Optimization of diesel engine performance and emission using waste plastic pyrolytic oil by ANN and its thermo-economic assessment.

The current study focuses on the engine performance and emission analysis of a 4-stroke compression ignition engine powered by waste plastic oil (WPO) obtained by the catalytic pyrolysis of medical plastic wastes. This is followed by their optimization study and economic analysis. This study demonstrates the use of artificial neural networks (ANN) to forecast a multi-component fuel mixture, which is novel and reduces the amount of experimental effort required to determine the engine output characteristics. The engine tests were conducted using WPO blended diesel at various proportions (10%, 20%, 30% by volume) to acquire the required data for training the ANN model, which enables better prediction for the engine performance by making use of the standard back-propagation algorithm. Considering supervised data obtained from repeated engine tests, an artificial intelligence-based model of ANN was designed to select different parameters of performance and emission as output layers; at the same time, engine loading and different blending ratios of the test fuels were taken as the input layers. The ANN model was built up making use of 80% of testing outcomes for training. The ANN model forecasted engine performance and exhaust emission with regression coefficients (R) at 0.989-0.998 intervals and a mean relative error from 0.002 to 0.348%. Such results illustrated the effectiveness of the ANN model for estimating emissions and the performance of diesel engines. Moreover, the economic viability of the use of 20WPO as an alternative to diesel was justified by thermo-economic analysis.