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Subspace identification method-based setpoints tracking control and its applications to the column cleaning process.

Zhiqiang Wang1, Zhiyuan Song2, Dakuo He2

  • 1Northeast Electric Power University, Jilin, China.

ISA Transactions
|December 17, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep autoencoder-based subspace identification method (SIM-DAE) to control tailing copper grade (TCG) and concentrate copper grade (CCG) in copper flotation processes, ensuring stable and efficient operations.

Keywords:
Column cleaning processH(∞) performanceSetpoint tracking controlSubspace identification

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Area of Science:

  • Mineral Processing Engineering
  • Control Systems Engineering
  • Artificial Intelligence in Chemical Engineering

Background:

  • The column cleaning process is critical for economic indicators in copper flotation, directly impacting tailing copper grade (TCG) and concentrate copper grade (CCG).
  • Accurate control of TCG and CCG is essential but challenging due to unknown parameters in the column cleaning process.
  • Existing methods struggle with the complexity and unmeasured parameters inherent in flotation column dynamics.

Purpose of the Study:

  • To develop a robust method for setpoint tracking control of TCG and CCG in copper flotation column cleaning.
  • To address the challenge of unknown system parameters by proposing an advanced system identification technique.
  • To ensure the stability and anti-interference capabilities of the control system for industrial application.

Main Methods:

  • Construction of a state-space model based on the two-phase model of flotation.
  • Application of a deep autoencoder-based subspace identification method (SIM-DAE) to identify unknown state-space matrices.
  • Utilization of a Lyapunov-Krasovskii function for stability and anti-interference analysis.
  • Design of a state feedback controller for setpoint tracking of TCG and CCG.

Main Results:

  • Successfully identified the state-space model matrices of the complex column cleaning process using SIM-DAE.
  • Verified the stability and anti-interference performance of the identified system.
  • Demonstrated the effectiveness of the designed state feedback controller in achieving setpoint tracking for TCG and CCG.
  • Validated the proposed methods through data experiments and an industrial field platform.

Conclusions:

  • The SIM-DAE method provides an effective approach for identifying system dynamics in flotation columns with unknown parameters.
  • The developed control strategy ensures accurate tracking of TCG and CCG setpoints, improving process economics.
  • The study confirms the practical feasibility and effectiveness of the proposed identification and control methods in industrial copper flotation operations.