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Abstract: Great efforts have been made to resolve the serious environmental pollution and inevitable declining of energy resources. A review of Chinese fuel reserves and engine technology showed that compressed natural gas (CNG)/diesel dual fuel engine (DFE) was one of the best solutions for the above problems at present. In order to study and improve the emission per-formance of CNG/diesel DFE, an emission model for DFE based on radial basis function (RBF) neural network was developed which was a black-box input-output training data model not require priori knowledge. The RBF centers and the connected weights could be selected automatically according to the distribution of the training data in input-output space and the given approximating error. Studies showed that the predicted results accorded well with the experimental data over a large range of operating conditions from low load to high load. The developed emissions model based on the RBF neural network could be used to successfully predict and optimize the emissions performance of DFE. And the effect of the DFE main performance parameters, such as rotation speed, load, pilot quantity and injection timing, were also predicted by means of this model. In resumé, an emission prediction model for CNG/diesel DFE based on RBF neural network was built for analyzing the effect of the main performance parameters on the CO, NO_x emissions of DFE. The predicted results agreed quite well with the traditional emissions model, which indicated that the model had certain application value, although it still has some limitations, because of its high dependence on the quantity of the experimental sample data.

[1] Fei, S.M., Liu, Z.T., Yan, Z.D., 2003. Knock prediction for dual fuel engines by using a simplified combustion model. Journal of Zhejiang University SCIENCE, 4(5):591-594.

[2] Ilkivová, M.R., Ilkiá, B.R., Neuschl, T., 2002. Comparison of a linear and nonlinear approach to engine misfire detection. Control Engineering Practice, 10:1141-1146.

[3] Kaoru, I., Youji, I., Hajime, M., 2003. Image restoration using the RBF network with variable regularization parameters. Neurocomputing, 50:177-191.

[4] Korres, D.M., Anastopoulos, G., Lois, E., Alexandridis, A., Sarimveis, H., Bafas, D., 2002. A neural network approach to the prediction of diesel fuel lubricity. Fuel, 81:1243-1250.

[5] Liu, Z.T., 2000. A Study of Combustion and Control Model of Natural Gas-Diesel Fuel Engine by Means of Neural Network. Ph.D thesis, Zhejiang University (in Chinese).

[6] Omatu, S., Khalid, M., 1996. Neuro-control and its Applications. Springer-Verlag, London.

[7] Yan, Z.D., Kriam, G.A., 1992. A predictive model for dual fuel D. I diesel engine performance and emission. ASME, 27:33-39.

[8] Yan, Z.D., Zhou, C.G., SU, S.C., Liu, Z.T., Wang, X.Z., 2003. Application of neural network in the study of combustion rate of natural gas/diesel dual fuel engine. Journal of Zhejiang University SCIENCE, 4(2):170-174.

[9] Zhou, B., Tan, D.M., Wei, D.Y., 2001. Prediction of the Emissions from internal combustion engine using a neural network. CSICE, 4(2):361-364.