A review on application of artificial neural network (ANN) for performance and emission characteristics of diesel engine fueled with biodiesel-based fuels. (October 2021) - Tuan Hoang, Anh; Nižetić, Sandro; Chyuan Ong, Hwai; Tarelko, Wieslaw; Viet Pham, Van; Hieu Le, Tri; Quang Chau, Minh; Phuong Nguyen, Xuan
Highlights: Mechanism, classification, and evolutions of ANN model for prediction. Performance of ANN model for predicting engine behaviors fueled with biodiesel. Challenges and future direction of ANN model for engine application. Abstract: Biodiesel has been emerging as a potential and promising biofuel for the strategy of reducing toxic emissions and improving engine performance. Computational methods aiming to offer numerical solutions were inevitable as a study methodology which was sometimes considered the only practical method. Artificial neural networks (ANN) were data-processing systems, which were used to tackle many issues in engineering and science, especially in some fields where there was a failure of the conventional modeling approaches. Thus, it was believed that the best choice was the development of a novel approach like the ANN model to anticipate engine performance and exhaust emissions with high accuracy. In this review paper, the structure and applicability of the ANN model were comprehensively evaluated. More importantly, the use of ANN with trained, tested, and validated data was introduced to determine the performance and emission characteristics of a diesel engine fueled with biodiesel-based fuel. In general, the ANN model could supply a relatively high determination coefficient as compared between predicted results and experimental data, showing that the ANN model could have a good ability to predict the engine behaviors with an accuracy higher than 95%.