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THiflJ

This is to certify that the

dissertation entitled

AN ENERGY AND COST ANALYSIS MODEL
TO EVALUATE THE COMBUSTION
OF FOOD PROCESSING WASTES

presented by

Steven Alonzo Sargent

has been accepted towards fulfillment
of the requirements for

Phone degree in Aqricu‘ltura]

Engineering
Technology

fl Major professor 5%

 

Date FEbY‘UBY‘y 3, 1984

MSU is an Affirmative Action/Equal Opportunity Institution 0- 12771

 

 

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RETURNING MATERIALS:
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AN ENERGY AND COST ANALYSIS MODEL
TO EVALUATE THE COMBUSTION
OF FOOD PROCESSING WASTES

By

Steven Alonzo Sargent

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Department of Agricultural Engineering

1984

This is to certify that the

dissertation entitled

AN ENERGY AND COST ANALYSIS MODEL
TO EVALUATE THE COMBUSTION
OF FOOD PROCESSING WASTES

presented by

Steven Alonzo Sargent

has been accepted towards fulfillment
of the requirements for

Ph.D. degmin Agricultural

Engineering
Technology

a 5 Major professor

 

 

Date b" T

MS U it u Affirmative Action/Equal Opportunity Institution 0-1277 1

ABSTRACT
AN ENERGY AND COST ANALYSIS MODEL

TO EVALUATE THE COMBUSTION
OF FOOD PROCESSING WASTES

By

Steven Alonzo Sargent

Technical and economic factors pertinent to conversion
of food processing wastes into recoverable energy were inves—
tigated. Combustion characteristics for a variety of wastes
were defined, leading to the selection of components for an
in-plant waste handling system for use in conjunction with
each of three boiler systems representing pile burning,
fluidized-bed combustion and suspension—firing technologies.
Life Cycle Costing techniques were chosen to determine the
total costs of the handling/combustion systems which would be
incurred over a fixed payback period.

Energy and cost calculations were incorporated into an
interactive computer model for analysis of individual food
processing firms. The model prompts the user for input
regarding the processing plant schedule, operating and loan
parameters, and fossil and waste fuel characteristics.
Projected annual savings in fuel and disposal costs are
compared with average annual costs to determine the break—

even point for cost—effective investment.

Steven Alonzo Sargent

The model was validated with conservative parameters
representing two sizes of Michigan apple juice processors.
Apple pomace was substituted for natural gas and #2 fuel oil.
Small processors were considered to be those generating 30
tons/day (27,215 kg/day) of pomace and requiring 10 million
Btu/hr (10.5 GJ/hr) of processing heat. Large processors were
those generating 100 tons/day (90,718 kg/day) of pomace and
requiring 30 million Btu/hr (31.5 GJ/hr) of heat. Disposal
costs for pomace were zero. A 14% loan interest rate with a
5—year payback period was employed, with other costs from
September, 1983.

The handling/combustion systems are not currently cost—
effective for either small or large apple juice processors.
Combustion of packaging wastes, in addition to pomace, would
reduce energy costs further and enable large processors to
invest in a suspension-fired system with 1.1 tons/day (965
kg/day) of polyethylene plastic, or with 3.0 tons/day (2,735
kg/day) of corrugated paper box.

The break-even point for investment was determined to be
most sensitive to processing waste flow rate, fossil fuel
price and imposition of disposal cost. The break-even point
was least sensitive to electrical cost and loan interest rate.
Large processors could invest in a suspension-fired system with
a 20% increase in fossil fuel price or in any system if dispo-
sal costs were imposed equal to 50% of the current rate. Small
processors would require a 100% increase in fuel price or
imposition of 100% of the disposal cost to seriously consider

combustion of apple pomace.

To Suzana,

my wife

and my best friend

ii

Come to me, all you who are weary and burdened,

and I will give you rest.

What good is it for a man to gain the whole world,
yet forfeit his soul? Or what can a man give

in exchange for his soul?

I tell you the truth, whoever hears my word
and believes him who sent me has eternal life
and will not be condemned; he has crossed over
from death to life.

Jesus Christ

111

Acknowledgements
I would like to express my sincere appreciation

to Dr. James F. Steffe, for his valued guidance,
encouragement, friendship and support as
committee chairman;

to Committee members
Dr. B.R. Tennes, Dr. G.R. Van Ee, Dr. A.K. Srivastava,
Dept. of Agricultural Engineering,
Dr. T.R. Pierson, Dept. of Agricultural Economics,
Dr. M.A. Uebersax, Dept. of Food Science and
Human Nutrition,

for their excellent input in their respective
disciplines and their supportive spirit as a
committee at each step of the research;

to my parents, Alonzo and Grace Sargent, for their love
and dedication to my brothers and me; Mom for
encouraging us to set high goals, and Dad for his
example of perseverance in achieving his goals;

to my brothers, Tom, Dave, Rob and Ed, for their love
and support over the years;

a minha querida familia brasileira, cujo amor tem sido
um grande encorajamento;

to John W. Larkin, a faithful friend at all times;

to Ray and Sharon Rice, for their continual encouragement
during times of transition and their example as a
couple committed to Jesus Christ and to each other;

and to my fellow graduate students and the faculty and

staff of the AE Dept., whose friendships have been
of such value to me personally.

iv

TABLE OF CONTENTS

Page
LIST OF TABLES . - viii
LIST OF FIGURES. x
LIST OF ABBREVIATIONS. xii
Chapter
1. Problem Definition and Analytical Approach . 1
1.1 Introduction to the problem . 1
1. 2 Analytical approach. . . . 2
Sytems analysis and models . . 2
Simulation as a decision-making tool. 3
1.3 Research objectives. . . . . . . 7
2. Characteristics of the Food Processing Industry 9
2.1 Energy consumption and cost. . . . . 9
By state and Industry Group . . . 9
Within the Food and Kindred Products
Group . . . . 14
2.2 Generation of food p lant w astes . 17
Biomass wastes . 17
National level. . 17
Michigan food processing indu stry 20
Michigan apple juice processing
industry. . . . . . 22
Other processing plant wa stes . 25
2.3 Waste utilization and disposal practices . 26
2.4 Conversion of processing wastes into energy . 30
Energy conversion methods . . 31
Selection of direct combustion process . 35
3. Technical Considerations for Processing Wastes . 37
3.1 Physical properties . . 37
Apple pomace . . . . . . . 37
Other processing wastes . . . . . 40
3.2 Waste combustion properties . . . . . 42
Solid fuel combustion theory. . . 42
Combustion emissions considerations 43
Sources of heat loss . . . . . 46
Waste heat recovery . . . . . . 47
Apple pomace . . . . 48
Other processing wastes 52

V

Chapter

3.3 Selection of handling/combustion system
components . . . . . . . . . . . .

Handling system . . . . . . . . . .
Combustion systems . . . . . . . . .
4. Engineering Cost Analysis. . . . . . . .

4.1 Analytical approach . . . . . . . . . .
4.2 Determination of total costs. . . . . . .
Fixed and variable costs . . . . . . .
Cost estimation . . . . . . . . .
Cost indexes . . . . . . . . .
Equipment scaling . .
Other parameters . . . . . . . . . .
4.3 Life-Cycle Cost Analysis. . . . . . . . . .
Cost analysis methods. . . . . . . . .
Time value of money. . . . . . . . .
Average Annual Cost. . . . . . .
4.4 Interpreting the analysis . . . . .
Break-Even Analysis. . .
Sensitivity Analysis . . . . . . .
4.5 Feasibility analysis data . . . . . .
Fixed cost data. . . . . . . . . . .
Equipment costs and power requirements
Equipment scaling. . . . . . . . . .
Variable cost data . . . . . . .

5. Energy and Cost Analysis Model . . . . . .

5.1 Model description . . . . . . . . . .
Initial considerations . . . . . . .
Input parameters . . . . . . . . .

Plant operating and economic para—
meters . . . . . . . . . . .
Biomass input parameters . .
Calculations for energy analysis .
Biomass energy savings . . . . . .
Calculations for the Life- -Cycle Cost
Analysis . . . . . . . . . .
Total costs. . . . . . . . . .
Investment savings (losses). . .

Output parameters. . . . . . .
Biomass Energy Analysis. .
Life- -Cycle Cost Analysis .

5.2 Verification and validation of the model.

The Michigan apple juice processing
industry . . . . . . . . . . . . .
Apple pomace input parameters. . .
Input parameters for additional in—

plant wastes . . . . . . . . . .

Feasibility analysis results . . . . .

Energy value of apple processing
wastes . . . . . . . . . . . . .

vi

Page

Chapter Page

5. (cont.) Life- Cycle Cost Analysis Results . . 107
Total costs for handling/combus—
tion. . . . . . . . 107

Average annual costs .and the break-
even point. . . . . . . . . . 109
Apple pomace. . . . . . . . . 109
In-plant processing wastes. . 111

Sensitivity of the analysis . . . 115
Effect of waste disposal cost 115
Effect of fossil fuel price . 118
Effect of loan interest rate. 118

Feasibility analysis discussion. . . 122

Fossil fuel costs, disposal costs,
loan interest rate. . . . . . 122

Additional wastes with fuel value 124

System selection considerations . 125

Potentials for improving cost-
effectiveness . . . . . . . . 126

6. Summary and Conclusions. . . . . . . . . . . . . 128

6.1 Model development and validation. . . . . . 128
Apple pomace waste . . . . . . . . . . . 129
Packaging wastes . . . . . . . . 131

6.2 Recommendations for further research. . . . 132

APPENDICES
1. Procedure and preliminary results for bulk
density and angle of repose for apple pomace133
2. Conversion factors and drying calculations
for apple pomace . . . . . . . . . . . . . 139
3. Equipment and manufacturers of system compo—
nents used in the analysis. . . . . . . . . 140
4. The effect of savings in disposal costs on
net annual savings for small processors . . 141
5. The effect of savings from disposal costs on
net annual savings for large processors . . 142
6. The effect of changes in fossil fuel price on

net annual savings for small and large pro-
Cessors O O I O O I O O C O O I O O O O O O 143

7. The effect of loan interest rate on net
annual savings for small processors . . . . 144
8. The effect of loan interest rate on net

annual savings for large processors . . . . 145

LIST OF REFERENCES . . . . . . . . . . . . . . . . . . 146

vii

LIST OF TABLES

Table Page

1. Energy trends for all Industry Groups in the top
10 consuming states . . . . . . . . . . . . . . . 10

2. Energy consumption and value of product shipments
for major U.S. Industry Groups, 1981. . . . . . . 12

3. Trends in energy costs by fuel type for major U.S.
Industry Groups and the Food and Kindred Products
GTOUP, 1967-1981. 0 o o o o o o o o o o o o o o o 13

4. Energy consumption and costs and the value of
product shipments for industries within the Food
and Kindred Products Group, 1981. . . . . . . . . 15

5. Major processed commodities and solid waste
estimates for Michigan, 1981. . . . . . . . . . . 21

6. Industrial conversion of by-products into usable
energy by direct combustion . . . . . . . . . . . 33

7. Bulk densities for selected industrial wastes . . 41

8. Federal stationary source emission performance
standards 0 O O O O I O O O O O O O O O O O O O O 45

9. Comparison of selected analyses for apple pomace
and fossil fuels. . . . . . . . . . . . . . . . . 49

10. Ash analyses for selected biomass and fossil fuels 50

11. Combustion characteristics of selected processing
wastes and residues . . . . . . . . . . . . . . . .53

12. Ash fusion temperatures for selected processing
wastes. . . . . . . . . . . . . . . . . . . . . . 56

13. Differences between direct combustion systems . . 67

14. Installed purchase costs and power requirements for
handling/boiler system components . . . . . . . . 82

15. Energy analysis for selected apple processing
wastes. . . . . . . . . . . . . . . . . . . . . . 102

16. Hourly biomass flow rates and annual savings for
small processors. . . . . . . . . . . . . . . . . 103

17. Hourly biomass flow rates and annual savings for
large processors. . . . . . . . . . . . . . . . . 105

viii

Table
18.

19.

20.

21.

22.

23.

Page
Net fuel values for selected apple processing
wastes. . . . . . . . . . . . . . . . . . . . . . 106

Total costs for small and large handling/combus—
tion systems. . . . . . . . . . . . . . . . . . . 108

Average annual costs and net annual savings for
small and large processors combusting apple pomacellO

Effect of packaging wastes on net annual savings
for small and large processors. . . . . . . . . . 112

Required amounts of packaging wastes for cost—
effective investment for small processors (in
addition to apple pomace) . . . . . . . . . . . . 113

Required amounts of packaging wastes for cost—
effective investment for large processors (in
addition to apple pomace) . . . . . . . . . . . . 114

ix

LIST OF FIGURES

Figure Page
1. Flow diagram illustrating the modeling and feed-
back phases of simulation technique . . . . . 5
2. Process apple utilization for juice and cider in
Michigan, 1976-1982 . . . . . . . . . . . . . 23
3. Michigan apple utilization for the 1982-1983
processing season . . . . . . . . . . . . 24
4. Waste generation and disposal in the U.S. Food
Processing Industry 28
5. Flowchart of apple utilization at a typical
processing plant. . . . . . . . . . . 38
6. Proposed processing waste handling system
components. 59
7. Biomass pile burning boiler . . . . . . . 63
8. Biomass suspension—fired combustor system . 64
9. Biomass fluidized-bed combustion boiler . 66
10. Representative investment costs for handling system
components. 83
11. Cost capacity exponents for the rotary drier and
combustors (1983 price base). . 85
12. General flowchart for the waste energy and cost
analysis model. . . . . . . . . . . . . 9O
13. Initial input parameters for processing plant
operating conditions. . . . 92
14. Input parameters for waste energy and cost
analysis. . . . . . . . . . . . . . 94
15. Example output of the energy analysis for apple
pomace, polyethylene and corrugated paper box . 97
16. Example output of the economic analysis for apple
pomace, polyethylene and corrugated paper box . 98

Figure Page

17. Effect of disposal cost on net annual savings
for small processors. . . . . . . . . . . . . . . 116

18. Effect of disposal cost on net annual savings for
large processors. . . . . . . . . . . . . . . . . 117

19. Effect of fossil fuel price change on net annual
savings for small processors. . . . . . . . . . . 119

20. Effect of fossil fuel price change on net annual
savings for large processors. . . . . . . . . . . 120

21. Effect of loan interest rate on net annual savings
for small processors. . . . . . . . . . . . . . . 121

22. Effect of loan interest rate on net annual savings
for large processors. . . . . . . . . . . . . . . 123

xi

List

of Abbreviations

 

ac
AP
Btu

CPB
d.b.

