Net, JavaScript and HTML are used during the ... spent many hours helping me
on VB.Net ...... Another GIS-based DSS was developed to calculate marginal.
A WEB-BASED SPATIAL DECISION SUPPORT SYSTEM FOR UTILIZING ORGANIC WASTES AS RENEWABLE ENERGY RESOURCES IN NEW YORK STATE
A Dissertation Presented to the Faculty of the Graduate School of Cornell University In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
by Jianguo Ma January 2006
© 2006 Jianguo Ma
A WEB-BASED SPATIAL DECISION SUPPORT SYSTEM FOR UTILIZING ORGANIC WASTES AS RENEWABLE ENERGY RESOURCES IN NEW YORK STATE
Jianguo Ma, Ph. D. Cornell University 2006
Organic waste is a terrible thing to waste because of its impact on the environment and increasing disposal cost. However, using anaerobic digestion (AD) technology, organic waste can be converted into useful energy while harvesting environmental and economic benefits at the same time. As the 3rd largest dairy state in the nation and the host for many food waste generators, New York State produces a large amount of organic waste. Recently there has been a renewed interest in farmbased co-digestion, which has created strong needs for research in this field. To date there are very limited data and tools available to help identify, locate and evaluate these resources. In addition, it is important to find a way to estimate food waste production from a variety of generating sources. Lastly, an economic model/tool is desirable to assess the costs and benefits of potential on-farm co-digestion projects. To address these issues, a web-based spatial decision support system (SDSS) is developed by integrating geographic information systems (GIS), the Internet, and modeling. ArcGIS, Manifold, VB.Net, JavaScript and HTML are used during the design process. This system consists of three modules: (1) Dynamic Mapping and Querying; (2) Food Waste Estimator; and (3) Co-digestion Economic Analysis. Module 1 is designed to help users dynamically explore the map of resources by displaying, zooming in/out to any extent, and selecting any combination of
information layers. Users can also create and print out customized maps. In addition, users can retrieve various data through queries that might be helpful in their decision making. Module 2 is designed to estimate food waste production from a variety of generating sources including: food processing facilities; supermarkets; fast food franchises; correctional facilities; restaurants; colleges / universities; K-12 public schools; hospitals; and nursing homes. Lastly, Module 3 is designed to provide a preliminary economic analysis for on-farm anaerobic digester systems, particularly using dairy manure and food waste for co-digestion. The ultimate goal of this study is to provide data and tools to identify organic waste streams as renewable energy resources and to relate these to development of onfarm co-digestion in New York State.
BIOGRAPHICAL SKETCH
Jianguo Ma was born at Aksu in Xinjiang Autonomous Region, China. He received his B.S. in Geological Engineering from Beijing Science and Technology University in 1994 and then a M.A. in Higher Education Administration from Peking University in 1997. Since 2000, he has studied in the Department of Biological and Environmental Engineering at Cornell University and was awarded a M.S. degree in 2003. His current research interests include: renewable energy (particularly agriculture-based bio-energy) and its impact on environment, biogeography, and rural development; integrated bio-refineries; and the application of geographic information systems and remote sensing technology.
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To Yulong and Albert.
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ACKNOWLEDGMENTS
The completion of this thesis would never have been possible without support that I have received from many individuals and organizations during my graduate study at Cornell University. The acknowledgement expresses my sincere appreciation to all of those who have helped me in various ways. First, thanks must go to my major advisor, Prof. Norman Scott, who has continuously supported and guided me in my research and study in past few years. I am most grateful for his spending so much valuable time with me and providing guidance, suggestions, encouragement, and constructive criticism. I have not only learned a great deal of knowledge from him but also academic professionalism and dedication to excellence. Without his supervision and help, I would have never been able to complete this research. Many thanks also go to other members of the Special Committee: Prof. Stephen DeGloria, Prof. Douglas Haith and Prof. Robert Thomas. They have given me far more guidance and support than that can be expected. I have greatly benefited from their multi-disciplinary knowledge and specialties which provide comments and advice from different perspectives. I am especially thankful for their continual support for improving on the writing of this thesis and presenting the results. Thanks to other faculty, staff, and graduate students who have helped me on my research in many ways. Brian Aldrich gave me numerous comments and feedback on my research. I am grateful for his friendship and encouragement. Randi Rainbow spent many hours helping me on VB.Net programming and patiently let me test my designed computer applications on the server that he manages. Dr. Arthur Lembo provided guidance on GIS and essentially led me to the Manifold world. The members
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of our research group (Stephan Minott, Liping Duan, Amy Risen, Crystal Powers, Arvind Chandrasekar, Michelle Wright, and Rodrigo Labatut) have supported me with useful comments and suggestions. The knowledge and experience I gained from them was of great benefit to my research. Particularly I would like to thank Michelle Wright for her direct assistance in data collection for a portion of this research. I am also grateful for cooperation with Peter Wright during field studies. Scott Inglis generously shared the raw data that he collected from field trips and also was always patient in our discussions about data issues. Also thanks to Jonathan Perry who has helped me in many ways in past few years while I was in Ithaca. Finally, I would like to acknowledge that this project was partially supported by New York State Energy Research and Development Authority (NYSERDA).
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TABLE OF CONTENTS
1
INTRODUCTION .................................................................................................. 1 1.1
2
3
Organic Waste: An Environmental and Economic Challenge ....................... 1
1.1.1
Dairy Manure.......................................................................................... 2
1.1.2
Food Waste............................................................................................. 4
1.2
Biogas Recovery through Anaerobic Digestion ............................................. 5
1.3
Summary......................................................................................................... 8
RESEARCH TOPIC IDENTIFICATION.............................................................. 9 2.1
Status of On-Farm Anaerobic Digestion in New York State ......................... 9
2.2
Research Needs ............................................................................................ 10
LITERATURE REVIEW..................................................................................... 14 3.1
Work to Date and Limitations ...................................................................... 14
3.1.1
Identify, Quantify and Locate Organic Waste Resources .................... 14
3.1.2
Economic Analysis of On-Farm Anaerobic Digester Systems ............ 16
3.1.3
GIS and Web-based Spatial Decision Support Systems....................... 18
3.2
Summary....................................................................................................... 22
4
RESEARCH OBJECTIVES................................................................................. 23
5
DATA ................................................................................................................... 24 5.1
Data Identification ........................................................................................ 24
5.2
Data Collection............................................................................................. 27
5.2.1
Non-Spatial Data .................................................................................. 27
5.2.2
Spatial Data .......................................................................................... 32
5.3 6
Data Processing ............................................................................................ 35
METHODS........................................................................................................... 36
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7
6.1
Framework.................................................................................................... 36
6.2
System Design Procedures ........................................................................... 38
6.3
System Components / Modules .................................................................... 38
6.4
The Software ................................................................................................ 40
RESULTS AND DISCUSSION........................................................................... 42 7.1
Geo-Spatial Database ................................................................................... 42
7.2
The Web Platform ........................................................................................ 44
7.3
Modules of the Spatial Decision Support System ........................................ 45
7.3.1
Module 1: Mapping and Querying ....................................................... 46
7.3.2
Module 2: Food Waste Estimator......................................................... 59
7.3.3
Module 3: Co-Digestion Economic Analysis....................................... 62
7.4
Database Update........................................................................................... 71
8
SUMMARY ......................................................................................................... 74
9
RECOMMENDATIONS FOR FUTURE RESEARCH ...................................... 76
APPENDICES.............................................................................................................. 78 Appendix A: Abbreviations...................................................................................... 78 Appendix B: Help Information and Examples on Mapping..................................... 79 Appendix C: Survey Form Used to Collect Technical and Financial Data from Dairy Farms with AD System in Northeast region .................................................. 97 Appendix D: Survey Form Used to Collect Data about Food Waste from Food Processors in New York State .................................................................................. 99 Appendix E: Definition of CAFO .......................................................................... 100 References .................................................................................................................. 103 Glossary...................................................................................................................... 110
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LIST OF FIGURES Figure 1. Locations of anaerobic digesters funded by NYSERDA………………….11 Figure 2. Total biogas production and biogas production per cow per day at AA Dairy…………………………………………………………………………………30 Figure 3. Engine generator performance at AA Dairy, 1998-2001………………….30 Figure 4. Framework of web-based SDSS…………………………………………...37 Figure 5. Distribution of CAFOs and food processors in New York State…………..42 Figure 6. Distribution of food processors within 20- and 50-mile radius of X Farm...43 Figure 7. Screen shot of the home page of the web platform………………………...45 Figure 8. Screen shot of SDSS Module 1: Mapping & Querying……………………46 Figure 9. Tool bar…………………………………………………………………….47 Figure 10. Query example screen shot #1……………………………………………52 Figure 11. Query example screen shot #2……………………………………………52 Figure 12. Query example screen shot #3……………………………………………53 Figure 13. Query example screen shot #4……………………………………………54 Figure 14. Query example screen shot #5……………………………………………55 Figure 15. Query example screen shot #6……………………………………………55 Figure 16. Query example screen shot #7……………………………………………56 Figure 17. Query example screen shot #8……………………………………………57 Figure 18. Query example screen shot #9……………………………………………57 Figure 19. Query example screen shot #10………………………………………..…58 Figure 20. Screen shot of SDSS Module 2…………………………………………...59 Figure 21. Screen shot of SDSS Module 3 …………………………………………..62 Figure 22. Detailed view of Module 3 (Part I)………………………………………..63 Figure 23. Detailed view of Module 3 (Part II)………………………………………64
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Figure 24. Screen shot of tool for updating food processor data (Part I)……………..72 Figure 25. Screen shot of tool for updating food processor data (Part II)…………....72 Figure 26. Screen shot of email for updating food processor data…………………...73
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LIST OF TABLES Table 1. Methane emission sources…………………………………………………..3 Table 2. List of farms that in various stage of installing AD in New York State…….10 Table 3. Operating on-farm AD Systems in Northeast region…………………….....28 Table 4. Specification summary of AD systems on AA Dairy and Matlink………….29 Table 5. Biogas usage on Matlink (8/22/2003 – 7/22/2005)………………………….31 Table 6. Summary of economics on Matlink…………………………………………31 Table 7 Data Sources and description of food wastes generators in New York……...33 Table 8 Environment, utility and other data.................................................................34 Table 9. Summary of food waste generators in New York…………………………...35 Table 10. Queries in Module 1………………………………………………………..49 Table 11. Statistics of food processor survey results……………………………...….60 Table 12. Formulas used to estimate various food waste production types…………..61 Table 13 Volume of manure and food waste fed to AD daily on Matlink Dairy…….68 Table 14. Estimate of revenues per cow………………………………...……………71
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1 1.1
INTRODUCTION
Organic Waste: An Environmental and Economic Challenge Organic waste is derived from biological origins such as food, animal waste,
yard trimmings, paper and cardboard, bio-solids, and sludge, which are produced wherever there is human habitation. Organic waste will naturally decay over time, returning its constituent elements to the earth. Many people think that organic waste is biodegradable and thus it does not cause a problem in our waste stream. Unfortunately, however, this is not the case. Organic waste like animal manure creates environmental issues such as excessive nutrients, water pollution, odor, and pathogens. While other organic waste is sent to landfill, it becomes compacted under the pressure of the rubbish above. This compaction drives the air out of the landfill, and the decomposition of organic waste becomes anaerobic (without oxygen). Anaerobic decay of organic waste releases methane, which is a potent greenhouse gas, into the atmosphere. The large amount of organic waste means that the amount of methane produced will likely have a noticeable impact on global warming. The acids that form through this decay process may lead to the contamination of soils and ground water. Decaying organic waste can also cause instability in the structure of the landfill site, sometimes causing areas of landfills to collapse. Besides environmental concerns, it is also costly to dispose of and treat organic waste. In 1997, the annual cost of food waste disposal was estimated at around $31 billion nationally (Kantor, et al., 1997). In New York, the landfill tipping fee is about $60/ton in upstate and can be as high as $100/ton in municipal areas (Bonhotal, 2004).
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2
Animal manure and food waste have long been blamed as a major source of air and water pollution. Among all types of organic waste, they have attracted much attention because of their large volume and impact on the environment. Therefore, this study focuses on these two waste types. 1.1.1
Dairy Manure
The dairy industry is the largest sector of New York agriculture and generates $1.8 billion revenue annually, which represents 56.6% of total agricultural income (New York Agriculture and Markets, 2000). There are 7,388 dairy farms and 670,003 milk cows in the state (USDA, National Agricultural Statistics Service, 2002). The State is ranked the third largest dairy state in U.S., after California and Wisconsin (New York Agriculture and Markets, 2000). Each cow produces, on average, 112 pounds of manure per day (EPA, 1999, 2003c). Statewide, dairy cows produce more than 28 billion pounds of manure per year. Therefore, the dairy manure production is significant. The term “animal waste” hereafter in this study refers to dairy manure unless specified. When the tons of manure produced are not dealt with properly, environmental problems including severe air and water pollution can result. Animal waste has the potential to contribute pollutants such as excessive nutrients (e.g., nitrate, phosphorous), organic matter, sediments, pathogens, hormones, antibiotics and ammonia to the waters that we use for drinking, swimming and fishing. For example, in August 2005, an earthen manure storage at Marks Farm in upstate New York failed and millions of gallons of manure were spilled into the recreational stream – Black River, killing thousands of fish (The Ithaca Journal, Aug 12, 2005). The complete clean-up and recovery of the water system may take a few years.
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In addition to water quality problems, animal waste can also contribute significantly to air quality problems, including dust, smog, greenhouse gases (e.g. methane), and odors (EPA, 2003). Livestock manure is primarily composed of organic material and water. Under anaerobic conditions methane (CH4) generation takes place by transformation of the volatile solids portion (VS) of the manure (EPA, 2003). EPA estimates 1997 U.S. methane emissions from livestock manure management at 17.0 MMTCE 1 (3.0 Tg, 1 Tg=106 tons), which accounts for 10% of total 1997 U.S. methane emissions. The majority of methane emissions come from large swine and dairy farms that manage manure as a liquid. A study by U.S. DOE shows similar estimates of methane production due to livestock manure and other agricultural sources (Table 1). Methane is a very potent greenhouse gas which has a Global Warming Potential (GWP) of 21 times carbon dioxide (EPA, 2003d). Therefore, it is important to find alternative ways of managing animal manure to mitigate the methane issue. Table 1 Methane emission sources (EERE, 1999) Methane emitted (1997) Source
Carbon equivalent
MMT*
% of U.S. total
MMT*
Enteric fermentation
5.36
18.41
112.56
Animal waste
2.77
9.52
58.17
Rice cultivation
0.43
1.48
9.03
Biomass burning
0.04
0.14
0.84
Total agricultural sources
8.6
29.55
180.6
Total (all U.S. sources)
29.11
611.31
Note: * - millions of metric tons; Source: Adopted from U.S. DOE, Office of Energy Efficiency and Renewable Energy, 1999.
