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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Bibliographic Details
Published in:Fuel (Guildford) Vol. 258; p. 116178
Main Authors: Qiao, Zongliang, Wang, Xingchao, Gu, Hui, Tang, Youfei, Si, Fengqi, Romero, Carlos E., Yao, XueZhong
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 15.12.2019
Elsevier BV
Subjects:
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
Optimal target operating condition database
Online
Data mining
Improved fuzzy clustering algorithm
ISSN:0016-2361, 1873-7153
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Summary:•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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ISSN:0016-2361
1873-7153
DOI:10.1016/j.fuel.2019.116178