FBC
FKPI
F0
ft
gal
ha
hr

kJ
kW
lb

LCC
lit

MC
MSW
NG
PB
PE
SF
UCR
w.b.
WFW
yd
yr

average annual costs

acre

apple pomace

British thermal unit

degrees Celcius

corrugated paper box

dry basis

degrees Fahrenheit

fluidized-bed combustor

Food and Kindred Products Industry
#2 fuel oil

foot

gallon

hectare

hour

kilogram

kiloJoule; MJ=megaJoule; GJ=gigaJoule
kiloWatt

pound

Life Cycle Cost analysis

liter

meter

moisture content

municipal solid waste
natural gas

pile burner combustor
polyethylene
suspension-fired combustor
Uniform Capital Recovery factor
wet basis

wet fruit waste

yard

year

xii

CHAPTER 1

Problem Definition and Analytical Approach

1.1 Introduction to the problem

Food processing plants require large quantities of
energy for processing operations and in turn generate sizable
amounts of solid and liquid waste materials. Rising costs
for both energy and disposal have dramatically increased
production costs in recent years and as a result represent a
major concern to management. While the types and quantities
of wastes generated by food plants vary widely, the potential
exists for in-plant conversion of currently unusable or
under-utilized wastes with fuel potential into recoverable
energy, or more specifically, steam and hot water. Energy
recovery by these means, if proven feasible, could result in
significant savings for the industry in terms of lowered
purchased energy costs and savings in waste disposal.
Characterization and analysis of energy and disposal costs
are dependent upon a basic understanding of several

parameters, most importantly,

1. operating conditions representative of food

processing plants;

2. biomass types, fuel potential and availability;

3. replacement equipment for handling and combustion of
biomass which is readily available and reasonably

priced; and

4. a cost analysis method capable of determining the

feasibility potential.

1.2 Analytical approach

1.2.1 Systems analysis and models

Such a complex problem can be efficiently analyzed by a
method known as systems analysis or systems research. This
method, in simplest terms, is the study of ”a complex set of
related components within an autonomous framework” (Dent and
Blackie, 1979). An understanding of the function of the
related components within a sub-system permits the synthesis
of the sub-systems in order to gain a better understanding of

the entire system being investigated. Thus very complex

problems can be analyzed by systematically studying the
interactions between components within a sub-system, between
sub-systems and finally between the system and the
surrounding environment (Rountree, 1977; Bingham and Davies,
1978).

Development of a procedure to examine the feasibility of
a new technology is essentially a means to model a defined
system. A model is a replica or representation of a real
object or system and can be constructed in three forms. An
iconic model is a physical representation of a real system;
an analog model utilizes the properties of one system to
represent another; and a symbolic model employs mathematical
relationships to describe a real system (Ackoff, et al.,
1962). A model can be static (with the system analyzed in
equilibrium) or dynamic (with changes in system behavior
analyzed over time). A dynamic model can be further
classified as stochastic for prediction of random-occurring
events, or deterministic for solutions based upon known input

variables.

1.2.2 Simulation as a decision-making tool

In the literature the distinction between modelling and
simulation tends to be nebulous. Modelling is the technique
of developing and using a model to determine the solution to

a problem, normally with the aid of a computer. Simulation

4

increases problem-solving capability by adding to the
modelling phase a second phase of experimentation (Wright,
1970). Feedback during model development and testing leads
to refinement or adjustment of the model structure. Later
during experimentation, the user's understanding of the
system behavior is enhanced as the results are analyzed
followed by variation of the input parameters. This cyclic
pattern of input/output/analysis/input provides in-depth

information for use in decision-making (Figure 1).

There are several advantages to the use of a
computer-based model for analysis of agricultural systems

(Dent and Blackie 1979).

1. Systems can be studied which would otherwise be

prohibitive due to high cost or inconvenience.

2. Non-existent systems can be explored.

3. Long-term effects can be evaluated.

4. The evaluator or model-builder has a method which

promotes thoroughness and objectivity in approaching

the problem.

Cautions associated with model usage were also noted.

 

1

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Figure 1. Flow diagram illustrating the modeling and feedback
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1. Many times estimates must be used in place of actual

data, which will affect accuracy of the results.

2. The entire process of problem analysis, data
collection, model construction and validation can be

very time consuming and costly.

3. Model validation is problematic, because results may
be distorted by programmed biases or unreliable
data. This may lead to false security in the

results of the simulation.

Since a model is designed to represent a real system, care
must be exercised during all phases of development to
maintain the integrity of the analysis. A model must not
only be verified (checked to ensure that the results are
mathematically correct), but also be validated (compared to
the real system for degree of accuracy). The decision-maker
must determine the suitability of the results in
accomplishing the objectives of the study.

Wright (1970) listed nine steps to be taken in

simulation.

1. Specify the problem and objectives.

2. Understand the system.

3. Formulate the initial system model.

4. Collect data.

5. Specify a detailed system model.

6. Program the computer.

7. Validate the model.

8. Experiment with the model.

9. Analyze the results.

1.3 Research objectives

Succeeding chapters illustrate the use of computer
modelling to determine the technical and economic feasibility
of utilizing food processing plant wastes as in-plant boiler
feedstocks. The Michigan apple juice industry was selected
for analysis to develop and validate the model. Research

objectives relating to technical feasibility were:

1. identify and characterize combustible wastes

generated by the industry;

 

identify optimal energy conversion technology;

determine current operating parameters of the apple

processing industry in Michigan;

identify and size components for biomass handling

and energy conversion systems.

Objectives relating to economic feasibility were:

select the appropriate cost analysis method;

collect fixed cost data for components of the
handling/conversion systems, and variable cost data

with respect to systems operation;

write and verify a computer model based upon the
technical and economic approach developed,
applicable for analysis of a variety of food

processing operations;

validate the model using parameters representive of
the Michigan apple juice industry and determine a)
the technical and economic feasibility of recovering
energy from apple pomace, b) the sensitivity of key
operating parameters, and c) the effects of

combusting other in-plant wastes in conjunction with

apple pomace on feasibility.

CHAPTER 2

Characteristics of the Food Processing Industry

2.1 Energy consumption and cost

2.1.1 By state and Industry Group

According to the U.S. Bureau of the Census (1983a,b),
the top 10 energy consuming states in 1981 accounted for 58
percent of all the energy purchased in the U.S., with Texas,
Pennsylvania and Louisiana in the top 3 (Table 1). All but 2
states decreased in consumption from 1980-1981, reflecting
the slowdown in the economy which occurred during this
period. Michigan showed the greatest decrease in the ratio
of 1981 to 1980 fuel purchased at 0.92, slightly below the
10-state average of 0.96. However energy costs increased in
all states, with ratios ranging from 1.12 for Indiana to 1.27
for Louisiana. California industries paid the highest for

energy ($6.47/mi11ion Btu or $6.83/GJ), over 100 percent that

10

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11

of Louisiana. Michigan firms paid an average of
$4.98/million Btu ($5.25/GJ), slightly above the U.S.
average.

In 1981, the Food and Kindred Products Industry (FKPI)
Group ranked sixth nationally in terms of fuel and electrical
energy consumption, purchasing 913.1 trillion Btu (963,000
GJ), or 7.8 percent of the total consumption of 11,562.?
trillion Btu (12,199,000 GJ) ( Table 2). The average energy
cost was $4.78/million Btu ($5.04/GJ) for all industry
groups, ranging from $3.72-$8.28/mi11ion Btu ($3.92-$8.73/GJ)
for 1981. Energy costs for the FKPI Group were $4.81/million
Btu ($5.07/GJ), up 17.6 percent from 1980 costs. Regarding
the value of product shipments, the FKPI Group ranked first,
with a value of $272,136.6 million, or 13.5 percent of the
total of $2,017,542.5 million.

Several trends in energy usage can be noted during the
period of 1967 - 1981 (Table 3). U.S. energy consumption
peaked in 1971 at 1,030.3 trillion Btu (1,087,000 GJ) before
declining to 958.8 and 913.1 trillion Btu (1,011,000 GJ and
963,000 GJ) in 1974 and 1981, respectively. The decrease
during that lO-year period partly reflects energy
conservation practices which were initiated as a result of
the increased fossil fuel costs beginning in the 1970's.
Greatest cost increases were for petroleum fuels, with 1029,
956 and 881 percent hikes for residual oil, fuel oil and
natural gas, respectively. Coke and breeze, coal and

electrical energy had price increases of 493, 464 and 340

12

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13

 

 

 

 

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14

percent, respectively, during this period.

2.1.2 Within the Food and Kindred Products Group

Within the FKPI Group, electrical energy usage increased
markedly from 9.3 to 15.5 percent of all energy purchased
during 1967-1981 (Table 3). The use of "other” fuels
(including liquid petroleum gases) also increased from 1.9 to
10.4 percent. Use of coal dramatically decreased from 24.0
to 13.0, while distillate and residual oil usage decreased
only slightly and there was no change in the use of coke and
breeze. Natural gas usage increased from 49.8 to 57.9
percent in 1971, before gradually declining to 51.6 percent
in 1981.

Energy consumption for the industries within the FKPI
Group ranged from 153.3 trillion Btu (161,700 GJ) for Grain
Mill Products to 50.2 trillion Btu (52,964 GJ) for Bakery
Products (Table 4). Preserved Fruits and Vegetables ranked
third with 113.6 trillion Btu (119,855 GJ), behind Sugar and
Confectionary Products with 127.8 trillion Btu (134,837 GJ).
Energy costs for this Group ranged from $3.63/million Btu
($3.83/GJ) for Sugar and Confectionary Products to
$6.09/million Btu ($6.43/GJ) for Miscellaneous Foods and
Kindred Products. These costs for the nine industries were
fairly dependent upon total energy consumption; the larger

operations generally had lower overall costs/heat unit than

4 /_ T"

15

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16

did the smaller ones due in part to the economy of scale of
operation (larger plants normally have higher ouput per unit
of total cost than do smaller plants).

In terms of the value of product shipment, Meat Products
were ranked first, with a total value of $65,909 million in
1981. Second and third were Dairy Products and Beverages,
with $36,942 and $36,075 million, respectively. Least valued
shipments were from Sugar and Confectionary Products, with
$16,283 million. Preserved Fruit and Vegetables ranked fifth
in value at $27,719 million.

From this overview, energy costs are substantial for the
Food and Kindred Products Industry Group. It was the sixth
largest energy purchaser in 1981, utilizing almost 8 percent
of the total energy, and ranked sixth most efficient in terms
of cost/heat unit. This industry shipped the highest valued

products of all Industry Groups.

17

2.2 Generation of food plant wastes

2.2.1 Biomass wastes

2.2.1.1 National level

In 1968 the Office of Solid Waste Management, 0.5.
Environmental Protection Agency, conducted the first
national survey of solid waste disposal by food
processors. The areas covered in the survey were for
canned, frozen and dehydrated foods, and later the

following conclusions were published (Hudson, 1978):

1. The food processing industry produces
approximately 18,600 million lb (8,437 million
kg) of solid residual per year.

2. Of this amount, fruit and vegetable processors
generate 93 percent of the residuals, or 17,200
million lb (7,802 million kg). Specialty

processors (baby foods, soup, stew, TV dinners,

,-l.
’ Ir...-

18

spaghetti) account for 4 percent or 741 million
lb (336 million kg), while seafood processors
account for 3 percent or 560 million lb (254

million kg).

An additional 637 million lb (289 million kg) are
disposed of as liquid waste by these industries,

or approximately 16 percent of the total wastes

generated.

 

19

The type and size of processing plant operation will
greatly influence the kinds of wastes produced. For example,
for each ton (1000 kg) of raw apples processed, the following
wastes are generated: 5000 gal (20,861 lit) of of waste water
containing 5.0 lb (2.5 kg) of suspended solids, and 600 lb
(300 kg) of solid residuals. From citrus processing,
however, 3000 gal (12,517 lit) of waste water are produced
for each ton (1000 kg) of raw fruit, along with 5.0 lb (2.5
kg) of suspended solids and 220 lb (440 kg) of solid
residuals (Woodruff and Luh, 1975).

Processing operations produce such solid residues as
peelings, trimmings, cores, stems, pits, culls of undesirable
fruits or vegetables, nut shells, kernal fragments and grain
hulls. The physical properties of these wastes will be
discussed in a later section. Liquid wastes arise from
several processing operations. Waste water with low levels
of suspended solids arises from hydro-handling systems and
product cleaning operations, which require periodic
replacement to remove accumulated field and chemical
contamination. This may be disposed in sewage or irrigation
systems, provided no toxic chemical levels are present.
Liquids containing toxic constituents, such as salt brines
from pickling cucumbers or lye from caustic peeling

operations, must be detoxified prior to disposal.

~.| I'—
‘ 4
Vi
L
h
J.

20

Peeling operations generate the largest amount of liquid
wastes in the food industry, followed by blanching operations
(White, 1973). Both of these latter wastes contain high
amounts of suspended solids and require some treatment before

being released to the environment.

2.2.1.2 Michigan food processing industry

Solid waste quantities produced by Michigan food
processors in the 1981—82 season were estimated from data
published by the Michigan Agricultural Reporting Service
(Table 5). Estimates for each crop were calculated by
multiplying the total production amount for each crop by the
corresponding waste fraction. The total amount of residues
for Michigan was estimated to have been 7,827.1 million lb
(3,550.3 million kg), fresh weight. The moisture content
(MC) for fresh fruits and vegetables has a range of 77 to 96
percent, wet basis, while processed food wastes from pressed
products may have MC as low as 52 percent for grape pomace to
over 70 percent for apple pomace, depending upon the method
and efficiency of the press.

The total amount of solid wastes for Michigan is
disproportionally higher than the national total, since
wastes from sugar beet processing accounted for 96.4 percent
of the MIchigan total. Without beet wastes, 283.0 million 1b

(128.4 million kg) of wastes were generated, which is 1.5

21

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22

percent of the national total for the fruit and vegetable

processing industry.

2.2.1.3 Michigan apple juice processing industry

In 1982 Michigan ranked third nationally in terms of
apple production, with a harvest of 980 million lb (444.5
million kg) (Figure 2). Of this amount, 365 million 1b
(165.6 million kg), or 37.2 percent, was consumed by the
fresh market and the balance of 615 million lb (279.0 million
kg), or 62.8 percent, was divided among the processed
products (Figure 3). The bulk of processed apples went for
juice and cider (37.5 percent), while the remainder decreased
in percentage as canned (14.6), frozen (9.8) and other,
including vinegar, jam and wine (0.9). During the past five
years the apple processing industry has been shifting to
produce more juice and cider and less canned and frozen
products to meet a growing consumer demand for juice. This
trend is expected to continue through the 1980's (Ricks,
1981).

