A Comprehensive Approach to Biodiesel Blend Selection Using GRA-TOPSIS: A Case Study of Waste Cooking Oils in Egypt

Marwa M. Sleem, Osama Y. Abdelfattah, Amr A. Abohany and Shaymaa E. Sorour ()
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Marwa M. Sleem: Faculty of Technological Industry and Energy, Delta Technological University, Quesna 32684, Egypt
Osama Y. Abdelfattah: Faculty of Technological Industry and Energy, Delta Technological University, Quesna 32684, Egypt
Amr A. Abohany: Faculty of Computers and Information, Kafrelsheikh University, Kafrelsheikh 33511, Egypt
Shaymaa E. Sorour: Department of Management Information Systems, School of Business, King Faisal University, Al Hofuf 31982, Saudi Arabia

Sustainability, 2024, vol. 16, issue 14, 1-25

Abstract: The transition to sustainable energy sources is critical for addressing global environmental challenges. In 2017, Egypt produced about 500,000 tons of waste cooking oil from various sources including food industries, restaurants and hotels. Sadly, 90% of households choose to dispose of their used cooking oil by pouring it down the drain or into their village’s sewers instead of using proper disposal methods. The process involves converting waste cooking oil (WCO) into biodiesel.This study introduces a multi-criteria decision-making approach to identify the optimal biodiesel blend from waste cooking oils in Egypt. By leveraging the grey relational analysis (GRA) combined with the technique for order preference by similarity to the ideal solution (TOPSIS), we evaluate eight biodiesel blends (diesel, B5, B10, B20, B30, B50, B75, B100) against various performance metrics, including carbon monoxide, carbon dioxide, nitrogen oxides, hydrocarbons, particulate matter, engine power, fuel consumption, engine noise, and exhaust gas temperature. The experimental analysis used a single-cylinder, constant-speed, direct-injection eight cylinder diesel engine under varying load conditions. Our methodology involved feature engineering and model building to enhance predictive accuracy. The results demonstrated significant improvements in monitoring accuracy, with diesel, B5, and B20 emerging as the top-performing blends. Notably, the B5 blend showed the best overall performance, balancing efficiency and emissions. This study highlights the potential of integrating advanced AI-driven decision-making frameworks into biodiesel blend selection, promoting cleaner energy solutions and optimizing engine performance. Our findings underscore the substantial benefits of waste cooking oils for biodiesel production, contributing to environmental sustainability and energy efficiency.

Keywords: waste cooking oil; biodiesel; engine performance; multi-criteria decision making (MCDM); GRA-TOPSIS method (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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