Abstract
The main goal of this study is to investigate the capability of several heuristic algorithms, including cuckoo search (CS), gravitational search algorithm (GSA), particle swarm optimization (PSO), and genetic algorithm (GA) in training neural networks 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 neural network trained by different algorithms are developed and investigated. The performance of each model is evaluated and compared using correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE). The expected results indicate that neural networks with parameters optimized by heuristic algorithms can be utilized to develop the model for the prediction of performance and exhaust emissions. The study also provided a better understanding of the effects of engine process parameters on performance and exhaust emissions.
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Do, Q.H., Tuan, T.T., Ha, L.T.T., Doan, T.T.H., Nguyen, T.V.A., Tan, L.T. (2020). Development of Artificial Neural Networks Trained by Heuristic Algorithms for Prediction of Exhaust Emissions and Performance of a Diesel Engine Fuelled with Biodiesel Blends. In: Dey, N., Ashour, A., Bhattacharyya, S. (eds) Applied Nature-Inspired Computing: Algorithms and Case Studies. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-13-9263-4_11
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