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Distributed Intelligent Control Method Based on State Self-Learning and Its Application in Cascade Processes
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AI
DOI：10.1109/TASE.2024.3524472.png)
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摘要
En 中文
The multiple-reactor cascade operation is a distinctive characteristic in the process industry. However, it is difficult to establish an accurate and global model for multi-reactor cascading processes. Moreover, the intricate and dynamic operating state of the reactor, coupled with rear reactors, poses significant challenges to the fine control of the entire process. Therefore, this paper proposes a distributed intelligent control method based on state self-learning. Initially, the time-varying dynamic model of each reactor unit is established by learning the parameters of the regression model at each state point, achieving a nonlinear description of the reactor under complex conditions. Subsequently, leveraging the dynamic model and the material conservation principle between reactors, multi-step collaborative prediction is conducted along the reactor cascade direction. Thirdly, distributed model predictive control based on error self-correction is adopted to realize distributed intelligent control of the cascade reactor. This method is verified in a zinc smelting leaching process. The results indicate its superiority over common methods, offering higher prediction accuracy for the cascade process and enabling more effective control of individual reactors through distributed intelligence, which provides a novel and promising control paradigm for the cascade process.
Keyword:
Inductors
Predictive models
Leaching
Industries
Zinc
Predictive control
Smelting
Intelligent control
Decentralized control
Optimization
Process industry
multi-reactor cascade process
state self-learning
collaborative prediction
autonomous control
期刊
IF:
6.4
论文数:
5.1K
被引数:
1.6W
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