Data-Based Predictive Control for Wastewater Treatment Process
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 pro...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8125676/ |
| _version_ | 1818927990566813696 |
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| author | Hong-gui Han Lu Zhang Jun-fei Qiao |
| author_facet | Hong-gui Han Lu Zhang Jun-fei Qiao |
| author_sort | Hong-gui Han |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T03:21:47Z |
| format | Article |
| id | doaj.art-ee99b7f76aa640dcb2ba101cfa325b88 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T03:21:47Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ee99b7f76aa640dcb2ba101cfa325b882022-12-21T19:55:12ZengIEEEIEEE Access2169-35362018-01-0161498151210.1109/ACCESS.2017.27791758125676Data-Based Predictive Control for Wastewater Treatment ProcessHong-gui Han0https://orcid.org/0000-0001-5617-4075Lu Zhang1https://orcid.org/0000-0002-1601-9596Jun-fei Qiao2Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing, ChinaBeijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing, ChinaBeijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing, ChinaWastewater 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.https://ieeexplore.ieee.org/document/8125676/Wastewater treatment processdata-based predictive control strategyself-organizing fuzzy neural networkadaptive second-order Levenberg-Marquardt algorithm |
| spellingShingle | Hong-gui Han Lu Zhang Jun-fei Qiao Data-Based Predictive Control for Wastewater Treatment Process IEEE Access Wastewater treatment process data-based predictive control strategy self-organizing fuzzy neural network adaptive second-order Levenberg-Marquardt algorithm |
| title | Data-Based Predictive Control for Wastewater Treatment Process |
| title_full | Data-Based Predictive Control for Wastewater Treatment Process |
| title_fullStr | Data-Based Predictive Control for Wastewater Treatment Process |
| title_full_unstemmed | Data-Based Predictive Control for Wastewater Treatment Process |
| title_short | Data-Based Predictive Control for Wastewater Treatment Process |
| title_sort | data based predictive control for wastewater treatment process |
| topic | Wastewater treatment process data-based predictive control strategy self-organizing fuzzy neural network adaptive second-order Levenberg-Marquardt algorithm |
| url | https://ieeexplore.ieee.org/document/8125676/ |
| work_keys_str_mv | AT hongguihan databasedpredictivecontrolforwastewatertreatmentprocess AT luzhang databasedpredictivecontrolforwastewatertreatmentprocess AT junfeiqiao databasedpredictivecontrolforwastewatertreatmentprocess |