Prediction of a diesel engine exhaust gases physical properties with artificial neural network

In recent years, ANN (artificial neural network) method has been used as an effective method for analyses of the characteristic parameters in internal combustion engines. Also, determination of the best network structure is an important part of the research work in this branch. So, this subject is the main idea of the current study. The most reliable network structure has been determined for prediction of two important engine after-treatment parameters. These parameters are pressure and temperature of the gases at EVO (exhaust valve opening) time. Outputs of four ANN models have been compared with the results of a reliable developed multi-zone combustion model. The ANN models, which have been considered in this research work, are MLP (Multi Layer Perception), RBF (Radial Basis Function), SOM (Self Organized Map) and GFF (Generalized Feed Forward) with training algorithms of LM (Levenberg Marquart) and MOM (Momentum), respectively. Finally, the MLP-LM model has been proposed as the most appropriate model.