Selection of sustainable lignocellulose biomass for second-generation bioethanol production for automobile vehicles using lifecycle indicators through fuzzy hybrid PyMCDM approach. (15th August 2022)

• Record Type: Journal Article
• Authors: Ramesh, P.; Arul Mozhi Selvan, V.; Babu, D.
• Is Part Of: Fuel. Volume 322(2022)
• Publication Date: 2022-08-15
• Subjects: MCDM -- Lignocellulose -- Bioethanol -- Second generation -- Python -- LCA -- Life Cycle Assessment; MCDM Multi-Criteria Decision Making -- EDAS Evaluation Based on Distance from Average Solution -- COPRAS COmplex PRoportional ASsessment -- TOPSIS Technique for Order of Preference by Similarity to Ideal Solution -- PROMETHEE Preference Ranking Organization Method for Enrichment Evaluations -- AHP Analytic Hierarchy Process -- LCA Life Cycle Assessment -- GHG Green House Gases -- ARAS A new additive ratio assessment -- WASPAS Weighted Aggregated Sum Product Assessment; Fuel -- Periodicals; Coal -- Periodicals; Coal; Fuel; Periodicals; 662.6
• DOI: 10.1016/j.fuel.2022.124240
• Languages: English
• ISSNs: 0016-2361

Abstract

Highlights: Local lignocellulos biomasses were identified based on Indian context. MCDM methodology has been utilized to prioritize sustainable lignocellulos biomass. Python based algorithm was developed to improve computation speed. Abstract: Petroleum prices are increasing day by day due to the essential requirement of fuel to run automobiles, agricultural engines and standby power plants. More utilization of fossil fuels leads to global warming, environmental pollution, ozone depletion, and climate change. The second-generation ethanol is produced from various feedstock, including agricultural wastes and lignocellulose biomass sources doesn't affect the food security in many countries and the blending of bio-ethanol in gasoline reduced the greenhouse gas emissions. As large quantity of Lignocellulose biomass is available in India; the present study aims to select sustainable Lignocellulose biomass for the production of second-generation ethanol in Indian context. Five Lignocellulose biomass (Sugarcane Bagasse, Rice straw, wheat straw, Moringa, and Vetiver) were identified and prioritized using Multi-Criteria Decision Making (MCDM) methodology. This study followed the Hybrid MCDM approach to prioritize the Lignocellulose biomass. Python-based Evaluation Based on Distance from Average Solution (PyEDAS) MCDM algorithm was developed, and the final ranking is compared with COmplex PRoportional ASsessment (COPRAS), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) & Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE). From the results, Vetiver grass ranked as the best alternative, and Sugarcane bagasse ranked as the worst alternative among five Lignocellulose biomass sources.