BAKERY WASTE AS A FEEDSTOCK TO
ANAEROBIC DIGESTION
By
J.K. EVICKS
Bachelor of Science in Biosystems Engineering
Oklahoma State University
Stillwater, OK
2006
Submitted to the Faculty of the
Graduate College of the
Oklahoma State University
in partial fulfillment of
the requirements for
the Degree of
MASTER OF SCIENCE
May, 2016
BAKERY WASTE AS A FEEDSTOCK TO
ANAEROBIC DIGESTION
Thesis Approved:
Dr. Timothy J. Bowser
Thesis Advisor
Dr. Douglas Hamilton
Dr. Enos Stover
Dr. Robert S. Frazier
ii
ACKNOWLEDGEMENTS
Dr. Tim Bowser inspired me to continue my education. He encouraged me to pursue a graduate
degree, and that gave me the extra determination to enroll. In addition, he helped me chart the
course for this project, and has been a fantastic resource in reviewing associated ideas and the
thesis itself.
Dr. Ross Stover was instrumental in coaching me through the approach, methods, equipment
associated with this project. He and his staff helped set up equipment, monitor digesters, and
perform other vital tasks to the project. The Stover Group not only provided the facility,
assistance, and bench analysis time, but also the many consumables required for the
characterization and post-testing. Additionally, Dr. Enos Stover sparked the idea of developing
this project during a facility site visit.
Dr. Doug Hamilton was a fantastic resource in discussing the theory behind the project. His
experience and expertise was very helpful in understanding some of the concepts and phenomena
behind anaerobic digestion.
Dr. Scott Frazier was very helpful in understanding energy use, CHP systems, and the important
considerations of the design and implementation of such systems.
iii
Acknowledgements reflect the views of the author and are not endorsed by committee
members or Oklahoma State University.
Name: J. K. EVICKS
Date of Degree: MAY 2016
Title of Study: BAKERY WASTE AS A FEEDSTOCK TO ANAEROBIC DIGESTION
Major Field: BIOSYSTEMS ENGINEERING
Abstract:
Anaerobic digestion (AD) is a technology that has been used over the past century to treat
organic waste. In the past few decades, interest has risen in this technology due to factors
surrounding waste, energy, and environmental stewardship. This research intends to
supplement the small amount of data available for using bakery waste in the AD process.
Pizza dough was used as the primary feedstock in this study to evaluate the biogas
potential through several likely Food to Mass (F/M) ratios. The best F/M ratio for this
process considering feed rate, COD removal, and gas production was determined to be
0.5 g COD / g VS. Although this product does perform well in the AD process, current
market rates for waste disposal and energy in Oklahoma would not support construction
of an industrial scale digester. This work could be expanded in the future by further
evaluation of bakery feedstock, potential feedstock for beneficial co-digestion, methane
content analysis, or further specific financial analysis.
iv
TABLE OF CONTENTS
Page
CHAPTER I – INTRODUCTION ............................................................................................... 1
1.1 ANAEROBIC DIGESTION HISTORY .......................................................................................... 1
1.2 ANAEROBIC DIGESTION PROCESS .......................................................................................... 1
1.3 LIMITATIONS .......................................................................................................................... 3
1.4 CURRENT RELEVANCE ........................................................................................................... 5
1.5 OBJECTIVES ............................................................................................................................ 9
CHAPTER II – MATERIAL AND METHODS ....................................................................... 10
2.1 FEEDSTOCK SELECTION AND COLLECTION ......................................................................... 10
2.2 SEED COLLECTION AND PREPARATION ............................................................................... 12
2.3 CHARACTERIZATION ........................................................................................................... 12
2.4 REACTOR PREPARATION ..................................................................................................... 12
2.5 GAS PRODUCTION AND MONITORING ................................................................................. 14
2.6 POST TESTING...................................................................................................................... 15
2.6.1 Soluble COD ................................................................................................................. 15
2.6.2 Volatile Acids and Alkalinities ...................................................................................... 15
CHAPTER III – THEORY / CALCULATIONS ...................................................................... 16
3.1 PROCESS MEASURES ............................................................................................................ 16
3.1.1 Organic Loading ........................................................................................................... 16
3.1.2 pH.................................................................................................................................. 16
3.1.3 Volatile Acids (VA) and Alkalinity ................................................................................ 17
3.2 CALCULATIONS .................................................................................................................... 17
CHAPTER IV – RESULTS ........................................................................................................ 21
4.1 ORGANIC LOADING .............................................................................................................. 21
4.2 METHANE CONCENTRATION ................................................................................................ 21
4.3 GAS PRODUCTION ................................................................................................................ 22
4.4 COD REMOVAL EFFICIENCY ................................................................................................ 23
4.5 BIOGAS PER UNIT COD APPLIED ......................................................................................... 24
4.6 RELATIONSHIP BETWEEN BIOGAS COMPONENTS ................................................................ 25
4.7 BATCH REACTOR CONSIDERATIONS .................................................................................... 25
4.8 FINANCIAL CONSIDERATIONS .............................................................................................. 26
SECTION V – CONCLUSIONS ................................................................................................ 32
5.1 BIOGAS POTENTIAL .............................................................................................................. 32
5.2 OPTIMAL ORGANIC LOADING RATE .................................................................................... 32
v
5.3 SIMPLE ECONOMIC ANALYSIS ............................................................................................. 33
5.4 FURTHER ANALYSIS ............................................................................................................. 33
REFERENCES............................................................................................................................. 35
APPENDICES .............................................................................................................................. 39
APPENDIX A – TRIAL 1 SUPPORTING DOCUMENTS (JANUARY 2013) ........................ 40
APPENDIX B – TRIAL 2 SUPPORTING DOCUMENTS (NOVEMBER 2013) .................... 50
APPENDIX C – TRIAL 3 SUPPORTING DOCUMENTS (JANUARY 2015) ........................ 59
vi
LIST OF TABLES
Table
Page
Table 1 – Bakery waste product nutrition vs. corn .................................................................... 8
Table 2 – Trial 3 Data & Calculations ..................................................................................... 18
Table 3 – Economic Analysis by MS Excel Calculation: Scenario 1 ...................................... 29
Table 4 – Economic Analysis by MS Excel Calculation: Scenario 2 (High Waste Disposal Cost)
................................................................................................................................................. 30
Table 5 – Economic Analysis by MS Excel Calculation: Scenario 3 (High Electricity Cost). 31
vii
LIST OF FIGURES
Figure
Page
Figure 1 - Anaerobic digestion process for manure (Girard et al. 2013) ......................................... 2
Figure 2 - USEPA food recovery hierarchy ..................................................................................... 6
Figure 3 - Pareto Chart Example ..................................................................................................... 7
Figure 4 - Reactor setup including digesters, flow cell bath, and computer. ................................. 14
Figure 5 - Biogas production of pizza dough. ................................................................................ 22
Figure 6 - COD Removal Efficiency……………………………………………………………..23
Figure 7 - Biogas per unit COD applied in AD trials. ................................................................... 24
Figure 8 - CHP Energy Balance..................................................................................................... 28
viii
CHAPTER I – INTRODUCTION
1.1 Anaerobic Digestion History
Anaerobic digestion (AD) has become a popular topic lately, but it is not a new technology. Jan
Baptista Van Helmont noticed in the seventeenth century that organic material produced
flammable gases during decomposition. John Dalton and Sir Humphrey Davy discovered in the
1800s that this flammable gas contained methane. Omelianski in the 1890s, then Sohngen in the
1910s continued this research, finding that this AD process had intermediate products of
hydrogen, carbon dioxide, and acetic acid, and those intermediates combined to form methane.
These discoveries led to the idea of intentionally using AD to treat waste and wastewater. For
example, Cameron in Exeter, England used AD in “septic tanks” in the 1890s to treat residential
wastewater, and used the resulting methane to power street lights and heat homes (Abbasi et al.
2012).
In the decades that have followed, millions of AD digesters have been installed worldwide. Most
of these digesters have been small or farm scale installations for animal and human waste in
Europe and China, with over nine thousand and eight million small scale units, respectively. The
United States began to install larger scale farm units in the 1970s, but performance issues and
costs made further development difficult. Renewable energy subsidies and assistance through
programs like the U.S. Environmental Protection Agency’s (EPA) AgStar program have helped
renew interest in this technology for farms. The AgStar program reported that an estimated 162
anaerobic digesters generated 453 million kWh of energy in the United States in agricultural
operations alone in 2010 (C2ES 2011).
