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Burcu Beykal1,2, Nikolaos A Diangelakis3,4, Efstratios N Pistikopoulos3,4

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Summary
This summary is machine-generated.

This study optimizes control for dynamic systems using surrogate models and data-driven methods, avoiding complex calculations. This approach effectively generates optimal control strategies for time-varying systems, demonstrated on a chemical reactor example.

Keywords:
Data-driven optimizationdynamic optimizationoptimal controlsurrogate modelingtime-varying systems

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Area of Science:

  • Chemical Engineering
  • Control Systems Theory
  • Computational Science

Background:

  • Time-varying systems are prevalent in chemical processes, necessitating efficient control strategies.
  • Traditional control optimization often requires full model discretization, which can be computationally intensive.
  • Systematic control is crucial for maintaining desired operational settings in dynamic industrial processes.

Purpose of the Study:

  • To develop a control optimization framework for time-varying systems that bypasses full model discretization.
  • To utilize surrogate modeling and data-driven optimization for deriving optimal control trajectories.
  • To evaluate the effectiveness of different surrogate forms and data-driven optimization strategies.

Main Methods:

  • Postulating nonlinear continuous-time control action trajectories using time-varying surrogate models.
  • Employing data-driven optimization to derive surrogate model parameters from high-fidelity model data.
  • Testing exponential and polynomial surrogate forms and comparing local/global, sample-based/model-based optimization strategies.
  • Validating the approach on a motivating example and a continuous stirred-tank reactor (CSTR) case study.

Main Results:

  • Successful derivation of optimal control trajectories without full model discretization.
  • Demonstrated consistency across various data-driven optimization strategies.
  • Favorable results obtained in the CSTR control case study, showcasing practical applicability.
  • Efficient control action trajectory generation using surrogate models and input-output data.

Conclusions:

  • The proposed data-driven surrogate modeling approach offers an effective alternative for optimizing control of time-varying systems.
  • This method reduces computational burden by avoiding full model discretization.
  • The framework is robust and applicable to complex dynamic systems, as evidenced by the CSTR case study.