Multi objective optimization of diesel engine performance and emission characteristics using taguchi-grey relational analysis

A promising solution to the problem of fuel depletion and energy security is the use of biodiesel as an alternative fuel for diesel engines. It is used in blended or pure forms without any engine modification. The aim of this study is to reduce smoke and nitrogen oxide (NOx) emissions while maintaining brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC) in a biodiesel-fuelled variable compression ratio (VCR) diesel engine. Experiments were conducted using a Taguchi design based L9 orthogonal array. The effects of three control parameters were investigated: engine load, blend ratio, and compression ratio (CR). The signal-to-noise (S/N) ratio of Taguchi was calculated based on their performance characteristics. Using a response table and a response graph, the optimal level of control factors was determined based on this grade. An analysis of variance (ANOVA) is used to estimate the individual effects of components. The results of the trials show that 75% engine load, 20% blend ratio, and a CR of 18 are the best combinations for reducing smoke and NOx. When compared to diesel this combination results in a 32.3% reduction in smoke, a 19.7% reduction in NOx emissions, a marginal decrease of 2.17% in BTE, and a 5.9% decrease in BSFC. It is evident that Taguchi design combined with grey relational analysis (GRA) can efficiently predict response values using an optimal combination of control factors.