1
Outside the farm application, many municipalities are now using AD to treat waste. In a 2013
AD feasibility study for areas in Louisiana, the National Renewable Energy Lab (NREL 2013)
reported that over 1450 municipal wastewater treatment plants (WWTPs) are using AD
technology to assist in treating wastes. Some of these were standalone units, and some were
partnerships between the municipality and local industry. In any case, the parties involved likely
benefit from waste treatment, heat, and power associated with AD.
Food processing facilities like Gill’s Onions in Oxnard, California and Stahlbush Island Farms in
Corvallis, Oregon are also using AD to treat organic processing wastes. In both cases, the large
volumes of food processing wastes are difficult to dispose of, so AD provided a sensible solution
to treat their inherent waste stream. In visiting these facilities in 2011, it was found that the units
provide a significant amount of power in addition to uniquely treating the organic waste. The
Gill’s unit is coupled with two fuel cells for a total of 600 kW (Gill’s Onions, n.d.) and the
Stahlbush unit is coupled with an electric generator for a total of 1.0 MW (Stahlbush Island
Farms, n.d.). This generated electricity provides much of the base or even total electric load for
each processing facility.
1.2 Anaerobic Digestion Process
As found in the research noted above, anaerobic digestion is a process of four steps – Hydrolysis,
Acidogenesis, Acetogenesis, and Methanogenesis. These steps produce intermediate products of
hydrogen, acetic acid, ammonia, and others to produce biogas – a mixture of methane carbon
dioxide, and other trace gases. Figure 1 (Girard et al. 2013) shows this process for animal
manure, but the process is much the same for carbohydrate and lipid-laden bakery products.
These four basic steps occur simultaneously in a continuous anaerobic digester to break down
organic materials and convert them to methane and other byproducts.
1
Figure 1 - Anaerobic digestion process for manure (Girard et al. 2013)
In the first step of hydrolysis, primary components of carbohydrates, proteins, and lipids are
broken down into more basic components of simple sugars, amino acids, and fatty acids (Abbasi
et al. 2012). Panico et. al (2015) mentions that this disintegration and depolymerization of the
primary components are extracellular processes catalyzed by enzymes such as cellulase, protease,
and lipase excreted by hydrolytic and fermentative bacteria. These enzymes either attach to the
solid particles or react with soluble substrates to enable the more basic components to be used at
an intracellular level. Raposo et al. (2011) mentions that particle size reduction can help increase
the speed of the hydrolysis process. This point is important because breaking down some organic
materials can be the rate-limiting step in the AD process. Since the rate of the digestion process
depends on hydrolysis under normal circumstances, then more material can be processed in a
shorter amount of time as particle size is reduced.
Girard et al. (2013) further mentions that the second step of acidogenesis and third step of
acetogenesis ferments those sugars and other products that became available in the hydrolysis
2
step. These steps make alcohols, carbonic acid, and volatile fatty acids (VFA) available to
acidifying microorganisms, which in turn are used to create hydrogen gas, ammonia, carbon
dioxide, acetic acid, and other compounds. This complex set of reactions must be balanced in
order to prevent methanogen inhibition in the next step. This inhibition can be caused from
excess VFA production related to high organic loading rates (Ahring, 1995).
The last step of methanogenesis completes the cycle by transforming those intermediate products
into methane. Part of this methane comes from division of acetic acid, and part comes from
combining hydrogen and carbon dioxide. Aceticlastic (acetate-utilizing) methanogens are
responsible for the division of acetic acid into methane and carbon dioxide, while hydrogenutilizing methanogens are responsible for combining the hydrogen and carbon dioxide to form
methane (Metcalf and Eddy, 2003). With a mixture of animal manure and organic industrial
waste, Ahring (1995) found wide variation between the specific methane activity (initial methane
production value) of the hydrogen-utilizing fraction and the acetate-utilizing fractions. He
mentioned that this balance of methanogen communities is dictated by the stability and organic
loading of the reactor.
1.3 Limitations
Anaerobic digestion is a robust process, but does require a balance of conditions to persist.
Correct proportions of carbohydrates, proteins, and lipids must be fed to the system to ensure the
system works as desired. For example, adequate carbohydrate levels are required to achieve
suitable gas production. Excessive protein contents can produce excessive ammonia which will
ultimately curb methane production. Similarly, excessive lipid content may cause instability or
intolerance to lower feed rates due to long chain fatty acid toxicity (Digman and Kim 2008).
Concurrently digesting materials in the same reactor, or co-digesting, may help to balance the
3
proportions of carbohydrates, lipids, and proteins and increase productivity (Zupančic et al.
2008).
In addition to achieving the best mixture for digestion, food materials can present their own issues
in logistics and handling as well. Food materials may be solid, liquid, or a mixture thereof. If
these materials are brought from another site for co-digestion, care should be taken to prevent
spillage or leakage that may present stormwater or wastewater concerns at a fixed site or en route
to the digester. The EPA Office of Water (2006) mentioned that food product spills can
contribute biochemical oxygen demand (BOD), total suspended solids (TSS), oil and grease, pH,
nitrogen, and other contaminants to storm water when improperly handled. When a precipitation
event occurs, any residual spilled material would be washed away, taking them to the receiving
stream. In addition, most leavened raw dough products will rise with temperature, potentially
causing overflow or issues with transportation. The containers or trailers of dough should be
refrigerated or stored in a way that prevent overflow. In summary, these different potential issues
for each type of waste must be considered and addressed in the design phase of a digester to
ensure that appropriate controls are implemented.
Food safety is of utmost importance in food manufacturing facilities. The US Food and Drug
Administration (FDA) website (2015) suggests that food manufacturing facilities employ several
programs to keep food safe. These may include programs such as Good Manufacturing Practices
(GMPs), Hazard Analysis of Critical Control Points (HACCP), pest management, and sanitation.
The HACCP plan in particular helps to identify and control hazards associated with food
manufacturing. One method to separate raw materials, finished goods, allergens, and waste
materials is color coding (Newslow, 2014). Different colored tools, utensils, scoops, brushes,
brooms, containers, and other materials are used for different purposes (direct food contact,
inedible waste, recycle, rework, floors, maintenance, etc.). These prevention programs would
4
need to be updated if a digester is constructed on site. In addition, food safety, pest management,
and environmental considerations would need to be made for storage of waste feedstock material
and solid byproducts outside or in an adjacent building. All of these processes will help to ensure
that food safety is not compromised onsite or inside the manufacturing facility.
1.4 Current Relevance
Anaerobic digestion is particularly relevant today for all organic wastes, but especially for food
wastes, where an estimated 30-40% of food is wasted in the United States every year. This
statistic amounted to an estimated 133 billion pounds of food in 2010 (USDA 2013). In addition
to the food waste problem, population is also steadily increasing in the United States at an
average rate of 0.65% per year over the next 30 years (USCB 2015). These two factors will
continue to present a monumental opportunity to treat organic waste by AD and other methods.
The EPA suggests a hierarchy in which food can be treated by reduction or recovery (USEPA
2014). This hierarchy includes a mixture of reuse opportunities and disposal methods in
decreasing preference as shown in the inverted pyramid in Figure 2.
5
Figure 2 - USEPA food recovery hierarchy
Source reduction is the preferred method because surplus food or waste would not be generated.
Although the majority of food is wasted by the end user, manufacturing facilities may consider
continual improvement processes to impact source reduction. For example, reducing product
scrap or shrink in the manufacturing process is one way to reduce food waste. Product scrap can
be caused by many processes in the manufacturing process from initial batch development to final
packaging. One method manufacturers use to identify and prioritize projects to reduce product
scrap is Pareto analysis. Ivancic (2014) mentions that the use of a Pareto chart helps to
understand and separate the essential few from the trivial many causes of defects or scrap. This
understanding comes from categorizing the frequency of defects and ranks them against other
categories (see example in Figure 3). In the bakery manufacturing case, categories of defects like
shape, color, dimensions, and other product attributes are some examples that would be desirable
to reduce. Once identified and prioritized, facility teams can be organized and deployed to
positively impact the causes of defects and reduce food scrap.
