4.7 Article

Multi-objective optimization of a diesel engine fueled with different fuel types containing additives using grey-based Taguchi approach

期刊

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
卷 29, 期 20, 页码 30277-30284

出版社

SPRINGER HEIDELBERG
DOI: 10.1007/s11356-021-18012-1

关键词

Biodiesel; Fuel additive; Multi-objective optimization; Exhaust emission; Taguchi method; Grey relational analysis

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This study investigated the effects of n-hexane and n-hexadecane additives on exhaust emissions and performance of a single-cylinder diesel engine fueled with biodiesel and diesel fuels. The results showed that rapeseed oil biodiesel was the best fuel type and hexadecane was the better additive for system optimization. The study also highlighted the importance of fuel type in optimizing emissions and engine performance.
Due to the reduction of fossil fuels' resources and their contribution to environmental problems, biodiesel fuels have attracted significant attention as substitutes for diesel fuels. However, since their NOx emissions are higher than that of diesel fuels in most cases and also because of their higher viscosity than diesel, fuel additives are used to enhance their properties and reduce emissions. In this study, the effect of n-hexane and n-hexadecane addition to biodiesel and diesel fuels on exhaust emissions and performance of a single-cylinder diesel engine was investigated by using grey-based Taguchi method. Fuel additive, the additive amount, and fuel type were considered as the operating parameters. Three fuel types including diesel, rapeseed oil biodiesel, and cottonseed oil biodiesel were used in this investigation, while n-hexane and n-hexadecane were considered as the two fuel additives. As well as, three levels were assigned to the additive amount which were 4, 8, and 12%. Based on the operating parameters and their levels, the plan of experiments was generated according to L-18 orthogonal array. Using grey relational analysis, this multi-response optimization problem was first transformed into a single response optimization. Then, this single system response, which is known as grey relational grade, was utilized in Taguchi approach for statistical evaluations. The results demonstrated that rapeseed was the best selection for fuel type compared to cottonseed and diesel in order to have the optimum system responses and hexadecane gave better results for system optimization in comparison with hexane additive. As well as, the analysis of variance showed that fuel type was the predominant operating factor influencing the grey relational grade which means fuel type was the most important parameter in the simultaneous optimization of exhaust emissions and engine performance. The Taguchi results also revealed that the optimum condition of engine performance and exhaust emissions happened when engine was fueled with rapeseed biodiesel containing 12% hexadecane as an additive. The confirmation test result validated the reliability of Taguchi approach in this investigation.

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