Single-tube ai closed-loop drying control method for window-type oxygen-enriched membrane fresh air complete machine

CN121701948BActive Publication Date: 2026-06-09GUANGXI TECHCAL COLLEGE OF MACHINERY & ELECTRICITY +1

Patent Information

Authority / Receiving Office: CN · China
Patent Type: Patents(China)
Current Assignee / Owner: GUANGXI TECHCAL COLLEGE OF MACHINERY & ELECTRICITY
Filing Date: 2026-02-13
Publication Date: 2026-06-09

AI Technical Summary

Technical Problem

Traditional window-type oxygen-enriched membrane fresh air systems suffer from problems such as high energy consumption, inaccurate regeneration timing, and interruption of fresh air supply during regeneration, which affect equipment efficiency and user experience.

Method used

The system employs an AI closed-loop control method, which collects data through temperature and humidity sensors and a power detection unit. It uses a pre-trained AI model to dynamically predict the regeneration duration and heating power, and combines a specific air valve structure to ensure the continuity of fresh air supply. Furthermore, it improves the system's adaptability and accuracy through incremental learning and dynamic threshold generation.

Benefits of technology

It enables on-demand control of drying and regeneration, significantly reduces energy consumption, ensures the continuity of fresh air and oxygen enrichment functions, improves system adaptability and user experience, and extends equipment lifespan.

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Abstract

The application discloses a single-tube AI closed-loop drying control method for a window-type oxygen-enriched membrane fresh air complete machine, and belongs to the technical field of air treatment equipment. The method comprises the following steps: collecting the temperature and humidity of the inlet and outlet of a drying tube, the environmental temperature and humidity, and the heater power data in real time, and inputting the data into an AI model to calculate the optimal regeneration duration and dynamic heating power control sequence, and calculating the air dew point of the outlet of the drying tube in real time, and triggering regeneration when the air dew point is not lower than a dynamic threshold; in the regeneration execution stage, the heater is controlled according to the power sequence output by the AI model, and the air duct leading to the oxygen-enriched membrane assembly is ensured to be continuously unblocked during regeneration through air valve switching to maintain fresh air supply. The application solves the problems of high energy consumption of a traditional single-tube regeneration system, inaccurate regeneration timing, and interruption of fresh air supply during regeneration, realizes intelligent and on-demand control of the drying regeneration process, significantly improves energy efficiency, and ensures the continuity of the oxygen-enriched fresh air function of the equipment.

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