Multipoint Feeding Strategy of Aluminum Reduction Cell Based on Distributed Subspace Predictive Control

Abstract

With the continuous development of large-scale aluminum reduction cells, the problem of the uniform distribution of alumina concentration in the cell has become more and more serious for the reduction process. In order to achieve the uniform distribution of the alumina concentration, a data-driven distributed subspace predictive control feeding strategy is proposed in this paper. Firstly, the aluminum reduction cell is divided into multiple sub-systems that affect each other according to the position of the feeding port. Based on the subspace method, the prediction model of the whole cell is identified, and the prediction output expression of each sub-system is deduced by decomposition. Secondly, the feeding controller is designed for each aluminum reduction cell subsystem, and the input and output information can be exchanged between each controller through the network. Thirdly, under consideration of the influence of other subsystems, each subsystem solves the Nash-optimal control feeding quantity, so that each subsystem realizes distributed feeding. Finally, the simulation results show that, compared with the traditional control method, the proposed distributed feeding control strategy can significantly improve the problem of the uniform distribution of alumina concentration and improve the current efficiency of the aluminum reduction cell.

Keywords

• aluminum reduction process
• alumina concentration
• subspace identification
• distributed predictive control