Data-driven decision-making for wastewater treatment process

• Published in: Control engineering practice 2020-03, Vol.96, p.104305, Article 104305
• Main Authors: Han, Hong-Gui, Zhang, Hui-Juan, Liu, Zheng, Qiao, Jun-Fei
• Format: Article
• Language: English
• Subjects: Data-driven decision-making method; Intelligent decision-making system; Long-term prediction method; Membrane fouling; Multi-category diagnosis method; Multi-warning method
• ISSN: 0967-0661; 1873-6939
• DOI: 10.1016/j.conengprac.2020.104305

Membrane fouling has become a serious issue for the safe operation of wastewater treatment process (WWTP). To deal with this problem, this paper proposes a data-driven decision-making method to reduce the incidence of membrane fouling in WWTP. The main novelties of this proposed data-driven decision-making method are threefold. First, a long-term prediction method, based on a self-organizing deep belief network (SDBN) and the multi-step prediction strategy, is developed to predict the membrane permeability. Second, a multi-warning method, based on an independent component analysis-principal component analysis (ICA-PCA) algorithm, is proposed to detect and warn membrane fouling with multiple indicators. Third, a multi-category diagnosis method, based on the kernel function, is designed to diagnose membrane fouling for providing the decision support. Finally, an intelligent decision-making system, consisting the above methods and required sensors, is developed for some real wastewater treatment plants. The experimental results demonstrated the efficiency and effectiveness of the proposed data-driven decision-making method.