A methodology for modeling batch reactors using generalized dynamic neural networks

This paper presents a methodology based on the application of dynamic artificial neural networks (DANNs) for modeling batch reactors. The network structure was designed by a specific method, called leave-one-out cross-validation. In order to reduce the number of input parameters, the multiway principal component analysis (MPCA) was employed. As a case study, sequencing batch reactor was selected to examine the suggested procedure. The results of DANN model were compared to the experimental data, extracted from the literature. Different statistical tools were used as the evaluation criteria for this comparison. The relative error of training and testing sets were 2.11% and 2.6%, respectively. The regression between the network outputs and the experimental data was more than 0.95. Therefore, the model developed in this work has an acceptable generalization capability and accuracy. In addition, it was proved that the implementation of MPCA with dynamic neural network could enhance the model performance. Furthermore, the comparison between the DANN model predictions with those of a mechanistic model revealed that the recommended model was over two and half times more accurate.