Performance and emission modeling of a 4-stroke diesel engine with biodiesel extracts from waste cooking oil blends with ZnO nanoparticle using ELM

Abstract

This research article provides the artificial neural network (ANN) model developed to predict the performance and emission behaviour of 4-stroke single cylinder diesel engine with biodiesel extracts from waste cooking oil blends prepared by transesterification method with ZnO nanoparticle in bimodal fuel. The ANN model was developed to predict the brake thermal efficiency (BTE), Brake-specific fuel consumption (BSFC), carbon monoxide (CO), carbon dioxide (CO2), nitrogen oxide (Nox) and un-burnt hydro carbons (UBHC) under the heterogeneous load conditions, biodiesel and NP content. An efficient learning algorithm namely extreme learning machine (ELM) algorithm is used for predicting performance and emissions characteristics. It was found that mean square error (MSE) of 0.00128 with 15 hidden neurons converging in 0.00452 s.