Juice processors must not only contend with increasing
energy costs, but also with a large volume of by-product in
the form of apple pomace (or presscake), the solid residue
which remains after the juice is pressed. Pomace is also
produced from wine and vinegar operations. An efficient

press will remove about 75 percent of the fresh weight of the

23

 

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25

apple, leaving the pomace at 65 percent MC (Kranzler and
Davis, 1981). Therefore from the 367 million lb (166.5
million kg) of apples pressed during the 1982-83 season,
about 92 million lb (41.6 million kg) of pomace required
disposal in Michigan.

For large processors, up to 100 tons (90,718 kg) of
pomace are produced per day of operation, posing a
significant disposal concern since pomace cannot be left in
the plant. The high moisture content and presence of soluble
sugars in pomace permits rapid fermentation, providing an
excellent media for microbial pathogens, insects and other
pests, as well as objectionable odors (Smock and Neubert,

1950).

2.2.2 Other processing plant wastes

Non-food processing wastes are generated from shipping,
canning, maintenance and office operations, most importantly
packaging materials, but also assorted solid wastes such as
office trash, floor sweepings and garbage. In some instances
field residues from cleaning or nearby harvest operations
must also be disposed. The food industry is the largest
consumer of packages, the next being the beverage industry.
For 1976, total packaging materials consumption was estimated
at 128.3 billion lb (58.2 billion kg) nationally (Mantell,
1975):

26

kg (billions) percent

Paper, paperboard 33.5 57
Glass 10.8 19
Metals 7.6 13
Wood 4.0 7
Plastics 2d _4

TOTAL 58.2 100

2.3 Waste utilization and disposal practices

Waste physical characteristics, governmental waste
discharge restrictions and disposal costs primarily determine
the method of disposal for the processor. Returning to the

data published by Hudson (1978):

1. For the industry as a whole, 79 percent of the
residuals or 14.6 billion lb (6,622 million kg) are
utilized as by-products, with the remaining 21

percent disposed as waste.

2. About 97 percent 14.2 billion lb (6,441 million kg)
of the residuals utilized as by-products are fed to

animals.

3. Of the 4.0 billion lb (1,814 million kg) disposed as

27

solid waste, 50 percent is placed in landfills (both
sanitary and Open), 49 percent is spread on the land

and 1 percent is burned on-site.

4. Liquid wastes, which carry 16 percent of the total
wastes (637 million lb or 289 million kg), are
disposed as follows - 50 percent released untreated
into streams, lakes, rivers, or oceans, with lesser
amounts disposed in public sewer systems. Very few

plants utilize on-site treatment or irrigation.

These data are summarized in graphic form (Figure 4).

Public concern and governmental legislation over
environmental contamination has reduced the number of wastes
which are considered safe for disposal (Hills and Roberts,
1981). Buried processing and municipal wastes (refuse tips)
may produce temperatures up to 300°F (150°C) as well as gases
such as methane, carbon monoxide and hydrogen sulfide due to
microbial decomposition under anaerobic conditions. Methane
may accumulate and begin to seep from the tip, creating the
danger of explosion. A pilot project to recover such gases
was initiated prior to 1979 at a municipal landfill in the
Los Angeles area, with estimates of recovery of 4.5 billion
gal (17 billion lit) per year over a 15 year period (Burnett,
1979). Several other examples of by-product disposal

practices and utilization follow.

28
ANNUAL SOLID WASTE

W

 

SEAFOOD SPECIALTY
3%

     
   
      
 

 

FRUIT AND
VEGETABLE
93%

DISPOSAL

 
   
    
   
   
   
  

UTILIZED
79%

 

 

LANDFILL
50%

 

 

 
 
  

ANIMAL FEED
97%

SPREAD ON LAND
49%

 

    

Figure 4. Waste generation and disposal in the U.S. Food
Processing Industry.

29

Cannery wastes make a very palatable and nutritious
animal feed supplement. Often the wastes are transported and
fed in the same form as when they are produced. Such wastes
may have high 'MC (above 50 percent) originating from
screening, trimming, peeling and pressing operations. Potato
and corn by-products are sold as cattle feed (Licht and
Revel, 1981), while apple pomace is given to cattle operators
for the cost of transportation. Pomace is fed to beef cattle
as a fiber substitute. Feeding to pregnant cows is not
recommended, since those fed pomace supplemented with
non-protein nitrogen gave birth to dead or weak calves, the
cause undetermined (Fotenot, et 81., 1977). Further studies
on the nutritional aspects of apple pomace in beef cattle are
currently in progress (Waller, 1983).

Prepared feeds are made from sugar beet pulp and citrus
pomace, which are dried, treated with nutrient additive and
packaged prior to shipment (Henderson and Kesterson, 1965).
These wastes account for the greatest portion of wastes
utilized as feed and'have the advantage of longer storage
periods and reduced loss of nutrients when compared to high
MC feeds due to less microbial action (Pugsley, 1975).
Recently, apple pomace became available as a flavor/fiber
ingredient for the baking industry (Apple. Fiber and Rice
Crunch; Mid-America Food Sales, Northbrook, Illinois 60062).
Other residues are by nature of low MC, and include grain

pieces and rice hulls; rice hulls, while added to feed

30

rations, are not recommended for use as a solitary feed due
to possible abrasion in the digestive tract of the animal
(Hsu and Luh, 1980).

Other uses of by-products with economic importance, are
most notably production of pectin from citrus wastes
(Henderson and Kesterson, 1965), bedding from hulls and
shells and extraction of chemicals (e.g., furfural from corn
cobs, Arnold, 1975). Soil incorporation of tomato and fruit
cannery wastes has been demonstrated by first spreading the
wastes on the topsoil at rates up to 223,050 1b/ac (250,000
kg/ha), followed by disking. No soil or groundwater
contamination by heavy metals occurred at these rates (Timm,
et al., 1980; Noodharmcho and Flocker, 1975). Conversion of
high MC wastes to humus is feasible by composting (Rose, et
31., 1965; Toth, 1973). Many growers dispose of apple pomace
as a mulch on their orchards, but must periodically
neutralize the acidity added by the pomace by incorporating
lime into the soil. The fresh pomace left in the orchard has
potential to act as a host for disease organisms, and adds to
weed control problems due to seeds which germinate the

following spring.

2.4 Conversion of processing wastes into energy

31

2.4.1 Energy conversion methods

Several processes exist which can transform biomass into
energy. Selection of the appropriate process is dependent
upon several factors: the physical state of the biomass
(initial moisture content, the heat value, physical
properties), the efficiency of the energy conversion process,
energy demands of the plant and economic feasibility. The
following methods have proven technically feasible for a wide
spectrum of processing wastes: anaerobic digestion, in which
microorganisms breakdown biomass materials to produce methane
gas and carbon dioxide; fermentation, in which yeasts ferment
simple sugars into ethanol; and thermochemical conversion, in
which heat is released by thermochemical reaction (White and
Plaskett, 1981).

Anaerobic digestion and fermentation are best suited for
conversion of wet processing residues to fuels, such as
cannery wastes (Lane, 1979). Citrus juice extractor residues
(peel, pulp, seeds) and the press liquor resulting from
dewatering of these residues, show potential for utilization
as substrates to produce fermented products (Graumlich,
1983). Of the three, thermochemical conversion produces the
highest amount of heat per unit of fresh product (Hall,
1981), and has proven cost-effective on an industrial scale

for conversion of a variety of waste materials into usable

32

energy, such as process steam or electricity (Table 6). With
assistance from governmental funding, Knouse Foods, Inc. has
demonstrated the possibility of converting apple pomace into
process steam and cogenerating electricity by direct
combustion (Schwieger, 1982).

Conversion of industrial solid wastes to useful energy
is growing in acceptability. In 1977 it was estimated that
approximately 15 percent of non-wood processing wastes were
converted into energy, equivalent to 90.0 trillion Btu
(94,900 GJ) (Tillman, 1977). In 1980 a plant in Cheboygan,
Michigan began combusting plastics, cellulose fibers and
factory and office trash, generating up to 28 million Btu/hr
(29.5 GJ/hr) and saving over $350,000/year in fossil fuel
costs and over $550,000/year in disposal costs (Reason,
1982).

Municipal solid wastes (MSW) are converted to produce
approximately 41.2 trillion Btu (43,400 GJ) per year in the
0.5. (Tillman, 1977). Composition of MSW was estimated to
be 80 percent organic combustibles (food wastes, paper,
plastics, leather, rubber, wood) and 20 percent inorganic
non-combustibles (glass, metal) (Baum and Parker, 1973).
Conversion of MSW requires extensive presorting to remove the
inorganic residues and has proven cost-effective on a
municipal scale basis.

There are three methods of thermochemical conversion of
biomass. Direct combustion occurs when the biomass is

oxidized with air in excess of stoichiometric requirments and

33

 

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34

held above the ignition temperature, assuring complete
combustion. The stoichiometric requirement is the
theoretical amount of oxygen necessary to completely oxidize
the carbon, hydrogen, sulfur and trace elements in the
biomass to produce primarily carbon dioxide, water vapor and
heat (Fryling, 1966). Derived fuels are obtained when the
combustion air is sub-stoichiometric (gasification) or absent
(pyrolysis, or carbonization). Gasification produces biogas,
or producer gas, while pyrloysis produces charcoal or char
liquid, both of low-to-medium heat value.

The derived fuels (methane, ethanol, biogas, charcoal
and char liquid) contain more heat value per unit of final
fuel weight and are therefore more economical to transport
and store than the raw residue. But direct combustion
results in the highest heat value per unit of raw biomass
material (Ha11,1981). The wood products industry has
utilized direct combustion for years to produce steam and
heat for drying kilns, and more recently to cogenerate
electricity (Jamison, 1979).

Conversion efficiencies of direct combustion, anaerobic
digestion and submerged microbial fermentation were
determined for apple and grape pomace (Kranzler, et al.,
1983). Direct combustion releases 752 Btu/lb (1,750 kJ/kg)
of fresh apples, whereas anaerobic digestion and fermentation
release 395.0 and 38.7 Btu/lb (920 and 90 kJ/kg),
respectively. Heat values for grape pomace were 550.3, 236.5

and 12.9 Btu/lb (1,280, 550 and 30 kJ/kg) of fresh grapes for

35

direct combustion, anaerobic digestion and fermentation,
respectively. Of the three methods, it was concluded that
direct combustion is the most efficient means to convert
pomace and least complicated method to retrofit to existing
steam generating systems. Anaerobic digestion was considered
viable, although less efficient, while fermentation was a
decidedly disadvantageous alternative for pomace conversion
due to the low heat value and complex equipment required.
Solid-state fermentation of apple pomace produced
approximately 4.3 percent alcohol by weight, and increased
protein in the remaining pomace by 50 percent, improving the

animal feed value (Hang, et 37., 1981).

2.4.2 Selection of direct combustion process

Thermochemical conversion of pomace by the direct
combustion process was selected as the optimal method for
in-plant conversion of fruit and vegetable processing wastes

to recoverable energy for the following reasons:

1. direct combustion generates the most heat per unit

fresh biomass;

2. in-plant production and combustion is more energy

efficient than other conversion processes;

36

efficient biomass combustion boiler systems are
readily available to the industry and have less
complex operation than those for other conversion

process;

the ash by-product of combustion accounts for a
small portion of the initial volume, greatly
reducing disposal costs, and has potential for
utilization as a fertilizer (Hsu and Luh, 1980) and
as a component for ornamental plant media (Regulski,

1983).

CHAPTER 3

Technical Considerations for Processing Wastes

3.1 Physical properties

3.1.1 Apple pomace

Handling and energy conversion of processing wastes are
highly dependent upon the physical characteristics of the
material. A description of a typical apple juice processing
plant will clarify the physical origin of pomace. Whole
apples brought to the processor are held in common storage
until scheduled for pressing (Figure 5). Lower grade fruits
(usually those not suitable for fresh market due to physical
blemishes, handling injury or small size) are inspected and
washed prior to grinding by a hammermill (Smock and Neubert,
1950). In plants with simultaneous canning or freezing
operations peelings and cores are also fed into the

hammermill.

37

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38

   

   

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nouoo
.ouoom

 

 

39

Upon grinding, rice hulls or loose cellulose fibers are
metered into the mixture at a rate of 2-4 percent
weight/weight basis as a press aid, which improves juice
extraction efficiency. Rice hulls are especially desirable
since the hard texture and waxy surface layer renders the
hulls almost totally impermeable to juice infiltration.
Press aids are believed to create channels for the juice to
flow more freely from the pulp during the pressing operation
(Moyer, 1967).

After pressing, a belt conveyor or screw auger is
generally used to remove pomace from the plant. Clumps of
pomace may form during conveyance, most notedly in the case
of the screw auger. Although particulate, pomace is very
cohesive due to a high moisture content (a minimum of 65
percent, wet basis) and a high sugar content (17.5 percent,
dry basis; Fotenot, et al., 1975). For this reason pomace
cannot be stored in the plant for extended periods of time,
since fermentation begins in the pile, creating objectional
odors and heat.

Little data is available regarding the mechanical
properties of apple pomace which would be useful in
conveyance and storage applications. Preliminary tests were
performed to obtain estimates for bulk density under at
compression and kinetic angle of repose. The bulk density
ranged from 8.4 lb/ft3 (135 kg/m3) for bone dry pomace, to
24.0 1b/ft3 (385 kg/m3) for pomace at 68 percent MC. The

4O

angle of repose was 370 and 320 for bone dry and 68 percent
MC pomace, respectively. A complete description of the

procedures and data are described (Appendix 1).

3.1.2 Other processing wastes

Bulk densities for processing plant wastes must be known
for accurate sizing of conveyance systems. Values for
several wastes range from 1.0 lb/ft3 (16.0 kg/m3) for
expanded polystyrene to 48.1 lb/ft3 (770.0 kg/m3) for oak
(Table 7). Those wastes with high initial MC (such as
uncompacted vegetable waste) will have significantly lower
bulk densities if dried.

Packaging wastes typically have a MC below 15 percent,
which is favorable for in-plant storage; however other
handling problems are created. Nails, staples and wire
bindings must be removed from such items as pallets, crates
and paper boxes in order to avoid excessive wear on
equipment. Metallic objects have been reported to ignite dry
biomass from sparks generated by handling equipment,
especially hammermill size reducers (Kut and Hare, 1981).
Removal of metal fasteners would increase hand labor costs,
while loose metal objects could be removed by an in-line
metal detector/removal system prior to any shredding
operations. Plastics likewise require no drying (2 percent

MC), although ambient temperatures must be maintained below

41

 

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42

the melting point in order to prevent blockage and untimely

maintenance.

