Publication

Abstract : The escalating worries regarding environmental sustainability and the exhaustion of fossil fuel resources have sparked concerns and extensive exploration for substitute fuels for internal combustion engines are being conducted alternative fuels for internal combustion engines are being conducted. In the current investigation prophecy of engine and emissions performance on a CI Engine using waste fry biofuel was carried out, by utilizing cutting-edge Machine Learning (ML) and Deep Learning (DL) techniques. Experimental records from engine tests under diverse RPM and load condition are scrutinized with these algorithms. Various algorithms and training functions were employed to educate the models. The analysis establishes that waste fry biofuel has a momentous influence on engine performance, encompassing brake thermal efficiency and specific fuel consumption, while also diminishing detrimental emissions such as NOx (Nitrogen Oxides), smoke, and CO (Carbon Monoxide). ML and DL models aid in foretelling engine operation and emission control strategies, accentuating the potential of waste fry biofuel as a sustainable and environmentally friendly alternative to traditional diesel fuel.