Robust prediction method for membrane permeability based on adaptive division according to working conditions

By using adaptive operating condition segmentation and nonlinear regression models, the problem of low accuracy in membrane permeability prediction in MBR wastewater treatment was solved, enabling effective early warning of membrane fouling and reducing energy consumption and costs.

CN116738869BActive Publication Date: 2026-07-24BEIJING UNIV OF TECH
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Patent Information

Application Number
CN202310300004.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-03-26
Publication Date
2026-07-24
Estimated Expiration
2043-03-26

AI Technical Summary

Technical Problem

Existing technologies in MBR wastewater treatment suffer from high energy consumption and high costs due to membrane fouling, and the accuracy of membrane permeability prediction is greatly affected by data imbalance, making it difficult to achieve effective early warning.

Method used

An adaptive classification method based on operating conditions is adopted. The input data is adaptively classified by radial basis function neural network and cross-entropy loss function. A nonlinear regression model is designed to establish the relationship between input and output variables, thereby improving the stability and accuracy of membrane permeability prediction.

Benefits of technology

It improves the dynamic error caused by data imbalance, enhances the accuracy of membrane permeability prediction, and provides an effective early warning capability for membrane fouling.

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Abstract

The membrane permeability robust prediction method based on working condition adaptive division belongs to the field of sewage treatment and resource utilization. Due to the influence of different working conditions on the MBR sewage treatment process, unbalanced samples exist in the membrane pollution related data, which may reduce the prediction performance. In order to solve this problem, the present application proposes a membrane permeability robust prediction method based on working condition adaptive division to predict the membrane permeability. First, a cross-entropy index based on spatial membership calculation is proposed, which can adaptively divide the membrane pollution related data set by evaluating the data fluctuation characteristics of the input information; second, a nonlinear regression model is used to predict the membrane permeability. The results show that the method can effectively predict the value of the membrane permeability.
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