3.2 Waste combustion properties

3.2.1 Solid fuel combustion theory

A review of combustion theory .will provide better
understanding of the fuel characteristics of the biomass
wastes. In order for sustained combustion of a solid
material to occur, three conditions must be satisfied -
proper temperature, time and turbulance. The fuel must be
confined for an adequate residence time above the ignition
point, the latter being that temperature at which combustion
becomes self-sustaining. Turbulance ensures that sufficient
oxygen is available to combine with hydrogen, carbon, sulfur
and trace elements released during the combustion reaction.
With these conditions met the three-stage combustion process
begins (Elliott, 1980).

During the first stage any remaining moisture is
evaporated from the fuel. This represents a net energy loss,
since the heat required to raise the water to the boiling
point and the heat of vaporization is equal to roughly 1,118
Btu/lb (2,600 kJ/kg) of water and the fuel temperature is

held near the 212°F (100°C). Upon vaporization the second,

43

or flaming combustion, stage begins in which volatile gases
evolve from polymeric compounds within the biomass as
temperatures rise from 300-1000°F (150-540°C). This phase
releases great amounts of heat as the ignition point is
surpassed and the process becomes self-sustaining. Gases
released include carbon monoxide (later oxidized to carbon
dioxide), the parafin series (e.g., methane), the olefin
series (e.g.,ethylene), the aromatic series (e.g. benzene),
and others, including sulfur compounds and water vapor (Perry
and Chilton, 1973). Finally, the remaining fixed carbon is
oxidized during the glowing combustion stage, releasing
carbon dioxide and leaving ash. The principal reactions
concerning direct combustion are described by the following

equations (Babcock and Wilcox, 1978).

For carbon:

c + 02 = co2 + 32,797 kJ/kg

For hydrogen:

2H +0 =2I-I

2 2 o + 142,119 kJ/kg

2

3.2.2 Combustion emissions considerations

Particulate emissions are the principal pollutants
arising from combustion of biomass materials. The amount of

fly ash carried by the stack gases varies with the method of

44

combustion, the biomass and the combustion system being
employed. Federal standards for stationary sources relevant
to biomass fuel combustion are 0.043 kg/MJ for particulates
in either coal/wood or gas/wood-fired boilers (Table 8). An
excellent review of particulate collectors can be found in
the Special Report by the editors of Power (1980). The
cyclone collector is commonly used to remove larger
particulates and may be adequate for efficient combustion
systems. Baghouse collectors can be added if emissions are
not met by the cyclone collectors, removing greater than 99.9
percent of the flyash. Wet scrubbers remove fines but
require substantial post-treatment of the waste water.
Electrostatic precipitators are also widely used where
biomass is burned in conjunction with coal.

The most toxic gaseous pollutants to economic crops are
sulfur dioxide, hydrogen flouride, ozone, nitrogen dioxide
and peroxyacetal nitrate. The principal mode of action is by
disruption of biochemical reactions upon pollutant uptake
through open stomata (Tibbitts and Kobriger, 1983). Only
sulfur dioxide and nitrogen oxides (NOx) emissions are
restricted by the federal government; however these latter
pollutants pertain normally to fossil fuels since biomass
fuels contain miniscule amounts of sulfur and nitrogen.
Conversion of atmospheric nitrogen to NOx occurs only when
combustor temperatures exceed 3,000°F (1650°C) (Babcock and

Wilcox, 1978).

45

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.1

 

 

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k.

46

3.2.3 Sources of heat loss

As stated previously, adequate turbulance is essential
to ensure stoichiometric requirements are met. This is
accomplished by introducing excess air into the combustion
chamber. Excess air is measured as the percent ambient air
added above the stoichiometric requirement, and varies with
the fuel, MC and combustor design. Inadequate excess air
causes incomplete combustion, resulting in unburned carbon
and carbon monoxide leaving the stack and carbon remaining in
the ash. Seventy-two percent of the heat value of carbon is
lost if CO is not oxidized. Too much excess air results in
fly ash being carried in the exhaust gases, severe cooling of
the combustion chamber and excessive heat loss, since the air
will be heated and exhausted to the atmosphere (Hughes,
1976). Thus precise monitoring of the combustion system is
fundamental in establishing and maintaining optimal
combustion conditions.

Combustion chamber temperatures must likewise be
controlled, since ash becomes molten when heated above the
respective fusion temperature. As the ash cools, it congeals
into a solid, glass-like substance known as slag. 'Deposits
of slag within a combustor or boiler create costly
maintenance problems and greatly reduce heat tramsfer

efficiency (Schwieger, 1980). Ash fusion temperatures have

47

been determined for fuel candidates and should be consulted
prior to fuel/air adjustments of combustion systems, since
the fusion temperatures vary between ash types.

Stack temperatures range from 390 to over 700°F
(ZOO-370°C). Although any exhaust gases above the ambient
temperature represent an energy loss, a minimum of 390°F
(200°C) should be maintained in order to avoid inadequate
updraft and condensation which causes corrosion in the heat
exchanger and stack (Bender, 1964). These heated gases are
the major source of heat loss for power boilers, with lesser
amounts being lost to hot ash removal, radiation losses from
the system and blowdown (flushing of hot water from boilers

as a maintenance routine).

3.2.4 Waste heat recovery

Recovery of waste heat is possible from these sources,
and methodology for energy accounting of food processing
operations has been described in detail by Singh (1978). A
study of food processing plants in the Pacific Northwest
identified exhaust gases to discharge up to 18 percent of the
total energy lost, followed by dryer exhausts and condenser
cooling water (Davis, et al., 1980). Significant savings in
energy costs have been realized by recovering waste heat from
exhaust gases from the dehydration of citrus wastes (Bryan,

1977) and from wastewater discharged to a drain (Combes and

 

48

Boykin, 1981).

As noted in Table 6, besides producing steam or hot
water many plants are also cogenerating electricity. In
several cases the processor changed from that of an
electrical importer to that of an exporter to the local power
grid. The pulp and paper industry has shown particular
promise, with estimates of 750 trillion Btu (791,000 GJ) in

energy savings per year (Johanson and Sarkanen, 1977).

3.2.5 Apple pomace

The primary constituent of pomace is cellulose.
Volatile and fixed carbon amount to 95.99 percent of bone dry
pomace with the remaining 4.0 percent as ash and 0.1 percent
as sulfur and trace elements (Table 9). Ash and sulfur
contents are much lower for pomace and other biomass wastes
than for coal, and sulfur is also less than that for #2 fuel
oil. Natural gas produces virtually no ash or sulfur. Rice
hulls contain an average of 17.4 percent ash, and when
present in pomace, will raise the overall ash content by less
than 1 percent.

Analyses of ash (Table 10) reveal the high level of
silicon in rice hulls (95.8 percent), which affects handling
characteristics. The presence of rice hulls in the apple
pomace is reflected by the high percentage of silicon, as

compared to wood or coal ash. Silicon is very abrasive, and

 

49

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51

would restrict use of pneumatic handling of dried pomace
containing rice hulls. Rice hulls are alternately used as
industrial abrasives for cleaning metal parts (Schwieger,
1982).

Bone dry apple pomace has a heat content of 7,780 Btu/lb
(18,100 kJ/kg), similar to that for wood, and approximately
60 percent that for coal, since it contains less fixed carbon
(Table 9). Rice hulls have a heat content of 5,757 Btu/lb
(13,390 kJ/kg). The ash fusion temperature of apple pomace
is 2700°F (1,482°C), higher than that for bituminous coal.
With combustion chamber temperatures held below the fusion
temperature slag deposits will be minimized. The heat
content of pomace, as with other biomass fuels, is inversely
related to MC. As MC increases from 0 to 20 to 65 percent,
the heat content decreases from 7,780 to 5,300 to 1,698
Btu/lb (18,100 to 12,330 to 3,950 kJ/kg), respectively
(Kranzler and Davis, 1981).

It would be advantageous to dry pomace prior to
combustion for several reasons. With a lower initial MC,
more heat would be available to produce steam; any moisture
in the fuel must be evaporated prior to combustion, therefore
more heat is lost with higher MC fuels. Handling dry pomace
requires much less power and has fewer equipment problems
than wet pomace and may be stored as a stable biological
product prior to combustion. Also, several biomass boilers
require a dry fuel for efficient energy conversion. Wood

chips are typically stored and combusted at 35 percent MC

 

52

(Schwieger, 1980), while 20 percent MC was suggested for
apple pomace as a compromise between net heat content and
drying costs (Kranzler and Davis, 1981). Pelletizing pomace
would produce a dense fuel with a higher heat content per
unit mass, but is very energy intensive and was not
considered as an option in this study (Swint, 1980).

Ash, fly ash and slag residues have been utilized in
several manners. Ash from a gasifier combustion system was
determined to be very acceptable with peat as a container
media for the nursery industry (Regulski, 1983). Fly ash
from coal combustion has proven acceptable as a component for
stabilizing aggregates for use as a highway paving
(Anonymous, 1976). Recently, coal cinders were evaluated for
use as a container media component, and found to contribute
significant amounts of micronutrients to growing plants;
heavy metals were also added, the possible toxic effects on
the plants to be determined later (Neal and Wagner, 1983).
These residues are sterile and inexpensive by-product

SOUI’CBS.

3.2.6 Other processing wastes

Many processing wastes have significant energy potential
(Table 11). Most cellulosic residues have heat contents in
the range of 6,000-10,000 Btu/1b (l3,956-23,260 kJ/kg) (dry

weight), which includes paper and wood packaging wastes, nut

 

53

Table 11. Combustion characteristics of selected processing

wastes and residues.

Heat Content

 

 

 

1 (dry basis) Ash
A. Packaging Wastes nggg Btuglb _£_
**1) Corrugated paper boxes 17,280 7,429 5.3
2) Brown paper 17.924 7.706 1.1
3) Paper food cartons 17,980 7,730 6.9
4) Waxed milk cartons 27,289 11.732 1.2
5) Plastic coated paper 17,917 7,703 2.8
6) Newspaper (packing) 19.724 8,480 1.5
**7) Polyethylene. poly-
propylene 44.l94 19.000 0.0
8) Polystyrene 40.123 17.250 ---
9) Polyamides (nylon) 29.657 12.750 ---
10) Polyesters 27.912 12.000 ---
ll) Polyurethane 26.749 11.500 ---
12) Polystyrene foam 42.147 18.120 ---
13) Polyvinyl chloride (PVC) 19.189 8.250 2.1
14) Vinyl 20.539 8.830 0.0
15) Softwood (pine) 21.283 9.150 0.1
16) Hardwood (oak) 20.194 8.682 0.1
B. Field Residues2
1) barley straw (spring) 18.000 7,739 5.3
2) barley straw (winter) 17.800 7.653 6.6
3) bean straw 18.000 7,739 5.3
4) oat straw 17,900 7,696 5.7
5) pea straw 17.900 7.696 7.7
6) potato foliage 17.300 7,438 13.5
7) rape straw 18.000 7,739 4.5
8) rye straw 18,200 7,825 3.0
9) sugar beet tops 15.400 6.621 21.2
10) wheat straw 3 17,600 7,567 7 1
11) corn stover (35% MC. w.b.) 10,730 4,613 4.0
12) corn cob (15% MC. w.b.)3 4 18.600 7,997 1.4
13) cotton gin trash (12.5% MC) 18.775 8,072 ---
C. Nut Shells and Fruit Pits5
1) almond (soft) 19.445 8.360 3.1
2) black walnut 18,608 8,000 0.3
3) chestnut 18,375 7,900 n.a.*
4) English walnut 18.608 8,000 0.8
5) filbert 19.306 8.300 0.7
6) peanut 20.469 8.800 8.8
7) pecan 20.818 8.950 1.8
8) apricot 19.817 8.520 0.7
9) cherry 18.143 7.800 0.8
10) Peach 19.073 8.200 0.4

Iablc

5.4

54

Table 11 (continued).

MOISTURE CONTENT HEAT CONTENT

 

 

 

1 (as received. (dry basis)
D. Assorted Solid Wastes wet ba51s) ggggg Btu/1b
1) Paper 10.2 17.612 7,572
2) Wood 20.0 20.033 8.613
3) Grass 65.0 17.894 7,693
4) Brush 40.0 18,375 7,900
5) Greens 62.0 16.461 7,077
6) Leaves 50.0 16.505 7.096
7) Leather 10.0 20.585 8.850
8) Rubber 8.2 26.353 11.330
9) Plastics 2.0 33,420 14,368
10) Oils. paints 0.0 31.168 13.400
11) Linoleum 2.1 19.329 8,310
12) Rags 10.0 17,798 7,652
13) Dirt 3.2 8,815 3,790
1”14) Wet fruit wastes 80.0 19.734 8.484
15) Fats 0.0 38,844 16.700
1 3

Baum and Parker. 1973: 2White and Plaskett, 1981: Claar. t al. 1979;

4Oursborn, st 51. 1978: 5Mantell. 1975: *not available: ‘* used in this

analysis.

55

.shells, fruit pits and field residues. Plastics, rubber,
fats and oils have higher heat contents due to the higher
proportion of hydrogen and carbon per unit. The greater
percentage of oxygen in biomass materials as compared to
plastics, reduces the heat content of these materials, since
the carbon and hydrogen are already partly oxidized (White
and Plaskett, 1981).

Combustion of plastics increases heat recovery
substantially, but requires special attention. Sudden
flare-ups in the combustion chamber can occur with flow rates
in excess of 10 percent of the total fuel (Hut and Hare,
1981). Combustion of poly-vinyl chloride (PVC) plastic
results in the release of chlorine, which combines with
hydrogen to form hydrochloric acid (HCl). Severe corrosion
occurs in the heat exchanger when HCl condenses on the
surfaces. Chlorine is also released from the burning of salt
in food processing wastes and paper products. As long as the
temperature in the heat exchanger and stack is maintained
above the condensation point of HCl (300-660°F or 150-350°C)
corrosion problems will be minimized (Baum and Parker, 1973).
The sulfur content for plastics (1-2 percent) is not
significant.

Biomass wastes with the highest ash contents were sugar
beet and potato foliage (21.2 and 13.5 percent,
respectively). All other reported values were below that for
peanut shells at 8.8 percent (Table 11). The most likely

fuel candidates for Michigan processing firms are within the

 

56

range for adequate emission control; values for cherry and
peach pits are less than 1 percent.

Ash fusion temperatures for paperboard, textiles, paper
and plastics will not be reached under normal operating
conditions in a biomass boiler, and therefore will produce

little or no slag (Table 12).