1
Million metric tons of carbon equivalent
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1.1.2
Food Waste
Food waste is organic matter derived from raw biological materials and mostly generated from the following processes: -
Waste from industrial food processing establishments.
-
Farm produce that does not meet supermarket purchase specifications.
-
Discards of blemished perishables and out-of-date foods at supermarkets.
-
Foods prepared by service establishments that are not served to customers.
-
Plate scraps from commercial and residential sources. The food industry generates large volumes of waste while producing food for
both local and global consumption. Studies of all types of foodservice operations have also identified numerous sources of food wastes generation. The US EPA has estimated that food wastes account for 11.4% of the total municipal solid wastes (MSW) (U.S. EPA, 2003). The organic faction is considerably higher for restaurants and food processing facilities. In 2000, an assessment of Georgia’s recovery potential of organic residuals from the food processing and institutional food sectors reported that 231,100 tons/year – mainly fruits and vegetables – plus 474,000 tons/year of institutional food waste were disposed of in landfills (Faucette and Governo, 2003). As available landfill space decreases, tipping fees as well as transportation costs increase, because trash must be shipped farther. There are 27 landfills in New York and the remaining capacity is only 7 years (Goldstein, 2001). The National Restaurant Association states that three out of five restaurants report paying more for trash removal now than just a few years ago. They also report that tipping fees have more than doubled since 1982. Therefore, food waste generators are faced with two problems: an economic one as well as an environmental one (Drummond, 1998).
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Foodservice and food retail outlets to some degree rely on alternative methods of disposal, including source reduction, recycling, and incineration. The most common form of recycling of food wastes is through composting. A nationwide survey found that, in 1997, there were 220 commercial composting sites (in 6 states) for food wastes. The study also found that about 68% of these composting sites accepted production food wastes, and about 50% accepted service food wastes from restaurants and cafeterias (Goldstein, 1997). Despite the increased use of composting in recent years, it is still an infrequently practiced method of food wastes disposal for most foodservice and food retail operations. In sum, food wastes, just like other solid wastes, take up landfill space and require shipping to reach a final disposal destination. Landfill space is shrinking, but generation of waste is not. As a result, reducing food wastes and finding beneficial uses for this material are important waste management issues in the United States today. 1.2
Biogas Recovery through Anaerobic Digestion Organic waste is most commonly disposed through either transportation to a
landfill or garbage disposals connected to a sewer system. Other waste management practices include recycling, composting, etc. However, in comparison, waste-toenergy (WTE) approach is attracting more and more attention because it can produce useful energy and other by-products. Co-firing and anaerobic digestion (AD) are two popular options. Co-firing projects are usually large-scale, whereby garbage, or municipal solid waste, is used as the fuel in a boiler. The high temperatures produced by the burning garbage turn the water to steam, which is then used to drive a turbine
6
generator that produces electricity. This approach is suitable for dry organic waste such as forest and crop residues, saw dust, and yard trimmings. In comparison, AD is considered a better approach for waste that has relatively high moisture content such as animal manure and food processing waste. AD is a biochemical degradation process that converts organic material, such as dairy manure, into methane (CH4) and other byproducts such as carbon dioxide (CO2), hydrogen sulfide (H2S) and other trace gases. Biogas produced in anaerobic digesters consists of methane (50%-80%), carbon dioxide (50%-20%), and trace levels of other gases such as carbon monoxide, nitrogen, and hydrogen sulfide (EPA, 1997). The relative percentage of these gases in biogas depends on the feed composition and management of the process. Just like dairy manure, food wastes have high ratios of volatile solids/total solids (VS/TS) (80-90%). It is estimated that a methane yield of 0.05-0.06 m3/kg (0.80-0.96 ft3/lb) VS can be achieved through anaerobic digestion of food wastes (Shin, et al., 2000). Pure methane yields about 1,000 Btu (or 252 kilocalories) of heat energy per cubic foot (0.028 m3) when burned. Thus, animal manure and food wastes have significant energy potential when used as feedstock for co-digestion for biogas production. Biogas is a source of renewable energy suitable for electricity and heat production. The digested effluent can then be separated and the solid residues be used to make compost for sale or as bedding material, while the liquid is spread in crop fields. Overall, there are many benefits of using AD to handle organic wastes: •
On-Site Farm Energy. By recovering biogas and producing on-farm energy, livestock producers can reduce energy purchases from electric and gas suppliers. Power production near to where it is consumed will also reduce transmission losses.
7
•
Reduced Odors. Biogas systems reduce offensive odors from manure storage facilities. These odors impair air quality and may be a nuisance to nearby communities. Biogas systems reduce these offensive odors because the volatile organic acids, which are the odor causing compounds, are consumed by bacteria to produce biogas.
•
Reduced Greenhouse Gas Emission. Methane contained in biogas can be captured for energy production, which will displace fossil fuels use. The released CO2 is from ‘short cycle’ carbon, i.e. the carbon in the organic matter was recently sequestered from the atmosphere. Therefore energy produced from this process is not considered to contribute to climate change.
•
Reduced Surface and Groundwater Contamination. Digester effluent is a more uniform and predictable product than untreated manure. Properly applied, digester effluent reduces the likelihood of surface or groundwater pollution.
•
Pathogen Reduction. Heated digesters reduce pathogen populations dramatically in a few days.
•
Reducing spread of weeds and disease. AD destroys virtually all weed seeds, so digested slurry can be spread with minimal risk of weed spread, reducing the need for costly herbicide and other weed control measures. From a technical perspective, the practice of mixing food wastes with dairy
manure in anaerobic digesters has shown that the addition of food wastes provides some benefits, including increased biogas production and possibly a reduction of H2S concentration in biogas 2 . Running an anaerobic digester with food waste alone runs the risk of shock loading the system and acidic conditions leading to failure. Mixing food waste with animal manure in an anaerobic digester provides buffering capacity to 2
Personal communication with Ted Matthew (previous Matlink owner) during field study in 2003.
8
prevent acidic conditions and continued microbial feeding when the food waste is not available. 1.3
Summary Organic waste, such as animal manure and food waste, has long been blamed
as a major source of air and water pollution. However, using appropriate technology, organic waste can become part of the solution instead of the problem. The most promising technology is probably AD which can convert organic wastes into biogas to produce energy while harvesting environmental and economic benefits. Utilizing animal manure and food waste as feedstock to on-farm AD systems to produce biogas for combined heat and power (CHP) is now economically viable for large-scale dairy operations if the system is designed and operated properly. This renewable source of economical “green” energy is friendly to the environment, considerate of neighbors, and appealing to some utility customers who are obligated to meet renewable energy portfolio goals. Increasingly stringent environmental regulations and shrinking landfill capacity have made organic waste disposal and treatment more difficult and costly. As the 3rd largest dairy state and a host of many food waste generators, New York faces high pressure in looking to effective ways of managing organic waste. Therefore, AD has gained growing interest among policy makers, the energy industry, and waste generators. There is expectation for continuous growth of on-farm co-digestion systems utilizing both animal manure and food waste as feedstock in New York State.
2 2.1
RESEARCH TOPIC IDENTIFICATION
Status of On-Farm Anaerobic Digestion in New York State Development of anaerobic digesters for livestock manure treatment and energy
recovery has accelerated at a fast pace over the past few years. In 2001 and 2002, the number of operating digesters has increased by nearly 30%. Most of these digesters are farm-scale systems. However, centralized digester applications for dairy operations are also emerging (U.S. EPA, 2003). In another report from EPA, as of 2002, there were 40 operating farm-based digester systems in the U.S. It is estimated that at least 40 additional systems are in various stages of planning. These operating systems produce the equivalent of approximately 4 MW per year while reducing methane emissions by nearly 124,000 metric tons of methane on a carbon-equivalent basis (U.S. EPA, 2004). The same trend has been observed in New York State. Before 2003, there were only three operating AD systems on dairy farms. Now there are at least 25 dairy farms that are in various stages of AD system development. Most of these farms have been awarded funding from New York State Energy Research & Development Authority (NYSERDA) (Aldrich, 2005). Table 2 describes the details. Figure 1 displays the spatial distribution of some of these systems.
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Table 2. List of Farms that in Various Stage of Installing AD in New York State 3 Farm Name Status Farm Name Status Cooperstown In Operation SUNY Morrisville Digester Holstein construction completed AA Dairy In Operation Sheland Farms Design Phase Farber Farms In Operation Allenwaite Farms Design Phase Matlink 4 In Operation True Farms Design Phase DDI In Operation Aurora Ridge Design Phase Farms Noblehurst Farms In Operation Perry Community Design Phase Digester Twin Birch Farms In Startup North Harbor Dairy Design Phase Spring Valley Farm In Startup Curtin Bros. Farm Design Phase El-Vi Farms In Operation Hardie Farms In Contract Negotiation Patterson Farms In Operation Bilow Feasibility Study Completed Corwin Duck Farm Under Construction Salem Dairy Feasibility Study Farmer Manure Completed Group Town of Perry: Under Construction Table Rock Farms Feasibility Study Emerling Completed Town of Perry: Under Construction Butler
2.2
Research Needs It is evident that there has been growing interest in AD among policy makers
and especially CAFO owners in New York State. However, there are very limited data and information about organic waste in the state, especially using food waste to mix with animal manure for co-digestion. Only few operating on-farm AD systems in the country including Matlink Farm (now Ridgeline Farm) in New York have attempted 3
Personal communication with Brian Aldrich, Extension Specialist, Cornell University; Updated information was from Dr. Norman Scott, Professor, Cornell University. 4 Matlink recently changed the ownership and is now called Ridgeline Farm.
11
using food waste as part of the feedstock to the digester. Successful results have been reported with increased biogas production and better gas quality (i.e. higher methane content and less hydrogen sulfide). However, systematic research is still needed in order to learn more about selecting the best food waste and the optimum mixture percentage with dairy manure.
Figure 1. Locations of anaerobic digesters funded by NYSERDA
Therefore, even though more and more people are fascinated by the promising future of on-farm co-digestion, there are many obstacles to be removed. There is still confusion about food waste in particular. Questions frequently asked include:
What is food waste?
12
What are the categories of food waste?
How much food waste is available for use as potential AD feedstock?
How can food waste production be estimated given limited information?
What are economics of food waste? Because on-farm AD is still a new application even though AD as a technology
has existed for a long time, many people especially CAFO owners are concerned about the question – “What are the costs and benefits of these co-digestion systems?” In addition, sources of organic waste are highly site-specific and only concentrated waste is potentially collectible and available for AD. The financial viability of AD is dependent on sufficient waste volumes in close proximity. It is important to locate an AD system to minimize the traffic impact and environmental impact. Ideal site placement requires proximity to waste sources, customers and the electricity grid. Depending on their roles, people may have different needs for information and help. On one hand, the policy makers from state and local governments are most interested in the big picture such as statewide distribution of organic waste resources, concentration, spatial pattern, etc. On the other hand, CAFO owners and businesses/contractors that specialize in WTE are most interested in detailed information that will help in planning or designing AD projects. Thus, there is strong interest in questions such as:
Where are generating sources of animal manure located?
Where are generating sources of food waste located?
How many and what food processors are located within, for example, 20 or 50 mile radius of my farm or digester site?
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To address these questions, a conventional analysis using texts and tables is no longer enough to describe the spatial aspect of information related to waste-to-energy. In addition, considering the large number of dairy farms in NYS, there are potentially many people interested in data and tools that can help better understand and explore the organic waste resources. Thus, a web-based spatial decision support system (SDSS) to identify organic waste as renewable energy resources will be an ideal tool to advance opportunities for co-digestion.
3 3.1
LITERATURE REVIEW
Work to Date and Limitations 3.1.1
Identify, Quantify and Locate Organic Waste Resources
There are 7,388 dairy farms and 670,003 milk cows in New York State (USDA, National Agricultural Statistics Service, 2002). These dairy cows produce up to 28 billion pounds of manure per year, which implies a considerable amount of potential energy. Research at Cornell University found that the energy potential would be 15,023 million ft3/year of biogas if every bit of dairy manure is collected and processed in a typical AD system. The biogas then could generate enough electricity to meet farm operation needs and the excess could supply about 46,508 households assuming an internal combustion engine-generator set of 20% efficiency is used (Ma, 2003). However, this is only a congregated assessment of energy potential from dairy manure in New York State. It is important to know how much waste is available. And it is also equally important to know where these waste resources are available because the financial and technical feasibility of an AD project depends on sufficient waste volumes in close proximity. To better understand and explore the energy potential of organic waste resources, ideally each of these farms should be identified not only by its name and herd size but also its geographical location (coordinates). However, this is a difficult task because of the vast number of dairy farms. Meanwhile, it is unnecessary to identify all of the dairy farms in the state. Previous study has shown that on-farm AD systems might be economically viable for only those farms with at least 400 milking
14
15
cows (Jewell et al., 1997). Thus, concentrated animal feeding operations (CAFOs) 5 seem to be the best candidates for installing AD systems. There are 616 dairy CAFOs which have about 250,000 milk cows, accounting for 36% of total population in the state (Ma, 2003). There are many ways to quantify dairy manure production. EPA estimates that a typical 1,400-lb dairy cow produces about 51 kg (112 lbs) of manure per day (U.S. EPA, 1999c). According to the standard developed by American Society of Agricultural & Biological Engineers (ASABE), the typical manure production is 55kg (120 lbs) per cow-day (ASABE, 2005). However, these theoretical values can be noticeably different from the real data on dairy farms in different regions because of diet, climate factors, etc. Unlike dairy manure, food waste is a more general term and its composition varies depending on the waste types. Also there are far more food waste generating sources than dairy farms. Therefore, it is important to define and identify those major generating sources that produce food waste in large quantity and in concentrated form. Connecticut Department of Environment Protection (CDEP, 2001) appointed a consulting firm to identify food waste generators statewide. The study identified such generators including: supermarkets, health care facilities (hospitals and nursing homes), colleges and universities, schools, correctional facilities, resort and conference facilities, and restaurants. A similar study was carried out by the same company for the Massachusetts Department of Environment Protection (MDEP, 2002). The purpose of these two studies was to facilitate food waste recycling.
5
A CAFO is defined as a lot or facility where a large number of animals (at least 200 mature milk cows for dairy CAFO) are raised in a confined area. For a more detailed definition about CAFO, check the appendix at the end of this document.
