Artificial Neural Network based prediction of a direct injected diesel engine performance and emission characteristics powered with biodiesel

Abstract

In this assessment, the ANN (Artificial Neural Network) offers the display of a diesel engine using biodiesel fuel to predict engine emissions and overall performance. In order to collect training and testing data for the planned ANN, a single-cylinder, 4-stroke diesel engine will be powered with biodiesel and diesel fuel varieties and will be run at variable load at stable engine rpm. Preliminary outcomes revealed that blends of biodiesel offer higher performance of an engine and enhanced emission qualities. An ANN model was progressed to be envisioning a relationship between brake thermal performance and exhaust emanations, such as carbon monoxide (CO), unburned hydrocarbon (HC), nitrogen oxides (NOx) and smoke intensity, the utilization of biodiesel-diesel blends and loads as input data. Approximately 70% of the overall experimental data was used for training, while 30% was used for testing. In this model, the standard Back-Propagation algorithm for the engine was used. It revealed that the ANN model can predict the engine output and exhaust emissions quite well with a regression coefficient lying closer to one, While the mean square error (MSE) was found to be very low.