Table 12. Ash fusion temperatures for selected processing
*

wastes.
ASE. 32.
Mixed waste 1,205

Paperboard, textiles 1,227
Plastics, rubber 1,261

Coal 1,330

 

*(Kut and Hare, 1981; Kranzler, et al., 1983).

Combustion of these residues would require the same
emission standards as those for waste-fueled combustors
listed previously (Table 8). Limits for HCl emission have
not been set at the federal level; however, the State of
Michigan allows a maximum of 0.07 mg/m3 when measured at the
property line of the plant. These requirements have been
easily met by all waste-fuel facilities in the State (Tilesz,
1983). Fly ash absorbs some HCl while being carried in the

flue gas, and any excess HCl can be satisfactorily removed by

57

water scrubbers (Baum and Parker, 1973). It should be

repeated

plastics.

that HCl is produced only by combustion of PVC

3.3 Selection of Handling/Combustion System Components

3.3.1 Handling system

Criteria for selection of system components were based

upon four general considerations:

pomace availability, including quantities produced,
length of processing season and plant processing

schedule;

types of handling equipment available to the

industry;

characteristics of the combustion furnaces,
including dependability, combustion efficiency,

retrofit potential and multifuel capability:

equipment costs relating to purchase, installation

and maintenance.

58

The advantages of drying wet processing wastes prior to
combustion has already been delineated. Apple pomace flow
rates (considered at 65 percent MC) were calculated for two
production rates of 30 and 100 tons pomace/day (27,215 and
90,718 kg/day), or 1.9 and 6.3 tons/hr (1,836 and 5,508
kg/hr), representative of a range of firms in the Michigan
apple juice industry. When dried to 15 percent MC, the flow
rates reduce to 1,667 and 5,000 lb/hr (756 and 2,268 kg/hr).
At these rates, 15 percent MC pomace would generate 10 and 30
million Btu/hr (10.5 and 31.5 GJ/hr). Calculations assumed a
production schedule of 16 hr/day, 25 days/month and a 5-month
processing season (i.e., from September to February).

Handling system components were evaluated and selected
from pertinent references and conversations with industrial
representatives, followed by sizing according to the pomace
flow rate (Figure 6). A rotary drier was selected due to the
capability for efficient drying of particulate, high MC
materials, and the flexibility for use in batch or continuous
operations. The drier was assumed to combust natural gas or
fuel oil, but the potential exists to reduce drying costs by
recovering waste heat from the boiler stack and introducing
into the drier. Waste heat recovery was not assumed in this
analysis.

The pomace should be agitated prior to drying to break
up any clumps which may have formed. Clumps passing through

the drier would be fed through a hammermill and reintroduced

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into the drier, since clumps case~harden at the surface,
thereby reducing drying efficiency. The dry surface acts as
an insulating barrier, hindering moisture diffusion from the
inside; the clumps could also block subsequent pomace flow
and disrupt the combustion process in the combustion chamber.

Upon drying, pomace would be transported by belt
conveyor or bucket elevator to a bulk collector for
short-term storage. Pneumatic handling would be advisable
only for pomace without rice hulls, since the high silica
content is very abrasive to transport piping. The hulls
could be separated from dried pomace by air classification,
which would then permit pneumatic handling and allow the
hulls to be recycled on a daily basis as a press aid. Air
classification of the rice hulls was not included in this
analysis.

The bulk collector would be located outside of the
building and adjacent to the boiler room. The purpose of the
collector would be for protection against adverse weather
with a maximum capacity of one day production. The
handling/combustion system was designed for a continuous
production schedule; storage would be necessary to prevent
accumulation of pomace within the plant in the event of brief
shutdowns of the boiler. Screw augers at the base of the
collector would transport and meter the pomace to the boiler.
Bridging of pomace can be avoided with an appropriate removal

system.

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61

3.3.2 Combustion systems

Several multifuel combustors are available for direct
combustion of biomass fuels, fossil fuels and combinations of
these fuels. Many of these combustors can be retrofitted to
existing boilers or purchased as an integral part of a
package boiler. Retrofit combustors require less capital
investment than the package boiler systems; however based on
conversations with industry representatives, the heat
recovery efficiency for retrofit applications decreases by
approximately 25 percent through heat losses between the
combustor and the heat recovery boiler. Package boilers
(those produced at the factory with the combustor and heat
exchanger boiler as a single unit) have heat recovery
efficiencies of 70-90 percent, depending on the method of
construction and flame adjustment.

Three package boiler systems were selected and evaluated
for the two pomace production rates assumed in this study.
Combustion technologies employed by these systems are known
as pile burning, fluidized-bed combustion and suspension
firing. The boilers are of the fire tube design and were
sized for energy generation of 10 and 30 million Btu/hr (10.5
and 31.5 GJ/hr), based on the respective pomace flow rates at
15 percent MC. Pile burning and suspension firing systems

have been extensively used by the wood products industry for

62

combusting wastes ranging from hogged brush to sawdust fines
(Bullpit, 1980). Fluidized-bed combustion is a relatively
new technology used for burning coal and municipal wastes on
a powerhouse scale, but also showing excellent promise for
use on a smaller scale for combusting biomass.

In pile burning systems (Figure 7), solid fuel is
introduced into the combustion chamber through the bottom
grate by a screw auger, forming a pile as it is pushed
outward. It may also be pneumatically fed into the chamber
from above, where it partially burns in suspension before
falling onto the grate. The fuel accumulates in a thick bed
pile with combustion occurring at the surface of the pile.
This permits fuels of 50-60 percent MC and of non-uniform
size to be combusted, since the extended residence time
facilitates drying in the combustion chamber. Furnace
designs are the dutch oven, fuel cell, cyclone, ‘wet cell,
inclined water-cooled pinhole grate, traveling-grate
spreader-Stoker and vibrating grate (Perry and Chilton,
1973).

Dry, particulate fuels (15 percent MC) are required by
suspension firing systems, in which the fuel is pneumatically
fed into the combustion chamber (Figure 8). Nearly complete
combustion occurs by proportionally metering the air with the
fuel flow rate. These systems have been installed in
powerhouse operations, and pulverized coal is routinely
combined with biomass fuels to increase heat output. Furnace

designs are the cyclonic and solid fuel burners (O'Grady,

63

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65

1980).

The current interest in fluidized-bed combustion systems
has developed because of the capability of burning a variety
of fuels up to 55 percent initial MC at non-uniform sizes.
The combustion air is forced upward through a bed of heated
sand maintained at approximately 1,700°F (927°C). The air
causes the sand particles to become fluidized (Figure 9), at
which time the fuel is introduced into the bed, dried and
combusted by the continuous agitation from the hot sand
particles. Addition of lime to the sand allows combustion of
high sulfur coals, the lime acting to absorb sulfur compounds
before release to the exhaust gases (LePori, et 37., 1981).

Due to the higher amounts of ash derived from
thermochemical conversion of cellulosic materials, a rigid
schedule must be maintained for removal of ash from the
combustion chamber to prevent slag formation. The trend has
been to design combustors which have automatic ash removal
systems in the grate area which permit continuous operation
by elimination of frequent manual cleaning. Several of these
systems also separate and reinject unburned char pieces back
into the combustion zone, improving efficiency up to 7
percent (Johnston Boiler Co., 1978).

These systems were selected to provide a greater choice
for the processor considering investing in biomass conversion
technology. Each has advantages and disadvantages (Table
13), and selection of a particular combustor is dependent

upon several technical factors, including size of operation,

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NC of the fuel, need for multi-fuel combustion, energy
recovery form required (e.g., steam, hot water, hot air) and
presence of abrasives in the biomass. Economic factors will
be discussed in Chapter 4. A list of the equipment and

manufacturers used in the analysis is provided (Appendix 3).

CHAPTER 4

Engineering Cost Analysis

4.1 Analytical Approach

Cost analysis is a necessary step in the process of
determining the overall feasibility of an engineering
project. An accurate knowledge of the anticipated costs and
returns of implementing a new technology is essential before
a decision can be reached regarding its adoption. In this
chapter (1) the essential elements of a cost analysis will be
outlined along with the basic criteria necessary in selecting
a cost analysis method, (2) an analytical method will be
presented for this problem and (3) techniques for
interpretation of the results will be discussed.

There is a basic difference between the accounting and
engineering approaches to cost analysis. The accountant
reviews the financial history of a firm in order to explain
its current economic status. Engineers, however, are
concerned with projecting the economic impact of adopting a

new technology on present and future costs. Thus an

69

70

appropriate cost analysis provides a base from which a
promising technology can be realistically evaluated in terms
of future returns to the company (Barish and Kaplan, 1978).
Projects requiring cost analysis are as diverse as energy
conservation/alternative energy sources, machinery
design/selection, building structures and new methods or
procedures for agricultural operations.

The first step in determining an appropriate cost
analysis method is to identify clear-cut, measurable
objectives, based upon the constraints of the intended firm.
Knowledge of investment requirements and firm constraints
establishes the system boundaries of the analysis.
Boundaries are necessary in order to control or limit the
number of parameters being investigated, enabling an
accurate, but manageable analysis. Thus the extent of the
analysis would be defined by those parameters having a direct
bearing on the feasibility of the investment - total costs,
loan interest rates, disposal costs (such as for processing
wastes), machinery power requirements, space limitations (in
buildings), potential to retrofit into the existing system,
etc.

The type of firm will also have bearing on the
objectives. Goverment agencies or institutions and large
firms are normally able to absorb capital outlays over a
longer period of time than smaller firms. Non-profit
organizations have different objectives than do private

firms, the former having objectives more subjective and

71

intangible than the latter which operate to maximize profits.
The availability of grants, special low-interest loans or tax
credits can also make some high-cost or high-risk ventures

feasible where they otherwise would not be.

4.2 Determination of total costs

4.2.1 Fixed and variable costs

Project costs must be identified and quantified, being
classified into two broad groups; as fixed (or investment)
costs and as variable (or operating and maintenance) costs.
Direct fixed costs are those associated with the purchase of
the investment - feasibility studies, equipment and
installation, insurance, loan interest and taxes. Indirect
fixed costs are associated with administration and plant
overhead expenses. Variable costs are reflected in the size
and frequency of operation and maintenance upkeep of the
investment - the price of energy, labor and repairs, and
production materials. Further discussion of variable costs
follow in a later section. Non-economic costs include
social, political and psychological costs, and are difficult
to estimate in terms of real dollars (Blanchard, 1978). For
feasibility analyses only present and future costs are

included in the calculation of total costs (Barish and

72

Kaplan, 1978).

4.2.2 Cost estimation

Often it is difficult to obtain exact values for such
costs as new equipment, insurance, taxes and maintenance due
to lack of data. Accepted methods for estimating these and
other costs have been discussed at length (Ostwald, 1974;

Humphreys and Katell, 1981).

4.2.2.1 Cost indexes

Cost indexes are compiled to provide a means of
converting past data into current costs. Various indexes are
published for cost factors such as new equipment, labor rates
and materials. They are established by collecting costs for
desired industries during a base year, and setting the

indexes equal to 100. Current costs can be determined by:

Present Cost =

Original Cost X (Present Index Value/Original Index Value)

Well-recognized indexes include the Marshall and Swift
all-industry indexes, the Engineering-News Record

Construction Cost Index and the U.S. Department of Labor

73

materials and labor indexes. Current Marshall and Swift
indexes are published in each issue of Chemical Engineering.
Caution must be exercised in using indexes, since they are
based on cost averages. They likewise should not be used for

periods exceeding 10 years (Peters and Timmerhaus, 1968).

4.2.2.2 Equipment scaling

At times cost data are needed for equipment sizes other
than those for which data has been obtained. A useful

formula for scaling of project size and cost is as follows:

Unknown equipment cost =

Known equipment cost x (Unknown size/Known size)n

where "n” is the cost capacity exponent for the given
equipment. Values for cost capacity exponents were derived
for individual equipment and installation costs in 1961
(Bauman, 1964). Plots on log-log scale were constructed for
equipment size vs. cost, and the slope of the line through
the points yielded the specific exponent for each piece of
equipment. Although the equation describes continuous
functions, in reality equipment costs are step functions,
since equipment are designed to perform over a fixed range.
However for purposes of feasibility studies, the costs can be

approximated by continuous functions. The average was 0.6

74

for all exponents, with a range from 0.16 to over 1.00.
Normally the factor corresponding to the type of equipment
being analyzed is selected; 0.6 is used only when another
factor is unavailable. Scaling should be confined to a
ten-fold range based on the data from which the factors are

derived.

4.2.2.3 Other parameters

Estimates for annual maintenance costs were given as
percent of new investment costs - simple processes: 2-6
percent; average operating conditions: 5-9 percent; and
severe or complex operating conditions: 7-11 percent (Peters
and Timmerhaus, 1968).

For analyses in which detailed data are available, a
more accurate determination of costs can be included. The
cost capacity exponent can be determined by the method
outlined above to provide greater accuracy in equipment
scaling. Calculation of corporate income taxes for a new
investment is dependent upon divulsion of proprietary
information such as taxable income. Knowledge of the tax
bracket, depreciation method and energy tax credits (for some
investments) would permit determination of exact taxes which
would be incurred over the life of a new investment. For
such a specific case depreciation of the investment would be

included only as it directly affects tax costs, using an

75

approved method for depreciation calculations. It should be
noted, however, that depreciation costs are not included as a
direct cost, since they have already been included in the

original purchase cost of the investment.

4.3 Life cycle cost analysis

4.3.1 Cost analysis methods

New technologies are many times evaluated and selected
or rejected solely on the basis of the lowest initial (or
purchase) costs. Another commonly used method, known as
payback period, calculates the length of time necessary to
pay off an investment, by dividing investment costs by the
anticipated savings. This payback period is compared to a
previously set maximum. Although both of these methods are
simple and involve little calculation time, very inaccurate
results and disasterous conclusions can result by overlooking
a group of costs which are far more expensive than initial
investment costs. These are operating and maintenance costs
and the time value of money.

Life Cycle Costing is a cost analysis method which
considers not only investment costs, but more importantly the
significant costs which would be incurred over the life of

the investment, in other words the total cost of ownership.

76

It is becoming widely adopted in the public and private
sectors for investments which are subject to substantial
operating and maintenance costs. Life cycle costing (LCC) is

influenced by four factors (Brown and Yanuck, 1980):

l. expectation of high energy costs during the life of

the investment,

2. a long life span,

3. savings in operating and maintenance costs resulting

from the new technology, and

4. high investment costs.

Life Cycle Costing has been effectively applied to
evaluate the viability of implementing retort pouch
technology (Williams, et 31., 1981), energy conservation in
residential housing (Jones and Harp, 1981) and farm machinery
selection (Rotz, et 31., 1983). Therefore the majority of
engineering projects requiring cost analysis would be best

served by LCC analysis.