16
Because there are many different waste types and also a vast number of generating sources, it is difficult to quantify food waste production. There have been a number of studies to estimate production of food wastes at a nationwide or statewide scale (Kantor, et al., 1997; Drummond, 1998). However, there are very few studies that estimate waste production from a specific type of generating source. A study in Kansas showed that food residuals production is about 0.289 lbs/meal at elementary schools, 0.195 lbs/meal at middle schools and 0.179 lbs/meal at high schools (Block, 2000). In college it is estimated that the food residuals generation is about 0.25 lbs/student (Clark and Law, 2000). The studies by CDEP (2001) and MDEP (2002) provide a set of formulas to quantify food wastes generated by different sources such as hospitals, nursing homes, colleges and universities, correctional facilities, resorts and conference properties, and restaurants. However, previous studies have failed to provide means to estimate the waste production from food processors. In fact, food processing wastes are considered the best feedstock for co-digestion because of large quantity and also good quality (i.e. uniformed composition and less contamination by non-organic matters). Compared to food waste generated by other institutions, waste from food processing facilities has more homogenous composition and less contamination by non-biodegradable materials such as plastics and metal. 3.1.2
Economic Analysis of On-Farm Anaerobic Digester Systems
AD as a technology is not new and actually has existed for thousands of years, especially in China and India. However, the application of AD on an industrial scale started only a few decades ago. The first on-farm AD system in the Northeast region was installed in the early 1980s. Even though the growth of on-farm AD projects has
17
accelerated at a fast pace in the past few years, the actual number of operating systems is still small nationwide. Therefore, there has been limited research studies of the economics of on-farm AD systems. Lusk (1996, 1998) compiled a list of all existing on-farm AD applications in the U.S. This report documented information such as digester designer, biogas production and usage, by-products, and economic benefits (e.g. savings on electricity and heating). These studies are limited because they are out of date and did not provide details about the economics of those AD projects. Furthermore, the costs and benefits of a potential AD project vary in different states because of difference in climate, financial incentive policies, etc. For example, heating might be needed to warm the digester in order to provide proper temperature for bacteria on farms in the Northeast region during winter, while not as much heating is necessary in states like California. This factor implies different savings on heating fuel. Therefore, previous studies focusing on dairy farms in New York State are particularly valuable. Jewell et al. (1997) analyzed the costs and benefits for groups of dairy farms in Upstate New York that were interested in AD. One of the findings is that economic scale is a deciding factor. For on-farm AD systems to be economically viable, Jewell et al. (1997) suggested that herd size should be larger than 400. The economics of on-farm AD systems also depend on the specific technology or equipment used. Most existing operations use internal combustion engine-generator sets to convert biogas to electricity and heat. The engine efficiency is normally around 20% for production of electricity. However, there are growing interests in new technology such as the microturbines and fuel cells. A research project at Cornell University found that using a fuel cell on-farm would be economically infeasible because of the high capital cost (Minott and Scott, 2001).
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A major limitation of previous research mentioned above is that on-farm AD systems using dairy manure only as feedstock were analyzed and co-digestion cases were not included. One reason is a lack of co-digestion applications in operation. Presently there is only one dairy farm in New York State (Matlink, new Ridgeline Farm) that regularly adds food waste to an AD for co-digestion. Wright and Inglis (2003) studied this farm and found that collecting and adding food waste to the digester caused additional investment such as permit registration fee, and a mixing tank with agitator for food waste and manure. However, this practice also brought in extra income from food waste tipping fees of about $200,000 per year for the farm. The biogas produced has ranged from 50% methane to as much as 71% methane (compared to 60% for most dairy-manure-only AD) as varying amounts of food waste to manure are introduced to the digester. In another recent study, five operating onfarm AD systems in New York are compared in terms of mass flow, nutrient flow, pathogen reduction, energy production and use, and economics (Wright, et al., 2004). It was found that there are significant differences from farm to farm in the cost of manure systems. The addition of food waste dramatically increased the amount of biogas produced and thus generated more combined electricity and heat (CHP). Thus, food waste tends to provide more profit to the system. However, these two studies did not provide an economic model to estimate the costs and benefits of future potential on-farm co-digestion systems. 3.1.3
GIS and Web-based Spatial Decision Support Systems
A Geographic Information System (GIS) is a computer system capable of assembling, storing, analyzing, and displaying geographically referenced information. Biomass resources such as organic waste are highly site-specific and the financial
19
feasibility of potential on-farm AD systems depends on resources within reasonable proximity. Therefore, spatial analysis is very important. GIS is an ideal tool to map natural resources (Morain, 1999). For this reason, GIS has been used extensively in many bio-energy studies (Liu et al., 1992; Rozakis et al., 2001; Ma et al., 2004). The studies by CDEP (2001) and MDEP (2002) also used GIS as a tool to map food waste generation in Connecticut and Massachusetts in order to facilitate development of composting or organics diversion infrastructure on a statewide or local basis. The GIS tool allowed the two states to map food waste generators by category, size, waste types, waste quantities, and other variables. In a more recent study (Ma, et al., 2004), a GIS model was developed for land-suitability assessment of potential energy systems featuring an AD coupled with an engine-generator set. A variety of environmental and social constraints, as well as economic factors, are integrated in the model to help determine the optimal sites for installing such systems. The model was then applied to Tompkins County, New York as a case study for demonstration. A siting suitability map was produced to identify those areas that are most suitable for distributed bio-energy systems using dairy manure. However, GIS is a complex tool and its database and powerful functions are normally used by well-trained people, which actually exclude those potential users who would otherwise benefit most from it. In addition, GIS is more than just a tool that is used to handle geographic data in digital form, display or create maps. In fact, GIS can be integrated with modeling, statistics, and analysis tools to carry out sophisticated tasks. Thus, it is a natural development to incorporate GIS with decision support systems (DSS), which is commonly known as spatial decision support systems (SDSS).
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DSS are special purpose tools that originated in the 1960s in primarily operation research and management science to address business problems. DSS is an extremely broad concept and its definitions vary depending upon the author's point of view (Druzdzel and Flynn 1999). Gorry and Morton (1971) define a DSS as being an "interactive computer-based systems that helps decision-makers utilize data and models to solve unstructured problems." Finlay (1994) and others define a DSS broadly as "a computer-based system that aids the process of decision making." No matter what the definition is, the basic idea of DSS is to provide a computer-based framework that integrates database management systems with analytical models and graphics to improve the decision-making process. SDSS are a class of computer systems in which the technologies of both GIS and DSS are applied to aid decision makers with problems that have a spatial dimension (Walsh, 1992). A common motivation for making SDSS accessible online is to support group decision-making (Kingston et al., 2000; Zhu et al., 2001). SDSS is mostly built upon GIS coupled with modeling. There are several strategies and approaches for the coupling of environmental models with a GIS (Nyerges, 1993; Fedra, 1996), which can range from loose to tight coupling. A loose coupling is just the transfer of data between models and GIS, and it is based on two separate systems and generally separate data management. A tight coupling is one with integrated data management, in which GIS and models share the same database. The tightest of couplings is an embedded or integrated system, in which modeling and data are embedded in a single manipulation framework (Crosbie, 1996; Fedra, 1996; Fedra and Kubat, 1993; Djokic and Maidment, 1993). Noon and Daly (1996) have proposed a GIS-based Biomass Resource Assessment, Version One (BRAVO), which is described as a DSS to assist the
21
Tennessee Valley Authority in estimating the costs for supplying wood fuel to any one of its 12 coal-fired power plants. In BRAVO, the GIS platform allows the efficient analysis of transportation networks so that accurate estimates of hauling distances and costs can be determined. Another GIS-based DSS was developed to calculate marginal cost of delivering wood chips to a specific location given road network maps and maps of farm-gate prices and supplies of wood chips from short rotation crops in Tennessee (Graham et al., 1997). SDSS are powerful tools. However, one of the issues is how to make the product easy to use and access. Since the emergence of the World-Wide Web in the mid-1990s, SDSS research has found a direction. The Internet extends the capabilities of SDSS to a large number of geographically dispersed users at a relatively low cost. Some of the most popular online geo-spatial applications, such as driving directions (e.g. Yahoo Maps and MapQuest), combine features of Internet mapping and decision support. Therefore, research into Web-based SDSS (WebSDSS) seems a natural consequence. Rinner and Jankowski (2002) described technical foundations and applications of WebSDSS. Sugumaran et al. (2004) developed a web-based DSS that prioritizes local watersheds in terms of environmental sensitivity. Choi et al. (2002) developed a web-based SDSS to assist with watershed hydrologic and water quality assessment for present and future land uses. Overall, there have been very limited SDSS applications and even less WebSDSS. There is a great need to be researched about how to integrate GIS, the Internet, modeling and databases to create a WebSDSS. This is especially true considering that new GIS software and other information technologies are advancing rapidly.
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3.2
Summary The literature review has found very few studies that have been conducted to
identify, quantify and locate organic waste as renewable energy resources. Food waste is a very complicated group of organic waste with many categories and numerous generating sources. This makes it particularly difficult to quantify food waste production. While formulas are available to estimate certain types of food waste, there has been no attempt to assess the production of food processing wastes which are considered the best candidates as feedstock for co-digestion because of its large quantity and good quality. The previous research on economic analysis of on-farm co-digestion is also limited. One of the major shortcomings is that they failed to provide an economic model that can simulate different scenarios of on-farm co-digestion application. There are few GIS applications related to biomass and bio-energy. However, these studies focus primarily on forest residues, wood residues, and energy crops. Food waste resources have been identified and mapped for the purpose of recycling. However, there is a lack of investigation into utilizing organic waste as a renewable energy resource. Lastly, a web-based SDSS is a useful solution to resource exploration and it has multiple benefits or capabilities such as spatial analysis, modeling, decision support, friendly user interface, and easy access. However, there have been very limited applications and it is still a relatively new research field -- integrating GIS, Internet, databases and modeling to create a WebSDSS.
4
RESEARCH OBJECTIVES
The primary goal of this study is to develop a web-based SDSS for utilizing organic wastes (dairy manure and food waste) as renewable energy resources for onfarm co-digestion in New York State. The specific objectives are defined as:
Create a geo-spatial database to identify and locate concentrated organic waste resources in New York.
Design a tool to help quantify the production of food waste (especially food processing waste).
Develop a model to simulate the costs and benefits of on-farm co-digestion systems.
Develop a system that integrates GIS, Internet, database and tools that can support users in making decision related to on-farm co-digestion application.
This study will increase awareness of various benefits of using animal manure and food wastes as AD feedstock to produce renewable energy. The results will be useful to policy makers, the public, energy investors, CAFO owners, food waste generators, and businesses that are specialized in AD applications. The web-based SDSS will be user-friendly and accessible to anyone through the Internet. The system will allow waste planners, haulers, entrepreneurs, and others to obtain many combinations of information about commercially generated organic wastes in New York. This will facilitate decisions about how to best target wastes for collection, which generators to target, how to structure collection routes and infrastructure, and where to site AD systems.
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5 5.1
DATA
Data Identification There are two types of data used in this study: non-spatial data and spatial data.
The term “spatial data” hereafter refers to GIS data or geo-referenced data, i.e. the attributes of all information are defined at unique locations in space. The identification of data is dependent on the objectives of this study. Nonspatial data are needed to address the objective regarding economic analysis of onfarm co-digestion systems. A set of variables concerning costs and benefits involved in AD system needs to be identified. The relationships among these variables should then be defined in order to simulate different scenarios of on-farm co-digestion application. Accordingly, case/field studies of operating on-farm AD systems in the Northeast region were conducted in 2002 and 2003. A survey (see Appendix C) was designed and mailed to the operators of these farms to fill out. The survey was intended to collect as much information as possible about the AD system from both technical and economic perspective with a focus on various costs and benefits. The specific data of interest are identified as: (1) capital costs (initial investment, life time and salvage values of various equipments); (2) operating costs (repair, maintenance, labor, insurance); and (3) benefits (electricity saving/sales, heating saving, compost sale, food waste tipping fees, etc.) In addition to these case studies, data collected by other researchers at Cornell University are also used in this study. As for spatial data, the highest priority is to locate dairy manure. CAFOs are considered the ideal candidates for on-farm AD implementation for several reasons:
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1. CAFOs have large number of animal units which means considerable amount of manure production. 2. CAFOs are generally operated in a confined environment which means the majority of the manure produced is collectible for AD. 3. CAFOs possess important resources such as land space, manure storage lagoons, and other equipment. 4. CAFO’s personnel have more experience in handling waste and thus necessary technical skills for AD operation and maintenance. 5. CAFOs are usually located far from high-density residential areas or urban areas, which may help minimize the odor issue and solve NIMBY (not-in-myback-yard) dilemma. 6. CAFOs are highly motivated to install AD on their sites because of increasing pressure from environmental regulations and foreseeable economic benefit. Therefore, CAFOs are expected to play a major role in developing on-farm AD and it is a priority to identify them. Even though there are 7,388 dairy farms in New York, only CAFO dairy data are collected for this study. Food waste is a generalized term and can refer to many categories of food residues. The financial and technical feasibility of an AD project depends on sufficient waste volumes in close proximity. As a result, any small food waste generators are omitted and only those generators that produce concentrated and large amounts of food waste are identified. Thus, it is important to narrow down the list of potential food waste generating sources. Based on literature review and data availability, nine major food wastes generators in New York are identified. •
Food processing facilities / plants
•
Supermarkets (large food retailer stores)
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•
Correctional facilities
•
Fast food franchises
•
Universities and colleges
•
K-12 public schools
•
Hospitals
•
Nursing homes
•
Restaurants Animal manure and food waste are major sources of air and water pollution if
not handled appropriately. This is verified by a recent manure spill accident in New York State. Thus, environmental constraints need to be considered when siting locations for potential ADs in order to minimize environmental impacts. Such data should include streams, rivers, canals, aquifers, etc. In addition, on-farm AD systems are essentially small-scale distributed generation power plants which will become part of the existing electric power system. They are preferably sited close to transmissions lines in order to minimize the interconnection costs and electricity loss. It is also possible for the on-farm ADs to deliver treated biogas directly into natural gas pipelines. Furthermore, AD contractors might be interested in identifying the utility service areas to evaluate the benefits from net-metering implementation. Thus, it will be useful to identify the data concerning utility infrastructure. Finally, there are other supplemental data need to be identified, such as political boundaries, agricultural census information in each county, and the transportation system.
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5.2
Data Collection 5.2.1
Non-Spatial Data
The collection efforts for non-spatial data are focused on studying the performance of on-farm AD systems from both technical and economic perspectives. There are only a few operating systems in New York State, even in entire Northeast region. Thus, it is practical to collect data from these operating systems through field studies. In the summer of 2003 and 2004, field studies were carried out to collect data from on-farm digesters in New York, Vermont, and Connecticut. A total of seven farms were studied (Table 3): four in New York, two in Connecticut, and one in Vermont. A survey (Appendix C) was developed to collect information about the farm, such as number of milking cows, manure production, digester type, digester dimensions, system designer, equipment, etc. Information about various costs and benefits (e.g. initial costs, repairs and maintenance, electricity and heating savings, byproduct sales, etc.) were determined The survey was given to AD system operators during the field studies. In some cases, the survey was sent to the contact person by email. Most returned surveys contained good quality data. Meanwhile, other members in the research group also have collected substantial amounts of data from field trips to farms. Most of these AD systems use biogas for combined heat and power (CHP) and only one of them uses biogas for only heating. An internal combustion enginegenerator set is the most common technology used to convert biogas to energy because this technology is more mature, reliable and economical compared to other options.
