Ranking of biomass pellets by integration of economic, environmental and technical factors
- 作者：Arifa Sultana ; Amit Kumar ; amit.Kumar@ualberta.ca
- 关键词：Multi-criteria ranking ; Biomass pellet ; PROMETHEE ; Bioeconomy ; Sensitivity analysis
- 刊名：Biomass and Bioenergy
- 出版年：2012
- 期刊代码：3_09619534
- 类别：pw
- 出版时间：April, 2012
- 卷：39
- 期：Complete
- 页码：344-355
- 文件大小：301 K
摘要
Interest in biomass as a renewable energy source has increased recently in response to a need to reduce greenhouse gas (GHG) emissions. The objective of this study is to develop a multi-criteria assessment model and rank different biomass feedstock-based pellets, in terms of their suitability for use in large heat and power generation plants and show the importance of environmental, economical and technical factors in making decision about different pellets. Five pellet聽alternatives, each produced from a different sustainable biomass feedstock i.e., wood, straw, switchgrass, alfalfa and poultry litter, are ranked according to eleven criteria, using the Preference Ranking Organization Method for Enrichment and Evaluation (PROMETHEE). Both quantitative and qualitative criteria are considered, including environmental, technical and economic factors. Three scenarios, namely base case, environmental and economic, are developed by changing the weight assigned to different criteria. In the base case scenario, equal weights are assigned to each criterion. In the economic and environmental scenarios, more weight is given to the economic and environmental factors, respectively. Based on the PROMETHEE rankings, wood pellets are the best source of energy for all scenarios followed by switchgrass, straw, poultry litter and alfalfa pellets except economic scenario, where straw pellets held higher position than switchgrass pellets. Sensitivity analysis on weights, threshold values, preference function and production cost indicate that the ranking was stable. The ranking in all scenarios remained same when qualitative criteria were omitted from the model; this indicates the stronger influence of quantitative criteria.