Predicting the engine performance using ethyl ester of fish oil with the aid of artificial neural network

Abstract

This article describes an application of an artificial neural network (ANN) model for predicting the performance, combustion and emission characteristics of a compression ignition engine using fish oil biodiesel. Experimental investigations are carried out in a single-cylinder constant speed direct injection diesel engine under variable load conditions. The performance, combustion and emission characteristics are measured using an exhaust gas analyzer, smoke meter, piezoelectric pressure transducer and crank angle encoder for different fuel blends and engine load conditions. The obtained data are recorded for each experiment and the associated data are used to train the simulation model using the back-propagation algorithm. The developed ANN model predicts the performance, combustion and exhaust emissions with a correlation coefficient (R) of 0.957–0.999 and a mean relative error of 0.02–3.97%. The root-mean-square errors were found to be low. The developed model has been found to predict the engine performance, combustion and emission parameters accurately for the range of data trained.