Sewage treatment process monitoring method based on KPLS and RWFCM

Abstract

The invention relates to the technical field of sewage treatment quality monitoring, and provides a sewage treatment process monitoring method based on KPLS and RWFCM. The method comprises the steps that firstly, collecting sewage treatment process data samples containing normal working conditions and abnormal working conditions wherein data of sewage treatment operation variables and data of effluent quality variables serve as an input data matrix and an output data matrix respectively, and the two matrixes are standardized; constructing a KPLS model, and solving a score matrix; then, based on an RWFCM algorithm, clustering the score matrix to obtain a membership matrix, and according to the membership matrix, carrying out abnormal working condition monitoring on the sewage treatment process; and finally, establishing a linear regression model of the membership matrix and the sample variables, solving a variable contribution matrix, and performing abnormal condition identification onthe sewage treatment process according to the variable contribution matrix. According to the invention, dimensionality reduction can be carried out on high-dimensional data, nonlinear data can be processed, the method is insensitive to outliers, and timeliness and accuracy of monitoring and identification in a sewage treatment process can be improved.

Description

technical field [0001] The invention relates to the technical field of sewage treatment quality monitoring, in particular to a sewage treatment process monitoring method based on KPLS and RWFCM. Background technique [0002] With the acceleration of urbanization and industrialization in our country, the society's demand for fresh water resources is increasing day by day. It is necessary to accelerate the construction of urban domestic sewage treatment and disposal facilities and improve the capacity of urban domestic sewage treatment. Activated sludge sewage treatment process is currently the main method of urban sewage treatment. Activated sludge purification of sewage mainly includes three processes: initial adsorption, microbial metabolism, formation of flocs and precipitation. Adsorption, decomposition and oxidation are carried out to separate it from sewage, so as to achieve the purpose of purifying sewage. [0003] At present, biochemical oxygen demand ([BOD]), chemi...

AI Technical Summary

Problems solved by technology

However, the sewage treatment process data is high-dimensional, nonlinear and has outliers. The traditional FCM algorithm cannot handle high-dimensional and nonlinear data and is very sensitive to outliers, which increases the difficulty of process monitoring and reduces the risk of failure. The reliability of detection has a great impact on the quality of sewage effluent, causing certain economic losses and even accidents

At the same time, the number of clusters of the FCM algorithm needs to be preset artificially, which has great limitations in practical applications.

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