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Table 3. Operating on-farm AD systems in Northeast region Farm AA Dairy
Location
# of Cows
Cushman Dairy
Candor, NY North Franklin, CT
500 750
DDI
Homer, NY
850
Fosters Brothers Dairy
Middlebury, VT
370
Freund Dairy
East Cannan, CT
250
Matlink Dairy
Clymer, NY
740
Noblehurst Farms, Inc.
York, NY
1100
Biogas Conversion Technology 130kW Caterpillar modified diesel engine-generator set 40kW Ford 6003A with Kohler generator Four 28kW Capstone microturbines / boiler 6 85kW Caterpillar modified diesel engine-generator set Burnham boiler (400,000 Btu/hour) Waukesha engine attached to a 145kW Marathon generator Caterpillar 3406NA attached to a 130kW Marathon generator
Biogas Use CHP CHP CHP / heating CHP Heating CHP CHP
Among all existing AD projects in the Northeast region, data from AA Dairy and Matlink (Ridgeline Farm) are the most valuable to this specific study. The reason is that these two digester systems are located in New York State which is the study area. AA Dairy is located in Central New York while Matlink is located in Western New York. More importantly, these two AD systems have been operated for a long time and the performance has been very consistent. The AA Dairy digester has been in operation since 1998 and Matlink installed the digester in 2001. Figure 2 shows the monitoring data on biogas production at the AA Dairy. From this figure, it is seen that during a 3-year span, the biogas production per cow per day was consistent with minor fluctuations with an average value of approximately 2.4m3/cow/day (85ft3/cow/day). Figure 3 reveals that electricity generation ranged from about 60kW to 80kW with an average of approximately 70kW (Peranginangin and Scott, 2003).
6
The Capstone microturbines failed after running for a short period of time. Currently, a boiler is used to generate heat. However, there are still efforts to install new microturbines.
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Therefore, the data collected from these two farms are most representative and credible. For the AA Dairy, data have been collected almost daily concerning biogas production and usage, electricity generation, engine hours, etc. The data used in this study cover a time span from May 21, 2001 to July 7, 2005. For Matlink, the data used in this study cover a time span from August 22, 2003 to July 22, 2005. The economic model of on-farm AD systems should be heavily dependent on these data when formulating the financial variables. Accordingly, data collection efforts are focused on AA Dairy and Matlink. Table 4 lists the technical specifications and summary of biogas and electricity generation of these two AD systems. Table 4. Specification summary of AD systems on AA Dairy and Matlink Digester Installation Date Digester Cover Hydraulic Retention Time (days) Feedstock % of CO2 in Biogas (%) % of CH4 in Biogas (%) Biogas Use Biogas Production (ft3/day) Biogas Production (ft3/cow-day) Annual Engine Hours (hrs/year) % of Engine Running Hours (%) Annual Electricity Generation (kWh) 7
Matlink Dec. 2001 soft top 21 manure + food waste 30 70 CHP 241,530 8 326 10 8,280 11 94.5 1,078,133 13
AA Dairy June 1998 soft top 40 7 manure 38 62 CHP 42,555 9 85 5,570 12 63.6 247,161 14
The digester on AA Dairy was originally designed to handle manure from 1,000 cows and the HRT was supposed to be 20 days. However, currently AA Dairy has a herd size of 500. Thus, the HRT becomes 40 days. 8 This value is an average of daily biogas production from 8/22/2001 to 7/22/2005. 9 This value is an average of daily biogas production from 5/21/2001 to 7/7/2005 10 This value is calculated as the equivalent to biogas production per cow. 11 This value is the annual engine hours calculated from monitoring data of one year span from 6/28/2004 to 6/28/2005. 12 This value is the annual engine hours calculated from monitoring data of one year span from 6/20/2004 to 6/20/2005. However, the data from previous years showed that the actual engine hours were greater. 13 This value is the annual electricity generation for one year span from 6/28/2004 to 6/28/2005. 14 This value is the annual electricity generation for one year span from 6/20/2004 to 6/20/2005.
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Biogas production (cubic feet/day/cow)
Biogas/cow
Total biogas
100
100,000
80
80,000
60
60,000
40
40,000
20
20,000
8/24/01
5/26/01
2/25/01
11/27/00
8/29/00
5/31/00
3/2/00
12/3/99
9/4/99
6/6/99
3/8/99
9/9/98
12/8/98
0 6/11/98
0
Total biogas production (cubic feet/day)
120,000
120
Date
Figure 2. Total biogas production and biogas production per cow per day at AA Dairy (Peranginangin and Scott, 2003; Minott and Scott, 2003) 120
100
80
60
40
20
0 6/8/98
9/6/98
12/5/98
3/5/99
6/3/99
9/1/99 11/30/99 2/28/00 5/28/00 8/26/00 11/24/00 2/22/01 5/23/01 8/21/01
Figure 3. Engine generator performance at AA Dairy, 1998-2001 (Peranginangin and Scott, 2003)
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Based on Table 4, the engine at the AA Dairy is running 63.6% of the time while the engine at Matlink is running about 94.5% of the time. The comparison also shows that the co-digestion of dairy manure and food waste at Matlink produces much more biogas (almost 4 times) compared to manure-only AD on AA Dairy. This agrees with the claim that mixing food wastes with dairy manure in anaerobic digesters significantly increases biogas production. In fact, there is more biogas produced at Matlink for the engine of 145 kW to use. Nearly 67.9% of the biogas produced is actually flared while only 31.3% of biogas is fed to the engine. The remaining biogas is fed to the boiler to generate heat for farm use (Table 5). Table 5. Biogas usage on Matlink (8/22/2003 – 7/22/2005) Flare Use Fed to Boiler Fed to Engine Total
Biogas Usage (ft3/day) 165,030 1,988 76,000 243,018
% of Total Production 67.9 0.8 31.3 100
Since Matlink is the only farm that uses both dairy manure and food waste for co-digestion, and the system has been operated successfully for a few years, more details about this farm, especially costs and benefits, need to be studied (Table 6). Table 6. Summary of economics on Matlink Costs / Revenues Total Initial Investment Total Annual Initial Investment Total Annual Operating Costs Total Annual Revenues Electricity Savings Electricity Sales Heating Savings Compost Sales Bedding Material Savings Food Waste Tipping Fee
$ 490,269 49,016 21,863 287,685 38,085 12,000 6,000 6,000 15,600 210,000
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5.2.2
Spatial Data
For any GIS related study, data collection is the most important task and usually takes a large amount of time. This is because the success of a GIS study is dependent on the quality and completeness of data used. This study is no exception. CAFO list was obtained from NYS Department of Environment Conservation (NYSDEC). The list is an Excel spreadsheet which provides information such as name, address, herd size, and owner of each CAFO as well as its location. The locations are in geographic coordinate format. Among the nine categories of food waste, food processing wastes are the best candidates for mixing with dairy manure for co-digestion based on various reasons: similar bio-physical characteristics (e.g. TS, VS and COD); large quantity; relatively good quality in terms of less contamination and uniformed composition. A list of 149 major food processing facilities in New York was extracted from a commercial database - Judge’s Peerless Food Processors North America 2003. These data are in tabular format and the information available includes name, address (street, city, and zip code), contact information (phone and fax numbers), and employee number of each food processor. To derive coordinates from physical address, a method called “geocoding” in GIS was used. Geocoding is the process of creating map features from addresses, place names, or similar information (Ormsby et al., 2001). In other words, geocoding converts textual descriptions of locations into geographic features. Essentially the same technology is used by MapQuestTM (http://www.mapquest.com/) or YahooMapTM (http://maps.yahoo.com/) in their dynamic location-mapping service. To quantify the food waste production, a survey (Appendix D) was developed and sent to each of the 149 food processing companies. Thirty two of them responded with information to various extents. It was found that the waste production varied
33
greatly even for the same category and size of food processors. In the case of the other eight major food waste generating sources, the data were obtained from different sources (Table 7). Table 7 Data sources and description of food wastes generators in New York Generator Category
Data Source
Data Description
Food processing Survey; facilities commercial database.
This dataset contains GIS coverage (points) representing facilities that are dedicated for food processing (e.g. dairy, vegetables, fruits, meat, etc.).
Hospitals
NYS Department of Health
This dataset contains information on all acute care facilities licensed by the NYS Health Department.
Nursing homes
NYS Department of Health
This dataset contains information on all nursing homes licensed by the NYS Health Department.
Colleges, universities
NYS Education Department
This dataset contains GIS coverage (points) representing colleges throughout NYS, including SUNY, CUNY, independent, military, nursing, and proprietary institutions.
K-12 public schools
NYS Education Department
This dataset contains GIS coverage (points) representing public schools throughout NYS.
Correctional facilities
NYS Office of Real Property Service
This dataset contains GIS coverage (points) representing facilities used by any governmental jurisdiction for housing within the criminal justice system.
Restaurants
NYS Office of Real Property Service NYS Office of Real Property Service
This dataset contains GIS coverage (points) representing facilities which serve full course meals with or without legal beverages. This dataset contains GIS coverage (points) representing facilities that usually belong to a chain and sell food and sundry items.
NYS Office of Real Property Service
This dataset contains GIS coverage (points) representing facilities that provide year-round, with counter service, limited menus and a drive-up window (e.g. McDonald's, Burger King, etc.).
Supermarkets (large retail food stores) Fast food franchises
Data on colleges/universities, K-12 public schools, hospitals and nursing homes were downloaded from New York State GIS Clearinghouse and the Cornell University Geospatial Information Repository (CUGIR). For food waste generators such as
34
supermarkets, correctional facilities, fast food franchises, and restaurants, they were extracted from a single large database maintained by NYS Office of Real Property Service (RPS). The points of these features were extracted in GIS environment using queries based on property class code which identifies the present use of the property. Besides these food waste data, supporting data sets (environment, utility, base map) are also collected from various sources (Table 8). These data include: Table 8 Environment, utility and other data Data Category
Data Source
Data Description
Agriculture census
U.S. Geological This data set contains the National Agricultural Survey Statistics Service, U.S. Department of Agriculture's 1997 Census data for the United States, presented by county. There are 25 categories of data which include information about farms, crops, livestock, values of products, and farm operator characteristics.
Active landfills
NYS Department of This data set contains landfills in New York State Environmental that are in active service. Conservation
Hydrography
U.S. Geological The data set contains the polygon and line water Survey features (streams and water bodies) of New York.
Transmission lines
NYS Department of This data set contains New York State Electric Public Service Transmission lines of 115 KV and above.
Natural gas pipelines
NYS Department of This data set contains the location, length, and Public Service owner of gas transmission lines across New York State
Electric company franchise areas
NYS Department of This data set contains electric company franchise Public Service areas for companies regulated by NYSDPS.
Natural gas company franchise areas
NYS Department of This data set contains Gas company franchise Public Service areas for companies regulated by the NYSDPS.
Principal aquifers
U.S. Geological This data set contains the shallowest principal Survey aquifers of New York State.
County/state boundary
U.S. Geological This data set contains the political boundaries of Survey counties in New York State.
35
Eventually, this study identified a total of 11,065 food waste generators in New York and the break down of categories is shown in Table 9. Table 9. Summary of food waste generators in New York # of % of Total Establishments Generator Category Establishments Food processors 149 1.3 Supermarkets 726 6.6 Fast Food Franchises 920 8.3 Correctional Facilities 226 2.0 Restaurants 3,605 32.6 Colleges / Universities 338 3.1 K-12 Public Schools 4,166 37.7 Hospitals 260 2.3 Nursing Homes 675 6.1 TOTAL 11,065 100.0
5.3
Data Processing The spatial data sets vary in terms of formats such as coordinate system,
projection, datum, etc. To address this issue, all of these data layers were manipulated and converted into a unified format, i.e. Universal Transverse Mercator (UTM) (zone 18) and North America Datum 1927 (NAD27). The software used in this process was ESRI® ArcGISTM(8.3), which is the most widely used GIS software. Some of the data sets that extend beyond the New York State boundary were clipped to match other data sets. As the result, a spatial database was created. To design the web-based GIS application, the GIS software called “Manifold” was used. The data layers created from ArcGIS were then imported into Manifold and virtually no changes were made during the conversion process. In the Manifold environment, the data layers were rearranged and reassigned with new symbols, color, visible scale, etc.
