Abstract
Due to the multi-variable, nonlinear, large time delay and strong coupling features of the wastewater treatment process, a recurrent high-order neural network is used to model the key water quality parameters(Chemical Oxygen Demand, Biological Oxygen Demand, Suspended Solid and Ammonia Nitrogen) for the wastewater treatment process, and the neural network is trained by an filtering algorithm. Operational data of a wastewater treatment plant is employed to illustrate the efficacy of the proposed modeling method. Meanwhile, the results are compared with feed-forward neural network and the general recurrent neural network to indicate the modeling accuracy of the recurrent high-order neural network.
Published Version
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