6
Figure 3 - Pareto Chart Example
Donating material to food banks, soup kitchens, shelters, or other end users is preferred after
source reduction. If products do not meet arbitrary company or customer specifications (such as
size, shape, or color), but are otherwise safe to eat, donation is a very positive outlet. The EPA
(2014) mentions that such donations could feed many people in the community without a
significant cost to the manufacturing facility or receiving entity. The EPA (2014) continues by
mentioning that donors also have limited liability from the Bill Emerson Good Samaritan Food
Donation Act and can realize enhanced tax deductibility. However, finding a recipient may be
difficult depending on the product type and volume; not everyone can store or use a van trailer
load of pizza dough within its original shelf life. In addition, minimum food safety requirements
must be met in order to donate food (FAO, 2011). Therefore, donation is not suggested if a
product has been potentially contaminated, tainted, or otherwise unsafe to eat.
Bakery product manufacturers traditionally have employed animal feed companies to process
their waste material for inclusion into animal feed. In this arrangement, the animal feed company
usually pays the manufacturer for their waste materials, depending on the quality and market
7
price for comparable animal feed constituents. It turns out that bakery products have nutritional
characteristics that put it on par with corn meal, so it can be used as a substitute for corn in
chicken feed with no discernable disadvantage to the animal. Table 1 shows the comparison of
key parameters of typical bakery products and corn from Shafey et al. (2011) to that of pizza
dough (manufacturer nutrition data). The key parameters of protein and fiber are slightly less for
pizza dough than for that of corn, but very similar.
Contents
Pizza Dough
Bakery Waste
Corn
(manufacturer)
Product (Shafey et.al
(Shafey et.al 2011)
2011)
Moisture
19.67%
7.16%
11.00%
Crude Protein
7.18%
12.40%
8.50%
Crude Fiber
1.80%
0.59%
2.20%
Table 1 - Bakery waste product nutrition vs. corn
In the absence of this animal feed solution, landfill or waste to energy facilities are likely the most
common alternative. This alternative requires little or no capital costs to transfer to the treatment
site, but these facilities charge fees (rather than pay market rates) for waste bakery materials.
Ongoing costs from switching from animal feed to landfill or waste to energy would change the
financial scenario for this waste material considerably.
Anaerobic digestion is between animal feed and landfill in this hierarchy of environmental
preference and has the potential to be financially preferable as well, depending on the market
conditions. Those market conditions for energy and waste in the United States do not support a
positive return on investment at this time, unless extreme energy or waste disposal rates are at
8
play (NREL 2013). As those rates increase over time, AD will be a more viable part of the
solution to the food waste dilemma.
1.5 Objectives
Given the realm of possible bakery products, it is prudent to investigate and document biogas
potential for these specific products so that educated decisions can be made regarding future AD
applications. There is a meager amount of information on anaerobic biogas potential on bakery
waste. Only two journal article examples were found that provided specific biogas potential
values for this material. One example of existing information is a study completed by Schievano
et al. (2009) which mentions biogas potential of 731 +/- 178 and 653 +/- 27.8 normal liters
biogas per kilogram TS for “Bakery Waste” and “Bakery Residues” respectively. The other
example is from Cabbai et al. (2013) which mentions methane potential of 476.28 normal
milliliters methane per gram VS for “Bakery” waste.
This study will provide additional information on the viability of bakery wastes as a feedstock to
AD. This research will provide the required information to determine potential biogas
production, associated capacity, and financial considerations of using this material.
The specific objectives that will be met include:
1. Biogas potential of pizza dough through five likely organic loading rates.
2. Optimal organic loading rate predicted for an industrial scale continuous reactor
considering feed rate, COD removal, and gas production.
3. Simple economic analysis for constructing a digester based on the products of digestion
at current market rates. This analysis will consider both the up-front and ongoing costs,
in addition to the savings and other benefits a digester may provide.
9
CHAPTER II – MATERIAL AND METHODS
An anaerobic digestion study was performed at an independent wastewater treatability laboratory
in Stillwater, Oklahoma. To determine the biogas potential for the different bakery products
proposed, a Biochemical Methane Potential (BMP) test was utilized. This methodology is a
relatively simple and inexpensive way to determine the potential production of different
feedstock. Because of these factors, this methodology has been widely used for preliminary
information since the late 1970s (Moody et. al. 2011).
In this process, each of the products were individually characterized, adjusted for nutrient levels,
then individually tested in a batch anaerobic digester in triplicate at five different feedstock
loading rates. After each test, the resulting products were tested to see what resulting effluent
loading would be treated after the process or off-site. The results of the different tests showed the
optimal batch reaction gas production, and continuous production can be estimated based on that
information.
2.1 Feedstock Selection and Collection
The feedstock considered for this study was an assortment of bakery products from an Oklahoma
food manufacturer. Biscuit dough, pizza dough, and handheld pies were selected for initial
characterization. These materials were chosen to give the manufacturer a potential reuse option
for the waste products that could provide energy for future needs.
10
Initial characterization of these three products showed volatile solids (VS), or the amount of
organic matter, in the range of 55% to near 70% for frozen pizza dough and baked biscuits,
respectively. After considering the time and resources required to evaluate three feedstocks at
three loading rates, it was determined that more data points for one feedstock (rather than fewer
data points for three feedstocks) may give a clearer picture into the range of loading rates and
biogas production for similar bakery products. Considering the relative amounts of the
manufacturing facility’s waste stream volume, pizza dough emerged as the best fit for the study.
Although pizza dough is made of simple ingredients such as flour, water, salt, shortening, and
baker’s yeast, differences in preparation can lead to differences in product quality. To increase
quality and consistency, pizza dough is made at an industrial scale in advance of restaurant or
home pizza preparation. The larger production scale coupled with tight quality tolerances reduces
the variation that may be seen in a residential or other small scale process (Limongi et al. 2012).
Such a manufacturing operation receives raw ingredients in bulk, mixes them together in large
(>1000 lb) batch mixers, rolls the dough into a continuous sheet by a series of reducing rollers on
a conveyor, then cuts it into the desired shape by a stamping or rotary cutter. After forming, the
dough can be shipped raw, frozen, baked, or some combination of these methods.
For this study, the pizza dough feedstock was collected directly from the manufacturer’s
production facility and preserved on ice in a small ice chest until characterization/preparation
later the same day. Due to the large volume and narrow specification for each product in the
manufacturing facility, each sample was considered to be homogeneous and representative of
similar product.
11
2.2 Seed Collection and Preparation
Seed is the bacteria that breaks down the organic feedstock in the anaerobic digestion process.
Granular seed sludge was obtained from a functional industrial anaerobic digestion treatment
facility in Oklahoma. The sludge was transported at ambient temperature, then used that same
day. The granular sludge was blended to reduce the granule size with a kitchen blender at a high
level for approximately 30 seconds. The relationships between seed/sample particle size and gas
production has not consistently been clarified in research (Raposo et al. 2011), so the blender
served to ensure the seed and sludge were added in accurate proportions as detailed in Reactor
Preparation below.
2.3 Characterization
Characterization is the process of finding the properties of a given material. This may include
properties of moisture content, organic content, nitrogen content, solids content, pH, and other
measures. In this case, characterization of the product and seed sludge was necessary to
determine the initial nutrient loading. This initial characterization included total solids (TS),
volatile solids (VS), chemical oxygen demand (COD), and pH. This characterization was
performed consistent with Standard Methods (APHA et al. 1998). The results of this
characterization appear in the appendices for each respective trial.
2.4 Reactor Preparation
In order to obtain a satisfactory nutrient loading, an appropriate sample size in relation to the
reactor size was calculated. 70-150 grams of COD per liter was selected as an appropriate
starting point for the reaction, depending on the F/M ratios used in that reactor set. These
amounts were suggested by Dr. Ross Stover through experience with similar material. To obtain
that sample concentration, Equation 1 was used.
