A laboratory multi-component waste gas treatment method based on AI adaptive adjustment

The AI-adaptive laboratory exhaust gas treatment system, combining CNN models and NSGA-II algorithms, dynamically identifies exhaust gas components and concentrations, generates optimal operating parameters, and solves the problems of high energy consumption and excessive emissions in laboratory exhaust gas treatment, achieving efficient and low-energy exhaust gas treatment.

CN122298172APending Publication Date: 2026-06-30NANJING NUODAN ENG TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
NANJING NUODAN ENG TECH CO LTD
Filing Date
2026-02-06
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

Existing laboratory waste gas treatment methods cannot match the fluctuations in waste gas composition and concentration in real time, resulting in high energy consumption from overtreatment or undertreatment, and potential risks of exceeding emission standards.

Method used

A laboratory multi-component waste gas treatment system based on AI adaptive adjustment is adopted. The system uses a CNN model to identify waste gas components and concentrations, and generates optimal operating parameters through the NSGA-II multi-objective optimization algorithm to dynamically control the adsorption, neutralization and catalytic oxidation units.

Benefits of technology

It achieves an accuracy rate of ≥96% for identifying waste gas components, an accuracy rate of ≥94% for identifying concentration levels, a system treatment efficiency of 97%, and a reduction in energy consumption of 15%-30%, ensuring that environmental protection standards are met and reducing the overall energy consumption of the system.

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Abstract

This invention provides a laboratory multi-component waste gas treatment method based on AI adaptive adjustment. The waste gas treatment system includes an adsorption unit, a neutralization unit, a catalytic oxidation unit, and a control system. The method further includes: S1: collecting raw data; S2: identifying waste gas components and obtaining optimal parameters; preprocessing the collected raw data such as noise reduction and normalization; identifying the component types and concentration levels of the waste gas using a CNN model; and outputting optimal parameters based on historical treatment data; S3: collaboratively adjusting according to the optimal parameters; and S4: providing feedback and alarms. This invention solves the problems of poor adaptability, high energy consumption, or excessive emissions associated with traditional fixed-parameter treatment. The component identification accuracy is ≥96%, the concentration identification accuracy is ≥94%, the treatment efficiency reaches up to 97%, and energy consumption is reduced by 15%-30%, achieving intelligent and refined environmental protection treatment.
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