Catalytic converter performance prediction and engine optimization when powered by diisopropyl ether/gasoline blends: Combined application of response surface methodology and artificial neural network

The main objective of this study is to develop a neural network model to predict high-accuracy responses and optimize the input parameters using response surface methodology to improve catalytic converter performance when powered by diisopropyl ether/gasoline blends. The engine exhaust gas was treated by a commercial catalytic converter by varying the brake power, engine speed, and compression ratio, and the pollutant levels were measured. Again, the experiment was repeated, and the exhaust gas was treated by a sucrose/alumina catalyst-coated converter and the results were compared. Experimentally obtained data were employed to develop a neural network and response surface methodology model. The developed artificial neural network yielded higher R2 values of over 0.9977 for commercial catalytic converter and over 0.99633 for sucrose/alumina catalyst-coated converter. Furthermore, mean square error values were less than 3 % for all the responses which indicates higher prediction accuracy. Similarly, the developed response surface methodology model exhibited a higher F-value and lower p-value which indicates the model is accurate. Moreover, the desirability factor of over 0.99 for both cases shows the model's high stability. The predicted optimum brake power of 8.01 kW at 2500 rpm and 9.5 compression ratio produced lesser emissions and the validation test error was found to be less than 4 %. Thus, the authors conclude that the combined application of artificial neural network and response surface methodology will be efficient in exhaust gas pollution level prediction and optimization.