12
Equation 1 – Dough Concentration (DC)
𝑔
𝐶𝑂𝐷
𝐶𝑂𝐷
𝐷𝑜𝑢𝑔ℎ 𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 ( ) = 𝐷𝑒𝑠𝑖𝑟𝑒𝑑 𝐶𝑂𝐷 (𝑔
) 𝑥 𝐴𝑣𝑔 𝑆𝑎𝑚𝑝𝑙𝑒 𝐶𝑂𝐷 (𝑔
𝐷𝑜𝑢𝑔ℎ)
𝐿
𝐿
𝑔
For Example:
150 𝑔 𝐶𝑂𝐷
𝐿
×
1 𝑔 𝐷𝑜𝑢𝑔ℎ
0.5 𝑔 𝐶𝑂𝐷
=
300 𝑔 𝐷𝑜𝑢𝑔ℎ
𝐿
Similarly, the seed sludge volume was calculated. The average VS % for the samples was used to
find the grams of VS per liter of seed. That unit conversion can be expressed as shown in
Equation 2.
Equation 2 – Seed Sludge VS Unit Conversion
𝑚𝑔
1𝑔
𝑆𝑒𝑒𝑑 𝑆𝑙𝑢𝑑𝑔𝑒 𝑉𝑆 = 𝐴𝑉𝐺 𝑉𝑆 % × 10,000 𝐿 ×
%
1000 𝑚𝑔
For Example: 6.1005% × 10,000
𝑚𝑔
𝐿
%
1𝑔
× 1000 𝑚𝑔 = 61.005
𝑔 𝑉𝑆
𝐿 𝑠𝑒𝑒𝑑
The samples were massed, diluted with 1 L of water, and blended for approximately 30 seconds
with a kitchen blender on the highest setting to achieve consistent concentrations. The Food to
Mass (F/M) ratio is the relationship of feedstock sample (food expressed as grams COD) as it
relates to seed sludge mass (seed mass expressed as grams VS). Different F/M ratios were
achieved by adding varying amounts of sample to each reactor, holding the volume of seed sludge
constant, and adding BOD dilution buffer to balance the volume to 400 mL. pH was measured
with an Oakton pH tester (model pHTestr 10) and adjusted to 7.2 as needed with a 1.0 N solution
of sodium bicarbonate.
13
2.5 Gas Production and Monitoring
Biogas was monitored with the Automatic Methane Potential Test System (AMPTS II,
Bioprocess Control, Sweden). This equipment features a system of 15, 500 mL reactors,
automatic stirrers, 15 inverted flow cells in a water bath, and a connected laptop computer with
associated proprietary software also from Bioprocess Control to log data (see Figure 4). This
system allowed up to 15 samples to be analyzed at once, with a resolution of 10 mL and precision
of +/- 1%. The biogas produced was logged to the associated computer as the inverted flow cells
raised, giving real time production information. Samples were monitored manually daily for pH
with a 0.4 % Thymolphthalein pH indicator solution, and pH was adjusted as needed manually
with a 3 M NaOH solution. Tests were concluded over a 2-6 week period.
Figure 4 - Reactor setup including digesters, flow cell bath, and computer.
14
2.6 Post Testing
After test completion, samples were analyzed to measure their COD removal efficiency. The
final pH, soluble COD, volatile acids (VA), partial alkalinity, and total alkalinity were measured
by Standard Methods (APHA et al., 1998) and/or the methods described below.
2.6.1 Soluble COD
To minimize the COD contribution of seed sludge, soluble COD was used to characterize the
final organic content of the reactors. 14 mL from each reactor were extracted, centrifuged, and
filtered through a vacuum filter, then the extract was diluted 10x to 100x to read within the
appropriate range of the COD vial. These samples were analyzed using Hach Method 8000
(Hach, 2009) with associated vials and Digital Reactor Block (DRB) 200 by Hach (Loveland,
CO).
2.6.2 Volatile Acids and Alkalinities
Volatile acids and alkalinities were run by taking a 200 mL sample from each reactor, and adding
1.0 N solution sulfuric acid to reach a pH of 5.7 and 4.3 for partial alkalinity and total alkalinity,
respectively. The volume of sulfuric acid was noted from each reactor, and the alkalinity was
calculated with equation 6. The difference between the partial alkalinity and total alkalinity
shows the quantity of volatile acids (Ripley et al. 1986).
Equation 6 – Alkalinity
𝐴𝑙𝑘𝑎𝑙𝑖𝑛𝑖𝑡𝑦 =
𝐻2 𝑆𝑂4 𝑉𝑜𝑙𝑢𝑚𝑒 × 𝐻2 𝑆𝑂4 𝑁𝑜𝑟𝑚𝑎𝑙𝑖𝑡𝑦 × 50,000
𝑆𝑎𝑚𝑝𝑙𝑒 𝑉𝑜𝑙𝑢𝑚𝑒
15
CHAPTER III – THEORY / CALCULATIONS
3.1 Process Measures
A variety of measurements are taken before, during, and after the reactions. These measurements
were used to calculate the gas production potential, digestion status, and COD removal efficiency.
3.1.1 Organic Loading
Chemical Oxygen Demand (COD) is one measure that helps determine of the organic loading of
a sample. Other methods, such as the Biological Oxygen Demand 5-day test (BOD 5) is a similar
measurement, but the COD test is completed in approximately 2 hours (versus 5 days for the
BOD 5 test). Total Solids (TS) & Volatile Solids (VS) give another perspective of the organic
loading of a sample and can also be done quickly. Many of these tests allow for quick insight
into the organic loading of the reactor so that appropriate process conditions can be calculated (de
Lemos Chernicharo 2007)
3.1.2 pH
pH is a measure of the Hydronium (H+) ion concentration. This indication of acidic or caustic
conditions, along the scale from 0-14, is used to help determine the suitability of an environment
for anaerobic bacteria to thrive. A narrow pH range from 6.5 to 7.5 is best suited for this
digestion process (Kondusamy and Kalamdhad 2014).
16
3.1.3 Volatile Acids (VA) and Alkalinity
Volatile acids and alkalinity measurements help determine the overall health of the reactor by
examining some of the intermediate steps of the process. Volatile acids are the acetogens, or
middle step of the anaerobic digestion cycle. Alkalinity works to buffer the acids in the system,
maintaining the pH in a narrow range from 6.5 to 7.5. If there is too much volatile acid
production, the system will fail due to a lack of alkalinity to offset the pH (Ripley et al. 1986).
3.2 Calculations
The initial characterization, reactor setup parameters, digestion reactions, and post testing results
are all valuable pieces of information that can help determine organic loading rates, F/M ratios,
and reactor design. Ratios or relationships such as the COD removal efficiency or biogas per unit
of COD can give more insight into the performance of the system. The data collected from
characterization, digestion, and post testing was compiled into a MS Excel spreadsheet for each
trial (see Table 2). An example of this raw data and the calculated values is shown in the table
below for Trial 3. The remainder of the raw data and calculations for each trial are located in
Appendix A, B, and C for Trials 1, 2, and 3 respectively.
17
Table 2 – Data and Calculations from Trial 3 Digester.
