A genetic algorithm-based artificial neural network model with TOPSIS approach to optimize the engine performance

ABSTRACT

This paper describes the application of an artificial neural network, genetic algorithm and analytic network process-technique for order preference by similarity to ideal solution for the selection of the optimum fuel blend. A single-cylinder, constant-speed direct-injection diesel engine with a rated output of 4.4 kW was used for exploratory analysis at different load conditions. Ten objectives – brake thermal efficiency, maximum rate of pressure rise, NOx, CO2, CO, HC, smoke, exhaust gas temperature, ignition delay and combustion delay were considered. The proposed ANN model is integrated with GA and the hybrid multi-criteria decision-making technique of ANP-TOPSIS to evaluate the optimum blend. First the ANN model was developed to predict the performance, combustion and emission parameters of the engine. A multi-layer perception network was used for non-linear mapping between input and output parameters. The performance of the ANN model is determined and shows the efficiency of the model to predict the performance, emission and combustion parameters, with a determination coefficient of 0.9627. Second, GA was used to determine the optimum load and blend based on the predicted ANN output parameter. Third, an approach based on the TOPSIS method was used for finding the best blend from the observed optimum parameters of GA.

KEYWORDS:

• Engine
• biofuels
• performance
• MCDM
• ANN
• genetic algorithm

Disclosure statement

No potential conflict of interest was reported by the author.