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Related Experiment Videos

Multi-objective optimization for model predictive control.

Willy Wojsznis1, Ashish Mehta, Peter Wojsznis

  • 1Emerson Process Management, 12301 Research Blvd., Austin, TX 78759, USA. Willy.Wojsznis@EmersonProcess.com

ISA Transactions
|March 27, 2007
PubMed
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This study introduces a novel multi-objective optimization technique for Model Predictive Control (MPC). It prioritizes constraints, economics, and control, ensuring robust performance in industrial applications like the Shell heavy oil fractionator.

Area of Science:

  • Control Engineering
  • Process Optimization
  • Industrial Automation

Background:

  • Model Predictive Control (MPC) is crucial for complex industrial processes.
  • Existing MPC techniques may struggle with dynamic multi-objective prioritization.
  • Handling constraints, economics, and control objectives simultaneously presents a challenge.

Purpose of the Study:

  • To develop a multi-objective optimization technique for MPC with prioritized objectives.
  • To dynamically assign weights for constraint handling, economic maximization, and control maintenance.
  • To ensure guaranteed solutions for complex industrial control scenarios.

Main Methods:

  • A three-level objective function prioritizing constraints, economics, and control.
  • Dynamic weight assignment to control and constraint variables nearing limits.

Related Experiment Videos

  • Utilization of priority structures, slack variable penalties, and model redefinition.
  • Implementation in an industrial control system for a distillation column (Shell heavy oil fractionator).

    • Main Results:

    • Successfully implemented a novel MPC technique in an industrial setting.
    • Demonstrated effective optimization and control of a distillation column.
    • Achieved dynamic prioritization of constraints, economics, and control objectives.
    • Validated the technique's ability to handle process outputs predicted to go out of limits.

    Conclusions:

    • The proposed multi-objective optimization technique enhances MPC performance.
    • It provides a robust and guaranteed solution for complex industrial control problems.
    • The method is effective for optimizing processes like the Shell heavy oil fractionator.