18
0.2002
0.2177
0.2316
0.0838
0.0035
0.0716
0.0783
0.0348
0.0303
0.0321
0.1930
0.2059
0.2189
0.0781
0.0033
0.0664
0.0722
0.0314
0.0273
0.0283
0.5207
0.5558
0.5906
0.2194
0.0100
0.1941
0.2087
0.1080
0.0946
0.0951
0.5167
0.5512
0.5861
0.2092
0.0090
0.1778
0.1933
0.0841
0.0731
0.0758
0.733
0.795
2.272
4.848
5.154
2.760
0.158
3.128
3.400
2.219
1.930
2.000
0.00
0.00
0.42
1.28
1.29
2.40
2.54
3.42
3.67
6.91
6.86
8.41
0
0
11.77
23.54
23.54
35.32
47.09
47.09
47.09
70.63
70.63
70.63
0.0%
0.0%
99.2%
99.2%
99.2%
95.3%
89.3%
91.6%
92.6%
77.9%
77.3%
79.7%
0.00
0.00
0.03
0.07
0.07
0.62
1.88
1.47
1.30
5.84
5.99
5.35
0.00
0.00
4.40
8.80
8.80
13.19
17.59
17.59
17.59
26.39
26.39
26.39
400
400
400
400
400
400
400
400
400
400
400
400
200
200
180
160
160
140
120
120
120
80
80
80
0
0
20
40
40
60
80
80
80
120
120
120
200
200
200
200
200
200
200
200
200
200
200
200
n/a
n/a
0.5
1
1
1.5
2
2
2
3
3
3
6.6
6.6
7.3
7.3
7.3
7.3
7.3
7.3
7.3
7.3
7.3
7.3
1
2
3
4
5
6
7
8
9
10
11
12
Biogas
per VS
removed
(L/g)
Biogas
per VS
applied
(L/g)
Biogas
per COD
removed
(L/g)
Biogas
Total
Final VS Biogas per COD
Produced applied
(g)
(L/g)
(NL)
Initial VS
(g)
% COD
Removed
Final
COD (g)
Initial COD
(g)
15
14
13
Total
Volume
(mL)***
12
BOD
Water
Volume
(mL)
11
Dough
Volume
(mL)
10
Seed
Volume
(mL)
9
F/M
8
pH
7
Reactor
Equation
The equations for the calculations performed in each of the respective cells of the spreadsheet
shown in Table 2 are listed below:
Equation 7 – Initial COD
𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝐶𝑂𝐷 (𝑔)
𝑚𝑔
𝑔
𝐿
= 𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑆𝑎𝑚𝑝𝑙𝑒 𝐶𝑂𝐷 ( ) ×
×
𝐿
1000 𝑚𝑔 1000 𝑚𝐿
× 𝑆𝑎𝑚𝑝𝑙𝑒 𝑉𝑜𝑙𝑢𝑚𝑒 (𝑚𝐿)
Equation 8 – Final COD
𝑚𝑔
𝑔
𝐿
𝐹𝑖𝑛𝑎𝑙 𝐶𝑂𝐷 (𝑔) = 𝐹𝑖𝑛𝑎𝑙 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑆𝑎𝑚𝑝𝑙𝑒 𝐶𝑂𝐷 ( ) ×
×
𝐿
1000 𝑚𝑔 1000 𝑚𝐿
× 𝑆𝑎𝑚𝑝𝑙𝑒 𝑉𝑜𝑙𝑢𝑚𝑒 (𝑚𝐿)
Equation 9 – % COD Removed
% 𝐶𝑂𝐷 𝑅𝑒𝑚𝑜𝑣𝑒𝑑 =
𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝐶𝑂𝐷 − 𝐹𝑖𝑛𝑎𝑙 𝐶𝑂𝐷
𝐼𝑛𝑖𝑡𝑎𝑙 𝐶𝑂𝐷
Equation 10 – Initial Volatile Solids
𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝑉𝑆 (𝑔) = 𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑆𝑎𝑚𝑝𝑙𝑒 𝑉𝑆% ×
10,000
1%
𝑚𝑔
𝐿 ×
𝑔
𝐿
×
1000 𝑚𝑔 1000 𝑚𝐿
× 𝑆𝑎𝑚𝑝𝑙𝑒 𝑉𝑜𝑙𝑢𝑚𝑒 (𝑚𝐿)
Equation 11 – Final Volatile Solids
𝑚𝑔
10,000 𝐿
𝑔
𝐿
𝐹𝑖𝑛𝑎𝑙 𝑉𝑆 (𝑔) = 𝐹𝑖𝑛𝑎𝑙 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑆𝑎𝑚𝑝𝑙𝑒 𝑉𝑆% ×
×
×
1%
1000 𝑚𝑔 1000 𝑚𝐿
× 𝑆𝑎𝑚𝑝𝑙𝑒 𝑉𝑜𝑙𝑢𝑚𝑒 (𝑚𝐿)
Equation 12 – Biogas per COD applied (L/g)
19
𝐿
𝑇𝑜𝑡𝑎𝑙 𝐵𝑖𝑜𝑔𝑎𝑠 𝑃𝑟𝑜𝑑𝑢𝑐𝑒𝑑 (𝐿)
𝐵𝑖𝑜𝑔𝑎𝑠 𝑝𝑒𝑟 𝐶𝑂𝐷 𝐴𝑝𝑝𝑙𝑖𝑒𝑑 ( ) =
𝑔
𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝐶𝑂𝐷 (𝑔)
Equation 13 – Biogas per COD removed (L/g)
𝐿
𝑇𝑜𝑡𝑎𝑙 𝐵𝑖𝑜𝑔𝑎𝑠 𝑃𝑟𝑜𝑑𝑢𝑐𝑒𝑑 (𝐿)
𝐵𝑖𝑜𝑔𝑎𝑠 𝑝𝑒𝑟 𝐶𝑂𝐷 𝑅𝑒𝑚𝑜𝑣𝑒𝑑 ( ) =
𝑔
𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝐶𝑂𝐷 (𝑔) − 𝐹𝑖𝑛𝑎𝑙 𝐶𝑂𝐷 (𝑔)
Equation 14 – Biogas per VS applied (L/g)
𝐿
𝑇𝑜𝑡𝑎𝑙 𝐵𝑖𝑜𝑔𝑎𝑠 𝑃𝑟𝑜𝑑𝑢𝑐𝑒𝑑 (𝐿)
𝐵𝑖𝑜𝑔𝑎𝑠 𝑝𝑒𝑟 𝑉𝑆 𝐴𝑝𝑝𝑙𝑖𝑒𝑑 ( ) =
𝑔
𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝑉𝑆 (𝑔)
Equation 15 – Biogas per VS removed (L/g)
𝐿
𝑇𝑜𝑡𝑎𝑙 𝐵𝑖𝑜𝑔𝑎𝑠 𝑃𝑟𝑜𝑑𝑢𝑐𝑒𝑑 (𝐿)
𝐵𝑖𝑜𝑔𝑎𝑠 𝑝𝑒𝑟 𝑉𝑆 𝑅𝑒𝑚𝑜𝑣𝑒𝑑 ( ) =
𝑔
𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝑉𝑆 (𝑔) − 𝐹𝑖𝑛𝑎𝑙 𝑉𝑆 (𝑔)
20
CHAPTER IV – RESULTS
4.1 Organic Loading
Initial characterization results showed that pizza dough feedstock had an average COD of 0.698
kg COD / kg dough (N = 6, SD = 0.185) and an average VS of 63.0% (N = 3, SD = 10.8%). See
Appendix A, B, and C for Trials 1, 2, and 3 respectively for the complete raw characterization
data for the feedstock and seed sludge.
4.2 Methane Concentration
This BMP test is primarily designed to measure the gross amount of biogas produced in this
reaction. For comparison, six tests were run with a scrubbing solution to see the percentage of
methane in the biogas. These tests showed methane concentrations of 34%, 58%, and 80% at 0.2,
0.4, and 0.6 F/M ratios, respectively. This data is not sufficient to draw conclusions from, and
does not seem to represent the typical trend of decreasing methane production with increasing
F/M ratios. Comparable average methane concentrations have been reported at 50-60% for food
waste by Kondusomy and Kalamdhad (2014), 60-70% for food waste by NREL (2013), and 4070% for all types of substrates by Abbasi et al. (2012).
21
4.3 Gas Production
As shown in Table 2, the feedstock and seed sludge produced biogas in varying amounts from
0.52 to 0.59 liters / gram COD added from 0.5 to 1.0 F/M ratios. The production declines sharply
to below 0.20 liters / gram COD at the 1.50 F/M ratio and beyond. As described in Stover
(2011), Ganapathi’s study mentions that this decline in gas production is due to volatile fatty acid
(VFA) accumulation/toxicity in batch reactor configurations. Figure 5 shows the relationship
between F/M ratio and biogas volume. The cumulative gas production at different F/M ratios
shown in this chart affirms that concept.
Figure 5 - Biogas production of pizza dough.
22
4.4 COD Removal Efficiency
This AD process is primarily designed to treat waste product. Many byproducts are produced in
the process, but the main motivation is usually to treat or reduce organic content in waste product
and/or wastewater. A useful measure for the effectiveness of that treatment is COD removal
efficiency. As with cumulative biogas production, the COD removal efficiency begins to decline
significantly at and above 1.50 F/M. Figure 6 shows this decreased removal efficiency as the
F/M ratio increases.
