Intelligent water quality prediction and regulation method in power plant descaling process

By standardizing and removing anomalies from water quality data from thermal power plants, an enhanced delay vector and covariate weighted matrix are constructed. Combined with local weights and anomaly factors, the problems of single data and poor adaptability to abnormal operating conditions in the descaling process of thermal power plants are solved, and high-precision water quality prediction and dynamic control are achieved.

CN121684541BActive Publication Date: 2026-07-24HANGZHOU HUADIAN JIANGDONG THERMAL POWER CO LTD
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Patent Information

Application Number
CN202610188523.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-02-10
Publication Date
2026-07-24
Estimated Expiration
2046-02-10

AI Technical Summary

Technical Problem

Existing technologies for descaling in thermal power plants suffer from problems such as limited data acquisition, insufficient utilization of variable correlation, poor adaptability to abnormal operating conditions, and low prediction accuracy, leading to incomplete descaling or excessive use of chemicals, which increases economic and environmental burdens.

Method used

By standardizing and removing anomalies from multi-source water quality monitoring data, an enhanced delay vector is constructed. Combined with local fluctuation suppression, water temperature deviation adjustment, reagent mutation suppression, and time decay weights, a covariate weighted matrix is ​​established. Direction-aware weights and anomaly sensitivity factors are introduced to form a global state vector. The model is trained using Huber loss and heteroscedasticity robust task loss of uncertainty factors.

Benefits of technology

It improves the accuracy and robustness of water quality prediction for descaling in thermal power plants, provides a reliable basis for dynamic control, ensures the interpretability and causal consistency of prediction results, reduces the impact of outliers and highly uncertain data, and improves the stability and prediction accuracy of the model.

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Abstract

The application provides an intelligent water quality prediction and regulation method in a power plant descaling process, and relates to the technical field of water quality prediction, and specifically comprises the following steps: collecting data recorded during chemical cleaning and descaling of a boiler of a power plant and performing data preprocessing to construct a power plant descaling water quality data set; introducing a local fluctuation suppression weight, a water temperature deviation adjustment weight, a reagent mutation suppression factor and a time decay weight to generate an enhanced delay vector; constructing a global state vector through a covariant weight and a direction perception expression; combining the global state vector, a reagent change amount and a temperature deviation to form a prediction input, adopting a heteroscedastic robust task loss based on a Huber loss and an uncertainty factor for optimization, and finally realizing multi-step prediction of a pH value, a Langelier saturation index, a calcium ion concentration, a silicate radical concentration, a conductivity and a turbidity in the next 15 minutes.
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Citation Information

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