Fuzzy C-means clustering-based on-line monitoring method for quality of slurry of desulfurization system

A technology of mean clustering and desulfurization system, applied in measuring devices, instruments, scientific instruments, etc., can solve the problems of slurry foaming and overflowing, unable to do laboratory analysis at any time, and only suitable for regular sampling or sampling inspection.

Inactive Publication Date: 2017-06-13
SOUTHEAST UNIV
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Problems solved by technology

When the accumulation of harmful substances in the slurry increases, the quality of the slurry gradually deteriorates, and this dynamic balance is broken. In the process of the whole desulfurization system, the main manifestations are: the foaming and overflow of the slurry and the occurrence of "poisoning" of the slurry in the absorption tower, resulting in a significant increase in the desulfurization efficiency. decreased, and at the same time, the dehydration of gypsum slurry was difficult, and CaCO 3 Phenomena such as excessive content
[0004] The causes and phenomena of slurry quality deterioration, the quality of slurry will be reflec

Method used

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  • Fuzzy C-means clustering-based on-line monitoring method for quality of slurry of desulfurization system
  • Fuzzy C-means clustering-based on-line monitoring method for quality of slurry of desulfurization system
  • Fuzzy C-means clustering-based on-line monitoring method for quality of slurry of desulfurization system

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Embodiment Construction

[0044] The online monitoring method of slurry quality in desulfurization system based on fuzzy C-means clustering in this embodiment includes the following steps:

[0045] Step 1. Obtain the corresponding desulfurization efficiency and slurry pH value under different slurry quality conditions (good, medium, and poor) through the data input interface, and calculate the limestone index reflecting the relationship between limestone flow, SO2 inlet concentration, and total flue gas gamma.

[0046] Step 2, using the fuzzy C-means clustering algorithm to determine the degree to which each data point belongs to a certain cluster. In the FCM clustering algorithm, it will finite sample set x={x 1 , x 2 ,...x n},x i ={Desulfurization efficiency, ph value, limestone index} is divided into category l (2≤l≤n). Any sample point will not be strictly divided into a certain category, but belongs to l different domains with a certain degree of membership.

[0047]

[0048]

[0049] ...

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Abstract

The invention discloses a fuzzy C-means clustering-based on-line monitoring method for the quality of slurry of a desulfurization system. The method comprises the steps of firstly analyzing parameters, which include the desulfurization efficiency, a pH value of the slurry, and limestone indexes of reflecting the relationship between the limestone flow and the SO2 inlet concentration as well as the total amount of a flue gas, of reflecting the quality characteristics of limestone slurry by using a correlation analysis method; and carrying out clustering analysis on the parameters reflecting the quality characteristics of the slurry by adopting a fuzzy C-means clustering algorithm, thereby achieving identification and diagnosis of the quality of the limestone slurry. A fuzzy slurry quality index is also provided on the basis according to the thought of a fuzzy membership, thereby achieving effective monitoring and quantitative evaluation on the desulfurization system. The quality of the limestone slurry can be quantitatively calibrated within a continuous time period and a basis is provided for adjustment and control of a slurry system.

Description

technical field [0001] The invention relates to a method for monitoring slurry quality of a desulfurization system, in particular to an online monitoring method for slurry quality of a desulfurization system based on fuzzy C-means clustering using an FCM clustering method and fuzzy pattern recognition, and belongs to the field of machine learning modeling. Background technique [0002] Cluster analysis is a kind of data mining and an important branch of unsupervised pattern recognition. The purpose of the clustering algorithm is to distinguish and classify things. In this process, there is no prior knowledge related to classification, and the internal similarity between things is the only criterion for category division. Therefore, as an unsupervised classification method, it divides an unlabeled sample set into several classes according to certain criteria, and gathers similar samples into one class as much as possible, and gathers dissimilar samples into different classes....

Claims

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Application Information

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IPC IPC(8): G01N33/00
CPCG01N33/00
Inventor 乔宗良司风琪姚学忠包文运孙瑞胡建垠
Owner SOUTHEAST UNIV
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