Multi-objective optimization of ternary blends of Algal biodiesel–diesel–1-decanol to mitigate environmental pollution in powering a diesel engine using RSM, ANOVA, and artificial bee colony

Abstract

This research presents an in-depth examination that utilizes a hybrid technique consisting of response surface methodology (RSM) for experimental design, analysis of variance (ANOVA) for model development, and the artificial bee colony (ABC) algorithm for multi-objective optimization. The study aims to enhance engine performance and reduce emissions through the integration of global maxima for brake thermal efficiency (BTE) and global minima for brake-specific fuel consumption (BSFC), hydrocarbon (HC), nitrogen oxides (NOx), and carbon monoxide (CO) emissions into a composite objective function. The relative importance of each objective was determined using weighted combinations. The ABC algorithm effectively explored the parameter space, determining the optimum values for brake mean effective pressure (BMEP) and 1-decanol% in the fuel mix. The results showed that the optimized solution, with a BMEP of 4.91 and a 1-decanol % of 9.82, improved engine performance and cut emissions significantly. Notably, the BSFC was reduced to 0.29 kg/kWh, demonstrating energy efficiency. CO emissions were lowered to 0.598 vol.%, NOx emissions to 1509.91 ppm, and HC emissions to 29.52 vol.%. Furthermore, the optimizing procedure produced an astounding brake thermal efficiency (BTE) of 28.78%, indicating better thermal energy efficiency within the engine. The ABC algorithm enhanced engine performance and lowered emissions overall, highlighting the advantageous trade-offs made by a weighted mix of objectives. The study's findings contribute to more sustainable combustion engine practises by providing crucial insights for upgrading engines with higher efficiency and fewer emissions, thus furthering renewable energy aspirations.