Research on Distributed Intelligent Control Method Based on State Self-Learning and Its Application in Cascade Processes

Academic Background

In the process industry, multi-reactor cascade operation is a distinctive characteristic. However, establishing an accurate and global model for multi-reactor cascade processes presents numerous challenges. The complex dynamic operating state of the reactors and the coupling relationships between front and rear reactors make it extremely difficult to finely control the entire process. Traditional control methods such as PID (Proportional-Integral-Derivative) control and fuzzy control perform well in single-variable and single-loop control processes but fall short in the collaborative control of multi-reactor cascade systems. Although Model Predictive Control (MPC) has been widely applied in the process industry, it still faces limitations in handling large-scale and nonlinear cascade processes, particularly in real-time control and optimization.

With the advancement of emerging technologies such as artificial intelligence and big data, exploring new control methods to achieve precise control over the entire process has become an urgent need. This paper proposes a distributed intelligent control method based on State Self-Learning (SSL), aiming to address the modeling and control challenges in multi-reactor cascade processes and provide a novel and efficient control paradigm for the process industry.

Source of the Paper

This paper is co-authored by Shulong Yin, Yonggang Li, Zhenxiang Feng, Bei Sun, and Huiping Liang, all from the School of Automation at Central South University. The paper was submitted on October 23, 2024, accepted on December 3, 2024, and published in the journal IEEE Transactions on Automation Science and Engineering. The research was supported by the National Natural Science Foundation of China (NSFC) and the Fundamental Research Funds for the Central Universities of Central South University.

Detailed Research Content

1. Research Process

a) Nonlinear Dynamic Modeling

The study first establishes a time-varying dynamic model for each reactor unit based on state self-learning. By learning the parameters of the regression model at each state point, a nonlinear description of the reactor under complex conditions was achieved. Specifically, the RBF neural network (Radial Basis Function Neural Network) was employed to learn the autoregressive model coefficients of the reactor under different states, thereby capturing the nonlinear dynamic characteristics of the reactor.

b) Multi-step Collaborative Prediction

Based on the dynamic model and the material conservation principle between reactors, the research team conducted multi-step collaborative prediction along the reactor cascade direction. By using the exit prediction information of the previous reactor as the future entrance information for the next reactor, collaborative prediction among cascaded reactors was realized. This method combines data-driven models with mechanistic knowledge, significantly improving prediction accuracy.

c) Distributed Intelligent Control

The study adopted a Distributed Model Predictive Control (DMPC) method based on error self-correction to achieve distributed intelligent control of cascaded reactors. By locally linearizing the nonlinear prediction model, this method transforms the complex nonlinear control problem into linear sub-problems, thereby simplifying the online optimization process.

2. Main Results

a) Effectiveness of Nonlinear Dynamic Modeling

By comparing the SSL-based modeling method with the direct neural network (NN) modeling method, the study validated the superiority of the SSL method in capturing the nonlinear dynamic characteristics of the reactor. Test data showed that the prediction errors of the SSL method were significantly lower than those of the NN method, especially demonstrating higher model accuracy under complex working conditions.

b) Effectiveness of Multi-step Collaborative Prediction

In the multi-step collaborative prediction experiments, the SSL method effectively transmitted prediction information between cascaded reactors, significantly improving overall prediction accuracy. Particularly in the dynamic changes of the cascade process, the SSL method exhibited stronger robustness.

c) Effectiveness of Distributed Intelligent Control

In the simulation experiments of the zinc smelting leaching process, the SSL-based distributed intelligent control method outperformed traditional PID control and NN-based distributed control methods in terms of control accuracy and response speed. This method was able to quickly track set points and maintain stable control under disturbance conditions.

3. Conclusions and Significance

This study demonstrates that the distributed intelligent control method based on state self-learning can effectively address the modeling and control challenges in multi-reactor cascade processes. Compared to traditional control methods, the SSL method shows significant advantages in modeling accuracy, prediction capability, and control effectiveness, providing a novel control paradigm for the process industry.

Research Highlights

1. Novel Modeling Method: The nonlinear dynamic modeling method based on state self-learning can update model parameters in real time, significantly improving model prediction accuracy.
2. Multi-step Collaborative Prediction: By combining data-driven models and mechanistic knowledge, collaborative prediction among cascaded reactors was achieved, enhancing overall prediction capability.
3. Distributed Intelligent Control: The Distributed Model Predictive Control (DMPC) method based on error self-correction simplifies the online optimization process and improves control accuracy and response speed.
4. Practical Application Validation: Simulation experiments in the zinc smelting leaching process validated the effectiveness and robustness of the method, providing strong support for its application in industrial production.

Research Value

The scientific value of this study lies in proposing an innovative method that combines state self-learning and distributed control, offering new insights for addressing the complex control challenges of multi-reactor cascades in the process industry. Its application value is reflected in the method’s ability to significantly improve production efficiency and stability in the process industry, providing technical support for industrial automation and intelligence. Moreover, the successful application of this method in zinc smelting and other process industries provides a reference for its promotion in similar industrial scenarios.