Performance-exhaust emission prediction of diesosenol fueled diesel engine: An ANN coupled MORSM based optimization

The present study investigates the usability of oxygenated fuel on the performance and exhaust emissions of a Diesel engine fueled with adulterated Diesel. In view of the engine experimentations, an Artificial Intelligence (AI) based Artificial Neural Network (ANN) model has been developed to predict outputs such as brake thermal efficiency (Bth), brake specific energy consumption (BSEC), oxides of nitrogen (NOX), unburned hydrocarbon (UBHC) and carbon monoxide (CO) with respect to engine load (%), Ethanol share (vol%) and Kerosene share (vol%). The proposed ANN model is found to be capable of mapping the input-output paradigms of ternary blends of Diesel-kerosene-ethanol (Diesosenol) with commendable accuracy. The combined results of error and correlation matrices with statistical analysis of ANN predicted outputs showed itself as robust and applicable mapping tool in Diesosenol platforms. Furthermore, the study incorporated Multi Objective Response Surface Methodology (MORSM) to find out the favorable engine operating condition. The trade-off study demonstrated that, kerosene share of 2.42% (by vol.) and Ethanol share of 10% (by vol.) at 74.14% engine load is the optimal, which was further validated by experimentation. The ANN coupled MORSM model thus developed is found to be an effective tool to predict engine outputs with minimal experimentation.