Multimodel Anomaly Identification and Control in Wet Limestone-Gypsum Flue Gas Desulphurization System
Sulphur dioxide, as one of the most common air pollutant gases, brings considerable numbers of hazards on human health and environment. For the purpose of reducing the detrimental effect it brings, it is of urgent necessity to control emissions of flue gas in power plants, since a substantial propor...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/6046729 |
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| author | Xiaoli Li Quanbo Liu Kang Wang Fuqiang Wang Guimei Cui Yang Li |
| author_facet | Xiaoli Li Quanbo Liu Kang Wang Fuqiang Wang Guimei Cui Yang Li |
| author_sort | Xiaoli Li |
| collection | DOAJ |
| description | Sulphur dioxide, as one of the most common air pollutant gases, brings considerable numbers of hazards on human health and environment. For the purpose of reducing the detrimental effect it brings, it is of urgent necessity to control emissions of flue gas in power plants, since a substantial proportion of sulphur dioxide in the atmosphere stems from flue gas generated in the whole process of electricity generation. However, the complexity and nondeterminism of the environment increase the occurrences of anomalies in practical flue gas desulphurization system. Anomalies in industrial desulphurization system would induce severe consequences and pose challenges for high-performance control with classical control strategies. In this article, based on process data sampled from 1000 MW unit flue gas desulphurization system in a coal-fired power plant, a multimodel control strategy with multilayer parallel dynamic neural network (MPDNN) is utilized to address the control problem in the context of different anomalies. In addition, simulation results indicate the applicability and effectiveness of the proposed control method by comparing with different cases. |
| format | Article |
| id | doaj-art-27e0387bca984fbabf1ae3caa4967507 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-27e0387bca984fbabf1ae3caa49675072025-02-03T05:52:24ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/60467296046729Multimodel Anomaly Identification and Control in Wet Limestone-Gypsum Flue Gas Desulphurization SystemXiaoli Li0Quanbo Liu1Kang Wang2Fuqiang Wang3Guimei Cui4Yang Li5Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaTechnology Research Center, Shenhua Guohua(Beijing) Electric Power Research Institute Corporation, Beijing 100025, ChinaSchool of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, ChinaSchool of International Studies, Communication University of China, Beijing 100024, ChinaSulphur dioxide, as one of the most common air pollutant gases, brings considerable numbers of hazards on human health and environment. For the purpose of reducing the detrimental effect it brings, it is of urgent necessity to control emissions of flue gas in power plants, since a substantial proportion of sulphur dioxide in the atmosphere stems from flue gas generated in the whole process of electricity generation. However, the complexity and nondeterminism of the environment increase the occurrences of anomalies in practical flue gas desulphurization system. Anomalies in industrial desulphurization system would induce severe consequences and pose challenges for high-performance control with classical control strategies. In this article, based on process data sampled from 1000 MW unit flue gas desulphurization system in a coal-fired power plant, a multimodel control strategy with multilayer parallel dynamic neural network (MPDNN) is utilized to address the control problem in the context of different anomalies. In addition, simulation results indicate the applicability and effectiveness of the proposed control method by comparing with different cases.http://dx.doi.org/10.1155/2020/6046729 |
| spellingShingle | Xiaoli Li Quanbo Liu Kang Wang Fuqiang Wang Guimei Cui Yang Li Multimodel Anomaly Identification and Control in Wet Limestone-Gypsum Flue Gas Desulphurization System Complexity |
| title | Multimodel Anomaly Identification and Control in Wet Limestone-Gypsum Flue Gas Desulphurization System |
| title_full | Multimodel Anomaly Identification and Control in Wet Limestone-Gypsum Flue Gas Desulphurization System |
| title_fullStr | Multimodel Anomaly Identification and Control in Wet Limestone-Gypsum Flue Gas Desulphurization System |
| title_full_unstemmed | Multimodel Anomaly Identification and Control in Wet Limestone-Gypsum Flue Gas Desulphurization System |
| title_short | Multimodel Anomaly Identification and Control in Wet Limestone-Gypsum Flue Gas Desulphurization System |
| title_sort | multimodel anomaly identification and control in wet limestone gypsum flue gas desulphurization system |
| url | http://dx.doi.org/10.1155/2020/6046729 |
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