Figure 6 – COD Removal Efficiency.
23
4.5 Biogas per Unit COD Applied
The amount of biogas produced per unit COD applied (Figure 7) is a similar trend to the
cumulative gas production found in Figure 5. The same concept of VFA accumulation in higher
F/M ratios holds true with this data set.
Figure 7 - Biogas per unit COD applied in AD trials.
24
4.6 Relationship Between Biogas Components
One key point to mention is the relationship between biogas, methane, and carbon dioxide.
Cumulative biogas production is plotted above, but the fractions of methane and carbon dioxide
do not stay consistent throughout the F/M range. Stover (2011) shows that methane production
follows the same general trend as biogas until the 1.5 F/M ratio point at approximately 65-75% of
the mixture, then drops considerably. Carbon dioxide stays consistent at approximately 25-35%
of the mixture until the 1.5 F/M ratio point, then increases rapidly to 50-75% of the mixture as
F/M ratio increases.
4.7 Batch Reactor Considerations
In addition, Stover (2011) discusses the differences of predicting biogas production and kinetics
from batch reactors and semi-continuous reactors. For example, the batch configuration has the
tendency to experience VFA accumulation/toxicity more readily in high initial F/M ratio loading
rates, where the semi-continuous configuration does not. While similar, the batch reactor
configuration used in this study does not have the capacity to accurately describe the biogas
kinetics that would be seen in semi-continuous or continuous reactor configurations. However,
the batch reactor configuration does give a starting point to predict the production rate in a
continuous reactor.
25
4.8 Financial Considerations
Considering the implementation of an industrial scale digester has many financial inputs. There
are costs and benefits that need to be weighed to see if the project makes sense to pursue. The
feedstock may have a current cost to dispose, be difficult to dispose, or may have a revenue
stream associated with beneficial reuse or recycle. The biogas product may be processed and
sold, used on site for direct combustion (oven, furnace, boiler, etc.), or used to power an engine
for combined heat and power (CHP) or electricity generation. In addition to the ongoing cost and
benefits, the infrastructure, equipment, and labor to install the digester also needs to be
considered.
Several approaches could be taken to find the financial viability of a project. This study will
examine an approach using estimated digester costs to determine an internal rate of return (IRR)
based on current market rates. IRR is a widely used financial calculation to determine the relative
benefit a project may deliver. It is expressed in terms of a percentage similar to a rate of return
on an investment, and Crundwell (2008) mentions that this feature makes it a familiar comparison
for managers in industry. Due to its scalability, IRR can be compared against other alternatives
of different cost or against the null (do nothing) alternative to help decide the best course of
action. This null alternative is usually the hurdle rate, or minimum attractive rate of return that an
organization will accept.
IRR is calculated by finding the rate of return that arises when the net present value of an
investment is zero. Crundwell (2008) describes the formula in equation 16. This equation is nonlinear, and cannot be solved directly. The suggested method is trial and error to satisfy the
equation, or by using functions such as MS Excel’s IRR function.
26
Equation 16 – IRR
In the current market, installing a digester solely on financial merit alone would not be feasible
due to bakery waste to animal feed revenue of $30-50 per ton and low electricity costs of $0.055
per kWh (blended rate with all fees considered). There is no net annual benefit in this situation.
However, those factors may be more costly, less reliable, or not available in the future, so an
estimation will be made involving disposal by waste to energy (WTE) or landfill (similar current
rate of $75/ton).
This study will evaluate three scenarios for financial viability. The first will examine the
feasibility of a CHP generator that will save WTE/landfill fees at current rates. The second and
third scenarios will consider sensitivity around WTE/landfill fees and electricity rates,
respectively that compare to the assumed hurdle rate of 15.0%.
Crundwell (2008) mentions that sensitivity analysis is another way to develop insight into the
relationship of the inputs and outputs of any particular model. This sensitivity relationship (S)
happens to be a partial derivative of one variable (c) with respect to another variable (b) given in
Equation 17.
Equation 17 – Sensitivity
27
All three of the following scenarios use a 1.0 million gallon reactor coupled with a 1.0 MW CHP
generator to consume biogas produced from 10,000 tons of bakery waste per year. This was a
proposed solution from an AD design firm to a bakery manufacturer in 2012.
Using many assumptions from this study (0.7 grams COD per gram of dough, 0.6 liters biogas
per gram of COD, and 65% methane) and a low heat value of 26 MJ per cubic meter of
biomethane (Frazier et al. 2014), the 10,000 tons of waste becomes an average of 2.04 MW of
biomethane.
GE’s Jenbacher Type 4 units are stated as roughly 85% efficient (each model & fuel source
varies). Approximately 40% of the outgoing energy is usable electricity, 45% is usable heat
energy, and 15% is wasted (GE Power n.d.). A simple energy balance is shown in Figure 8 to
illustrate the input and outputs from such a system, using the available 2.04 MW of biomethane.
In reality, the facility would be able to utilize all available electricity from the process, but would
only be able to utilize about 200 kW of the heat energy at this time.
15% (306 kW) Waste
40% (816 kW) Electricity
2,041 kW
Biomethane
45% (918 kW) Heat
Figure 8 - CHP Energy Balance.
28
In this first scenario in Table 3, the bakery waste was being disposed in a landfill or waste to
energy facility (i.e. no revenue is being received from animal feed reuse). Even with that
assumption, the IRR to install such a digester was a mere 1.02% compared to the hurdle rate of
15.0%. Many other assumptions were made in this scenario and outlined in the comment column.
Bakery Waste to Anaerobic Digester Economic Analysis
Investment
Description
Investment Cost
Estimated Cost
Comment
$
14,000,000 Budgetary Estimate from an AD design firm, no financing
Costs
Description
Operating & Maintenance Costs
Solids Application
Annual Animal Feed Revenue Loss
Total Annual Costs
Estimated Annual Cost
$
163,200
$
50,000
$
$
213,200
Comment
Estimated $200/kW e1 @ 816 kW for internal combustion CHP
Estimate
Assuming no current revenue
Benefits
Description
Reduced Electrical Costs
Reduced Natural Gas Costs
Reduced Wastewater Charges
Bakery Waste Disposal Elimination
Total Annual Savings
Estimated Annual Benefit
$
360,835
$
24,000
$
90,000
$
750,000
$
1,224,835
Comment
816 kW @ 24hr/d, 335 days/yr2, current rate of $0.055/KWH
Will still have gas usage - assume 80% savings from $30,000
Will still have effluent - assume 40% savings from $150,000
Assuming 10,000 tons @ $75/ton tip fee
Net Annual Savings
Internal Rate of Return3
$
1,011,635
1.02%
Notes
1
Operation & Maintenance costs from the International Energy Agency (2010).
2
Assume downtime of 30 days per year, leaving 335 days of operation
3
IRR calculated by IRR formula in MS Excel
Assumptions
Cost of Electricity
Cost of Waste to Energy
Rebate for Bakery Waste
$0.055 per KWH
$75 per ton
$0 per ton
Table 3 – Economic Analysis by MS Excel Calculation: Scenario 1
29
Table 4 shows a scenario with the same reactor/CHP generator as Table 2, except the cost of
waste disposal is adjusted to meet the hurdle rate of 15.0%. At an extreme cost of $213.30 per
ton of waste, the hurdle rate is met.
Bakery Waste to Anaerobic Digester Economic Analysis - High Landfill Cost
Investment
Description
Investment Cost
Estimated Cost
Comment
$
14,000,000 Budgetary Estimate from an AD design firm, no financing
Costs
Description
Operating & Maintenance Costs
Solids Application
Annual Animal Feed Revenue Loss
Total Annual Costs
Estimated Annual Cost
$
163,200
$
50,000
$
$
213,200
Comment
Estimated $200/kW e1 @ 816 kW for internal combustion CHP
Estimate
Assuming no current revenue
Benefits
Description
Reduced Electrical Costs
Reduced Natural Gas Costs
Reduced Wastewater Charges
Bakery Waste Disposal Elimination
Total Annual Savings
Estimated Annual Benefit
$
360,835
$
24,000
$
90,000
$
2,133,000
$
2,607,835
Comment
816 kW @ 24hr/d, 335 days/yr2, current rate of $0.055/KWH
Will still have gas usage - assume 80% savings from $30,000
Will still have effluent - assume 40% savings from $150,000
Assuming 10,000 tons @ $75/ton tip fee
Net Annual Savings
Internal Rate of Return3
$
2,394,635
15.00%
Notes
1
Operation & Maintenance costs from the International Energy Agency (2010).
