An online quantitative mixing system for wet chemicals suitable for the new materials industry

By combining machine learning models to adjust the changes in chemical reagents in real time and dynamically adjusting the amount of chemicals added, the problems of out-of-control and low efficiency of the wet chemical compounding system in the prior art are solved, and an efficient and accurate chemical compounding process is achieved.

CN119005920BActive Publication Date: 2025-08-22ZHENGFAN TECH (HUZHOU) CO LTD
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Patent Information

Application Number
CN202411498696.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-10-25
Publication Date
2025-08-22
Estimated Expiration
2044-10-25

AI Technical Summary

Technical Problem

The existing online quantitative mixing system for wet chemicals cannot intelligently adjust the amount of chemicals added, resulting in problems such as out-of-control reactions, inaccurate product quality, low production efficiency and high cost.

Method used

Combining machine learning models, the changes in chemical reagents are predicted and adjusted in real time, and through feature extraction and data analysis, the amount of chemicals added is dynamically adjusted to cope with different changes, including obvious changes, normal changes and non-obvious changes.

Benefits of technology

Improves flexibility and accuracy of the mixing process, avoids violent reactions and production delays, reduces energy and time costs, and optimizes system efficiency and product quality control.

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Abstract

The present invention discloses an online quantitative mixing system for wet chemicals suitable for the new materials industry, which relates to the field of chemical mixing technology, including a formula calculation and raw material preparation module, an initial addition and mixing control module, a feature extraction and data analysis module, a change classification module, a normal change processing module, an obvious change adjustment module and an insignificant change adjustment module; the formula calculation and raw material preparation module prepares various chemicals according to a pre-set formula and stores them in different raw material tanks. The present invention, by combining a machine learning model, predicts and adjusts the changes of chemical reagents in real time, and flexibly responds to different changes in the mixing process. When there is a significant change, the addition amount is reduced, and when the change is not obvious, the addition amount is increased. Under normal circumstances, the initial trace amount is maintained, thereby improving the mixing accuracy and efficiency, avoiding violent reactions and delays, reducing energy and time costs, and optimizing production efficiency and product quality control.
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