR) + (lube * UR) + (lab) LDcost_usdf.Value = finalc End Sub Private Sub ld_CAN_Click() GRLD_W.LDUSDF.Text = "" GRLD_W.LDDF.Value = False Unload LDCOST_W End Sub Private Sub ld_OK_Click() Sheet2.Range("c12").Value = LDcost_usdf.Value GRLD_W.LDUSDF.Text = LDcost_usdf.Value GRLD_W.LDCOST.Text = " --" Unload LDCOST_W End Sub 243 Form - Grinder cost Private Sub gd_CAL_Click() Dim II II = Val(gdx1.Text) Dim EL EL = Val(gdx2.Text) Dim SV SV = Val(gd SV.Text) / 100 Dim SMH SMH = Val(gdx3.Text) Dim UR UR = Val(gdUR.Text) / 100 Dim Interest Interest = Val(gdx5.Text) Dim Insurance Insurance = Val(gdx6.Text) Dim tax tax = Val(gdx7.Text) Dim main main = Val(gdx8.Text) Dim fuel fuel = Val(gdx9.Text) Dim lube lube = Val(gdx10.Text) Dim lab lab = Val(gdx11.Text) 'Calculate AII, depreciation, and final cost Dim AAI AAI = II * (1 - SV) * (EL + 1) / (2 * EL) + 0.15 * II AAI_V.Text = Format(AAI, "0.00") Dim depre depre = II * (1 - SV) / EL gdx4.Value = Format(depr e, "0.00") Dim finalc finalc = II * (1 - SV) / (EL * SMH) + (Interest / 100 + Insurance / 100 + tax / 100) * AAI / SMH + (main / 100) * depre / SMH + (fuel * UR) + (lube * UR) + (lab) gdcost_usdf.Value = Format(finalc, "0.00") End Sub Private Sub gd_CAN_C lick() GRLD_W.GDUSDF.Text = "" GRLD_W.GDDF.Value = False Unload GDCOST_W End Sub 244 Private Sub gd_OK_Click() Sheet2.Range("c12").Value = gdcost_usdf.Value GRLD_W.GDUSDF.Text = gdcost_usdf.Value GRLD_W.GDCOST.Text = " --" Unload GDCOST_W End Sub 245 Forms - Forwarder cost Private Sub fwd_CAL_Click() Dim II II = Val(fwdx1.Text) Dim EL EL = Val(fwdx2.Text) Dim SV SV = Val(FWDSV.Text) / 100 Dim SMH SMH = Val(fwdx3.Text) Dim UR UR = Val(FWDUR.Text) / 100 Dim Interest Interest = Val(fwdx5.Text) Dim Insurance Insur ance = Val(fwdx6.Text) Dim tax tax = Val(fwdx7.Text) Dim main main = Val(fwdx8.Text) Dim fuel fuel = Val(fwdx9.Text) Dim lube lube = Val(fwdx10.Text) Dim lab lab = Val(fwdx11.Text) 'Calculate AII, depreciation, and final cost Dim AAI AAI = II * (1 - SV) * (EL + 1) / (2 * EL) + 0.15 * II AAI_V.Value = Format(AAI, "0.00") Dim depre depre = II * (1 - SV) / EL fwdx4.Value = Format(depre, "0.00") Dim finalc finalc = II * (1 - SV) / (EL * SMH) + (Interest / 100 + Insurance / 100 + tax / 100) * AAI / SMH + (main / 100) * depre / SMH + (fuel * UR) + (lube * UR) + (lab) fwdcost_usdf.Value = Format(finalc, "0.00") End Sub Private Sub fwd_CAN_Click() CTL_WINDOW.FWDUSDF.Text = "" CTL_WINDOW.FWDDF.Value = False Unload FWDCOST_W End Sub Private Sub fwd_OK_Click() 246 Sheet2 .Range("c12").Value = fwdcost_usdf.Value CTL_WINDOW.FWDUSDF.Text = fwdcost_usdf.Value CTL_WINDOW.FWDCOST.Text = " --" Unload FWDCOST_W End Sub 247 REFERENCE S 248 REFERENCE S Achat, D.L., Deleuze, C., Landmann, G., Pousse, N., Ranger, J. and Augusto, L., 2015. 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