2
Assume downtime of 30 days per year, leaving 335 days of operation
3
IRR calculated by IRR formula in MS Excel
Assumptions
Cost of Electricity
Cost of Waste to Energy
Rebate for Bakery Waste
$0.055 per KWH
$213 per ton
$0 per ton
Table 4 – Economic Analysis by MS Excel Calculation: Scenario 2 (High Waste Disposal Cost)
30
Table 5 shows a scenario with the same reactor/CHP generator as Table 2, except the cost of
electricity is adjusted to meet the hurdle rate of 15.0%. At an extreme cost of $0.2657 per kWh
of electricity, the hurdle rate is met.
Bakery Waste to Anaerobic Digester Economic Analysis - High Electric Cost
Investment
Description
Investment Cost
Estimated Cost
Comment
$
14,000,000 Budgetary Estimate from an AD design firm, no financing
Costs
Description
Operating & Maintenance Costs
Solids Application
Annual Animal Feed Revenue Loss
Total Annual Costs
Estimated Annual Cost
$
163,200
$
50,000
$
$
213,200
Comment
Estimated $200/kW e1 @ 816 kW for internal combustion CHP
Estimate
Assuming no current revenue
Benefits
Description
Reduced Electrical Costs
Reduced Natural Gas Costs
Reduced Wastewater Charges
Bakery Waste Disposal Elimination
Total Annual Savings
Estimated Annual Benefit
$
1,743,162
$
24,000
$
90,000
$
750,000
$
2,607,162
Comment
816 kW @ 24hr/d, 335 days/yr2, current rate of $0.055/KWH
Will still have gas usage - assume 80% savings from $30,000
Will still have effluent - assume 40% savings from $150,000
Assuming 10,000 tons @ $75/ton tip fee
Net Annual Savings
Internal Rate of Return3
$
2,393,962
15.00%
Notes
1
Operation & Maintenance costs from the International Energy Agency (2010).
2
Assume downtime of 30 days per year, leaving 335 days of operation
3
IRR calculated by IRR formula in MS Excel
Assumptions
Cost of Electricity
Cost of Waste to Energy
Rebate for Bakery Waste
$0.2657 per KWH
$75 per ton
$0 per ton
Table 5 – Economic Analysis by MS Excel Calculation: Scenario 3 (High Electric Cost)
31
SECTION V – CONCLUSIONS
5.1 Biogas Potential
The biogas potential of hand-tossed pizza dough through five likely Food to Mass (F/M) ratios
was evaluated. The range of gas produced was from 0.50 to 0.70 liters / gram COD added from
0.25 to 1.0 F/M ratios. The biogas production of 0.70 liters / gram COD corresponds well to the
research from Schievano et al. (2009), which had biogas production values of 731 +/- 178 and
653 +/- 27.8 normal liters biogas per kilogram TS for “Bakery Waste” and “Bakery Residues”
respectively.
5.2 Optimal Organic Loading Rate
The optimal organic loading rate predicted for an industrial scale continuous reactor based on
Biochemical Methane Potential (BMP), feed rate, COD removal, and gas production appears to
be 0.5 g COD/g VS. The gas production seems to peak near a F/M ratio of 1.0 g COD / g VS,
and it would be best to stay in the productive range of the system versus the failure range of the
system. In conjunction with this approach, the COD removal efficiency starts to decrease
significantly after 1.0 g COD / g VS.
32
5.3 Simple Economic Analysis
A simple economic analysis was examined for constructing a digester based on the products of
digestion. At the current market rates in Oklahoma, the IRR is approximately 1.02%. By
adjusting the waste disposal and electricity costs to meet the assumed hurdle rate of 15.0%, the
disposal cost for waste would need to be over $200/ton, or electricity would need to cost almost
$0.27/kWh. With those results considered, other drivers would need to exist in order to pursue
such an installation for economic benefit. In another part of the country or world, or at different
economic conditions, this scenario may have a more positive outlook.
5.4 Further Analysis
The results of this study could be expanded or refined by further analysis. This may include:

Semi-continuous bench testing on one particular feedstock or a combination to further
define optimal F/M ratios. The different F/M ratios from batch testing used in this study
approximates continuous operating F/M ratio, but continuous or semi-continuous testing
at steady state would be the best indicator of how the feedstock would perform.

Potential feedstock(s) for beneficial co-digestion. As indicated, pizza dough has a high
carbohydrate and lipid concentration which is beneficial for methane production. Pairing
the pizza dough with another feedstock high in protein may increase yield. On the
contrary, some feedstocks may inhibit yield or cause digester failure. Likely
combinations should be investigated, then run in additional BMP tests.

Methane content analysis. Methane is the most valuable product of the AD process, and
therefore the biogas stream is more valuable to combust if it has a higher methane
content. Conversely, contaminants such as carbon dioxide and hydrogen sulfide may
have to be scrubbed out of the biogas before combustion, depending on the application.
A more exact percentage could be gleaned by experimental comparison (i.e. scrubbing
33
these contaminants in some samples, while holding others constant) or through gas
chromatography.

Further specific financial analysis. There are many variables to economic feasibility of
anaerobic digesters, so specific analysis should be performed for each prospective
application. This will ensure that facility details are incorporated and current market
rates for capital and other costs are considered.
34
REFERENCES
Abbasi, T., Tauseef, S.M., & Abbasi, S.A. (2012). Biogas Energy. (pp. 11-12). doi: 10.1007/9781-4614-1040-9
Ahring, B.K. (1995). Methanogenesis in thermophilic biogas reactors. Antonie Van Leeuwenhoek,
67(1), 91-102. doi:10.1007/BF00872197
APHA, AWWA, & WEF. (1998). Standard Methods for the Examination of Water and
Wastewater. Washington, DC.
Cabbai, V., Ballico, M., Aneggi, E., & Goi, D. (2013). BMP tests of source selected OFMSW to
evaluate anaerobic codigestion with sewage sludge. Waste Management, 33(2013), 1626–1632.
doi:10.1016/j.wasman.2013.03.020
Center for Climate and Energy Solutions. (2011). Climate Techbook: Anaerobic Digesters.
Retrieved September 17, 2015 from: http://www.c2es.org/docUploads/AnaerobicDigesters.pdf
Crundwell, F.K. (2008). Finance for Engineers: Evaluation and Funding of Capital Projects. (pp.
167-180, 277-281). doi: 10.1007/978-1-84800-033-9
de Lemos Chernicharo, C. (2007). Anaerobic Reactors, Volume 4. (pp. 13-14). Retrieved from:
http://app.knovel.com/hotlink/toc/id:kpARV00008/anaerobic-reactors-volume/anaerobicreactors-volume.
Digman, B. & Kim, D.S. (2008). Review: Alternative Energy from Food Processing Wastes.
Environmental Progress, 27(4), 528-529. doi: 10.1002/ep.10312
FAO. (2011). Global food losses and food waste – Extent, causes and prevention. Retrieved
November 24, 2015 from http://www.fao.org/docrep/014/mb060e/mb060e.pdf
Frazier, R.S., Hamilton, D., & Ndegwa, P.M. (2014). Anaerobic Digestion: Biogas Utilization
and Cleanup. (Oklahoma Cooperative Extension Service Fact Sheet BAE-1752). Retrieved
October 29, 2015 from http://pods.dasnr.okstate.edu/docushare/dsweb/Get/Document-7877/BAE1752web.pdf
GE. (n.d.). Jenbacher Type 4. Retrieved March 14, 2015 from
https://powergen.gepower.com/products/reciprocating-engines/jenbacher-type-4.html
Gills Onions. (n.d.). Waste to Energy. Retrieved October 29, 2015 from
http://www.gillsonions.com/waste-to-energy
35
Girard, M., Palacios, J.H., Belzile, M., Godbout, S., & Pelletier, F. (2013). Biodegradation Engineering and Technology. In R. Chamy and F. Rosenkranz (Eds.), Chapter 10 Biodegradation
in Animal Manure Management. doi: 10.5772/56151
Hach. (2009). Oxygen Demand, Chemical. Retrieved December 9, 2015 from
http://www.hach.com/asset-get.download-en.jsa?code=55143
International Energy Agency. (2010). Combined Heat and Power. (IEA ETSAP - Technology
Brief E04 – May 2010). Retrieved March 10, 2016 from http://www.iea-etsap.org/web/etechds/pdf/e04-chp-gs-gct_adfinal.pdf
Ivancic, V. (2014). Improving the Decision Making Process Through the Pareto Principle.
