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

This paper addresses artificial 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 specific 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, modified NSGA-II by incorporating diversity preserving mechanism called the ε-elimination algorithm was used for multi-objective optimization process. Six objectives, maximization of brake power and minimization of BSFC, PM, NOx, 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 finding the best compromise solution from the obtained set of Pareto solutions.