A hybrid method of modified NSGA-II and TOPSIS to optimize performance and emissions of a diesel engine using biodiesel

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• 作者：Mir Majid Etghani ; Mohammad Hassan Shojaeefard ; Abolfazl Khalkhali ; Mostafa Akbari
• 关键词：Performance ; Emissions ; Castor oil biodiesel ; ANN ; NSGA-II ; TOPSIS
• 刊名：Applied Thermal Engineering
• 出版年：2013
• 出版时间：25 September, 2013
• 年：2013
• 卷：59
• 期：1-2
• 页码：309-315
• 全文大小：1441 K

文摘

This paper addresses arti?cial neural network (ANN) modeling followed by multi-objective optimization process to determine optimum biodiesel blends and speed ranges of a diesel engine fueled with castor oil biodiesel (COB) blends. First, an ANN model was developed based on standard back-propagation algorithm to model and predict brake power, brake speci?c fuel consumption (BSFC) and the emissions of engine. In this way, multi-layer perception (MLP) network was used for non-linear mapping between the input and output parameters. Second, modi?ed NSGA-II by incorporating diversity preserving mechanism called the ¦Å-elimination algorithm was used for multi-objective optimization process. Six objectives, maximization of brake power and minimization of BSFC, PM, NO_x, CO and CO₂ were simultaneously considered in this step. Optimization procedure resulted in creating of non-dominated optimal points which gave an insight on the best operating conditions of the engine. Third, an approach based on TOPSIS method was used for ?nding the best compromise solution from the obtained set of Pareto solutions.