Prediction of spark ignition engine performance responses fueled with fusel oil/gasoline blends by artificial neural network

Abstract

In the present study, the performance parameters of a single-cylinder, air-cooled spark ignition (SI) engine using fusel oil-gasoline fuel blends were predicted by artificial neural network (ANN). The SI engine was operated with gasoline/fusel oil (10% and 20%) blends at different engine load (1000, 2000, 3000, 4000, 5000, 6000, 7000 and 8000 Watt) and compression ratios (8.00, 9.12 and 10.07) to obtain data essential to create the ANN model. In the constructed ANN model, brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC) are chosen as output parameters, while engine load, compression ratio (CR) and fusel oil ratio are chosen as input factors. 75% of the test results were employed to train the ANN. The performance of ANN model was determined by comparing it with the data produced from the part not used for training. According to the found data, ANN model estimated engine performance parameters such as BTE and BSFC by an overall regression coefficient (R) at 0.99384. Simultaneously, mean absolute percentage error (MAPE) were found as 5.027% and 7.847% for BTE and BSFC, respectively. When ANN results and experimental results are compared for BTE and BSFC responses, it is determined that ANN results are close to experimental results with an error rate of less than 5%.