Model predictive control for the self-optimized operation in wastewater treatment plants: Analysis of dynamic issues |
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| Affiliation: | 1. System Engineering Department, University of Malaga, Campus de Teatinos s/n, 29071, Malaga, Spain;2. System Engineering Department, University of Malaga, Campus de Teatinos s/n, 29071, Malaga, Spain;3. Dept. of Mechanical, Chemical and Materials Engineering, University of Cagliari, Via Marengo, 2, I-09123 Cagliari, Italy;4. Dept. of Civil and Environmental Engineering, Aalto University, P.O. Box 12100, FI-00076 Aalto, Finland;5. Department of Chemical Engineering, Federal University of Campina Grande, 58429-140 Campina Grande, Brazil;6. Centre for Intelligent Systems, Faculty of Science and Technology, University of Algarve, Campus de Gambelas, 8005-139 Faro, Algarve, Portugal;7. System Engineering Department, University of Malaga, Campus de Teatinos s/n, 29071, Malaga, Spain |
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| Abstract: | This paper describes a procedure to find the best controlled variables in an economic sense for the activated sludge process in a wastewater treatment plant, despite the large load disturbances. A novel dynamic analysis of the closed loop control of these variables has been performed, considering a nonlinear model predictive controller (NMPC) and a particular distributed NMPC-PI control structure where the PI is devoted to control the process active constraints and the NMPC the self-optimizing variables. The well-known self-optimizing control methodology has been applied, considering the most important measurements of the process. This methodology provides the optimum combination of measurements to keep constant with minimum economic loss. In order to avoid nonfeasible dynamic operation, a preselection of the measurements has been performed, based on the nonlinear model of the process and evaluating the possibility of keeping their values constant in the presence of typical disturbances. |
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| Keywords: | Self-optimizing control Process optimization Activated sludge process Model predictive control |
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