Carbon-saving type deodorization control method and system based on gas online monitoring

By deploying sensor arrays and multi-layer convolutional neural networks to construct an odor pollutant diffusion model, and using the Monte Carlo algorithm to simulate the diffusion path, the emission valves are dynamically adjusted, solving the problem of inaccurate prediction of gaseous pollutant diffusion paths in existing technologies, and achieving precise pollutant control and environmental protection.

CN121091676BActive Publication Date: 2026-06-19SHANGHAI MUNICIPAL ENG DESIGN INST (GRP) CO LTD +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
SHANGHAI MUNICIPAL ENG DESIGN INST (GRP) CO LTD
Filing Date
2025-09-08
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

Existing technologies struggle to accurately predict the diffusion paths of gaseous pollutants, making it impossible for traditional models to dynamically adjust preset emission valves in the face of sudden weather events, thus affecting the effectiveness of pollutant control.

Method used

By deploying a sensor array to collect gaseous environmental data, using a multi-layer convolutional neural network to extract the diffusion characteristics of odor pollutants, constructing an odor pollutant diffusion model, and using the Monte Carlo algorithm to simulate the diffusion path, the control priority of the preset emission valve is determined so as to dynamically adjust the opening and closing status of the emission valve.

Benefits of technology

It enables precise control of odor pollutant concentrations, reduces pollution control costs, safeguards ambient air quality, and reduces pollution risks.

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Abstract

This application relates to the field of big data acquisition technology and provides a carbon-saving deodorization control method and system based on online gas monitoring. The method includes: deploying a corresponding sensor array according to a preset sensor deployment strategy to collect gaseous environmental data, obtaining a multi-dimensional spatiotemporal dataset; extracting features from the multi-dimensional spatiotemporal dataset using a multi-layer convolutional neural network to obtain a diffusion feature set of odor pollutants; constructing an odor pollutant diffusion model based on the diffusion feature set; simulating the probability distribution of the diffusion path of the odor pollutants using a Monte Carlo algorithm based on the odor pollutant diffusion model to generate a diffusion topology; determining the control priority of a preset emission valve based on the diffusion topology; and controlling the opening and closing state of the preset emission valve according to the control priority to ensure that the concentration of the odor pollutants meets a preset concentration condition. This method helps improve the prediction accuracy of the diffusion path of gaseous pollutants.
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