 

77

4.3.2 Time value of money

Besides including variable costs, a second advantage of
LCC is consideration of the time value of money. In order
for a firm to make a major investment such as a biomass
handling/combustion system, capital would be required, often
obtained by a loan. The present value of a sum of money is
worth more than its value would be at a later date since the
real value of money decreases due to inflation. This concept
is referred to as the time value of money, which is often
approximated by the cost of borrowing money, and is included
in the interest rate charged by lenders. An example would be
helpful.

One thousand dollars would be worth only $900 after one
year at 10 percent annual inflation; in other words it would
take $1100 of future dollars next year to be equivalent to
$1000 of present dollars at this inflation rate. Interest
rates are set to cover losses due to inflation as well as to
provide a margin of profit. The cost of interest (or the
opportunity cost of owned capital) is thus an inherent cost
of any analysis concerning the cost of capital.

There are six basic formulas for determining the present
or future worth (value) of money, encompassing a variety of
situations. These are: Single Compound Amount, Single

Present Worth, Uniform Capital Recovery, Uniform Present

78

Worth, Uniform Sinking Fund and Uniform Compound Amount. An
excellent description of these formulas and accompanying
examples is presented in Brown and Yanuck (1980).

Present worth techniques utilize the above formulas in
order to calculate the current value of future dollars.
Three techniques pertinent to feasibility analysis are (1)
net present value, which calculates the present value of
total costs over the life of the investment, (2) uniform

annual costs, in which present and future costs are divided

 

into equal annual costs over the life of the investment, and
(3) internal rate Q; return, which determines the discount
rate at which present value costs and benefits are equal.
Internal rate of return is the least recommended of the
three; even though more accurate, it involves complicated
iterations by computer or by trial and error. The decision
to use either net present value or uniform annual costs would
be dependent upon loan restrictions, and in some cases upon
figuidelines for feasibility studies required by the firm or

organization .

43.3.3 Average Annual Cost

After consideration of the above formulas and methods,
1:he Uniform Capital Recovery (UCR) factor was selected for
lase in this analysis. The UCR factor is used to determine

‘t:he Average Annual Cost of a loan based on a fixed interest

79

rate and loan repayment period:

Average Annual Cost =

Principle x UCR + Yearly Operating and Maintenance Costs

where UCR =
i(1+i)n
(1+i)n - 1
i 2 interest rate (decimal)

n = number of interest periods

4.4 Interpreting the analysis

4.4.1 Break-Even Analysis

It is mandatory to determine the length of time which
swould be necessary to pay back the total costs of a new
:investment. Use of the payback period method was previously
‘nnentioned, but should be limited to inexpensive, short-term
investments since the time value of money and variable costs
are ignored. Knowledge of the Average Annual Cost (AAC) of
tflae proposed investment and the resultant savings (or losses)
as compared to the current system permits calculation of the

tzime necessary to recover the AAC. The point at which AAC

80

equal savings is known as the break-even point. With the
Break-Even Analysis the cost/savings history of the
investment can be plotted for determination of the true
payback period as well as future savings to be expected by
the investment (Park, 1973). Energy-related projects are
normally considered viable if the payback period is 5 years

or less (Lapeau, 1983).

4 . 4 .2 Sensitivity Analysis

Once the results of a cost analysis are available it is
important to gain an understanding as to which individual
cost variables influence the overall feasibility of the new
investment. For example, how would the break-even point be
affected by fluctuations in loan interest rates or electrical
and fossil fuel costs? The influence of such parameters is
determined by performing a Sensitivity Analysis, in which
total costs are calculated as each of the costs is varied,
one at a time. The value of this method is that even though
a Particular investment may not be presently feasible,
proZiections can be made as to the economic conditions
den‘anded for it to become feasible. Sensitivity Analysis can
also be used to optimize the system by investigating
intel‘actions between the parameters and to explore areas of

risk or uncertainty (Allen, 1972).

81

4.5 Feasibility Analysis Data

4.5.1 Fixed cost data

4.5.1.1 Equipment costs and power requirements

Costs and power requirements were obtained from
manufacturer representatives for each of the handling system
components in August, 1983 (Table 14). These data were based
on component sizes calculated for a plant biomass output of
12.500 lb/hr (5,670 kg/hr), w.b., which would represent a
large processor. Installation estimates were included with
Purchase costs for all components. Power requirements
in(Iluded all motors related to fuel handling and combustion
air. The rotary drier was the costliest handling system
component, accounting for 86 percent of the total handling
COSts (Figure 10), and 64 percent of the power requirement.
The storage bin was sized to contain the biomass from a

Single day's production (at 15 percent MC).

82

Table 14. Installed purchase costs and power requirements for
handling/boiler system components.

Handling System
Belt conveyor
Rotary drier
Hammermill
Bucket elevator
Storagetfin

Screw auger

 

Total

Boiler? Systems
Pile burning
Fluid 1 zed—bed

Suspension-fired

Purchase Costs
14,595
259,560
13,081
8,549
11,610
3,445

 

310,840

863,720
734,500
348,906

($)

Power
Consumption (kW)

 

0.67
111.63
54.00
4.50
2.70
173.50

 

144.00
119.00
103.00

 

Manufacturers' prices for August 1983 for a large processor with
biomass output of 12,500 lb/hr (5,670 kg/hr).

83

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4.5.1.2 Equipment scaling

Costs for equipment sizes other than those obtained were
estimated in the feasibility model by use of the scaling
formula previously explained. Cost capacity exponents were
derived for each of the boiler systems as outlined by Bauman
(1964). Installed costs for two boiler sizes were plotted on
log-log graph paper, the slope recorded as the exponent
(Figure 11). The exponents were 0.36, 0.65 and 0.18 for pile
burning, fluidized bed and suspension-fired boiler systems,
respectively. The exponent used for the handling system was
0.53, based on a composite of values reported by Bauman
(1964) for belt and screw conveyors (0.53) and by Perry and
Chilton (1973) for a vertical carbon steel tank (0.52). The
actual cost for the rotary drier was used in the analysis
based on plant biomass output: 0-6,000 lb/hr (0-2721 kg/hr) =
3129.780 and 6,000-16,000 lb/hr (2721-7257 kg/hr) = $259,560.

A power consumption exponent was derived as above for
eatimation of power requirements for scaled equipment. The
exP<>flent was determined from manufacturer's data for power
°°“Sumption of two equipment and boiler sizes. From these
data the analysis should be valid for processors requiring
firm“ 1 million Btu/hr (1.05 GJ/hr) to 100 million Btu/hr
(105.5 GJ/hr).

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86

4.5.2 Variable cost data

Local sources were consulted for variable cost data in
August, 1983. Electrical rates were obtained from the local
utility company based on the sum of the demand rate
($5.90/kw), base rate ($0.0243/kw-hr),fuel-cost adjustment
(50.00672/kw-hr) and 4 percent Michigan sales tax. It was
assumed that the processor owned the transformer, qualifying
it for the least expensive primary rate. Natural gas cost
$4.795/1ooo {:3 ($0.169/1000 lit) and #2 fuel oil cost
$0.948/gallon ($0.2504/lit). The fuel oil price had
significantly dropped by 17 percent since July 1982, while
the natural gas price increased 4 percent during the same
Period. The sharp decrease in the fuel oil price reduced the
difference in net costs between the two fuels to
$6.771/million Btu ($7.1438/GJ) for fuel oil and
34-795/million Btu ($5.0593/GJ) for natural gas.

Disposal costs were $18.30/ton ($16.60/1000 kg), based
on a large capacity trailer 40 yd3 (31 m3). The loan
interest rate for a firm in good economic standing was 14
percent annually for a fixed rate loan with a 5-year payback
period. Insurance for boiler and related machinery would be
$653/yr. The extra labor rate was fixed at $10.00/hr.
SevEral costs were determined to be irrelevent to the

ana:I-lrsis and therefore not included: administrative costs for

87

the new system were assumed to be equal to present costs;
salvage value of the old system was assumed to equal to the
removal costs; and property tax increases were estimated at
one percent of investment costs. Specific tax calculations
based on depreciation of the investment were not made since

individual processor tax brackets vary widely.

CHAPTER 5

Energy and Cost Analysis Model

5.1 Model description

5.1.1 Initial considerations

In planning the computer model, the overriding objective
was to create a tool from which industries other than the
apple juice industry may be evaluated. This model would
serve as a reference from which refinements may be made as
deemed necessary after further experimentation and analysis.
Having access to a Cyber CDC 750 computer at Michigan State
University, it was decided to program the feasibility
approach as described in the previous chapters as a
"user-friendly" model. A user-friendly model is one in which
the user can directly interact with the program from a
terminal by means of a series of prompts, or questions, from

the model. Fortran 5 was selected as the program language

88

89

due to broad processing capability and wide usage in
engineering disciplines.

Upon completion of the programming stage, the model was
verified by comparison of model responses with hand
calculations. Clear documentation was inserted to facilitate
later use. The program can be stored in compiled form for
future use as a low-cost teaching aid in energy or cost
analysis.

The model is divided into two parts, a biomass energy
model to calculate biomass net heat content and fossil fuel
savings, linked with a cost analysis model (Figure 12). The
user is given the option to perform either the biomass energy
analysis or both analyses, depending upon the study being
conducted. Single or multiple parameters may be varied at
the completion of an analysis to aid in simulation, avoiding
re-initialization of all parameters. Parameters which may be
varied relate to boiler size and type, biomass and/or fossil
fuel type, loan interest rate and payback period. The option
to begin a new analysis or exit is also available.

The following sections describe in detail model

structure and operation.

5.1.2 Input parameters

90

C D

 

   
   
 

  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

   
   
    
     
    
  
   

 

 

 

 

 

 

     
   

 
 

 

 

 

 

SELECT WASTE ENERGY ANALYSIS
r—p ANALYSIS
ENERGY & COST ANALYSIS
JPLANT OPERATING
—l: l PARAMETERS
I FOSSIL FUEL
“"5 I PARAMETERS ]‘ 3"
[ WASTE FUEL
h-f l CHARACTERISTICS
-t OUTPUT
(l. Net heat content
2. Initial flow rate
3. Dried flow rate
’ ‘ 4. Haste fuel value
PERFORM ENERGY . .
ANALYSIS LSLPrOJected saVings
A Fl. Annual variable costs
PERFORM COST 2. Total invest. costs
ANALYSIS 3. Average annual costs
4. Break-even period
5. Annual savings
Lg. Disposal savings
SELECT
PARAMETER
CHANGES END 3
Figure 12. General flowchart for the waste energy and cost

analysis model.

91

5.1.2.1 Plant operating and economic parameters

All variables were programmed for English units, due to
almost exclusive usage within the industry for measurement of
power generation and energy consumption. This description
will assume selection of the complete energy/cost analysis.
Plant operating conditions of significance are requested
which relate to the processing schedule, waste disposal cost,
utility rates and peak heat demand. Variables affecting the
cost analysis of the proposed system for input are extra
hours to be required, hourly salary for the worker, loan

interest rate and desired loan repayment period (Figure 13).

5.1.2.2 Biomass input parameters

A maximum of 3 biomass types can be analyzed with a
single run, and consider single or multiple processing
seasons. Parameters required for fossil fuel inputs are
current fuel type (natural gas, #2 fuel oil or other) and
price. Those related to the biomass are biomass heat content
(dry weight), initial MC, dried MC, daily production rate,
processing season length, and opportunity cost (or the value
for alternate use). Finally selection of from 1 to all 3

combustion systems is requested, followed by combustor

 

‘ START

    

92

 
    
 

  

ANALYSIS
DESIRED

 

PROCESSING
SCHEDULE

T DISPOSAL
COSTS
I

UTILITY
RATES

I

EXTRA HOURS
FOR WORKER

l
WAGE RATE

1

LOAN INTEREST
RATE

1

PAYBACK
PERIOD

 

 

 

T Lia-III L---vTL--JT inn-ul inn-.—

 

"""*l"""'l""ll""'l'r""‘l
L-L—i.

 

I

PEAK HEAT
DEMAND

 

WASTE ENERGY ANALYSIS

 

ENERGY_§ COST ANALYSIS

 

 

 

I TO BIOMASS
'— :> INPUTS
Figure 13. Initial input parameters for processing plant

operating conditions.

93

efficiency (Figure 14).

5.1.3 Calculations for energy analysis

The net heat content of the biomass(es) is determined by
first calculating the amount of heat required by the drier to
reduce the MC as specified by the input conditions. A drier
efficiency of approximately 50 percent was assumed by
utilizing 1700 Btu/lb (3954 kJ/kg) of water, to provide
drying costs using the fossil fuel. Second, the water
remaining in the biomass after drying was assumed to require
1300 Btu/lb (3024 kJ/kg), to account for evaporation within
the boiler prior to combustion. The difference of these heat
losses from the heat content (for bone dry material) is the
net heat content of the biomass used in the calculations in
the energy analysis. A summary of the drying calculations is

presented (Appendix 2).

5.1.3.1 Biomass energy savings

Hourly savings from costs of disposing the biomass (if
any) are next added to hourly savings in fossil fuel costs
(including the boiler efficiency) less drying costs, to
arrive at the net hourly savings when combusting the biomass.
The net hourly savings can be converted to annual savings and

summed for each of the biomass fuels being analyzed yielding

v1

I‘l

94

FROM
INITIAL
INPUT

      
 

   
   
   
 
 
  

CURRENT
FOSSIL FUEL
TYPE

 

OTHER
Yes FUEL PRICE 1

j

‘ SINGLE OR
2 °r 3 SEPARATE ]
_ SEASONS

chance i J

 

 

     
   
 
    

CHANGE

PRICE FROM

1983
?

 

 

NUMBER

 

WASTE HEAT
CONTENT (n)

I

L
T W l...
I
l
l

 

(n)

 

 

DAILY WASTE (n)
GENERATION

 

 

(n)

 

 

 

SEASON LENGTH
FOR WASTE
* OPPORTUNITY
COST
SELECT

BOILER
SYSTEM

(n)

  
  

     
  

individ. systems PERFORM
i! ENERGY

I BOILER & COST
all 3 EFFICIENCY FANALYSIS

I? -
ignite 14. Input parameters for waste energy and cost analysis.

 

 

95

the total annual savings.

5.1.4 Calculations for the Life-Cycle Cost Analysis

5.1.4.1 Total costs

Annual electrical costs are estimated for the handling
and boiler systems, based on the respective scaling
exponents. Maintenance, property tax and insurance costs are
summed to obtain an annual total for system operating costs.
Total investment costs are calculated from the respective
scaling exponents for the handling and boiler systems and for

the sized drier.

