Data-based robust model predictive control for wastewater treatment process. (October 2022)

• Record Type: Journal Article
• Authors: Han, Hong-Gui; Wang, Chen-Yang; Sun, Hao-Yuan; Qiao, Jun-Fei
• Is Part Of: Journal of process control. Volume 118(2022)
• Page Start: 115
• Page End: 125
• Publication Date: 2022-10
• Subjects: Data-driven -- A learning method -- Robust model predictive control -- Wastewater treatment process; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2022.08.013
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: Data-driven methods are widely used in wastewater treatment processes (WWTPs). However, disturbances are not fully considered during the control process, which may affect the stable operation of WWTPs. To address this issue, a data-driven robust model predictive control (DRMPC) method is proposed for WWTPs with disturbances. First, an interval type-2 fuzzy neural network (IT2FNN), which has high robust performance to disturbances, is used to identify dynamic behavior of WWTPs. Second, based on IT2FNN, a robust stabilizing controller is developed to weaken the influence of disturbances on control performance and stability. Third, the proposed controller ensures constraint satisfaction and input-to-state stability (ISS). Finally, the feasibility of DRMPC strategy is verified on the benchmark simulation model No. 1 (BSM1). The experimental studies indicate that the proposed DRMPC is capable of running stably and tracking accurately. Graphical abstract: Highlights: Identify dynamic behavior of WWTPs by using IT2FNN. Reduce the influence of disturbances on control performance and stability via the DRMPC algorithm. The proposed controller ensures constraint satisfaction and input-state stable (ISS). The feasibility of DRMPC strategy is verified on the benchmark simulation model No. 1 (BSM1).