A systematic approach to data-driven modeling and soft sensing in a full-scale plant

摘要

The well-known mathematical modeling and neural networks (NNs) methods have limitationsnto incorporate the key process characteristics at the wastewater treatment plants (WWTPs)nwhich are complex, non-stationary, temporal correlation, and nonlinear systems. In this study,na systematic methodology of NNs modeling which can be efficiently included in the key modelingninformation of the WWTPs is performed by selecting the temporal effect of the hydraulics basednon multi-way principal components analysis (MPCA). The proposed method is applied fornmodeling wastewater quality of a full-scale plant, which is a Daewoo nutrient removal (DNR)nprocess. Through the experimental results in a full-scale plant, the efficiency of the proposednmethod is evaluated and the prediction capability is highly improved by the inclusion of thenhydraulics term due to the optimized structure of neural networks.