Performance and emission prediction of a compression ignition engine fueled with biodiesel-diesel blends: A combined application of ANN and RSM based optimization

• Published in: Fuel (Guildford) 2020-06, Vol.269, p.117472, Article 117472
• Main Authors: Aydın, Mustafa, Uslu, Samet, Bahattin Çelik, M.
• Format: Article
• Language: English
• Subjects: Artificial neural network; Artificial neural networks; Biodiesel; Biodiesel fuels; Biofuels; Brakes; Carbon monoxide; Compression; Diesel; Diesel engine; Diesel engines; Diesel fuels; Emission analysis; Emissions; Emissions control; Exhaust emission; Exhaust gases; Gas temperature; Injection; Mathematical models; Mixtures; Multilayers; Neural networks; Nitrogen oxides; Optimization; Parameters; Photochemicals; Prediction; Regression coefficients; Response surface methodology; Thermodynamic efficiency
• ISSN: 0016-2361; 1873-7153
• DOI: 10.1016/j.fuel.2020.117472

In the present study, the performance and emission parameters of a single cylinder diesel engine powered by biodiesel-diesel fuel blends were predicted by Artificial Neural Network (ANN) and optimized by Response Surface Methodology (RSM). The data to be used for ANN and RSM applications were obtained by using biodiesel/diesel fuel blends at different engine loads and various injection pressures. ANN model has been developed to predict the outputs such as brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), exhaust gas temperature (EGT), nitrogen oxides (NOx), hydrocarbons (HC), carbon monoxide (CO) and smoke regarding engine load, biodiesel ratio and injection pressure. A feed-forward multi-layer perceptron network is used to show the correlation among the input factors and the output factors. The RSM is applied to find the optimum engine operating parameters with the purpose of simultaneous reduction of emissions, EGT, BSFC and increase BTE. The obtained results reveal that the ANN can correctly model the exhaust emission and performance parameters with the regression coefficients (R2) between 0.8663 and 0.9858. It is seen that the maximum mean relative error (MRE) is less than 10%, compared with the experimental results. The RSM study demonstrated that, biodiesel ratio of 32% with 816-W engine load and 470 bar injection pressure are the optimum engine operating parameters. It is found that the ANN with RSM support is a good tool for predict and optimize of diesel engine parameters powered with diesel/biodiesel mixtures.