Application of fuzzy-assisted grey Taguchi approach for engine parameters optimization on performance-emission of a CI engine

Abstract

ABSTRACTWith the prevalent interest in diesel engine performance and emission, it is necessary to define optimum engine operating condition with less number of experiments for the efficient and effective outcome. In this article, the combined effect of input parameters viz. engine load and types of fuel blend in controlling BSFC, NOx, UHC, and CO output variables in a diesel engine are investigated. In this study, a fuzzy-assisted grey Taguchi method has been proposed to optimize the engine load and types of fuel used. The main purpose of using fuzzy inference system is to convert the multi-response into an equivalent single objective optimization. Optimum input factor corresponding to estimated values of output response has been obtained by employing fuzzy grey reasoning grade. The computed factor combination based on the highest ranking of fuzzy grey grade is validated through a confirmation experiment. Based on grey-fuzzy approach optimum engine parameter is found D85B10E5 at 100% load. ANOVA analysis of Grey Fuzzy Grade reveals engine load is the most significant input factor influencing engine output. Based on the results it is concluded that grey-fuzzy-Taguchi approach can be a more useful tool to ameliorate performance and emission of an engine compared to simple grey relational grade.Abbreviations: CI: compression ignition; BSEC: break specific energy consumption; NOx: nitrogen oxides; UHC: unburned hydrocarbon; CO: carbon monoxide; GRA: grey relational analysis; GRC: grey relational coefficient; GRG: grey relational grade; ANOVA: analysis of variance; DAQ: data acquisition; S/N: signal to noise ratio; FIS: fuzzy interface system; GFG: grey fuzzy grade