Prediction of spark ignition engine performance with bioethanol-gasoline mixes using a multilayer perception model

Abstract

ABSTRACT: The current study examines the impact of bioethanol blends on spark-ignited engine performance using multilayer perception modeling. Waste pomegranate fruits (WPF) from a nearby fruit market are used to make bioethanol. After 72 hours, the fruit juice mixture yielded 1.1 ± 0.3 mLgm−1 ethanol. Each of four ethanol mixes was tested at different speeds to determine the engine's indicated and braking power, volumetric, thermal, and mechanical efficiency. The addition of ethanol increased volumetric efficiency by up to 25% and indicated power by up to 20%. In contrast, ethanol proportions showed thermal efficiency variations. In order to forecast performance parameters, a multilayer perception model with feed forward back propogation is used. 25% of the test data was used to validate the MLP model. The accuracy of designed network was checked by root mean square error, mean squared error and Mean Absolute Percentage Error and higher values of regression coefficient. The constructed MLP model predicted values that were highly accurate and with acceptable error. The results proved that the MLP model can be effectively implemented for predicting engine performance and hopeful prospects for waste pomegranate ethanol at commercial level.