Machine learning of weighted superposition attraction algorithm for optimization diesel engine performance and emission fueled with butanol-diesel biofuel

Abstract

• Butanol-diesel biofuel shows promise for use in diesel engines. • Machine learning of weighted superposition attraction (WSA) algorithm is used. • Optimization improves engine performance and reduces emissions. • Statistical significance of regression models and ANOVA confirms optimization. • Further research needed on economic, environmental, and dynamic optimization. Machine learning (ML) is a subset of artificial intelligence (AI) and computer science that employs data and algorithms and mimics human learning to self-enhance its accuracy. In biofuel research, butanol is widely recognized as a prospective alternative biofuel. Butanol addition in diesel or combustion engine has been more and more studied recently. Gaining a comprehensive comprehension of butanol performance and emission characteristics using machine learning approach is an essential milestone in investigating alcohol-based biofuel addition in diesel engines. However, few studies investigated butanol effect on diesel engine emissions using machine learning for optimization. A novel optimization study is needed. This work aims to investigate the newly developed and efficient machine learning, weighted superposition attraction (WSA) algorithm, to optimize the emission and performance of diesel engines fuelled with butanol-diesel biofuel. Mathematical modeling between the factors (butanol (vol.%) and BMEP (bar)) and the responses (BTE (%), BSFC (g/kWh), Exhaust Temperature Texh ( o C), NOx (g/kWh), CO (g/kWh), HC (g/kWh), and Smoke Opacity (%)) are governed using regression modeling. The optimized and best factor levels are determined employing the machine learning of WSA Algorithm. Confirmations are carried out. Optimization results indicate that the BTE is maximized, and the remainder of the responses are minimized.

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