Multipoint Feeding Strategy of Aluminum Reduction Cell Based on Distributed Subspace Predictive Control
Abstract
:1. Introduction
- (1)
- The large aluminum reduction cell is divided into several subsystems according to the position of the feeder. Compared with the work in [14,15], the difference is that this paper considers the influence of each feeding port caused by the flow of the electrolyte between subsystems on the alumina concentration near other feeding ports.
- (2)
- Inspired by the work of [22], this paper designs the controller by establishing a prediction model between the feed rate and alumina concentration in each subsystem, and the input and output information can be exchanged between each subsystem through the network.
- (3)
- Compared with the traditional timing grouping feeding strategy, a new distributed control feeding strategy is designed in this paper, so that each feeding device is controlled by an independent controller. Each feeder works in coordination with the influence of other subsystems’ feeding, realizing on-demand distributed feeding, and improving the control performance of each subsystem [23].
2. Design of Distributed Feeding Control Scheme for Aluminum Electrolysis
3. Distributed Subspace Predictive Control
3.1. Data-Driven Distributed Prediction Model
3.2. Design of Distributed Predictive Controller for Aluminum Reduction Cell System
3.3. Determination of Parameters of Aluminum Reduction Cell Prediction Model and Design of Data-Driven Distributed Predictive Control Algorithm
- Step 1 At the k sampling time, take the initial value of the control input variable of each subsystem and pass the initial value to other subsystems, so that the iteration ordinal l = 0;
- Step 2 Use the last iteration value calculate the value of iteration l + 1 for the ith subsystem;
- Step 3 Pass the calculation result to other subsystems through the network;
- Step 4 If the Nash optimality is satisfied for all subsystems or the maximum number of iterations is reached, the iteration is ended, otherwise, return to the second step;
- Step 5 Each subsystem executes the optimal control signal and uses it as the initial value at the next moment;
- Step 6 End the calculation of this sampling time, and wait for the next sampling time k + 1.
4. Simulation Experiments
4.1. Data Acquisition
4.2. Control Effect without Any Interference
4.3. The Control Effect when the Feeding Amount of the Feeder Is Inconsistent with the Actual Set Value
5. Conclusions
- (1)
- Each feeding device is controlled by an independent controller, and the distributed control method which combines the advantages of centralized and decentralized control is adopted, overcoming their shortcomings.
- (2)
- The mutual influence between the various subsystems and the influence of sudden interference are considered. For example, when the feeding amount is inaccurate, the controller can also control the concentration of alumina well to ensure the stability of the reduction cell.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Subsystem | MSE without Interference | MSE with Interference |
---|---|---|
Subsystem 1 | 0.0309 | 0.0387 |
Subsystem 2 | 0.0306 | 0.0667 |
Subsystem 3 | 0.0140 | 0.0203 |
Subsystem 4 | 0.0156 | 0.0161 |
Subsystem 5 | 0.0414 | 0.0475 |
Subsystem 6 | 0.0421 | 0.0633 |
Subsystem 1 | 0.0309 | 0.0387 |
Average | 0.0291 | 0.0421 |
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Cui, J.; Wang, P.; Li, X.; Huang, R.; Li, Q.; Cao, B.; Lu, H. Multipoint Feeding Strategy of Aluminum Reduction Cell Based on Distributed Subspace Predictive Control. Machines 2022, 10, 220. https://doi.org/10.3390/machines10030220
Cui J, Wang P, Li X, Huang R, Li Q, Cao B, Lu H. Multipoint Feeding Strategy of Aluminum Reduction Cell Based on Distributed Subspace Predictive Control. Machines. 2022; 10(3):220. https://doi.org/10.3390/machines10030220
Chicago/Turabian StyleCui, Jiarui, Peining Wang, Xiangquan Li, Ruoyu Huang, Qing Li, Bin Cao, and Hui Lu. 2022. "Multipoint Feeding Strategy of Aluminum Reduction Cell Based on Distributed Subspace Predictive Control" Machines 10, no. 3: 220. https://doi.org/10.3390/machines10030220
APA StyleCui, J., Wang, P., Li, X., Huang, R., Li, Q., Cao, B., & Lu, H. (2022). Multipoint Feeding Strategy of Aluminum Reduction Cell Based on Distributed Subspace Predictive Control. Machines, 10(3), 220. https://doi.org/10.3390/machines10030220