Heat treatment process matching regulation and control method and system based on dewatered sludge calorific value estimation

By constructing a deep neural network-process coupling mapping map, the problem of nonlinear mapping in sludge calorific value estimation and process control was solved, realizing an efficient and stable sludge thermal treatment process and improving the accuracy of parameter recommendation and system response capability.

CN122044098APending Publication Date: 2026-05-15GUANGZHOU CHENGYUAN ENVIRONMENTAL PROTECTION EQUIP ENG CO LTD
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Patent Information

Application Number
CN202610084220.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-22
Publication Date
2026-05-15

AI Technical Summary

Technical Problem

Existing technologies lack precision and intelligence in sludge calorific value estimation and process control. They cannot effectively adapt to the complex nonlinear mapping between calorific value characteristics and operating parameters of various equipment, resulting in large deviations in parameter recommendations. This makes it difficult to ensure optimal energy consumption or safe operation. Furthermore, they lack closed-loop adaptive learning and online model optimization mechanisms, exhibiting lag and poor adaptability in the face of sludge characteristics and dynamic fluctuations in operating conditions.

Method used

A calorific value-process coupling mapping spectrum based on deep neural networks is constructed. By collecting and preprocessing sludge physicochemical property data, a nonlinear mapping spectrum is generated using a deep neural network model. Physicochemical indicators are acquired in real time and process parameter setpoints are generated. Combined with an online learning mechanism and SHAP value analysis, the data is dynamically updated to achieve end-to-end intelligent control.

Benefits of technology

It achieves end-to-end intelligent generation of everything from the multidimensional physicochemical characteristics of sludge to key operating parameters, improving the accuracy and consistency of parameter recommendations, shortening system response time, ensuring that the thermal treatment process operates within a high-efficiency and stable range, possessing good versatility and scalability, and reducing reliance on expert experience.

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Abstract

The invention provides a heat treatment process matching regulation and control method and system based on dewatered sludge calorific value estimation, and the method comprises the steps: collecting physicochemical property data of dewatered sludge from different sources in batches, pairing the physicochemical property data with optimal operation parameters under three typical heat treatment processes, and carrying out multi-layer statistical screening and characteristic standardization; and training by adopting a deep neural network to form a calorific value-process coupling mapping model. The model can automatically calculate the feeding rate, the air supply amount, the hearth temperature gradient and the residence time matched with the current equipment working condition according to the sludge characteristics detected in real time. And online feedback error monitoring and model adaptive fine tuning are realized, and the feature contribution degree is quantified through SHAP. According to the method, the accuracy and the consistency of parameter recommendation are remarkably improved, the characteristic mode can be quickly identified and the optimal regulation and control strategy can be output especially for dewatered sludge with large component fluctuation and complex sources, the system response time is greatly shortened, and the heat treatment process is always kept in an efficient and stable operation interval.
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