An investigation on data mining and operating optimization for wet flue gas desulfurization systems
•The optimal target database of operating conditions for WFGD was investigated.•An improved fuzzy clustering (IFC) algorithm was proposed for data clustering.•L-G ratio, pH and slurry density were obtained to minimize unit SO2 removal cost.•An overall continuous optimal operation database was establ...
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| Vydané v: | Fuel (Guildford) Ročník 258; s. 116178 |
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| Jazyk: | English |
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Kidlington
Elsevier Ltd
15.12.2019
Elsevier BV |
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| ISSN: | 0016-2361, 1873-7153 |
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| Abstract | •The optimal target database of operating conditions for WFGD was investigated.•An improved fuzzy clustering (IFC) algorithm was proposed for data clustering.•L-G ratio, pH and slurry density were obtained to minimize unit SO2 removal cost.•An overall continuous optimal operation database was established.
Finding the best conditions for current operation from historical data is of great meaning for power plants. In order to obtain the optimal conditions, a comprehensive evaluation criterion, using the minimum cost as an objective function, was established for a wet flue gas desulfurization (WFGD) system in this paper. A basic procedure was presented to set up the database of the system operating target conditions. To improve the accuracy of mining target data, an improved fuzzy clustering (IFC) algorithm was proposed. This algorithm used K-means results as initial conditions and fuzzy C-means algorithm as analytical method. Besides, the information entropy was also utilized in the IFC algorithm as an evaluation index. Results indicated that the proposed algorithm was more accurate than typical K-means and fuzzy C-means in data clustering. Additionally, the operating data for the WFGD system of a 600 MW unit were selected as the modeling samples. In this model, the unit SO2 removal cost was selected as the criterion for the assessment of operating conditions. The operating condition data were divided into different operating clusters based on the unit load and inlet SO2 concentration of the WFGD system. Using pH, liquid-gas ratio, and slurry density as an initial condition, optimal steady-state operating data were obtained. Finally, an overall operation database of this system was established, which could successfully obtain the continuous optimal operating conditions and provide operating guidance. |
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| AbstractList | •The optimal target database of operating conditions for WFGD was investigated.•An improved fuzzy clustering (IFC) algorithm was proposed for data clustering.•L-G ratio, pH and slurry density were obtained to minimize unit SO2 removal cost.•An overall continuous optimal operation database was established.
Finding the best conditions for current operation from historical data is of great meaning for power plants. In order to obtain the optimal conditions, a comprehensive evaluation criterion, using the minimum cost as an objective function, was established for a wet flue gas desulfurization (WFGD) system in this paper. A basic procedure was presented to set up the database of the system operating target conditions. To improve the accuracy of mining target data, an improved fuzzy clustering (IFC) algorithm was proposed. This algorithm used K-means results as initial conditions and fuzzy C-means algorithm as analytical method. Besides, the information entropy was also utilized in the IFC algorithm as an evaluation index. Results indicated that the proposed algorithm was more accurate than typical K-means and fuzzy C-means in data clustering. Additionally, the operating data for the WFGD system of a 600 MW unit were selected as the modeling samples. In this model, the unit SO2 removal cost was selected as the criterion for the assessment of operating conditions. The operating condition data were divided into different operating clusters based on the unit load and inlet SO2 concentration of the WFGD system. Using pH, liquid-gas ratio, and slurry density as an initial condition, optimal steady-state operating data were obtained. Finally, an overall operation database of this system was established, which could successfully obtain the continuous optimal operating conditions and provide operating guidance. Finding the best conditions for current operation from historical data is of great meaning for power plants. In order to obtain the optimal conditions, a comprehensive evaluation criterion, using the minimum cost as an objective function, was established for a wet flue gas desulfurization (WFGD) system in this paper. A basic procedure was presented to set up the database of the system operating target conditions. To improve the accuracy of mining target data, an improved fuzzy clustering (IFC) algorithm was proposed. This algorithm used K-means results as initial conditions and fuzzy C-means algorithm as analytical method. Besides, the information entropy was also utilized in the IFC algorithm as an evaluation index. Results indicated that the proposed algorithm was more accurate than typical K-means and fuzzy C-means in data clustering. Additionally, the operating data for the WFGD system of a 600 MW unit were selected as the modeling samples. In this model, the unit SO2 removal cost was selected as the criterion for the assessment of operating conditions. The operating condition data were divided into different operating clusters based on the unit load and inlet SO2 concentration of the WFGD system. Using pH, liquid-gas ratio, and slurry density as an initial condition, optimal steady-state operating data were obtained. Finally, an overall operation database of this system was established, which could successfully obtain the continuous optimal operating conditions and provide operating guidance. |
| ArticleNumber | 116178 |
| Author | Tang, Youfei Wang, Xingchao Qiao, Zongliang Gu, Hui Si, Fengqi Romero, Carlos E. Yao, XueZhong |
| Author_xml | – sequence: 1 givenname: Zongliang surname: Qiao fullname: Qiao, Zongliang email: qiaozongliang@seu.edu.cn organization: Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, Southeast University, Nanjing 210096, China – sequence: 2 givenname: Xingchao orcidid: 0000-0002-6136-7717 surname: Wang fullname: Wang, Xingchao organization: Energy Research Center, Lehigh University, Bethlehem, PA 18015, USA – sequence: 3 givenname: Hui surname: Gu fullname: Gu, Hui organization: School of Energy and Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China – sequence: 4 givenname: Youfei surname: Tang fullname: Tang, Youfei organization: Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, Southeast University, Nanjing 210096, China – sequence: 5 givenname: Fengqi surname: Si fullname: Si, Fengqi organization: Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, Southeast University, Nanjing 210096, China – sequence: 6 givenname: Carlos E. surname: Romero fullname: Romero, Carlos E. organization: Energy Research Center, Lehigh University, Bethlehem, PA 18015, USA – sequence: 7 givenname: XueZhong surname: Yao fullname: Yao, XueZhong organization: Datang Environment Industry Group Co., Ltd., Nanjing 211106, China |
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| Snippet | •The optimal target database of operating conditions for WFGD was investigated.•An improved fuzzy clustering (IFC) algorithm was proposed for data... Finding the best conditions for current operation from historical data is of great meaning for power plants. In order to obtain the optimal conditions, a... |
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| SubjectTerms | Air pollution control Algorithms Clustering Criteria Data mining Desulfurization Desulfurizing Electric power generation Entropy (Information theory) Evaluation Flue gas Flue gas desulfurization Improved fuzzy clustering algorithm Information processing Initial conditions Minimum cost Objective function Online Optimal target operating condition database Optimization Pollution control equipment Power plants Slurries Sulfur dioxide Unit loads |
| Title | An investigation on data mining and operating optimization for wet flue gas desulfurization systems |
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