Modeling wastewater treatment plant via hierarchical neural networks
污水处理过程的递阶神经网络建模

A hierarchical neural networks based on the mechanism of activated sludge process is introduced for modeling the wastewater treatment plant (WWTP) which includes multivariable and multi-nonlinear subsystems with serial structure. This approach combines the neural network and the mechanism model in a serial configuration; and the nonlinear uncertainties of the activated sludge process are estimated by neural networks. A stable learning algorithm and the theoretical analysis are given for this model based on the relations of various modeling errors among sub-processes. Operational data of a wastewater treatment plant illustrate the efficacy of this modeling approach.