6 6.1
METHODS
Framework For designing web-based GIS applications, it is always based on the same
model called client/server. The clients are those who connect with the Web and are the end-users of the data. The servers are storage unit of information and also process the requests from the clients and return the corresponding information to them. Client-side and server-side applications are two general solutions to providing geospatial data to end-users without requiring them to have complicated or expensive GIS software on their own machines. In client-side processing, the client’s Web browser is enhanced to support GIS functionality (by means of Java applets, plug-ins, applications, etc.), which require time for downloading and installation. In server-side applications (using an ASP), the client’s Web browser is only used to generate server requests and display the results while the central server does the processing. The distinction between client- and server-side processing is important in the distribution of geospatial information because of the size of the database involved. Because the SDSS to be designed in the present study is oriented towards the general public, a server-side design was chosen, i.e. minimize the client’s processing and maximize the server load and accessibility. This approach facilitates maximum usability, or in other words, the system is technically an “easy access system.” This way, the system would not require the client to download any plug-ins or Java applets. The client will be freed from such responsibilities while using the SDSS. The coupling of models with a GIS is the major issue in a SDSS. There are several strategies and approaches for the coupling of environmental models with a
36
37
GIS, which can range from loose to tight coupling. In this study, the economic model and GIS are two separate subsystems. This is because that the spatial database is supported by many queries which provide information and assistance for potential decision making. A tight coupling with economic model would make the entire SDSS too complicated. Lastly, the success of a SDSS largely depends on the effectiveness of its user interface. A well-designed graphics user interface can free the user from learning complex commands and makes the program easy to use. Based on the discussion above, a general framework was proposed to guide the design of web-based SDSS in this study and it is depicted in Figure 4. Client
User Interface Web Browser Textual & Graphic Interface
Query
GIS
Image/Map Input
Output
WWW
Data Hyperlinks
Model Tool
GIS Software
GIS Database
Server
Figure 4. Framework of web-based SDSS
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6.2
System Design Procedures The most important and first step of designing a SDSS is to identify who will
be using the SDSS because this will determine the purpose of the system in terms of the decision being made and the outputs it must supply. Only after identifying the audiences or end-users of the product to be designed, can the design proceed to the next steps. Razavi (1995) has proposed structured methodology for developing GIS applications, which comprises the following stages: o Requirement analysis o Prototyping o Construction o Structural testing The four stages constitute the foundation of an effective GIS application development in this research. The purpose of “requirement analysis” is to acquire a complete understanding of the problem and to specify the requirements of the software to be used to resolve the problem. “Prototyping” begins with the customization of the interface controls and project components and further defines the data flow. In the “construction” phase, programming is required for the application to be developed. The purpose of “structural testing” is to identify application software defects (bugs) and to resolve as many of these as possible. 6.3
System Components / Modules By integrating components such as databases, GIS, the Internet and modeling,
this web-based spatial decision support system (SDSS) is designed to provide data, information and tools to help users in their decision-making. In general, the system components are determined by the research objectives. Accordingly, three modules are
39
proposed: (1) A Web-based GIS system that provides graphical display (dynamic mapping) and tabular reports (querying); (2) A Web-based tool that enables users to estimate the waste production from a variety of food waste generating sources; and (3) A Web-based economic model that can simulate the costs and benefits from AD codigestion systems based on inputs from users. Even though much effort has been made to identify and collect as much data as possible, there is always a need to update the databases by either adding new data or modifying existing data. Thus, it is an important issue to design an effective mechanism to maintain and update the database. Because the system is intended for use by people who may be unfamiliar with the technology involved in this study such as GIS, important considerations must be made in interface design. It is important that the interface be designed in an intuitive and user-friendly fashion, and that the functionality does not exceed what users can comprehend (Herold, et al., 2005). Therefore, Graphic User Interface (GUI) is another significant component of SDSS and its primary goal is to provide a simple, welldocumented and easy-to-use interface. The user interface should support decision makers through all decision-making phases, and is the key to the successful use of any SDSS. It enables a dynamically interactive session in a real-time exchange of information between the user and the system (Malczewski, 1999). Philosophies and guidelines for designing a good GUI for SDSS have been discussed in many studies (Armstrong and Densham, 1990; Heywood et al., 1995; Jankowski, 1995; and Carver et al., 1996). Lastly, a web site needs to be created as a platform to host the SDSS and also provide related information and serve as part of the GUI.
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6.4
The Software ESRI’s ArcGIS is the most widely used GIS software and was employed to
create and process the spatial data used in this study. However, to design a web-based GIS application, it is not an easy decision to choose the right software simply because there are few existing web-based GIS applications for reference. There are a variety of GIS products available on the market today. For this study it was a crucial requirement that the GIS software be able to provide web-based mapping service. A quick search ended up with a list of potential candidates including: ArcIMS (ESRI), Manifold, GenaServer (GenaWarehouse), AltaMap Server (GeoMicro), GeoMedia Web Map and GeoMedia WebEnterprise (Intergraph), MapXtreme NT and MapXtreme Java (MapInfo) and MetaMAP Internet Geospatial Database and Map Server (MetaMAP). After consulting with GIS experts and also comparing the functions, reliability and cost, the list was narrowed down to top two contenders, i.e. ESRI’s ArcIMS and Manifold. Manifold was eventually selected for developing the web-based GIS even though it is a relatively new GIS software package. ArcIMS was not chosen because of its high cost ($7,500). In comparison, the entire Manifold package is only $245. Also ArcIMS has a reputation for being unstable in providing the IMS services. Based upon the Microsoft Windows operating system, Manifold is virtually a "word processor for maps" in which maps can be edited in a similar fashion as Microsoft Word. Manifold includes a large number of capabilities to work with different types of data at the same time. Particularly, Manifold includes a built-in, enterprise-class, powerful Internet map server (IMS). The map server allows browsing, panning, and zooming within maps that users choose to publish as well as support for queries, geocoding, hyperlinks, information tools and layer selection if
41
desired. Advanced users can customize the map server to create spectacular Internet pages. In addition, Manifold IMS works with any Windows HTTP server and provides pre-built templates for use in ASP and ASP.NET Windows IIS environments. It works with standard web browsers and requires no plug-ins or expensive middleware. It even works perfectly with Microsoft's ASP .NET environment and with the latest generation of Microsoft .NET servers. Thus, eventually Manifold was chosen as the software to develop the IMS applications due to the following features: o Ease of use o Relative low cost o Ease of administration o Expanded spatial SQL operators which allows for more flexible database queries o Web server built into GIS software Part of the system design in this study involves web development. The software selected is VB.NET, which is Microsoft’s latest Web development platform. VB.NET is now fully part of Visual Studio, sharing the development environment with Microsoft Visual C++.NET, Microsoft Visual C#.NET, and several other programming tools. VB.Net is an ideal tool for developing Web Forms which is a programming model for Internet users’ interfaces based on ASP.NET. Web Forms applications are designed to be displayed by Web browsers. The controls on Web Forms are visible in the client’s Web browser (i.e. on the end-user’s computer), but the functionality for the controls resides on the Web server that hosts the actual Web application. VB.Net has become a great choice for programming at all levels.
7 7.1
RESULTS AND DISCUSSION
Geo-Spatial Database The foundation of developing a web-based SDSS is to build a geo-spatial
database first. After identification, collection and processing, the data were assembled as a database in ArcGIS. This database can produce a set of maps to help visualize and evaluate the geographic distribution of dairy manure and food waste resources in New York State. Different information layers can also be combined and superimposed, or overlaid, to help discover their spatial relationships. This database has been used to provide mapping products for New York State Energy Research and Development Authority and other interested parties. An example is shown in Figure 5.
Figure 5. Distribution of CAFOs and food processors in New York State
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Individual CAFO owners and companies that specialize in AD design and planning are interested in more detailed information about waste resources. Upon request, mapping products and tabular information were also created from this geospatial database. For example, Figure 6 shows the food processors that are located around a specific farm that made the request for information. In addition, a table corresponding to this map was provided to list the food waste sources, waste type, volume, distances, and contact information.
Figure 6. Distribution of food processors within 20- and 50-mile radius of X Farm
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7.2
The Web Platform A web site (URL: http://wastetoenergy.bee.cornell.edu) was developed in order
to provide a platform to present the SDSS. Figure 7 shows the screen shot of the homepage where the SDSS is made accessible by clicking on an image. The “Overview” section gives an introduction to this research (background, research objectives and data used) and then explains why this web site was created. The “Decision Support” section describes each of the three modules in the SDSS. Under the “Background Info” menu, the keywords related to this study are described, including: biomass and bio-energy, organic waste, waste-to-energy, AD, and GIS. Links are also provided to enable users to further explore these subjects. The remaining menus provide information about the research team, acknowledgement of assistance received during research, and updates about the web site. In particular, a menu – “User’s Guide” - was designed. The success of a decision support system largely depends on the effectiveness of its user interface, which in turn is partially determined by the effectiveness of the user's guide. Thus, the “User’s Guide” is a very important part of this web site. In this section, helpful information and examples are provided for different subjects (e.g. mapping, querying, general questions). The user's guide is primarily focused on Module 1 because most users are unfamiliar with GIS and mapping. The queries also need to be explained because they are created based on the designer’s expert knowledge. In addition, the querying results are dynamically linked with mapping (i.e. the map will update after running a specific query). Therefore, it is important to provide sufficient assistance to help users run the queries successfully and then interpret the results correctly.
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A complete User's Guide which compiles help information and examples for all subjects can be viewed or downloaded as a PDF document. A list of frequently asked questions (FAQs) is also available from the menu under “User’s Guide.”
Figure 7. Screen shot of the home page of the web platform
7.3
Modules of the Spatial Decision Support System Based on the research objectives, three modules were designed in this system:
(a) Module 1: Mapping & Querying; (b) Module 2: Food Waste Production Estimator; and (c) Module 3: Co-digestion Economic Analysis. These modules are designed in sequence in order to address questions (evaluation, planning, and economic
46
assessment) that might be raised in a potential project. Therefore, they are closely related and supplement each other within the system. 7.3.1
Module 1: Mapping and Querying
This module is designed to help users dynamically explore the map by displaying, zooming in/out to any extent, and selecting any combination of information layers. Users can also create and print out customized maps. In addition, users can retrieve various data through queries that might be helpful in their decision making. A screen shot of this module can be seen in Figure 8.
Figure 8. Screen shot of SDSS Module 1: Mapping & Querying
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On the screen, there are primarily six sections. On the upper left is a tool bar (Figure 9) which can be used to manipulate the mapping service. The tool set includes: "Zoom to Center", "Zoom In", "Zoom Out", "Zoom Box", "Zoom to Fit", "Find Info", “Track Line”, “Tracker Area”, "Print", and "Help Info".
Figure 9. Tool bar Brief descriptions of these tools are given below. For more details and how to use these tools, refer to Appendix B. Zoom In - Magnify the view as if seen from a closer distance. Zoom Out - Reduce the view as if seen from farther away. Zoom to Fit - Zoom so that the component fits within the current window. Zoom to Center - Pan the view so that the spot clicked is centered. Zoom Box - Zoom to the size of the cursor box drawn with the mouse. Info Tool - - Show data fields for object. Track Line - - Measure distance between two or more points. Track Area - - Measure the area of a polygon. Print - - Customize map and print the map layout. Help Info & Examples - - Description and examples about specific tasks. Update - - Dynamically update the food processor database.
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Under the tool bar is the map which will update whenever users employ a tool or run a query. The map dimension is 480x360 pixels. Further under the map is an area where the querying results will be displayed as a table. In the middle is a layer pane where users can select or deselect any information layers depending on their specific interest. Each layer represents one type of geographic feature (data) which might be either points, lines, or areas. Be sure to press the "Apply" button after checking on/off layers in order to make the changes effective. Right next to the layer pane is a legend which uses symbols and colors to distinguish different layers seen in a map or print layout. To increase the speed of interactive mapping service and also to better organize the layers, a customized legend (an image) is created and used to replace the original "live" legend which will redraw every time it is updated. The image is in PNG (Portable Network Graphics) format. The rest of the screen is filled with various queries which are designed based on the developer's expert knowledge with the anticipation that these queries might be helpful in the user's decision-making. The queries were written by using Structured Query Language (SQL). Four of the queries might be useful to some users like governmental officials who are most interested in the big picture such as statewide distribution of organic waste resources, concentration, spatial pattern, etc. The other queries might be useful to individual owners of CAFOs who are most interested in detailed data/information when evaluating, planning or designing on-farm anaerobic digester systems. Potential users may also include those companies that specialize in AD design and planning business.
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Table 10. Queries in Module 1 Title
Description This query provides a table that shows how those CAFOs are distributed among counties in NYS. This query helps find CAFOs in each county. The results show CAFOs’ names, number of cows, address, city and county located. This query helps find a list of CAFOs that have, at least, a certain number of milking cows that you specify. The result will show: CAFO name, number of cows, city and county, and other info. This query helps find those active landfills in each county. The querying results will show: name, address, city and county where they are located, zip and region. This query helps identify any CAFO on the map.
This query helps identify and locate all CAFOs that located within a specified distance from any point you define.
This query helps identify and locate all food processors that are located within a specified distance from any point you define.
This query helps find all food processors around a specified CAFO within a given distance. This query helps find a list of food processors producing wastes that you specified. There are totally seven types of food processing waste. This query helps find all food processors around a specified CAFO within a given distance. In addition to generating a map and table, a circle will be actually drawn on the map. This circle clearly defines the boundary of the area interested. This query helps find the hydrographical features (such as streams, shorelines, river banks, and canals) near a specified CAFO within given distance.
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Table 10 (Continued) There are eight queries like this one. Each of them is designed to help users find one type of food waste generators around a specified CAFO within a given distance. The querying result will show such information as: owners' names, address, city where they are located, and zip code.
This spatial decision support system is web-based and is intended to serve as many potential users as possible. It is assumed that most users have no background in geographic information systems (GIS) and Internet mapping. Therefore, the help information is very detailed and step-by-step instructions are available. More help information on mapping and querying can be found in Appendix B. The help information can be accessed not only from the web site menus but also from the module window. In particular, help information on queries is a click away throughout the SDSS window, since it is placed at the same location as the query. Just a simple click on
will bring up a window displaying the help
information and examples. This window is a "floating" one that can be left open or closed at user’s convenience. The "Helper Window" will pop-up whenever a help link -
is clicked but it is always updated in the same window. Given the collection of data (layers) and queries, this SDSS module offers
many possibilities for searching or creating user-specified information and mapping services. However, it would be difficult and unnecessary to list and explain all those possible application scenarios. Because the most frequently asked question regarding this SDSS is to how locate organic wastes around any CAFO, it is desirable to use such an example to illustrate the basic functions of this system. This example also shows how to save the tabular querying results and create and print a customized map.
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Suppose a CAFO owner wants to find out all food processors that are located within a specified distance from his farm (El-Vi Farms), s/he would want to use this query – “Create a circle around a CAFO and find FPs (food processors) within”. This query finds all food processors around a specified CAFO within a given distance. In addition to generating a map and table, a circle will actually be drawn on the map. This circle clearly defines the boundary of the area involved. The general procedures to carry out this task will be as following: Step 1: Check on or off the boxes in front of the layers so that "CAFO Dairy ", "CAFO Labels", "Food Processors ", "FP Labels", "Supermarkets", "Fast Food Franchises", and "County" are selected. The user can select more layers if he wants. However, the map will be cleaner and easier to read by checking off those unnecessary layers. Be sure to press "Apply" afterward to make the changes effective.
Step 2: Enter "El-Vi Farms" in the text box after "CAFO Name:" and then enter "25" in the text box after "Radius" (Figure 10). Be sure to spell the CAFO name correctly. There is a link - "CAFO List" - which has a complete list of all CAFO names.
Step 3: Press the "Query" button to run it. The map will be updated so that all food processors located within 25 miles of El-Vi Farms are shown at the center of the map. A table of these food processors will also be generated and displayed under the map.
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Figure 10. Query example screen shot #1
Figure 11. Query example screen shot #2
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Step 4: Right click anywhere in the area that displays the querying results and then select "Export to Microsoft Excel." Then an Excel window will pop up and the querying results will be retrieved and displayed as an Excel sheet (Figure 12, 13). This only applies to Windows XP users. If someone is using different operating system, they might just highlight the table and then copy-paste to Excel or Word.
Figure 12. Query example screen shot #3
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Figure 13. Query example screen shot #4
Step 5: Now press the button - "Apply" - on the layers pane. The map will be redrawn and go back to the original extent. Meanwhile, a blue circle will be shown on the map (Figure 14). The circle might be solid if there is more than one food processor that falls within the circle. Or, the circle might be semi-transparent if there is only one food processor selected.
