Dynamic–static​ model for monitoring wastewater treatment processes. (March 2023) - Han, Hong-Gui; Sun, Chen-Xuan; Wu, Xiao-Long; Yang, Hong-Yan; Zhao, Nan; Li, Jie; Qiao, Jun-Fei
Abstract: Data-driven models (DDMs) are widely developed for monitoring wastewater treatment processes (WWTPs). However, DDMs, derived from invalid or noisy datasets, may fail to capture the dominant features of WWTPs and further result in inferior monitoring results. To solve this issue, a dynamic–static model is designed to monitor WWTPs. Primarily, the operational status of WWTPs is divided by a receding condition partition strategy, which can prevent the mutual interference of fluctuations among different operational conditions. As to the operational conditions without invalid datasets, the dynamic features of WWTPs are extracted by a dynamic intelligent model (DIM). DIM is built using an interval type-2 fuzzy neural network to mimic the dynamic relationship between process variables accurately. Meanwhile, the unreliable results caused by invalid datasets are compensated by a static statistical model. This model is developed to describe static properties using historical datasets, which are copied to execute the monitoring of WWTPs with a similarity discrimination mechanism. Finally, the proposed dynamic–static model is validated by several experiments in terms of total nitrogen removal under multiple operational conditions. The experimental results illustrate that the proposed model can ensure continuous and reliable monitoring of WWTPs. Highlights: In this work, a dynamic–static model is designed to monitor WWTPs. The operational status of WWTPs is divided by a receding condition partition strategy. As to the operational conditions without invalid datasets, the dynamic features of WWTPs are extracted by a dynamic intelligent model. The unreliable results caused by invalid datasets are compensated by a static statistical model.