Optimization of diesel engine operating parameters fueled with palm oil-diesel blend: Comparative evaluation between response surface methodology (RSM) and artificial neural network (ANN)

Abstract

Engine performance and emission characteristics of palm oil-diesel blends tested on single-cylinder diesel engine by several engine loads and injection advances. Exhaust emissions and smoke were recorded using MRU Delta 1600L and MRU Optrans 1600 model gas analyzer, respectively. Brake thermal efficiency (BTE), exhaust gas temperature (EGT), carbon monoxide (CO), hydrocarbon (HC), smoke and nitrogen oxides (NOx) were optimized as output factors considering engine load, injection advance and palm oil percentage as input variables using response surface methodology (RSM) and artificial neural network (ANN). The developed ANN and RSM models showed superior predictive certainty with big R2 (correlation coefficient) values. The RSM models showed better performance and have higher R2 values than ANN models. The developed RSM model has R2 values over 0.90 while the R2 values of ANN model are between 0.88 and 0.95. The values of mean relative error (MRE) and root mean square error (RMSE) for all the responses were low. Optimum responses were found by 69.11%, 196.25 ppm, 0.126%, 189.764 ppm, 155.49 ℃ and 30.75%, respectively for smoke, NOx, CO, HC, EGT and BTE with optimum operating factors as 17.88% palm oil percentage, 35 °CA injection advance and 780-watt engine load. The applied models gave good results that are beneficial for estimating and optimizing the engine performance and emission characteristics.