Intelligent Partition of Operating Condition-Based Multi-Model Control in Flue Gas Desulfurization
Flue gas emission is an inevitable procedure in the course of electricity generation, which would pose a severe threat to human health, and has an adverse effect on our environment. Due to the fact that the environment in practical flue gas desulfurization system fluctuates frequently, system parame...
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9164958/ |
id |
doaj-87e75f24977a4b96b709628e50d921cb |
---|---|
record_format |
Article |
spelling |
doaj-87e75f24977a4b96b709628e50d921cb2021-03-30T03:24:10ZengIEEEIEEE Access2169-35362020-01-01814930114931510.1109/ACCESS.2020.30158889164958Intelligent Partition of Operating Condition-Based Multi-Model Control in Flue Gas DesulfurizationXiaoli Li0https://orcid.org/0000-0002-8627-6221Quanbo Liu1https://orcid.org/0000-0002-6974-0125Kang Wang2https://orcid.org/0000-0002-8403-7606Fuqiang Wang3Guimei Cui4Yang Li5Faculty of Information Technology, Beijing University of Technology, Beijing, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, ChinaShenhua Guohua (Beijing) Electric Power Research Institute Corporation, Beijing, ChinaSchool of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, ChinaSchool of International Studies, Communication University of China (CUC), Beijing, ChinaFlue gas emission is an inevitable procedure in the course of electricity generation, which would pose a severe threat to human health, and has an adverse effect on our environment. Due to the fact that the environment in practical flue gas desulfurization system fluctuates frequently, system parameters tend to vary constantly during the operating process, thus control performance with traditional strategies tends to be suboptimal in most cases. To address this problem, some insight into operating conditions must be gained prior to taking proper control strategy. Therefore, in this article, based on actual measurements in 1000 MW Unit Wet Limestone FGD System for a coal-fired power plant, a kind of intelligent operating condition partition method is combined with the multi-model adaptive control strategy. Specifically, analysis and partition of operating condition is carried out in the first place, then adaptive multi-model control is implemented with the combination of parallel dynamic neural network and partition results. Additionally, the applicability of proposed control mode is investigated through different simulation examples. At the same time, to further enhance the flexibility of multi-model control structure, some possible improvements on it is also discussed.https://ieeexplore.ieee.org/document/9164958/Clusteringfeature selectionflue gas desulfurizationmultiple modelsneurocontrol |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiaoli Li Quanbo Liu Kang Wang Fuqiang Wang Guimei Cui Yang Li |
spellingShingle |
Xiaoli Li Quanbo Liu Kang Wang Fuqiang Wang Guimei Cui Yang Li Intelligent Partition of Operating Condition-Based Multi-Model Control in Flue Gas Desulfurization IEEE Access Clustering feature selection flue gas desulfurization multiple models neurocontrol |
author_facet |
Xiaoli Li Quanbo Liu Kang Wang Fuqiang Wang Guimei Cui Yang Li |
author_sort |
Xiaoli Li |
title |
Intelligent Partition of Operating Condition-Based Multi-Model Control in Flue Gas Desulfurization |
title_short |
Intelligent Partition of Operating Condition-Based Multi-Model Control in Flue Gas Desulfurization |
title_full |
Intelligent Partition of Operating Condition-Based Multi-Model Control in Flue Gas Desulfurization |
title_fullStr |
Intelligent Partition of Operating Condition-Based Multi-Model Control in Flue Gas Desulfurization |
title_full_unstemmed |
Intelligent Partition of Operating Condition-Based Multi-Model Control in Flue Gas Desulfurization |
title_sort |
intelligent partition of operating condition-based multi-model control in flue gas desulfurization |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Flue gas emission is an inevitable procedure in the course of electricity generation, which would pose a severe threat to human health, and has an adverse effect on our environment. Due to the fact that the environment in practical flue gas desulfurization system fluctuates frequently, system parameters tend to vary constantly during the operating process, thus control performance with traditional strategies tends to be suboptimal in most cases. To address this problem, some insight into operating conditions must be gained prior to taking proper control strategy. Therefore, in this article, based on actual measurements in 1000 MW Unit Wet Limestone FGD System for a coal-fired power plant, a kind of intelligent operating condition partition method is combined with the multi-model adaptive control strategy. Specifically, analysis and partition of operating condition is carried out in the first place, then adaptive multi-model control is implemented with the combination of parallel dynamic neural network and partition results. Additionally, the applicability of proposed control mode is investigated through different simulation examples. At the same time, to further enhance the flexibility of multi-model control structure, some possible improvements on it is also discussed. |
topic |
Clustering feature selection flue gas desulfurization multiple models neurocontrol |
url |
https://ieeexplore.ieee.org/document/9164958/ |
work_keys_str_mv |
AT xiaolili intelligentpartitionofoperatingconditionbasedmultimodelcontrolinfluegasdesulfurization AT quanboliu intelligentpartitionofoperatingconditionbasedmultimodelcontrolinfluegasdesulfurization AT kangwang intelligentpartitionofoperatingconditionbasedmultimodelcontrolinfluegasdesulfurization AT fuqiangwang intelligentpartitionofoperatingconditionbasedmultimodelcontrolinfluegasdesulfurization AT guimeicui intelligentpartitionofoperatingconditionbasedmultimodelcontrolinfluegasdesulfurization AT yangli intelligentpartitionofoperatingconditionbasedmultimodelcontrolinfluegasdesulfurization |
_version_ |
1724183536458530816 |