A new approach for clustering in desulfurization system based on modified framework for gypsum slurry quality monitoring

Wet flue gas desulfurization (WFGD) is very important in reduction of SO2 emission in power plant for its lower investment cost, higher desulfurization efficiency and useful by-products. Healthy slurry works as the premise of performance analysis and optimization in WFGD system. However, little rese...

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Vydané v:Energy (Oxford) Ročník 148; s. 789 - 801
Hlavní autori: Gu, Hui, Cui, Yanfeng, Zhu, Hongxia, Xue, Rui, Si, Fengqi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Elsevier Ltd 01.04.2018
Elsevier BV
Predmet:
Air pollution control
algorithms
byproducts
Calcium carbonate
case studies
Clustering
Computer simulation
Data mining
Data processing
Desulfurization
Desulfurizing
EKFCM
Electric power generation
Emissions
Emissions control
Entropy
Flue gas
Flue gas desulfurization
Gypsum
Gypsum slurry quality
industry
Monitoring
Performance indices
Pollution control equipment
Power plants
Slurries
Sub-TDFO strategy
Sulfur
Sulfur dioxide
WFGD
Gypsum slurry quality
Sub-TDFO strategy
WFGD
EKFCM
ISSN:0360-5442, 1873-6785
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Shrnutí:Wet flue gas desulfurization (WFGD) is very important in reduction of SO2 emission in power plant for its lower investment cost, higher desulfurization efficiency and useful by-products. Healthy slurry works as the premise of performance analysis and optimization in WFGD system. However, little research has been conducted on monitoring gypsum slurry quality deterioration. In this paper, an on-line clustering framework has been proposed to monitor gypsum slurry quality in desulfurization system based on data mining. Compound parameterγ, is put forward to remove the influences to slurry quality from gas volume and inlet SO2 concentration to CaCO3 slurry flow. Thus, after the simplification, desulfurization efficiency, pH value and γ are taken as parameters for gypsum slurry quality monitoring. A new clustering method, EKFCM, based on improved fuzzy clustering algorithm, Kmeans and fuzzy C-means combined with entropy theory is proposed. EKFCM is superior to basic FCM in finding the clustering number without prior knowledge when dealing with off-line data, verified by a self-defined function with validity indexes comparison. A new rule, Sub-TDFO, with time decay inserted into “first in first out” in the subset, is proposed to the framework for on-line learning. Another self-defined function is added to the former one for on-line process simulation to verify the effectiveness of the proposed framework. Then, WFGD system in a 600 MW unit is worked as the clustering case study for gypsum slurry quality on-line monitoring and quantization. The clustering results from the proposed on-line framework can be applied to illustrate the process of gypsum slurry quality variation. Moreover, this method be used in other industry processes data for its on-line features. •A on-line clustering framework is proposed for gypsum slurry quality monitoring.•Put forward parameter γ, as a significant strategy to remove complex influences.•EKFCM proposed has the capacity of self-adaptation without prior knowledge.•Sub-TDFO strategy for WFGD analysis is with time decay taken into consideration.
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ISSN:0360-5442
1873-6785
DOI:10.1016/j.energy.2018.01.175