Paper Info
Title
Data-Based Predictive Control for Wastewater Treatment Process.
Abstract
Wastewater treatment process (WWTP) has long been a challenging industrial issue due to its built-in uncertainties and discontinuous measurement of system states. To solve this problem, in this paper, a data-based predictive control (DPC) strategy. based on the available sensing measurements, is proposed to control the dissolved oxygen (DO) concentration in WWTP. First, a self-organizing fuzzy neural network, which can adjust both the structure and parameters simultaneously, is developed to identify the real-time states of WWTP. Second, an improved nonlinear predictive control method is designed to reduce the online computation complexity by transforming the constrained conditions into an unconstrained nonlinear programming problem. Then, an adaptive second order Levenberg-Marquardt algorithm is developed to derive the control law of DPC. Third, the theoretical analysis on the stability is also given to confirm the prerequisite of any successful application of DPC, Finally, the proposed DPC strategy is applied to the Benchmark Simulation Model No. 1. Experimental results demonstrate that the control performance of the proposed DPC is better than some existing methods.
Year
DOI
Venue
2018
10.1109/ACCESS.2017.2779175
IEEE ACCESS
Keywords
Field
DocType
Wastewater treatment process,data-based predictive control strategy,self-organizing fuzzy neural network,adaptive second-order Levenerg-Marquardt algorithm.
Optimal control,Control theory,Computer science,Model predictive control,Nonlinear programming,Online computation,Nonlinear predictive control,Fuzzy control system,Artificial neural network,Distributed computing
Journal
Volume
ISSN
Citations 
6
2169-3536
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Hong-Gui Han147639.06
Lu Zhang216340.09
Jun-Fei Qiao379874.56