Ekonomska Misao i Praksa, 23(2), 633-655. Retrieved from
http://search.proquest.com.argo.library.okstate.edu/docview/1641820041?accountid=4117
Kondusamy, D., & Kalamdhad, A.S. (2014). Pre-treatment and anaerobic digestion of food waste
for high rate methane production – A review. Journal of Environmental Chemical Engineering,
2(3), 1821–1830. doi: 10.1016/j.jece.2014.07.024.
Ley, C., Klein, O., Bernard, P., & Licata, L. (2013). Detecting outliers: Do not use standard
deviation around the mean, use absolute deviation around the median. Journal of Experimental
Social Psychology, 49(4), 764–766. doi:10.1016/j.jesp.2013.03.013
Limongi, S., Simões, D.R.S., & Demiate, I.M. (2012). Production of pizza dough with reduced
fermentation time. Food Science and Technology (Campinas), 32(4), 793-797. Retrieved
September 26, 2015, from http://www.scielo.br/scielo.php?script=sci_arttext&pid=S010120612012000400010&lng=en&tlng=en.10.1590/S0101-20612012005000093
Metcalf & Eddy, Inc. (2003). Wastewater Engineering Treatment and Reuse. Boston: McGraw
Hill.
Moody, L.B., Burns, R.T., Bishop, G., Sell, S.T., & Spajic, R. Using Biochemical Methane
Potential Assays to Aid in Co-Substrate Selection for Co-Digestion. Applied Engineering in
Agriculture, 27(3), 433‐439.
National Renewable Energy Laboratory. (January 2013). Feasibility Study of Anaerobic
Digestion of Food Waste in St. Bernard, Louisiana. (NREL/TP-7A30-57082). Retrieved October
7, 2015 from: http://www.nrel.gov/docs/fy13osti/57082.pdf
Newslow, D. (2014). Food safety management programs: Applications, best practices, and
compliance. Beaverton: Ringgold Inc. Retrieved from
http://search.proquest.com.argo.library.okstate.edu/docview/1651905181?accountid=4117
Panico, A. d'Antonio, G. Esposito, G. Frunzo, L. Iodice, P. & Pirozzi, F. (2015). The Effect of
Substrate-Bulk Interaction on Hydrolysis Modeling in Anaerobic Digestion Process.
Sustainability, 6(12), 8348-8363. doi:10.3390/su6128348
Raposo, F., De la Rubia, M.A., Fernández-Cegrí, & V., Borja, R. (2012). Anaerobic digestion of
solid organic substrates in batch mode: An overview relating to methane yields and experimental
36
procedures. Renewable and Sustainable Energy Reviews, 16(1), 861–877. doi:
10.1016/j.rser.2011.09.008
Ripley, L.E., Boyle, W.C., & Converse, J.C. (1986). Improved Alkalimetric Monitoring for
Anaerobic Digestion of High-Strength Wastes. Water Pollution Control Federation, 58(5), 406411. Retrieved from: http://www.jstor.org/stable/25042933
Schievano, A., D’Imporzano, G., & Adani, F. (2009). Substituting energy crops with organic
wastes and agro-industrial residues for biogas production. Journal of Environmental
Management, 90(8), 2537–2541. doi:10.1016/j.jenvman.2009.01.013
Shafey, T.M., Alodan, M.A., Al-Batshan, H.A. Abouheif, M.A., Alamri, M.S. & Al-Ruqaie, IM.
(2011). Performance, Egg Characteristics and Economic Impact of Laying Hens Fed Extruded
Bakery Waste. Journal of Animal and Veterinary Advances, 10(17), 2248-2252. doi:
10.3923/javaa.2011.2248.2252
Stahlbush Island Farms. (n.d.). Energy. Retrieved October 29, 2015 from
http://www.stahlbush.com/sustainable-farming/energy/
Stover, T.R. (2011). Anaerobic Digestion Optimization for Enhanced Renewable Biomethane
Production (Doctoral Dissertation). Retrieved from ProQuest (UMI Number: 3474699).
United States Census Bureau. (2015). Projections of the Size and Composition of the U.S.
Population: 2014 to 2060 Population Estimates and Projections – Current Population Reports.
(Publication P25-1143). Retrieved August 6, 2015 from:
http://www.census.gov/content/dam/Census/library/publications/2015/demo/p25-1143.pdf
United States Department of Agriculture. (2013, June 4). News Release: USDA and EPA Launch
U.S. Food Waste Challenge. (Release No. 0112.13). Retrieved August 6, 2015 from:
http://www.usda.gov/wps/portal/usda/usdahome?contentidonly=true&contentid=2013/06/0112.x
ml
United States Environmental Protection Agency. (December 2006). Industrial Stormwater Fact
Sheet Series: Sector U: Food and Kindred Products Facilities. (EPA-833-F-06-036). Retrieved
November 24, 2015 from:
http://water.epa.gov/polwaste/npdes/stormwater/upload/sector_u_food.pdf
United States Environmental Protection Agency. (2010). U.S. Farm Anaerobic Digestion
Systems: A 2010 Snapshot. USEPA AgSTAR. Retrieved September 28, 2015 from:
http://www.epa.gov/outreach/agstar/documents/2010_digester_update.pdf
United States Environmental Protection Agency. (2015, February 9). Anaerobic Digestion.
Retrieved August 6, 2015 from: http://www.epa.gov/waste/conserve/foodwaste/fd-anaerobic.htm
United States Environmental Protection Agency. (2014, November 20). The Food Recovery
Hierarchy. Retrieved August 6, 2015 from: http://www.epa.gov/foodrecovery/
United States Food and Drug Administration (2015, May 15). Guidance & Regulation. Retrieved
November 24, 2015 from: http://www.fda.gov/Food/GuidanceRegulation/default.htm
37
Zupančic, G.D., Uranjek-Ževart, N., & Roš, M. (2008). Full-scale anaerobic co-digestion of
organic waste and municipal sludge. Biomass Bioenergy, 32(2), 162–167. doi:
10.1016/j.biombioe.2007.07.006
38
APPENDICES
39
APPENDIX A – TRIAL 1 SUPPORTING DOCUMENTS (JANUARY 2013)
40
41
42
43
44
45
46
47
48
49
APPENDIX B – TRIAL 2 SUPPORTING DOCUMENTS (NOVEMBER 2013)
50
51
52
53
54
55
56
57
58
APPENDIX C – TRIAL 3 SUPPORTING DOCUMENTS (JANUARY 2015)
59
60
61
62
63
64
65
66
67
VITA
John Kyle Evicks
Candidate for the Degree of
Master of Science
Thesis: BAKERY WASTE AS A FEEDSTOCK TO ANAEROBIC DIGESTION
Major Field: Biosystems Engineering
Biographical:
Education:
Completed the requirements for the Master of Science in Biosystems
Engineering at Oklahoma State University, Stillwater, Oklahoma in May 2016.
Completed the requirements for the Bachelor of Science in Biosystems
Engineering at Oklahoma State University, Stillwater, Oklahoma in May 2006.
Experience:
Environmental Manager
 The Bama Companies, Inc. (Tulsa, OK) – July 2007 to Present
 Leading corporate environmental compliance efforts and sustainability
program for three facilities in the Tulsa area
 Managing capital improvement projects
Project Engineer Trainee
 Leprino Foods (Allendale, MI) – May 2006 to July 2007
 Developed and implemented capital improvement projects for
mozzarella cheese facility
 Led and participated in employee teams to solve process or other issues
Professional Memberships:
Vice Chair, American Society of Agricultural and Biological Engineers, OK
Section