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Multi-Rate Dual-MPC Layered Operational Optimization Control for Dense Medium Separation Process
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DOI：10.1109/TASE.2026.3674591.png)
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Abstract
En 中文
Dense medium separation (DMS) process is one of the most effective clean coal technologies. Layered operational optimization control for DMS process typically entails fast-time-scale density adjustment in basic loop process and slow-time-scale ash content control in operational process. Unfortunately, the multi-rate, time-varying and uncertainty problems of DMS process make it difficult to design layered operational optimization control methods. To address these issues, this paper proposes a multi-rate dual-MPC (Model Predictive Control) layered operational optimization control approach. For the basic loop process, a multi-rate MPC controller is designed by employing lifting technique and reconstructing the output prediction vector. For the operational process, a neural learning-based MPC control strategy is developed, incorporating control error entropy to enable online updates of model parameters under uncertain conditions. Finally, experiments were conducted using real data from the DMS process and an industrial-grade controller to verify the effectiveness of the proposed algorithm. Note to Practitioners—In real-world DMS process, due to the different lengths of each pipeline, there are different time delays among each equipment. In the experimental part of this paper, only manual alignment of the data is relied on. In the actual application process of the proposed algorithm, practitioners need to adjust the algorithm delay according to the actual situation. In future research, we will also improve the algorithm in this paper for the delay situation.
Keywords:
Dense medium separation process
model predictive control
multi-rate
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