Intervention of Artificial Neural Network with an Improved Activation Function to Predict the Performance and Emission Characteristics of a Biogas Powered Dual Fuel Engine

Biogas is a significant renewable fuel derived by sources of biological origin. One today’s research issues the effect biofuels on engine efficiency. The experiments are complicated, time consuming and expensive. Furthermore, evaluation cannot be carried out beyond permissible limit. purpose this to build an artificial neural network successfully for dual diesel with view overcoming experimental difficulties. Authors used load, bio-gas flow rate n-butanol concentration as input parameters forecast target variables in analysis, i.e., smoke, brake thermal efficiency (BTE), carbon monoxide (CO), hydrocarbon (HC), nitrous-oxide (NOx). Estimated values results were compared. error analysis showed that built model has quite accurately predicted results. This been described value Coefficient determination (R2), which varies between 0.8493 0.9863 normalized mean square (NMSE) 0.0071 0.1182. potency Nash-Sutcliffe coefficient (NSCE) ranges from 0.821 0.8898 BTE, HC, NOx Smoke. effectively emulated on-board efficiency, emission, combustion features dual-fuel biogas taking Swish activation mechanism (ANN) model.