Region-specific biomass feedstock selection for gasification using multi-attribute decision-making techniques

Biomass-based energy systems are significant and sustainable options for economic progress, especially in the developing world. Usable form of energy can be extracted from biomass through gasification. Among the various types of biomasses with different physico-chemical characteristics, a ranking fr...

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Bibliographic Details
Published in:International journal of sustainable engineering 2021-09, Vol.14 (5), p.1101-1109
Main Authors: George, Joel, Arun, P., Muraleedharan, C.
Format: Article
Language:English
Subjects:
Analytic hierarchy process
Analytical Hierarchical Process (AHP)
Biomass
Biomass energy production
Biomass ranking
Correlation coefficient
Correlation coefficients
Decision making
Feasibility
Gasification
gasification feasibility
Mathematical analysis
Multi Attribute Decision Making
Raw materials
Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)
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Summary:Biomass-based energy systems are significant and sustainable options for economic progress, especially in the developing world. Usable form of energy can be extracted from biomass through gasification. Among the various types of biomasses with different physico-chemical characteristics, a ranking framework assists in the best choice based on feasibility for gasification. Selection of appropriate biomass from a finite number of alternatives based on relevant properties that influence gasification is possible through multiple attribute decision making techniques. In this study, 10 different biomasses identified within the geographical proximity of southern India are ranked based on six experimentally determined characteristics, pertinent for gasification. A hybrid model combining Analytical Hierarchical Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is formulated to rank the biomasses based on gasification feasibility. Among the different feedstocks considered, coconut shell is identified as the best feedstock for gasification. The hybrid model developed has been validated using two different multi-attribute decision making approaches - Euclidian distance-based approximation and Simple Additive Weighting. Spearman's rank correlation coefficient obtained for the mutual comparison of ranks is above 0.9 indicating acceptable agreement between the ranks obtained using the three methods.
ISSN:1939-7038
1939-7046
DOI:10.1080/19397038.2020.1790058