Multi-Objective Optimization Using Fuzzy-Logic of Exhaust Emissions and Performance of Mahua and Jatropha Biodiesel–Bio-Additive Blends

Abstract

Background: The main challenge for the widespread use of biodiesel in compression ignition engines is to reduce exhaust emissions without compromising the performance of the engine; to do this, the key responses (oxides of nitrogen (NOx), smoke, unburnt hydrocarbon (HC), carbon monoxide (CO), brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC)) are varied in counterproductive ways by fuel and operating variables.

Methods: The aim of this study is to combine these six responses into a single intelligently interpretable multi-response performance index (MRPI) for 20 % mahua (MEOM) and jatropha (MEOJ) methyl-ester blends with and without a bio derived additive in a variable-compression-ratio (VCR) diesel engine using a hierarchical Mamdani fuzzy inference system (FIS) and an environment-weighted rule base.

Results: The operating points were ranked, the analysis of variance (ANOVA) was used to allocate the contributions of the factors and the ranking was independently confirmed by the TOPSIS multi-criteria method and a rule-base sensitivity analysis. In tThe smoke, HC and CO emission of the optimum for the jatropha biodiesel-additive blend (B20MEOJBA3ml) at the compression ratio of 17.5. it was found that around 24 % less, 12 % less and 36 % less respectively compared to the plain Mahua blend at the same compression ratio with no gain in NOx with slight increase in efficiency.

Conclusion: The same point was also ranked by TOPSIS (ρ = 0.77) and the optimal was not affected by the different rule bases based on the environment, balance and performance, respectively, indicating the robustness of the fuzzy recommendation.