Experimental investigation and performance optimization of ci engine for fuel produced from waste plastics

Abstract

newline The present study offers a sustainable and practical approach to managing waste plastic by addressing the disparity between global plastic production and the generation of plastic waste. A series of experimental assessments were executed on a single-cylinder stationary diesel engine utilizing a blend of WPO as the fuel. The engine was run under different load conditions for WPO10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and WPO100% at no load, full load and an overloaded (120%) engine capacity. Engine performance parameters were gauged using a soft data acquisition system and a computer-linked interface. An escalation in the proportions of WPO results in a corresponding rise in emission level particularly NOx levels attributed to the heightened heat release rate and combustion temperature. This research delves into advanced optimization methods like Teaching-Learning-Based Optimization (TLBO), JAYA and artificial neural network (ANN) modelling for the purpose of predicting and enhancing the performance parameter of engine. The comparison of the proposed algorithms is employed in order to discover novel optimization techniques for forecasting and enhancing the performance parameters of a WPO fuel and its mixtures in a CI engine. Finally Obtained results validated by the experimental results selected input parameters from chosen optimization techniques. The ANN model was established employing 85% of test results for training purposes. The ANN model anticipated engine performance and exhaust emissions with regression square coefficients (R2) ranging from 0.9809 to 0.9588 and a mean relative error between 0.122% and 0.348%. The investigation suggested that WPO30 fuel has the potential to be incorporated in diesel engine.