IDI diesel engine performance and exhaust emission analysis using biodiesel with an artificial neural network (ANN)

• Published in: Egyptian journal of petroleum Vol. 26; no. 3; pp. 593 - 600
• Main Authors: Prasada Rao, K., Victor Babu, T., Anuradha, G., Appa Rao, B.V.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2017
• Subjects: Artificial neural network; Indirect diesel injection engine; Isopropanol; Mean square error; Rice Bran Methyl Ester; Artificial neural network; Rice Bran Methyl Ester; Isopropanol; Indirect diesel injection engine; Mean square error
• ISSN: 1110-0621
• DOI: 10.1016/j.ejpe.2016.08.006

Biodiesel is receiving increasing attention each passing day because of its fuel properties and compatibility. This study investigates the performance and emission characteristics of single cylinder four stroke indirect diesel injection (IDI) engine fueled with Rice Bran Methyl Ester (RBME) with Isopropanol additive. The investigation is done through a combination of experimental data analysis and artificial neural network (ANN) modeling. The study used IDI engine experimental data to evaluate nine engine performance and emission parameters including Exhaust Gas Temperature (E.G.T), Brake Specific Fuel Consumption (BSFC), Brake Thermal Efficiency (B.The) and various emissions like Hydrocarbons (HC), Carbon monoxide (CO), Carbon dioxide (CO2), Oxygen (O2), Nitrogen oxides (NOX) and smoke. For the ANN modeling standard back propagation algorithm was found to be the optimum choice for training the model. A multi-layer perception (MLP) network was used for non-linear mapping between the input and output parameters. It was found that ANN was able to predict the engine performance and exhaust emissions with a correlation coefficient of 0.995, 0.980, 0.999, 0.985, 0.999, 0.999, 0.980, 0.999, and 0.999 for E.G.T, BSFC, B.The, HC, O2, CO2, CO, NOX, smoke respectively.