5.1.4.2 Investment savings (losses)

The uniform capital recovery (UCR) factor is calculated
from the loan interest rate and repayment period, from which
is determined the Average Annual Cost of the investment for
the duration of the loan period. Annual investment savings
(or losses) are determined by subtracting the AAC from the
annual total savings. The actual break-even point is also
calculated in terms of the number of months of operation

required on biomass in order to recover the AAC.

96

5.1.5 Output parameters

5.1.5.1 Biomass energy analysis

A table is printed for each biomass type, identifying
the system size, waste fuel type and fossil fuel being
substituted (Figure 15). Variables listed are: heat content
of bone dry material, heat which would be generated and
recovered from combustion, and the heat required for drying
(if any). Pre- and post-drying flow rates, fuel value per
unit mass, and hourly and yearly savings in fossil fuel and
disposal costs are also printed. Determination of flow rates
is essential for additional system design with respect to
equipment throughput and storage capacity, while heat

available for recovery can be used in sizing boiler systems.

5.1.5.2 Life-Cycle Cost Analysis

A heading lists the key operating and investment
conditions succeeded by variable and fixed costs printed in
tabular form (Figure 16,A). For wastes in addition to the
principle waste being evaluated, another table is printed for

the flow rates which would be required of that waste in order

97
LARGE PROCESSOR REPLACING #2 FUEL OIL

.--------—-----—------------—--—---——---—-—-—-
—-—--——---—--—-------—---——-——-—-—-—-—-——-—---

FOR BIOMASS FUEL #1 REPLACING #2 FUEL OIL

WITH INITIAL MOISTURE CONTENT OF 65. PERCENT
HEAT CONTENT (BTU/LB, BONE DRY):

HEAT GENERATED FROM COMB. (BTU/LB):

HEAT REQUIRED TO DRY (BTU/LB, FRESH WT):

WET WASTE FLOW RATE (LB/HR):

DRIED WASTE FLOW RATE (LB/HR):

WASTE FUEL VALUE (S/TON):

PROJECTED SAVINGS BURNING THIS WASTE:

FOR WASTE FUEL #2 REPLACING #2 FUEL OIL
WITH INITIAL MOISTURE CONTENT OF 2. PERCENT

HEAT CONTENT (BTU/LB, BONE DRY):

HEAT GENERATED FROM COMB. (BTU/LB):

HEAT REQUIRED TO DRY (BTU/LB, FRESH WT):
WET WASTE FLOW RATE (LB/HR):

DRIED WASTE FLOW RATE (LB/HR):

WASTE FUEL VALUE (S/TON):

PROJECTED SAVINGS BURNING THIS WASTE:

$/HOUR:
$/YEAR:

FOR WASTE FUEL #3 REPLACING #2 FUEL OIL

WITH INITIAL MOISTURE CONTENT OF 10. PERCENT
HEAT CONTENT (BTU/LB. BONE DRY):

HEAT GENERATED FROM COMB. (BTU/LB):

HEAT REQUIRED TO DRY (BTU/LB, FRESH WT):

WET WASTE FLOW RATE (LB/HR):

DRIED WASTE FLOW RATE (LB/HR):

WASTE FUEL VALUE (S/TON):

PROJECTED SAVINGS BURNING THIS WASTE:

.--.—--——-—_.—-.—---—---—u-—-——_“..-——-—-.——.—-—
-----———------—--------——-—----—----a—-—----—.

(apple pomace)

5147.059
16.87846

105.490
210980.786

40.133
80266.314

(corrug. paper
box)

375.000
75.47008

14.151
28301.279

Figure 15. Example output of the energy analysis for apple
pomace, polyethylene and corrugated paper box.

TONS/DAY

FUEL VALUE:

LOAN:

FOR A PLANT PRODUCING 100.
WITH A 5.-MONTH PROCESS SEASON

30000000. BTU/HR SYSTEMS
SUBSTITUTING FOR #2 FUEL OIL
$ 6.7714/MILLION BTU

14.PERCENT;
WITH DISPOSAL COSTS OF $0.

5.YEAR PAYBACK

PER TON

Ra

 

REQUIRED FLOW RATE FOR ADDITIONAL WASTE IN ORDER 1
FOR BREAK-EVEN OF AVERAGE ANNUAL COSTS TO OCCUR.

COMBUSTOR

WASTE #2

PILE BURNING
FLUIDIZED BED
SUSPENSION FIRED

WASTE #3

PILE BURNING
FLUIDIZED BED
SUSPENSION FIRED

LB/HR,WET WT

(polyethylene)
923.9
718.8
132.9
(corr. paper box)
2620.4
2038.7
0

377.

 

COMBUSTOR

PILE BURNING
FLUIDIZED BED

SUSPENSION FIRED 16517.5

HANDLING AND COMBUSTION SYSTEM COSTS
(VARIABLE AND FIXED COSTS)

($)

OPERATING
(ANNUAL)

UTILITY
(ANNUAL)
HANDLING COMBUSTOR

16517.5 13709.0
16517.5 11329.0
9805.8

28822.0
28822.0
28822. O

HANDLING COMBUSTOR

39620.0
33364.0
20272. 9

INVESTMENT

(TOTAL)
HANDLING COMBUSTOR]

304551.1 863720.0
304551.1 734500. 0
304551.1 348906. 0 J

==--———------------------—--_-—---——-—-—-—---—-—---——--———-—---——-~—----—--
W--—----..-----------.---.-—-.-—-----.----—----—..---..--—:—-

P ILE BURNING
FLUIDIZED BED

lJSPENSION FIRED

(MONTHS)
408740.8 9.69
364844.5 8.65
239436. 3 5.67

-89192.4 4
-45296.1
801H2.1 J

ANNUAL DISPOSAL SAVINGS. $0.

‘ .........................................................................

Figure 16.

Example output of the economic analysis for
apple pomace, polyethylene and corrugated paper

box.

99

for the system to break-even (B). Utility costs, total
operating costs and total investment costs (in dollars) are
listed for each of the three handling/boiler systems (C).
Average Annual Costs, the number of months of operation on
biomass required for payback of AAC, and annual investment

savings (losses) are also printed for each system (D).

5.2 Verification and Validation of the Model

5.2.1 The Michigan apple juice processing industry

5.2.1.1 Apple pomace input parameters

The model was verified by comparing energy and cost data
determined by the model with known values derived by hand
calculations. Values for input variables were selected to
represent two sizes of Michigan apple juice processors.
Operating conditions were set to evaluate the technical and
economic feasibility for the processing situations, one for a
small processor generating 30 tons/day (27,215 kg/day) of
apple pomace (AP), and the other for a large processor
producing 100 tons/day (90,718 kg/day). Handling and boiler

systems were sized for these pomace flow rates, assuming a

100

maximum energy requirement of 10 million Btu/hr (10.5 GJ/hr)
for small processors and 30 million Btu/hr (31.5 GJ/hr) for
large processors. The processing schedule was calculated for
16 hours/day, 25 days/month over a 5-month processing season
(e.g., from mid-September to mid-February).

The annual loan interest rate was fixed at 14 percent,
with a 5-year payback period. It was also assumed that the
total investment cost was satisfied by the loan; in other

words, no initial down payment was made.

5.2.1.2 Input parameters for additional in-plant wastes

In many processing plants significant energy may be
available from waste streams other than pomace. From the
list of waste fuel candidates (Table 11), three were selected
for analysis of waste fuel and economic feasibility:
polyethylene (PE), corrugated paper box (CPB) and wet fruit
waste (WFW). Criteria for selection of these wastes were, 1)
the likelihood of waste availability at the plant (packaging
wastes and biomass from concurrent or other season processing
operations), and 2) representation of a range of wastes from
better candidates (PE and CPB, which require no drying) to
the worst candidate (WFW with a high MC of 80 percent).

Small processors were assumed to generate 30 tons/day
(27,215 kg/day) of WFW, equivalent to that of AP, and l
ton/day (907 kg/day) of both PE and CPB packaging waste.

101

Large processors were likewise assumed to generate 100
tons/day (90,718 kg/day) of WFW and 3 tons/day (2,721 kg/day)

of PE and CPB waste.

5.2.2 Feasibility Analysis Results

5.2.2.1 Energy value of apple processing wastes

The model was validated by performing an energy and cost
analysis for apple juice processors in Michigan. The energy
analysis was performed for the four processing wastes, AP,
WFW, CPB and PE. Heat generated from combustion of the dried
materials was estimated for AP, WFW and CPB at 6,418, 7,016
and 6,556 Btu/lb (14,928, 16,319 and 15,249 kJ/kg),
respectively (Table 15). Heat loss to drying amounted to
15.6 percent for AP and 18.5 percent for WFW of the net heat
content for each waste after drying. Polyethylene had the
highest heat generation estimate at 18,594 Btu/lb (43,250
kJ/kg). Flow rates for AP and WFW were reduced 59 and 77
percent, respectively, after drying.

Annual savings anticipated from the decrease in fossil
fuels was a function of the biomass flow rate and heat
content (Table 16). A small processor could expect a net
savings of $44,820/year, substituting AP for natural gas
(NG), and $63,294 substituting for #2 fuel oil (F0).

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104

Combustion of PE would save $18,946 and $26,755/year in place
of NG and F0, respectively, since the high heat content would
offset the low flow rate of l ton/day (907 kg/day). Savings
from combustion of CPB and WFW were minimal, due to the low
flow rate of the former and the high drying costs and
resultant low flow rate of the latter.

Large processors could anticipate savings of $149,400
and $210,981/year substituting AP for N6 and F0,
respectively; PE would save $56,838 and $80,266 in place of
NG and F0, respectively (Table 17). Savings for CPB and WFW
were proportionately low.

Net fuel values for the four candidates ranged from
$1.00/ton ($0.91/1000 kg) for WFW replacing NC, to
$214.04/ton ($194.17/1000 kg) for PE substituting for F0
(Table 18). Relative fuel values as compared to pomace =
$1.00 were: PE = $12.70: CPB = $4.50; and WFW a $0.10.

Savings from reduction in biomass and packaging wastes
would greatly increase annual savings, as current disposal
rates are $18.30/ton ($16.60/lOOO kg). The effects of
disposal costs and other variables will be discussed in the

section concerning the Sensitivity Analysis.

 

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107

5.2.2.2 Life-Cycle Cost Analysis Results

5.2.2.2.1 Total costs for handling/combustion systems

The analysis assumed that the investment capital was
obtained entirely by a loan at an annual loan interest rate
of 14 percent and a payback period of 5 years. Apple pomace
was substituted for either natural gas or #2 fuel oil and
assumed no costs for disposal: in other words it was disposed
of as a local animal feed at no charge to the processor.

To purchase a handling/combustion system, small
processors would need to borrow $735,128, $513,184 or
$439,851 for the pile burning (PB), fluidized-bed (FB) or
suspension-fired (SF) systems, respectively (Table 19).
Annual operating costs would be $52,032 $43,263 or $39,712
for PB, FB or SF systems, respectively.

A large processor would require a loan of $1,168,271,
$1,039,051 or $653,457 to purchase a PB, FB or SF system,
respectively, while annual operating costs would be $68,443,
$62,186 or $49,095 for these same systems. Of the total
costs which would be incurred over the 5-year payback period,
interest costs would account for an average of 25 percent,
investment costs for 55 percent and operating costs for 20

percent.

 

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109

5.2.2.2.2 Average annual costs and the break-even point

5.2.2.2.2.l Apple pomace

Average annual costs (AAC) were calculated for small and
large processors with the following results (Table 20). For
small processors, AAC were $167,834, $192,745 and $266,163
for SF, PB and PB systems, respectively. For large
processors, AAC for these same systems were $239,436,
$364,845 and $408,741.

Waste fuel savings were subtracted from estimates of the
AAC to obtain the net annual savings (or losses) for each of
the three handling/combustion systems evaluated for the two
boiler sizes. None of the systems would be cost-effective
for either small or large processors under the economic
conditions assumed in this analysis, since net annual losses
were projected in each case. The losses for large processors
would range from a maximum of $259,34l/year for a PB system
substituting for natural gas (NG) to a minimum of $28,455 for
a SF system replacing fuel oil (F0). Losses for small
processors would be a maximum of $221,343/year for a PB
system substituting for N6, and a minimum of $104,540 for a

SF system substituting for F0.

110

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111

5.2.2.2.2.2 In-plant packaging wastes

The analysis was repeated for small and large processors
including the packaging wastes polyethylene (PE) and
corrugated paper box (CPB). While varying widely, small
processors were assumed to generate l ton/day (907.2 kg/day)
each of PE and CPB, and large processors 3 tons/day (2,721.5
kg/day) each of PE and CPB (Mantell, 1975). Under these
conditions, large processors could invest in a SF system
substituting these biomass fuels (in addition to AP) for F0,
and save $80,112/year (Table 21). The next least-cost system
would be a SF system substituting for N6, which would lose
$13,157/year.

The flow rates of PE and CPB required for break-even of
AAC were calculated. (These rates would be in addition to
AP.) Small processors would require a minimum of 3.9
tons/day (3,545 kg/day) of PE only, or 11.1 tons/day (10,053
kg/day) of CPB only to break-even with a SF system (Table
22). Large processors would need a minimum of 1.1 tons/day
(965 kg/day) of PE or 3.0 tons/day (2,735 kg/day) of CPB for

investment in a SF system to be cost-effective (Table 23).

112

 

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115

5.2.2.2.3 Sensitivity of the analysis

5.2.2.2.3.l Effect of waste disposal cost

Additional savings would be realized from combustion of
processing wastes if costs were being incurred for disposal.
(Commercial carriers typically charge $18.30/ton, or
$16.60/lOOO kg, for local disposal.) The analysis was
performed with the same operating and economic constraints
and included disposal costs of 100 percent of the current
rate and 50 percent ($9.15/ton, or $8.30/lOOO kg). For small
processors the SF system replacing NG would become
cost-effective as diSposal costs approached 100 percent
(Figure 17). Pomace substitution for F0 would allow purchase
of PB and SF systems, with the latter yielding savings of
$31,338/year.

Large processors could invest in all but the PB system
if replacing NG and incurring 50 percent of the disposal
costs (Figure 18). Savings of $ll,018/year for the FB system
and $136,427/year for the SF system could be anticipated.
With full disposal costs replacing NG, net annual savings for
all systems would range from $193,584 to $362,889. For large

processors substituting for F0, all systems would be

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118

cost-effective for disposal costs of 50 and 100 percent, with
net annual savings ranging from $28,703 for the PB system, to

$424,469 for the SF system. (See Appendices 4 and 5.)