Step 6: Use the "Zoom Box" tool -
- and draw a rectangle around the circle to
zoom in (Figure 15).
Step 7: The user can use the same tool to adjust the map extent or further zoom in to see more details.
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Figure 14. Query example screen shot #5
Figure 15. Query example screen shot #6
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Step 8: Click the printer button -
- on the tool bar to create and print out a
customized map. After the button is clicked, a new window will pop up and ask user to enter some inputs (Figure 16).
Figure 16. Query example screen shot #7
Step 9: Enter the map title and author information and select the components (legend, north arrow, and date/time) that the user wants to show on the final map (Figure 17).
Step 10: A customized map will be created (Figure 18). The user can always go back to make changes by clicking on the button - "Go Back".
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Figure 17. Query example screen shot #8
Figure 18. Query example screen shot #9
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Step 11: Now the user can print it out by clicking on the "Print" button on the upper right of the window (Figure 19). Note that this button will not be shown on the map printed out.
Figure 19. Query example screen shot #10
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7.3.2
Module 2: Food Waste Estimator
This module (Figure 20) is designed to estimate food waste production from a variety of generating sources including: food processing facilities / plants; supermarkets; fast food franchises; correctional facilities; restaurants; colleges / universities; K-12 public schools; hospitals; and nursing homes.
Figure 20. Screen shot of SDSS Module 2: Food Waste Production Estimator
The screen is divided into two sections. On the left is a web form where users can enter inputs. The Module 1 can be used to obtain some of the inputs for this web form. For example, the number of employee information about supermarkets can be found by using the “Query.” Or, users can find some contact information (e.g. address,
60
phone #, etc.) which may eventually lead them to find the data needed. After entering the data and then pressing the "Calculate" button, the results will be displayed on the same page. The literature review found that formulas for estimating waste production from different types of generating sources are available in previous research. However, there has been no attempt to develop formulas to quantify the food processing waste which is considered the best candidate for co-digestion. To address this issue, a survey was developed and sent to each of the 149 food processing companies that are covered in the database for New York State. A total of thirty-two of surveys were returned. The results showed that the waste production varies significantly even for the same category and size of food processors. A regression analysis seems to be unreasonable. Therefore, a simple method to estimate food waste production was developed. The thirty-two food processors were grouped by waste types (i.e. bakery, dairy, fruit, meat, mixes, and vegetables). The average waste production and number of employee were calculated. Then, for each category of food processors, the average of waste production per employee was calculated dividing the waste production by the number of employee. Table 11 shows the results. Table 11. Statistics of food processor survey results
Waste Type Bakery Dairy Fruits Meat Mixes Vegetables
Average Waste Production (lbs/year) 146,537 1,294,157 975,418 1,280 163,398 143,111
Average Number of Employees 145 166 170 62 375 275
Average Waste Production per Employee (lbs/year) 1,011 7,800 5,738 21 436 520
These results combined with formulas obtained from the literature (CDEP, 2001; MDEP, 2002) were used to estimate food waste production from a variety of
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generating sources (Table 12). A web form was developed to provide a convenient and user-friendly interface. Table 12. Formulas used to estimate various food waste production types Food Processors Bakery Food wastes (lbs/yr) = N of employees * 1,011 lbs/employee/yr
Dairy Food wastes (lbs/yr) = N of employees * 7,800 lbs/employee/yr
Fruit Food wastes (lbs/yr) = N of employees * 5,738 lbs/employee/yr
Meat Food wastes (lbs/yr) = N of employees * 21 lbs/employee/yr
Mixes Food wastes (lbs/yr) = N of employees * 436 lbs/employee/yr Vegetables Food wastes (lbs/yr) = N of employees * 520 lbs/employee/yr
Hospitals Food wastes (lbs/yr) = N of beds * 5.7 meals/bed/day * 0.6 lbs food wastes/meal * 365 days/yr
Nursing Homes and Similar Facilities Food wastes (lbs/yr = N of beds *3.0 meals/bed/day * 0.6 lbs food wastes/meal * 365 days/yr
Colleges, Universities, and Independent Preparatory Schools Residential Institutions Food wastes (lbs/yr) = 0.35 lbs/meal * N of students * 405 meals/student/yr Non-Residential Institutions (e.g., community colleges) Food wastes (lbs/yr) = 0.35 lbs/meal * N of students * 108 meals/student/yr
K-12 Public Schools Elementary Schools Food wastes (lbs/yr) = 0.289 lbs/meal * N of students * 150 meals/student/yr Middle Schools Food wastes (lbs/yr) = 0.195 lbs/meal * N of students * 150 meals/student/yr High Schools Food wastes (lbs/yr) = 0.179 lbs/meal * N of students * 150 meals/student/yr
Correctional Facilities Food wastes (lbs/yr) = l.0 lb/inmate/day * N of inmates * 365 days/yr
Supermarkets Food wastes (lbs/year) = N of employees * 3,000 lbs/employee/yr
Restaurants Food wastes (lbs/year) = N of employees * 3,000 lbs/employee/yr
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7.3.3
Module 3: Co-Digestion Economic Analysis
This module (Figure 21) is designed to provide a convenient way for potential users to evaluate the economics when considering installing an anaerobic digester on their farms, especially using dairy manure and food waste for co-digestion. The screen is divided into two sections. On the left is a web form where users can enter inputs. After inputting information and then pressing the "Calculate" button, the results will be displayed on the same page. On the right of the screen is a page which provides background information about why and how this module is designed.
Figure 21. Screen shot of SDSS Module 3: Economic Analysis of On-farm AD Systems
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With inputs from users, this tool can estimate manure production, food waste mixture, digester capacity, biogas production, and electricity generation. It also can be used to estimate various costs (e.g. capital costs and operating costs) and benefits (e.g. electricity savings/sales, compost sales, and food waste tipping fees). A set of variables concerning costs and benefits involved in AD system need to be identified. The relationships among these variables are then defined in order to simulate different scenarios of an on-farm co-digestion system. Figure 22 and 23 show the details regarding the actual layout as well as variables and questions that are used for carrying out the analysis.
Figure 22. Detailed view of Module 3 (Part I)
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Figure 23. Detailed view of Module 3 (Part II)
This economic model is based on field/case studies of farm-based AD systems in the Northeast region. Data collected from two typical on-farm AD projects (AA Dairy and Matlink) in New York State is particularly important because these systems have been operated for a long time with consistent performance. Thus, the data are representative and reasonable to be used in making assumptions and deriving equations in this cost-benefit model. Many variables are involved in this model and their relationships are defined in the following equations. AIj = (IIj - SVj)/2
[1]
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Where: AI – average investment II – initial investment SV – salvage value j – various equipment J from 1 to n
AICj = AIj * IRj
[2]
Where: AIC – annual interest charge IR – interest rate
Dj = (IIj – SVj)/ULj
[3]
Where: D – depreciation UL – useful life
ACCj = AICj + Dj
[4]
Where: ACC – annual capital cost
TII = Σ (IIj)
[5]
Where: TII – total initial investment
TACC = Σ (ACCj)
[6]
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Where: TACC – total annual capital cost
TARC = Σ (ARCj)
[7]
Where: TARC – total annual repair cost
TAOC = TARC + L + IS
[8]
Where: TAOC – total annual operating cost L – annual labor cost IS – insurance
L = LH * HR * 344 15
[9]
Where: LH – labor hours per day HR – labor’s hourly rate
TAC = TACC + TAOC
[10]
Where: TAC – total annual cost
TAR = ESaving + ESale + Heating + Compost + Tipping + Bedding Where: 15
Assuming there are 344 working days a year.
[11]
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TAR – total annual revenue ESaving – electricity savings ESale – electricity sales to the grid Heating – heating savings Compost – compost sales Bedding – savings on bedding materials Tipping – Tipping fees for collecting food waste
TAP = TAR – TAC
[12]
Where: TAP – total annual profit While the relationships among the variables above are clearly defined mathematically, variables such as biogas production and electricity generation have to be based on empirical data collected from AA Dairy and especially Matlink Dairy for the case of co-digestion. Table 13 shows the amount of dairy manure and food waste fed to the digester daily at Matlink during a time span from October 30, 2003 to June 28, 2005. The average manure production is 14,744 gallons per day which is then divided by the head size (740) to yield the manure production of 20 gallons per cow per day. Thus, this number is used to estimate the manure production based on inputs on “Number of Milking Cows” from users (see Figure 22). The food waste volume is calculated using the equation below: FW = M / (100 – P) * P Where: FW – food waste volume M – manure volume
[13]
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P – mixing percentage of food waste to total feedstock
The digester capacity is calculated using the equation below: DC = HRT * (M + FW)
[14]
Where: DC – digester capacity HRT – hydraulic retention time
Table 13 Volume of manure and food waste fed to AD daily on Matlink Dairy
Date 10/30/2003 12/8/2003 1/5/2004 1/26/2004 2/23/2004 3/25/2004 5/24/2004 7/19/2004 8/23/2004 9/16/2004 10/25/2004 11/15/2004 12/20/2004 1/31/2005 2/22/2005 3/22/2005 4/26/2005 5/24/2005 6/28/2005 Average
Manure Gallons % of Total per day Feedstock 12,000 55 15,000 50 14,000 74 14,000 54 12,000 46 12,000 55 17,000 53 13,000 76 15,000 50 15,000 56 15,000 56 22,000 76 15,000 60 14,000 80 12,000 67 15,000 60 15,000 56 13,000 46 15,000 50 14,474 59
Food Waste Gallons % of Total per day Feedstock 10,000 45 15,000 50 5,000 26 12,000 46 14,000 54 10,000 45 15,000 47 4,000 24 15,000 50 12,000 44 12,000 44 7,000 24 10,000 40 3,500 20 6,000 33 10,000 40 12,000 44 15,000 54 15,000 50 10,658 41
Based on data in Table 3, the biogas production for AA Dairy digester is 2.4 m3/cow/day (85 ft3/cow/day) for digester using dairy manure only. In comparison, the
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equivalent to biogas production per cow per day for Matlink digester is 9.2 m3/cow/day (326 ft3/cow/day). We can assume that the difference (6.8 m3/cow/day or 241 ft3/cow/day) between these two numbers implies the biogas production from food waste on per-cow basis. The average daily food waste volume is 10,658 gallons or about 14 gallons per cow per day. Thus, we can get an estimated of biogas production of 17.2 ft3 from a gallon of food waste, while it is 4.3 ft3/gallon for manure. Thus, the biogas production from co-digestion is calculated from the equation below: BP = M * 4.3 + FW * 17.2
[15]
Where: BP – biogas production (ft3/day) Based on data from both AA Dairy and Matlink, the conversion rate of biogas to electricity is about 0.03 kWh/ft3. The electricity generation is estimated assuming all biogas is fed to an internal combustion engine-generator of 20% efficiency. Due to maintenance such as changing engine oil because of corrosive hydrogen sulfide, the system will be briefly shut down every 2-3 weeks. The engine on Matlink Dairy operates 94.5% of the available hours per year. In addition, there is always a gas leakage issue. Therefore, in reality not all of the biogas produced is actually used for electricity generation. The estimated value here reflects the adjustment. For a typical on-farm anaerobic co-digestion system, the biogas is usually used for generating electricity which is then used for farm operations such as milking, pumping, etc. Thus, there will be savings on electricity. If food waste is used as feedstock, there will be tipping fee. Besides these two incomes, potential revenue sources may also include: o Selling excess electricity back to the grid;
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o Savings on heating fuel; o Compost sales; o Savings on bedding materials from compost. The currently operating on-farm digester systems in New York State have some or all of these income sources. Therefore, these revenues depend on the system design and business plan. Additional equipment and investments are usually required to achieve these incomes. For example, interconnection cables and transformers are needed in order to sell electricity back to the grid. Dairy farms are energy intensive operations. Electricity is used on a farm for many purposes such as milking, lighting, ventilation, manure handling, etc. A previous study (NYSERDA, 2003) found that the average electricity usage on a freestall dairy farm is 811 kWh/cow/year. Meanwhile, the average retail electricity price is about $0.0817/kWh in New York State (Energy Information Administration, 2003). Thus, if the user can not enter an accurate annual electricity cost, then the savings on electricity can be estimated by multiplying these two numbers with the number of cows on the farm. The income for selling electricity will be calculated by multiplying excess electricity with the retail price based on the net-metering law in New York State. The food waste tipping fee is calculated as $0.054/gallon based on data from Matlink and the annual tipping fee can be estimated accordingly. Based on data from AA Dairy and Matlink, the income sources such as heating savings, compost sales and bedding material savings primarily depend on herd size. The factors derived to help estimate the revenues from such sources is shown in Table 14.
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Table 14. Estimate of revenues per cow Source Revenues ($) Heating Saving 6,000 Compost Sales 6,000 Bedding Material Saving 15,600 7.4
Revenues/cow ($) 8 8 21
Database Update The various generating sources of organic waste constantly change over time
in terms of size, products, location, etc. There is always a need to update the database by either adding new data or modifying existing data. In addition, the person who maintains the database is not necessarily the same person who designed it. This will make maintenance difficult, especially if the responsible person does not have knowledge or experience in database management. Thus, it is important to design an effective mechanism to reduce the responsibilities for maintaining and updating the database. While there are many types of data which might be updated, the priority is given to food processor data. The reason is that such data are most interesting to potential users. It is crucial to offer complete and updated data regarding food processors for this web-based SDSS. The database updating function can be started by clicking on the icon -
- on
the tool bar. This will lead users to a web form that can be used to enter food processor information to dynamically update the food processor database. The requested information includes: company name, address, contacts, location (street and coordinates), waste type and corresponding waste volume. Figure 24 and 25 shows the interface of this tool.
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Figure 24. Screen shot of tool for updating food processor data (Part I)
Figure 25. Screen shot of tool for updating food processor data (Part II)
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After selecting the “Submit” button, the inputs will be directly sent to a Microsoft Access data file that has been created and stored on the server. At the same time, the inputs will be sent by email to the person who is responsible for maintaining the database (Figure 26). In the email, there are two links. If the inputs seem to be reasonable or accurate, the person will click on the first link to approve the new record and only then will the Access database be updated. If the inputs look unreasonable (such as testing inputs), the person will click on the second link to delete/disapprove the new record and then the record will be removed from the database automatically. So, during the updating process, the procedures are automatic and do not actually require opening the database. This will minimize the time and effort for maintaining the database.
