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Global optimization of distillation columns using surrogate models.

Tobias Keßler1, Christian Kunde2, Nick Mertens3

  • 1Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106 Magdeburg, Germany.

SN Applied Sciences
|August 18, 2020
PubMed
Summary

This study optimizes distillation columns using an iterative Kriging method for global optimization. The Kriging approach significantly improves results for non-ideal columns compared to local optimization methods.

Keywords:
DistillationGlobal optimizationKrigingSurrogate models

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Area of Science:

  • Chemical Engineering
  • Optimization Techniques

Background:

  • Distillation column optimization is crucial for process efficiency.
  • Deterministic global optimization is needed to avoid local minima in complex problems.
  • Mixed-integer nonlinear programming (MINLP) challenges arise in ideal and non-ideal column design.

Purpose of the Study:

  • To investigate surrogate-based optimization of distillation columns.
  • To employ an iterative Kriging approach for deterministic global optimization.
  • To determine optimal setups and operating conditions for distillation processes.

Main Methods:

  • Utilized an iterative Kriging approach as a surrogate model.
  • Applied deterministic global optimization to avoid local optima.
  • Conducted case studies on ideal and non-ideal distillation columns formulated as MINLP problems.

Main Results:

  • The adapted Kriging approach achieved results comparable to direct global optimization for ideal columns.
  • Significant improvements were observed for non-ideal distillation columns compared to multistart local optimization.
  • The method effectively addressed MINLP challenges in distillation column design.

Conclusions:

  • Iterative Kriging is an effective surrogate-based optimization strategy for distillation columns.
  • This approach offers a robust solution for complex, non-ideal distillation systems.
  • The Kriging method enhances the reliability of finding global optima in chemical process optimization.