Anaerobic digester systems (ADS) for multiple dairy farms: A GIS analysis for optimal site selection

Thompson, Ethan; Wang, Qingbin; Li, Minghao

doi:10.1016/j.enpol.2013.06.035

Cited by 51 publications

(25 citation statements)

References 13 publications

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“…This approach plainly runs the risk of selecting sub-optimal sets of n locations. Thompson et al (2013) applied a 'maximum attendance' locationallocation algorithm to select locations that could be served with manure within 22km network distance. However, the resulting locations are driven by demand where is defined on the supply of organic materials with large overlapping service areas for the facilities that were located.…”

Section: Discussionmentioning

confidence: 99%

“…of biomass). Thompson et al (2013) identified optimal AD locations amongst farms too small to have an AD facility themselves, using a 'maximum attendance' algorithm to select locations that could be served with manure within 22km network distance. Other work includes Sliz-Szkliniarz and Vogt (2012) used a suitability analysis to identify areas suitable for potential AD facilities with animal manure as the major supply variable and then applied a focal / neighbourhood analysis to determine 'optimal' locations.…”

Section: Renewable Energy Facility Locationmentioning

confidence: 99%

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Locating bioenergy facilities using a modified GIS-based location–allocation-algorithm: Considering the spatial distribution of resource supply

et al. 2015

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Section: Discussionmentioning

confidence: 99%

Section: Renewable Energy Facility Locationmentioning

confidence: 99%

Locating bioenergy facilities using a modified GIS-based location–allocation-algorithm: Considering the spatial distribution of resource supply

et al. 2015

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“…The arcs represent the existing transport connections between the nodes. An example of discrete topological structure is Thompson et al (2013) which use Geographical Information System (GIS) analysis to create and map a data layer of polygons that indicate all of the potential locations where, based on suitability criteria, anaerobic digester system can be installed.…”

Section: Agricultural Servicesmentioning

confidence: 99%

A Solution Approach for Agricultural Service Center Location Problem Using TOPSIS, DEA and SAW Techniques

et al. 2015

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In this research, agricultural service centers have been introduced as a strategic decision to improve efficiency of agricultural supply chain. Since the location problems in agriculture exhibit several features, such as their large scope and size, and demonstrate increased levels of complexity, so the main aim of this study is developing an integrated Multi-Attribute Decision Making (MADM) approach considering all aspects of the Agricultural Service Center Location Problem (ASCLP) including: customers, service suppliers, and also technical suitability of candidate location. In this regard two main MADM approaches are developed. The first approach was the combination of Simple Additive Weighting (SAW), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Data Envelopment Analysis (DEA). The second approach is developed to improve the solutions for some cases which the first approach was unable to reach to the non-dominated solutions. Before completing the MADM approaches, initially a Delphi fuzzy-analytical hierarchy process survey has been completed to extract the objectives and attributes of ASCLP and their local weight, which is published in previous work by the authors. The main contribution of current chapter is to consider more comprehensively the attributes in solving a service facility location problem such as: distances to demand points and also suppliers, cultivated area of demand points, population of demand points, number of cultivated crops, and the ration of irrigated cultivated lands to dried cultivated lands. The developed approach was able to use all mentioned decision methods, simultaneously. In the second approach, the developed attributes are used, two scores (maxi-min and maxi-max) for each candidate location is computed and nondominated solutions are identified. To prove the capability of the selected attributes one case study is addressed. The results reveal that the chosen attributes to a very large degree can lead to the non-dominated solutions.

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“…For example, it may be possible to implement community-scale ADS serving multiple small and medium farms within the same geographic regions [18]. Indeed, in the European Union nations of Germany, Denmark, France, Austria, and the United Kingdom, community-scale ADS have been implemented to process manure from multiple farms as well as food processing waste and municipal organic residuals.…”

Section: Conclusion and Potential Policy Implicationsmentioning

confidence: 99%

“…Public funds may be better used to continue support of stand-alone systems on large farms and to spur technical and operational solutions, such as shared, regional ADS facilities (See Thompson et United States: A Case Study in Vermont al. for a study on the feasibility of centralized ADS in Addison County, Vermont [18]). …”

Section: Conclusion and Potential Policy Implicationsmentioning

confidence: 99%

Energy Outputs and Financial Returns of On-Farm Biodigester Systems in the United States: A Case Study in Vermont

Wang¹,

Thompson²,

Valchuis³

et al. 2017

IJSGE

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This study assesses the investments, energy outputs, and financial returns of on-farm anaerobic digester systems (ADS) by farm size through a case study in Vermont and discusses the potential policy implications. Detailed data on the initial investments, production of electricity and other marketable products, operational expenses, and income, collected through surveys of eight operating ADS on dairy farms in Vermont, are used to estimate the return on equity (ROE), return on assets (ROA), and other financial indicators for small, medium, and large farms. The primary survey data indicate that the average investment was $1.35 million for small and medium farms (75-500 cows) and $2.44 million for large farms (>500 cows). Financial analysis indicates that the ROE and ROA were 12.54% and 13.50% for large farms but only 0.73% and 1.07% for small and medium dairy farms, respectively. Whereas the technology of ADS developed in the United States seems to favor large farms in terms of both energy production and financial returns, the centralized ADS developed in Europe and low-cost mini digesters developed in China may have potentials for small and medium farms to develop more economically viable ADS in the United States.

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Anaerobic digester systems (ADS) for multiple dairy farms: A GIS analysis for optimal site selection

Cited by 51 publications

References 13 publications

Locating bioenergy facilities using a modified GIS-based location–allocation-algorithm: Considering the spatial distribution of resource supply

Locating bioenergy facilities using a modified GIS-based location–allocation-algorithm: Considering the spatial distribution of resource supply

A Solution Approach for Agricultural Service Center Location Problem Using TOPSIS, DEA and SAW Techniques

Energy Outputs and Financial Returns of On-Farm Biodigester Systems in the United States: A Case Study in Vermont

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