Figure 26. Screen shot of email for updating food processor data
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SUMMARY
Organic waste (e.g. dairy manure and food waste) is an important problem because of its large volume and impact on the environment. However, using appropriate technology, organic waste may become part of the solution instead of the problem. Under the pressure of increasingly restrict environmental regulations and shrinking landfill space, recently there has been growing interest in using anaerobic digestion (AD) technology to convert such waste into useful energy while harvesting environmental and economic benefits at same time. As the 3rd largest dairy state in the nation and the host for a large number of food waste generators, New York State produces significant amount of organic waste. The waste poses a major threat to the environment and on the other hand, can be realized for energy as a renewable energy resource. However, there have been limited data and information concerning these resources. In addition, the organic waste sources are highly site-specific and spatial data/analysis is necessary in order to assist AD project development. To address these issues, a geo-spatial database has been developed to identify and locate those major generating sources including: CAFOs, food processors, supermarkets, correctional facilities, fast food franchises, restaurants, colleges / universities, K-12 public schools, hospitals and nursing homes. These entities are selected because of their concentrated waste output. Data concerning the energy utility systems and the environment are also included in the database because these data will be useful when evaluating the feasibility of on-farm AD systems.
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Furthermore, to expand the functions of this database as well as the accessibility for potential users, a web-based spatial decision support system (SDSS) has been designed by integrating spatial data, a geographic information system (GIS), the Internet, and decision support. This system consists of three modules: (1) dynamic mapping and querying; (2) food waste production estimator; and (3) economic analysis of on-farm co-digestion systems. These modules are designed in sequence to address different questions (evaluation, planning, and economic assessment) that might be raised in a potential project. Therefore, they are closely related and supplement each other within the system. A set of variables regarding costs and benefits involved in an AD system were identified. The relationships among these variables are defined in order to simulate different scenarios of on-farm co-digestion applications. This study will help increase awareness of various benefits of using animal manure and food wastes as feedstock for anaerobic digestion to produce renewable energy. The results will be useful to policy makers, the public, bioenergy investors, farmers, and food wastes generators. The combination of the reporting, database and GIS capabilities will allow waste planners, haulers, entrepreneurs, and others to obtain combinations of information about commercially generated organic wastes in New York. It can be used to facilitate decisions about how best to target wastes for collection, which generators to target, how to structure collection routes and infrastructure, and where to site AD systems. The ultimate goal of this study is to promote and facilitate the utilization of organic wastes (dairy manure and food waste) as renewable energy resources through anaerobic digestion technology in New York State.
9
RECOMMENDATIONS FOR FUTURE RESEARCH
This study is one of the early applications that have attempted to integrate GIS, spatial data, decision support tools, and the Internet to develop a web-based system for utilizing organic waste as a renewable energy resource. There are many areas that can be improved in the future. First, the economic model is preliminary. It would be better if both annual and accumulative cash flows could be described. It should also include investment indicators, such as net present value (NPVs) and internal rates of return (IRR), which are more useful in evaluating the economic viability of an AD project. Secondly, the food waste data sets were not joined into a single data file because it would significantly slow down the querying activities. However, it is possible to use programming to change the query interface so that the queries for those eight food waste generators could be incorporated into a single query, even though the data files are still separated. Transportation data are not included in the database. It is not because the data cannot be found. In fact, detailed data for all streets and roads in New York were collected. However, the WebSDSS was significantly slowed down after adding this transportation data because of its large file size. Therefore, these data were deleted from the database in order to maintain the system at reasonable download speed. A possible remedy to this problem is to create a new dataset concerning the transportation system by including major roads only, which will reduce the file size. The distance appearing in queries or measured by using “TrackLine” tool is Euclidean distance. It will not likely be the real transportation distance. Also the
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distance may be inaccurate if there are water bodies in the area. Improvement should be made to estimate the real distance. Another useful feature to be developed is to track and collect users’ input for Module 3 – “Co-digestion Economic Analysis” because such information will help evaluate their real needs and concerns. This will eventually provide basis for redesign or improvement of the system. In addition, the designed system should be made accessible to the public at an earlier stage and leave enough time to collect feedback, comments and suggestions from users. This information should be analyzed and integrated into the newer version. Finally, because Manifold is a new GIS software product, there are many more functions and powers to be discovered to improve the SDSS. One interesting direction is to couple the economic model tightly with the GIS database. This will be a challenging project because complicated programming will be involved and the data layers might need to be converted into raster format which would reduce the processing speed.
APPENDICES Appendix A: Abbreviations
Terms
Abbreviation
AD
Anaerobic Digestion
CAFO
Concentrated Animal Feeding Operations
CHP
Combined Heat and Power
DG
Distributed Generation
DSS
Decision Support System
ESRI
Environmental Systems Research Institute, Inc.
GIS
Geographical Information Systems
GUI
Graphic User Interface
IIS
Internet Information Server
IMS
Internet Map Server
MSW
Municipal Solid Wastes
NYSERDA
New York State Energy Research & Development Authority
TS
Total Solids
VS
Volatile Solids
WTE
Waste-to-Energy
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Appendix B: Help Information and Examples on Mapping Tools Description
Zoom In - Magnify the view as if seen from a closer distance. This button lets you zoom in on the location you click and display more details on the map. Every click will enlarge the area by 2X. Be aware that zooming in too much will not display any more details and could result in a blank map. Zoom Out - Reduce the view as if seen from farther away. This button lets you zoom out on the location you click and display a larger area with less detail on the map. Some layers such as "CAFOs Lables" and "FPs Lables" will become invisible in order to make features on the map less crowded. Every click will reduce the scale by 2x. Be aware that zooming out too much will make the map unreadable. Zoom to Fit - Zoom so that the component fits within the current window. This button zooms the display to the full extent of the map area. If multiple layers are selected, clicking this button will zoom the map out to the extent that will show the limit of the layer with largest extent. (In most cases, clicking this button will show the extent of the New York State boundary if the "County" layer is selected.)
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Tip: This button is especially useful when you are unsure of you location. You can always use this button to zoom back out to the full map extent and start over. Zoom to Center - Pan the view so that the spot clicked is centered. This button lets you move the map area by clicking on an area, the map will be redrawn with the center of the map located where you clicked the mouse. Tip: After zooming in to a desired area, clicking this button first and then double clicking on any food processor (red triangle) on the map, a new window will open to display the web site of that specific food processor. Zoom Box - Zoom to the size of the cursor box drawn with the mouse. By clicking and holding the mouse button down in the upper left corner and dragging a box over to the bottom right and releasing the mouse button the map will be redrawn in the area you outlined with the box. Tip: This button is especially useful when you try to zoom in or out a specific location. Compared to "Zoom In" and "Zoom Out", it it much easier to define the extent of target area. You can use this button combined with "Zoom to Fit" to repeatedly define the area of interest until you are satisfied. Info Tool - - Show data fields for object. Shows all fields in all drawings in a map that is published, excluding the ID field and intrinsic fields. This button will retrieve the attribute information of the chosen feature on a selected active layer. The results are shown in the area right below the map.
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Track Line - - Measure distance between two or more points. This button will draw a line between any two points that are defined by clicking on the map using the mouse. The points and line will be dynamically displayed on the map. The screen coordinates are automatically converted into the coordinate system of the component served by IMS. You can continue to the link more points until you press the "Reset" button (shown under the map). The Euclidean distance value is shown in the area (status bar in yellow color) right below the map. A list of unit options are available to choose from, including: foot, meter, kilometer, mile, and nautical mile.
Track Area - - Measure the area of a polygon. The button will draw a polygon of any size and shape to be defined by clicking on the map using the mouse. Both of the points and polygon will be dynamically displayed on the map. The Euclidean area of the polygon will be reported in the area (status bar in yellow color) right below the map. A list of unit options are available to choose from, including: foot, meter, kilometer, mile, and nautical mile. Print - - Print the map layout. This button will capture the map with layers selected and extent defined by you and then open a new window. There you can create a customized map by entering title and author information as well as selecting components of the map (legend, north arrow, and date). After the map is created, you can print it directly from that window. Help Info & Examples - - Show descriptions and examples to help carry out different tasks. This button is shown in many places on the screen where help might be needed. Clicking on it will open a new window which provides description about that specific
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feature (mapping or query) and also list examples to help users to correctly use that function. Update - - Dynamically update the food processor database. This button will lead you to a web form that can be used to enter food processor information to dynamically update the food processor database. The inputs will be directly sent to a Microsoft Access data file. At the same time, the inputs will be sent by email to the person who is responsible for maintaining the database. In the email, there are two links. If the inputs seem to be reasonable or accurate, the person will click on the first link to approve the new record and only then the Access database will be updated. If the inputs look unreasonable (such as testing inputs), the person will click on the second link to delete/disapprove the new record and then the record will be removed from the database automatically. So, during the updating process, the procedures are automatic and do not actually require opening the database. This will minimize the time and effort for maintaining the database. Expand Heading - Open a tool such as Layers. This button lets you to expand the panes such as the Layers, Legend, or Queries and make them visible. Contract Heading - Close a tool such as Layers. This button lets you to contract the panes such as the Layers, Legend, or Queries and make them invisible. This is useful when you don't need any of those panes and want to make the screen less crowded.
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About the Layers Each layer represents one type of geographic feature (data) which might be either points, lines, or areas. The layers at the top of the list are drawn on top of those below it. Some layers (e.g. "Aquifers" and "Ag Census") are semi-transparent in order to make the layers below visible. Users can select any combination of information layers simply by checking on or off the boxes in the front. For best function and visual effect, here are some tips: •
Always press "Apply" button after select or deselect layers.
•
Always have the "County" layer selected. This layer shows the political boundary of counties in New York State.
•
Have less layers selected when displaying a map in small scale (large area).
"CAFO" stands for concentrated animal feeding operations, which are large-scale farms. (A more detailed definition is available in the "Frequently Asked Questions".) "FP" stands for food processor. The image below provides further description about some layers in particular.
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About the Legend A legend is a display that uses symbols and colors to distinguish different layers seen in a map or print layout. To increase the speed of interactive mapping service and also to better organize the layers, a customized legend (an image) is created and used to replace the original "live" legend. Most layers are listed on the legend except those layers of areas such as "County" or "Ag Census" because there are too many symbols. However, they still can be identified by using "Info Tool".
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Examples Example 1: How do I zoom in to an area of interest? This example shows how to use the tool bar to locate an specific area in New York State. The general procedures are as following: Step 1: Click the "Zoom Box" button -
.
Step 2: Draw a box around the area you are interested (western New York in this example) by clicking and holding the mouse button down in the upper left corner and dragging a box over to the bottom right. Then release the button.
Note: It is also OK to use "Zoom In" and "Zoom Out" to accomplish the same task. However, the "Zoom Box" is a more convenient tool. Step 3: The map will immediately update and display the area selected.
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Step 4: You can continuously zoom in to smaller area by drawing another box.
Step 5: To change the area of interest, you can click the original extent and start over again from step 1.
- "Zoom to Fit" to go back to
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Example 2: How do I display the layers of interest? This example shows how to select or deselect layers to meet customized needs. The general procedures are as following: Step 1: Only check the small boxes in front of the layers that you are interested. You can select any combination of layers and there is no limit on number. Step 2: Then press the "Apply" button on the bottom of the Layers Pane. The map will immediately update and display only the layers that you just selected.
Note: It is recommended to leave the "County" layer checked in most cases because it defines the boundary of New York State as well as that of all 62 counties.
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Example 3: How do I identify an object and find its attribute information? This example shows how to locate a specific object (which can be a point, line, or area) on the map and display the information about this object. Note: If you just want to find the names of certain CAFO or food processor, you can zoom in (see Example 1) to an area until county level. The names (lables) of all CAFOs and food processors will be automatically displayed on the map. This doesn't apply to any other layers.
To identify objects on other layers, the general procedures are as following: Step 1: Select the layers that you are interested (see Example 2). In this example, only "Food Processors" and "County" are selected.
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Step 2: If the objects (e.g. points) are too crowded on the map, you need to zoom in to enlarge the area first. Step 3: Click the button -
(Info).
Step 4: Click right on a specific object of interest. (In this example, the object is a food processor seen in the blue circle.) Step 5: The information about the object clicked will be displayed in the blank area below the map.
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Note: If the objects are lines or areas, the general procedures are similar. You can click anywhere on the line or in the area to select the object. Once a line is clicked, it will be highlighted in red color and the information is shown in the section below map (see image below). This example shows that the identified stream (a line feature) is "Fall Creek".
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To select objects from an area-layer (e.g. "Elec Utility"), you need to make sure this specific layer on tops of other area-layers. Or, to make it simpler, you can just select this area-layer and deselect any other area-layers. Once an area is clicked, its boundary will be highlighted in red color. Also the area will be filled with small grids to distinguish from other areas (see image below). This example shows that the identified region (an area feature) is serviced by the utility company - "NYSEG" for electricity.
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Example 4: How do I create and print a customized map? This example shows how to create and then print out a map that displays the area and layers that you are most interested. The general procedures are as following: Step 1: Create a map that has correct extent and layers. Step 2: Click on the button -
.
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Step 3: A new window will pop up (see image below) and asks you to enter some inputs.
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Step 4: Enter the map title and author information and select the components (legend, north arrow, and date/time) that you want to show on the final map.
Step 5: A customized map will be created. You can always go back to make changes by clicking on the button - "Go Back".
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Step 6: Now you can print it out by clicking on the "Print" button on the upper right of the window. Note that this button will not be shown on the map printed out.
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Appendix C: Survey Form Used to Collect Technical and Financial Data from Dairy Farms with AD System in Northeast region Farm Information
Anaerobic Digester System Information
Owner/Manager
System Type
Mailing Address
System Installed Date System Designer -- Phone
Digester Dimensions Financing for the system (e.g. grant)
-- Fax Acreage
Manure Parameters
# of cows (total)
% VS % TS
Retention Time (RT) (days)
-- Milkers -- Heifers
Biogas Production
-- Calves
Biogas Parameter
% CH4
Milk Production
% CO2
Manure Collection Method Manure mgt. method before AD installation
Food Wastes Added to manure into digester
Bedding Materials
Compost Production
%H2S Type
Volume
Capital Costs Initial Cost ($)
Useful Life (years)
Digester Cover for digester Room hosting digester Separator Manure storage Pipeline Engine-generator set Engine Building Electric wires & switch equipment Meters Tanks Boiler Flare Others
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Salvage Value ($)
Repairs & Maintenance ($/yr OR %) *
Remarks
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Model
Specifications
Engine Generator
Pumps
Purpose / function**
Horsepower (hp)
Initial Cost ($)
Useful Life (years)
Salvage Value ($)
Repairs & Maintenance ($/yr)
A B C D E F Other
Annual Operating Costs Hour Rate ($/hr)
Hours/wk
Labor Rate ($/hr)
Labor Hours (hrs/wk)
Truck Tractor Manure spreader Irrigation equipment Other (
)
Other operating costs $/yr
Remarks
Insurance Fuel (engine and steam heat) Other (
)
Revenues / Benefits $/yr
Remarks
Milk Selling or saving on electricity Saving on fuel for heating Selling compost Saving on bedding materials Saving on odor control equipment Saving on odor control labor Other
Unit price:
Remarks
Appendix D: Survey Form Used to Collect Data about Food Waste from Food Processors in New York State Please supply the following information: 1.