5.2.2.2.3.2 Effect of fossil fuel price

Prices for NC and F0 were converted to $/million Btu
(S/GJ). Natural gas was priced at $4.795/million Btu
($5.059/GJ) and #2 fuel oil at $6.77/mi11ion Btu ($7.14/GJ).
Using F0 as a base, fuel prices were increased by 20, 50 and
100 percent, or to $8.124, 510.155 and $13.54/million Btu
($8.571, 510.714 and $14.28/GJ), respectively. Systems for
small processors would become feasible only with fuel price
increases in excess of 100 percent (Figure 19). Large
processors could afford to invest in a SF system with a 20
percent increase to $8.124/million Btu ($8.571/GJ) (Figure
20). Investment in PB and FB systems would not be
cost-effective until fuel prices rise by nearly 100 percent.

(See Appendix 6.)

5.2.2.2.3.3 Effect of loan interest rate

Loan interest rates were varied from 10 to 18 percent
for each of the systems. None of the small or large systems
would become cost-effective even if the unlikely rate of 10

percent financing were obtained (Figure 21). For a loan

 

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122

obtained at 10 percent, net annual losses for small
processors were reduced by 9 percent, while for large
processors losses were reduced by 12 percent for substitution
of NG, and by 16 percent for substitution of F0 (Figure 22).

(See Appendices 7 and 8.)

5.2.3 Feasibility Analysis Discussion

5.2.3.1 Fossil fuel costs, disposal costs, loan interest
rate

The cost analysis projected net annual losses for
investment in the systems with economic constraints
representing current processing operations. Net annual
savings were projected by the Sensitivity Analysis when key
parameters were varied over a range of operating conditions.
As a result projections can be made as to the economic
conditions which would justify such an investment. Fossil
fuel costs played a major role in cost evaluation of biomass
and other renewable fuels from 1973-1981, when fuel prices
increased dramatically. However fuel prices have stabilized
since then (at least for the foreseeable future), and
break-even points have become extended due to the equivalent
fuel value of the biomass in question remaining fairly

static. The price for #2 fuel oil has even decreased during

123

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124

the past year. Fossil fuel prices would need to increase
over 15 percent ($7.785/million Btu, or $8.214/GJ) in order
for the least-expensive system (SF) to become cost-effective
for a large processor.

Waste disposal costs are beginning to rival fossil fuel
costs as a significant concern for many industries. Although
few apple juice processors currently pay for disposal,
increased regulation is reducing the number of disposal sites
considered safe, along with the types and quantities of
wastes which can be disposed of in the remaining sites.
Large systems employing FB or SF boilers would become
cost-effective if disposal costs of 50 percent ($9.15/ton or
$8.30/lOO kg) of the current cartage fee were imposed.

While interest rates were shown to be substantial costs,
they were not a significant factor in comparison with
disposal and fossil fuel costs, and therefore should not be

given first priority in evaluating cost-effectiveness.

5.2.3.2 Additional wastes with fuel value

The fuel value of PE and CPB packaging wastes has been
shown to contribute significantly to the payback of AAC for
the combustion systems. Therefore the availability of these
and/or other waste with fuel potential must be examined in
the energy and cost analyses for specific processing

situations. Wet fruit wastes (with MC of 80 percent) were

 

‘u-fi'i‘q

(14-

 

125

determined to be infeasible for direct combustion, due to the
excessive amount of water which must be removed. Such
processing wastes as cherry and peach pits would appear to
have potential for significant fuel savings during other

processing seasons.

5.2.3.3 System selection considerations

Variations in capital investment and operating costs are
the primary reasons for the distinct differences in the
average annual costs of the three combustion systems.
However, each prospective investor must carefully weigh not
only the value calculated for net annual savings or losses,
but also the advantages and drawbacks for each system. The
SF system represents the least-cost alternative for
applications permitting a dry (15 percent MC), particulate
and non-abrasive solid fuel. This system would be optimal in
situations with a highly uniform waste, and in some cases can
be retrofitted to an existing boiler.

The PB and PB systems have higher capital investment and
operating costs, but can burn fuels with as much as 60
percent MC. For applications in which pomace could be
combusted immediately, storage would not be required,
eliminating the need for the storage bin and accompanying
handling equipment. Thus pomace would require minimal

drying, reducing the size of the rotary drier which is the

126

costliest of the handling system components. The PB and FE
systems can combust non-uniform solid fuels as well, allowing
greater flexibility in the types of wastes converted. These
systems which require higher capital expenditure may prove to
be cost-effective at current fossil prices with reductions in
investment costs, depending upon individual processing
situations.

Increases in electrical costs were not considered
significant, because rate increases are often based on
increased fuel costs at the generating utility. Since the
fuel value for pomace is calculated from the current value of
the fossil fuel it replaces, the savings from pomace
combustion will be higher, offsetting the increased utility

COStS.

5.2.3.4 Potentials for improving cost-effectiveness

Other means of cost reduction at the plant may play
important roles, in the economic feasibility of pomace
combustion. The availability of other in-plant wastes with
fuel value (waste paper, shipping material, used pallets, and
processing wastes such as cherry or peach pits) could allow a
processor to combust for more than 16 hours/day or beyond the
5-month process season assumed in this analysis. Local

sources of inexpensive solid fuel might also be available.

5"

127

A processor may have a lower energy demand than that
associated with the pomace production rates for these
calculations. In this case average annual costs would be
less, since a smaller-sized boiler could be purchased. By
storing excess pomace, combustion could be extended beyond
the 5-month processing season, further reducing fossil fuel
costs, with a relatively small increase in investment costs
for a larger storage system. The potential also exists for
recovery of waste stack heat in order to offset fossil fuel
costs in pomace drying, and for recycling of rice hulls by
air classification from pomace containing the press aid. The
latter source would reduce purchase costs of rice hulls and

permit pneumatic handling of dry pomace.

 

CHAPTER 6

Summary and Conclusions

6.1 Model Development and Validation

Technical and economic factors were evaluated pertinent
to in-plant conversion of food processing wastes into
recoverable energy. A waste handling system was proposed for
use in conjunction with three direct-combustion boiler
systems. With proper equipment and operation, gaseous and
particulate emissions from combustion of biomass and other
wastes can be controlled to meet state and federal standards,
while disposal volume can be reduced by a minimum of 90
percent.

Life Cycle Cost Analysis techniques were selected to
evaluate cost-effectiveness, based on determination of
average annual costs over the loan payback period. From the
approach derived from these investigations, a computer model
was developed to perform energy and cost feasibility analyses
for food processors with energy requirements in the range of
1 million Btu/hr (1.05 GJ/hr) to 100 million Btu/hr (105.5

GJ/hr).
128

 

129

6.1.1 Apple pomace waste

The model was validated with variables representing
operating conditions for two processing plant sizes for
Michigan apple juice processors. Small processors were
assumed to generate 30 tons/day (27,215 kg/day) of apple
pomace and require 10 million Btu/hr (10.5 GJ/hr) of heat for
processing. Large processors were assumed to generate lOO
tons/day (90,718 kg/day) and require 30 million Btu/hr (31.5
GJ/hr) of heat. Using investment and operating costs from

September 1983, the following conclusions were made:

1. The break-even point for conversion to an apple
pomace handling/combustion system is most sensitive
to pomace flow rate, waste disposal costs and fossil
fuel costs, and least sensitive to electrical costs

and loan interest rates.

2. The proposed systems are not currently
cost-effective for processors incurring no disposal
costs for pomace removal; however analysis of
individual apple juice Operations is warranted,
particularly for large processors. Variations in

plant production schedule and energy demand, the

 

130

availability of other in-plant wastes with fuel
value or further restrictions in waste disposal
methods and materials could interact to make
investment in a handling/combustion system an

economically viable alternative.

With a 20 percent increase in fossil fuel price to
$8.124/million Btu ($8.571/GJ), large processors
could purchase a suspension-fired system. Small
processors could purchase this system only after

fuel price increases of more than 100 percent.

For large processors incurring disposal costs
greater than $9.15/ton (38.30/1000 kg), or 50
percent of the current cartage fee, investment would
be cost-effective for either fluidized-bed or
suspension-fired systems replacing natural gas or #2

fuel oil.

Small processors could purchase a system if
currently spending $18.30/ton ($16.60/lOOO kg) in
disposal costs. Those replacing natural gas could
invest in a suspension-fired system, while those
replacing #2 fuel oil could purchase either a

fluidized-bed or suspension-fired system.

 

131

6.1.2 Packaging wastes

Other in-plant wastes with fuel value could be combusted
to reduce fossil fuel and disposal costs. Packaging wastes
in particular show promise due to low moisture content and
relatively high heat content. Further analysis for
combustion of polyethylene or corrugated paper box (in

addition to apple pomace) revealed:

1. A large processor could invest in a suspension-fired
system with the availability of 1.1 tons/day (965
kg/day) of polyethylene, or 3 tons/day (2,735
kg/day) or corrugated paper box.

2. A small processor would require 3.9 tons/day (3,545
kg/day) of polyethylene, or 11.1 tons/day (10,053
kg/day) of corrugated paper box for investment in

the suspension-fired system.

3. Wet processing wastes, with moisture contents above
80 percent, wet basis, would not be cost-effective
as a directly-combusted fuel due to excessive drying

requirements for efficient combustion.

 

132

6.2 Recommendations for Further Application

The model was developed to aid in assessing the overall
economic feasibility of converting food processing wastes
into recoverable energy. In order to perform the analysis
representative of the apple juice processing industry,
conservative assumptions were made with respect to operating
parameters and investment costs. Other food processing
industries could be similarly analyzed and assessed,
including the sugar beet, grape juice/wine, and cherry
processing industries, each of which generates substantial
quantities of wastes.

The need exists to charactize these industries, in terms
of the types and quantities of wastes generated, method and
distribution of waste disposal, and processing energy
requirements. With a greater data base available, the model
could be employed in the I evaluation of these
handling/combustion systems for individual processing plants.
The model could be further refined and expanded to analyze
and select an optimal system from a variety of handling or

combustion system components.

rm

 

APPENDICES

133

Appendix 1. Procedure and preliminary results for bulk
density and angle of repose for apple pomace.

Procedure

Apple pomace at an initial MC of 68 percent was
obtained from a local cider processor and held in a drying
oven at 100°C for various periods resulting in pomace
samples of 62, 42 and 0 percent MC. Bulk densities were
determined by compressing a known mass of uncompressed
pomace under progressive loads in a graduated chamber.

The angle of repose was calculated by filling a one
liter cylinder with pomace, vertically lifting the
cylinder, and allowing the pomace to seek its own level.
Three replicates were performed for each MC, in which the
mean radius and height of each pile were measured. The

mean angle of repose was calculated by the equation:

angle of repose = tan-1(height/radius)

This value is referred to as the 'kinetic' angle of
repose, since it combines both kinetic and static features
in the measurement presented in this discussion. A
kinetic measurement would allow the pomace to fall a known
distance before measuring the angle, while a static
measurement would be determined by the angle at which a

tilted pile begins to slide in on itself (Mohsenin, 1970).

 

 

134

Results and discussion

Two trends are evident from the data for bulk density
(Figure (A): first, pomace compresses very little over the
range of O-l4O kg/mz, and second, bulk density increased
with increasing MC. Densities ranged from 135-146.5
kg/m3 for bone dry pomace, to 385-410 kg/m3 for fresh
weight (68 percent) pomace. There were no major
differences in slopes of the lines indicating the
compression rate to be independent of MC. The presence of
sugar solutes in the pomace causes it to clump with slight
pressure at higher moisture contents.

The angle of repose (Figure B) was 36.60 for bone
dry, and 41.9, 33.3, and 32.10 for 42, 62 and 68 percent
MC, respectively. Lesser angles for the higher MC were
not due to the pomace being more fluid, but rather being
more adhesive. During measurements, clumps formed from
compaction against the cylinder wall and fell under weight
of gravity, causing non-uniform particulate movement. An
alternative to this method might be that of pomace at rest
on a horizontal surface being tilted until movement begins
(which would be the static angle of repose as previously
described). The tangent of the static angle would be the

static coefficient of friction (Ross and Kiker, 1967).

 

 

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136

 

 

 

1 l 1 l
20 4O 60 80 100

 

MOISTURE CONTENT
(% w.b.)

The effect of moisture content on angle of repose
of apple pomace.

 

ff; may): ‘5’: ii
i .

 

137

Knowledge of these values at various MC would be
especially useful when transferring pomace by means of
bulk containers where tilting is involved. With the
present method, an improvement could be made by allowing
the sample to free-fall a short distance upon leaving the

cylinder to reduce clumping tendencies.

Summary

These tests have estimated some mechanical properties
of apple pomace, while revealing the complexity of
collecting these data. Bulk density and clumping were
proportional to MC and compression; both are important
aspects for flow or mechanical conveyance, such as might
be necessary for calculation of tilt angle or screw auger
power requirements. Compression could be measured with
higher forces (above 140 kg/m2 measured in this study)
and correlated to clumping 'tendencies and MC. Pomace
behaved similar to grain (when clumping was not a factor
during measurement of angle of repose), in that it was
free flowing and the angle of repose decreased with
decreased MC.

Problems which might be encountered during handling
of fresh pomace relate mainly to moisture content. Sugar
deposits could accumulate considerably on handling

equipment without imposition of a rigorous cleaning

 

 

138

program between uses. Clumping would without doubt
increase power requirements, causing excess wear on parts
and raising fuel or electrical costs. Storage life would

be greatly reduced as well.

 

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2:

 

139

Appendix 2. Conversion factors and drying calculations for
apple pomace.

a) Conversion factors:

 

1 ac = 0.4046 ha

1 Btu = 1.055 kJ

1 Btu/1b = 2.326 kJ/kg
OF = 1.8 x 00 + 32

1 gal (U.S.) = 3.7854 lit
1 lb = 0.45359 kg

1 Ib/fc2 = 4.8824 kg/m2

1 lb/ft3 = 16.0185 kg/m3
1 ton = 907.18 kg

1 yd3 = 0.7645 m3

b) Drying calculations:

 

1. Amount of water dried: (65% to 15% MC, w.b.)

75% of fresh weight pressed as juice, 25% remains
as pomace @ 65% MC (Kranzler and Davis, 1981).

*Dry weight:
1 kg-—.65 kg water = .35 kg dry matter

*Weight at 15% MC:

x—.35

x .15

 

.35/.85 = .4118 kg
*Per unit pomace:
1.00 - .41 = .59 kg water to be dried

X

2. Heat required for drying: (drying efficiency=60%)
3954 kJ/kg water = x kJ/.59 kg water

2333 kJ/k pomace @ 65% MC
(1003 Btu 1b)

X

 

 

 

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146

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