A Comparative Study of Machine Learning Techniques in Prediction of Exhaust Emissions and Performance of a Diesel Engine Fuelled with Biodiesel Blends
2021
Quang Hung Do, Shih-Kuei Lo and Jeng-Fung Chen
Biodiesel has been receiving increasing attention because of its fuel properties and compatibility with petroleum-based diesel fuel. Therefore, it is necessary to measure the engine performance and exhaust emissions of engines using petroleum-based diesel fuel and biodiesel blends. The main goal of this study is to investigate the capability of several machine learning (ML) techniques including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), general regression neural network (GRNN), radial basis function (RBFN), and support vector regression (SVR) for predicting performance and exhaust emissions of the diesel engine fuelled with biodiesel blends. The case application is a Hyundai D4CB 2.5 engine together with B0, B10 and B20 biodiesel blends which are popularly used in Vietnam. The engine process parameters are used as inputs and the outputs include predicted torque and NOx emission. Different predicting models based on ML techniques are developed and validated. The performance of each model is evaluated and compared using root mean squared error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and correlation coefficient (R). The obtained results indicate that SVR can be used to develop the model for the prediction of performance and exhaust emissions. The study also provides a better understanding of the effects of engine process parameters on performance and exhaust emissions.
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