Company Name:
2.
Contact Person: ________________________
Phone: _______________________
Email: _________________________
Fax: _________________________
3.
Total Volume of Food Products Processed: ______________________________
Please fill in the information to the extent that you can do so easily. Waste Sources
Waste Amount
(Example) Fruit Waste
1,000 gallon/day
Chemical Oxygen Demand (COD)
Total Solids (TS)
Volatile Solids (VS)
30,000 mg/l
30%
25%
Present Waste Disposal / Treatment Methods
Fruit Wastes
Vegetable Wastes Dairy Product Wastes Bakery Wastes
Meat Wastes
Other Wastes (Specify) Other Wastes (Specify) Additional Comments:
_____________________________________________________________________ _____________________________________________________________________ Please return this form to the following address using the enclosed envelope (postage paid): Prof. Norman Scott, 216 Riley-Robb, Ithaca, NY 14853 OR, Fax: (607) 255-4080
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Appendix E: Definition of CAFO
An Animal Feeding Operation (AFO) is defined as a lot or facility (other than an aquatic animal production facility) where animals have been, are, or will be stabled or confined and fed or maintained for a total of 45 days or more in any 12-month period, and the animal confinement areas do not sustain crops, vegetation, forage growth, or post-harvest residues in the normal growing season. A Concentrated Animal Feeding Operation (CAFO) generally has large number of animal units and is operated in confined environment. Based primarily on herd size, CAFOs are divided into two groups: large CAFOs and medium CAFOs. Large CAFO is defined as an AFO if it stables or confines as many or more than the number of animals specified in any of the following categories:
700 Mature Dairy Cows, whether milked or dry
1,000 Veal Calves
1,000 Cattle, other than mature dairy cows or veal calves (Cattle includes but is not limited to heifers, steers, bulls and cow/calf pairs)
2,500 Swine, each weighing 55 pounds or more
10,000 Swine, each weighing less than 55 pounds
500 Horses
10,000 Sheep or Lambs
55,000 Turkeys
30,000 Laying Hens or Broilers, if the AFO uses a liquid manure handling system
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125,000 Chickens (other than laying hens), if the AFO uses other than a liquid manure handling system
82,000 Laying Hens, if the AFO uses other than a liquid manure handling system
30,000 Ducks, if the AFO uses other than a liquid manure handling system
5,000 Ducks, if the AFO uses a liquid manure handling system Medium CAFO is defined as an AFO if the type and number of animals that it
stables or confines falls within any of the following ranges:
200-699 Mature Dairy Cows, whether milked or dry
300-999 Veal Calves
300-999 Cattle, other than mature dairy cows or veal calves (Cattle includes but is not limited to heifers, steers, bulls and cow/calf pairs)
750-2,499 Swine, each weighing 55 pounds or more
3,000-9,999 Swine each weighing less than 55 pounds
150-499 Horses
3,000-9,999 Sheep or Lambs
16,500-54,999 Turkeys
9,000-29,999 Laying Hens or Broilers, if the AFO uses a liquid manure handing system
37,500-124,999 Chickens (other than laying hens), if the AFO uses other than a liquid manure handling system
25,000-81,999 Laying Hens, if the AFO uses other than liquid manure handling systems
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10,000-29,999 Ducks, if the AFO uses other than a liquid manure handling system
1,500-4,999 Ducks, if the AFO uses a liquid manure handling system and pollutants are discharged in one of the following ways: o Into waters of the State through a man-made ditch, flushing system, or other similar man-made-device, or o Directly into waters of the State that originate outside if the facility and pass over, across, or through the facility or otherwise come into direct contact with the confined animals.
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70. U. S. Environmental Protection Agency (EPA). Global Warming Potentials, 2003d 71. Voivontas, D., D. Assimacopoulos, and E. G. Koukios, 2001, Assessment of Biomass Potential for Power Production: a GIS based Method, Biomass & Bioenergy, 20, p101-112 72. Walsh, M. R., 1992, "Toward spatial decision support systems in water resources", Journal of Water Resources Planning and Management. 109 (2): 158-169. 73. Wright, P., and Scott Inglis, An Economic Comparison of Two Anaerobic Digestion Systems on Dairy Farms, Presented in ASAE Annual International Meeting, Las Vegas, NE, July 27-30, 2003, paper#: 034154 74. Wright, P., Scott Inglis, Jianguo Ma, Curt Gooch, Brian Aldrich, Alex Meiser and Norman Scott, Preliminary Comparison of Five Anaerobic Digestion Systems on Dairy Farms in New York State, Presented in ASAE Annual International Meeting, Ottawa, Ontario, Canada, August 1-4, 2004, paper#: 044032
Glossary
AD (Anaerobic Digestion): Anaerobic digestion is a biochemical degradation process that converts complex organic material, such as animal manure, into methane (CH4) and other byproducts such as carbon dioxide (CO2), hydrogen sulfide (H2S) and other trace gases. An anaerobic digester is a device that promotes such process to produce and capture the methane. ArcGIS: An integrated collection of GIS software products developed by ESRI. ArcGIS is the comprehensive name for the current suite of GIS products by ESRI. ArcIMS: ArcIMS (ArcInternet Map Server) is an online mapping product produced by ESRI. It is a GIS that is designed to serve maps across the Internet. Sometimes these maps are just static images allowing simple panning and zooming, whilst others are more complex pages. Examples of interactive maps served with ArcIMS include maps with layers that can be turned on and off, or with features containing attributes that can be queried. Biogas: Gas produced when organic matter of animal or plant origin ferments in an oxygenfree environment. Usually, biogas produced in anaerobic digesters consists of methane (~ 60%), carbon dioxide (~ 40%), and trace levels of other gases such as carbon monoxide, nitrogen, and hydrogen sulfide. The relative percentage of these gases in biogas depends on the feed composition and management of the process. Biogas is a source of renewable energy with a significant potential and it is suitable for electricity and heat production and can also be used as a transportation fuel. CAFO (concentrated animal feeding operation): A CAFO generally has large number of animal units and is operated in confined environment. Based primarily on herd size, CAFOs are divided into two groups: large and medium. Client: On the Internet, a program that requests files or services from a server.
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Co-digestion: Anaerobic digestion that uses more than one type of organic wastes. In this study, co-digestion specifically refers to anaerobic digestion of dairy manure and food waste. Database: A collection of interrelated information managed and stored as a unit, usually on some form of mass-storage system, such as magnetic tape or disk. A GIS database includes data about the spatial location, shape, and attributes of geographic features. DG (Distributed Generation): Small, modular, decentralized, grid-connected or off-grid energy systems located in or near the place where energy is used. Depending on the size of nearby loads and the capacity of the distribution line to which it is connected, the maximum size of distributed generation can vary from a few hundred kW to 5 MW. DSS (Decision Support Systems): Information technology and software specifically designed to help people make decisions. It is a data processing mode emphasizing user friendliness and ad hoc query, reporting and analysis capabilities. ESRI (Environmental Systems Research Institute, Inc.): A world leader in GIS (geographic information system) software and technology. ESRI is the maker of ArcGIS. GIS: Geographic Information System. A GIS is a computer system capable of capturing, storing, analyzing, and displaying geographically referenced information; that is, data identified according to location. HTML: (HyperText Markup Language) Hypertext Markup Language is the authoring software language used on the Internet's World Wide Web and used for creating World Wide Web pages. HTML is not a full-blown programming language and therefore it is essentially static in nature. HTML is parsed by the web browser when a web page downloads and consists of tags (commands to tell the browser how to render the text, where to load in graphics etc on the web page) as well as the actual text. Image Map: An image containing one or more invisible regions, called hotspots, which are assigned hyperlinks. Typically, an image map gives visual cues about the information made
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available by clicking each part of the image. For example, a geographical map could be made into an image map by assigning hotspots to each region of interest on the map. Internet: The Internet, or simply the Net, is the publicly available worldwide system of interconnected computer networks that transmit data by packet switching using a standardized Internet Protocol (IP) and many other protocols. It is made up of thousands of smaller commercial, academic, domestic and government networks. It carries various information and services, such as electronic mail, online chat and the interlinked web pages and other documents of the World Wide Web. Internet Information Server: IIS (Microsoft Internet Information Services or Server) is a set of Internet based services for Windows machines. Originally supplied as part of the Option Pack for Windows NT, they were subsequently integrated with Windows 2000 and Windows Server 2003. The current (Windows 2003) version is IIS 6.0 and includes servers for FTP, SMTP, NNTP and HTTP/HTTPS. Earlier versions also included a Gopher server. JavaScript: A cross-platform, World Wide Web scripting language developed by Netscape Communications. JavaScript code is inserted directly into an HTML page. Layer: The GIS data model represents the world by sub-dividing features on the earth's surface according to a specific theme. Each theme is then geo-referenced and called layer. A layer usually contains of both spatial and attribute data. Manifold: Manifold System 6.50 is an integrated system that simultaneously works with vector drawings, satellite and aerial photos, other raster images, raster data, multi-channel remote sensing images, 2D and 3D surfaces and terrain simulations, multilayered maps, user supplied or automatically generated labels and a vast range of database table formats. Manifold IMS: Manifold IMS works with standard web browsers and requires no plug-ins, no programming and no costly middleware. The map server is built into Manifold itself so that Manifold can be used as a WYSIWYG development environment to create a project.
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Map labels: labels on a map indicating names of places or events. Map projection: A mathematical model that transforms the locations of features on the Earth's surface to locations on a two-dimensional surface. Because the Earth is threedimensional, some method must be used to depict a map in two dimensions. Some projections preserve shape; others preserve accuracy of area, distance, or direction. Metadata: Data that is used to describe other data. Metadata is information about a particular data set which may describe, for example, how, when, and by whom it was received, created, accessed, and/or modified and how it is formatted. Some metadata, such as file dates and sizes, can easily be seen by users; other metadata can be hidden or embedded and unavailable to computer users who are not technically adept. Metadata is generally not reproduced in full form when a document is printed. Microsoft Access: Microsoft Access is a relational database management system from Microsoft, packaged with Microsoft Office Professional which combines the Jet relational database engine with a graphical interface. The development environment provides productivity-enhancing features for both advanced developers and beginning users. It can use data stored in Access/Jet, SQL Server, Oracle, or any ODBC-compliant data container. NAD27: North American Datum 1927. It is used as a reference point used in the making of maps (typically in UTM projections for the North American continent). Organic Waste: Organic waste is the term used to describe those wastes that are readily biodegradable, or easily breakdown with the assistance of micro-organisms. Organic wastes consist of materials that contain molecules based on carbon. This includes food waste, green waste, and also wastes arising from grease traps. Organic waste however, does not include for example, plastic or mineral oil products. Plug-in: A “client program" that is used to expand the functionality of a "host program", such as a sequencer or digital audio editor. The host provides the plug-in with some type of input
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data such as digital audio samples, which is then processed to generate new output, such as effected digital audio. A plug-in is often run seamlessly from within a host program appearing to be part of the standard interface. One plug-in can be used by multiple host programs that share the same plug-in format. Plug-in applications are programs that can easily be installed and used as part of the Web browser. PNG: Portable Network Graphics (PNG) format was designed to be a patent-free successor to the GIF format. Though not designed specifically for the Web, PNG offers particular benefits in this environment such as improved image compression ( 10 to 30 percent smaller than GIFs), two dimensional interlacing, storage of text with the an image making it possible for search engines to gather information and offer subject searching for images in a standard way. Query: A question, especially if asked of a database by a user via a database management system or GIS. SDSS (Spatial Decision Support Systems): A customized computer-based information system that utilizes decision rules and models and incorporates spatial data. Server: A computer connected to a network whose primary function is to act as a library of information that other users can share. These computers offer services on a network. On the World Wide Web, a server is a computer that runs the Web server software, which responds to HTTP protocol requests. Servers can also be called hosts. Spatial data: Data pertaining to the location and spatial dimensions of geographical entities. Spatial data are classified as point, line, area, or surface. SQL: SQL (Structured Query Language) is a standard interactive and programming language for getting information from and updating a database. Although SQL is both an ANSI and an ISO standard, many database products support SQL with proprietary extensions to the standard language. Queries take the form of a command language that lets you select, insert, update, find out the location of data, etc.
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TCP/IP: (Transmission Control Protocol & Internet Protocol), TCP is one of the main protocols in TCP/IP networks. Whereas the IP protocol deals only with packets, TCP enables two hosts to establish a connection and exchange streams of data. TCP guarantees delivery of data and also guarantees that packets will be delivered in the same order in which they were sent. URL: (Uniform Resource Locator) A string that supplies the Internet address of a Web site or resource on the World Wide Web, along with the protocol by which the site or resource is accessed. The most common URL type is http://, which gives the Internet address of a Web page. Some other URL types are gopher://, which gives the Internet address of a Gopher directory, and ftp://, which gives the network location of an FTP resource. UTM (Universal Transverse Mercator): A widely used planar coordinate system, extending from 84o north to 80 o south latitude and based on a specialized application of the Transverse Mercator projection. The extent of the coordinate system is broken into sixty 6 degrees (longitude) zones. Within each zone, coordinated are usually expressed as meters north or south of the equator and east from a reference axis. For locations in the Northern Hemisphere, the origin is assigned a false easting of 500,000 and a false northing of 0. VB.NET: Visual Basic .NET is an object-oriented computer language that can be viewed as a spiritual evolution of Microsoft's Visual Basic (VB) implemented on the Microsoft .NET framework. Its introduction has been controversial, as significant changes were made that broke backward compatibility with VB and caused a rift within the developer community that may or may not be resolved with the introduction of Visual Studio 2005. Web browser: A client application that is used to view, download, upload, surf or otherwise access documents on Web pages. Technically, a web browser uses HTTP to make requests of web servers throughout the Internet on behalf of the browser user. Popular Web browsers include: Firefox, Internet Explorer, Mozilla, Opera and Safari.
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Web GIS: A Geographic Information System specifically designed to be “broadcast” on the Internet. Such a system usually combines GIS functionality (where the geographic databases are concerned) with web site design and administration. World Wide Web: A distributed database of information stored on servers connected by the Internet and special-purpose software for browsing, searching and downloading. The Web presents the user with documents, called web pages, full of links to other documents or information systems. Web pages include text as well as multimedia (images, video, animation, sound). The web is only one of several Internet environments, including email, Internet Relay Chat, FTP (File transfer Protocol), and Usenet news groups.
