Artificial neural network approach to predict the engine performance of fish oil biodiesel with diethyl ether using back propagation algorithm

Abstract

An artificial neural network (ANN) model is developed to predict the engine performance of fish oil biodiesel blended with diethyl ether. Engine performance and emission characteristics such as brake thermal efficiency, hydrocarbon, exhaust gas temperature, oxides of nitrogen (NO_x), carbon monoxide (CO), smoke and carbon dioxide (CO₂) were considered. Experimental investigations on single-cylinder, constant speed, direct injection diesel engine are carried out under variable load conditions. The performance and emission characteristics are measured using an exhaust gas analyser, smoke metre, piezoelectric pressure transducer and crank angle encoder for different fuel blends and engine load conditions. In this model, a back propagation algorithm is used to predict the performance. Computational results clearly demonstrated that the developed ANN models produced less deviations and exhibited higher predictive accuracy with acceptable determination correlation coefficients of 0.97–1 and mean relative error of 0–3.061% with experimental values. The root mean square errors were found to be low. The developed model produces the idealised results and it has been found to be useful for predicting the engine performance and emission characteristics with limited number of available data.

Keywords:

• artificial neural network
• correlation coefficient
• root mean square error
• fish oil biodiesel
• diethyl ether
• engine performance