Method for predicting treated-water permanganate index achieved by ozone and biological activated carbon process of water treatment plant

The artificial neural network predicts the permanganate index of ozone bioactivated carbon effluent in the water plant, which solves the problem of delay in determining exceeding the standard caused by low monitoring frequency, and achieves real-time early warning and improves emergency response capabilities.

WO2025055086A1 Publication Date: 2025-03-20SHANGHAI NATIONAL ENGINEERING RESEARCH CENTER OF URBAN WATER RESOURCES CO LTD
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Patent Information

Application Number
PCT/CN2023/129105
Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
Priority Date
2023-09-13
Filing Date
2023-11-01
Publication Date
2025-03-20

AI Technical Summary

Technical Problem

In the ozone bioactivated carbon process in the existing tap water plants, the monitoring frequency of the permanganate index is not high, and the performance of the real-time reaction process cannot be performed. There is a delay in determining the concentration of the permanganate index exceeding the standard.

Method used

The data acquisition, preprocessing and prediction modules are established using artificial neural networks to collect and pretreat key water quality indicators and process operation parameter variables in real time. The artificial neural network is used to predict the permanganate index concentration of ozone bioactivated carbon process effluent, and the prediction results are output to display them visually.

Benefits of technology

Real-time prediction of the permanganate index of ozone bioactivated carbon process effluent in the water plant has been achieved, shortening the early warning time for the risk of organic matter exceeding the standard, improving the emergency treatment capacity of the water plant, and ensuring the safety and stability of the water supply quality.

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Abstract

Provided is a method for predicting a treated-water permanganate index achieved by an ozone and biological activated carbon process of a water treatment plant. The method comprises: establishing a data collection and retrieval module, so as to perform data collection and retrieval regarding a key water quality indicator variable and a process operation parameter variable, which impact the performance and effect of an ozone and biological activated carbon process; establishing a data preprocessing module, so as to preprocess the collected and retrieved data; using an artificial neural network to establish a prediction module for a permanganate index concentration in treated water subjected to the ozone and biological activated carbon process, wherein the prediction module uses the preprocessed data as an input to predict the permanganate index concentration in treated water subjected to said process, and outputting a predicted value; and establishing a prediction-result output module, wherein the output module outputs a prediction result. The method is applicable for providing, for an ozone and biological activated carbon process section of a water treatment plant, an early-warning function for indicating when a treated-water permanganate index exceeds the standard, such that the time taken to provide an early warning for indicating when the concentration of an organic matter in treated water exceeds the standard can be shortened in an intelligent manner, thereby improving the emergency response capability of a water treatment plant.
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