Waste tire continuous thermal cracking feeding control method and system based on machine learning

By using machine learning to predict and adjust the feed rate through multi-objective optimization, the problem of slow response in feedback control in existing technologies has been solved, and automation and stability improvement of the waste tire pyrolysis process have been achieved.

CN121950339APending Publication Date: 2026-05-01SHAN XI TIAN BO XIN YU HUAN BAO KE JI YOU XIAN GONG SI
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Patent Information

Application Number
CN202610145248.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-02-02
Publication Date
2026-05-01

AI Technical Summary

Technical Problem

Existing technologies rely on human experience and cannot achieve continuous adaptive adjustment. Furthermore, the thermal inertia and hysteresis of the pyrolysis reaction cause slow feedback control response, making it difficult to effectively suppress parameter fluctuations and affecting process stability and safety.

Method used

A machine learning-based approach is employed to monitor historical process parameter sequences and future operating parameters, using a long short-term memory network to predict the future state of the pyrolyzer, and adjusting the feed rate using a multi-objective optimization framework to achieve stable control of temperature and pressure.

Benefits of technology

It enables early detection of fluctuations in operating conditions, avoids chain oscillations caused by adjusting a single parameter, improves the level of automation and safety margin of the production process, and reduces reliance on the experience of operators.

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Abstract

The invention discloses a waste tire continuous thermal cracking feeding control method and system based on machine learning, and relates to the technical field of plastic waste recovery. The method comprises the following steps: acquiring a historical rear end socket temperature sequence and a historical internal pressure sequence of a cracker in a historical time window, and a rotation angular velocity and feeding rate sequence in a future preset time window; on the basis of the long and short-term memory network, predicting a predicted end socket temperature sequence and a predicted internal pressure sequence in a future window; with improvement of temperature, pressure and feeding rate stability as multiple targets, an adaptive feeding rate sequence is searched and output in a feeding rate adjustment space through digital twinborn simulation and an intelligent optimization algorithm in combination with the prediction sequence; and finally, carrying out feeding control on the cracker according to the sequence in a preset time window. According to the method, prospective optimization control over the cracking working condition is achieved, the hysteresis quality of traditional control is effectively overcome, and the stability, safety and automation level of the continuous thermal cracking process are improved.
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