Monitoring method of slurry quality in wet flue gas desulfurization system based on fuzzy C-means clustering

Abstract

In this paper, a method of slurry quality monitoring and diagnosis in Wet Flue Gas Desulfurization(WFGD) system was proposed based on feature extraction of slurry quality and Fuzzy C-means(FCM) clustering. Focusing on the WFGD system of a 600 MW unit in a certain power plant, a new index for slurry quality monitoring was put forward. And clustering centers could be obtained to be the standard modes for slurry quality identification by adopting FCM to perform clustering analysis, in which the desulfurization efficiency and pH were regarded as feature information. Slurry quality diagnosis could be realized eventually by calculating the membership between the unknown samples and the standard modes of slurry quality. Furthermore, a fuzzy quantitative monitoring index was presented to quantitatively monitor the slurry quality state during its actual operation according to the theory of fuzzy membership. On the basis of diagnostic analysis of the field operating data, it demonstrates that the method raided in this dissertation can monitor the slurry quality state efficiently, providing foundation for operation adjustment.