دورية أكاديمية
Multimodel Anomaly Identification and Control in Wet Limestone-Gypsum Flue Gas Desulphurization System
| العنوان: | Multimodel Anomaly Identification and Control in Wet Limestone-Gypsum Flue Gas Desulphurization System |
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| المؤلفون: | Xiaoli Li, Quanbo Liu, Kang Wang, Fuqiang Wang, Guimei Cui, Yang Li |
| المصدر: | Complexity, Vol 2020 (2020) |
| بيانات النشر: | Hindawi-Wiley, 2020. |
| سنة النشر: | 2020 |
| المجموعة: | LCC:Electronic computers. Computer science |
| مصطلحات موضوعية: | Electronic computers. Computer science, QA75.5-76.95 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1076-2787 1099-0526 |
| العلاقة: | https://doaj.org/toc/1076-2787Test; https://doaj.org/toc/1099-0526Test |
| DOI: | 10.1155/2020/6046729 |
| الوصول الحر: | https://doaj.org/article/27e0387bca984fbabf1ae3caa4967507Test |
| رقم الانضمام: | edsdoj.27e0387bca984fbabf1ae3caa4967507 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 10762787 10990526 |
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| DOI: | 10.1155/2020/6046729 |