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Robust Multi-Objective Global Optimization of Stochastic Processes With a Case Study in Gradient Elution

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This study introduces a new algorithm for robust multi-objective process optimization, enhancing reliability in chromatography by accounting for environmental variability. The method ensures process stability and performance under uncertain conditions.

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Area of Science:

  • Chemical Engineering
  • Process Optimization
  • Analytical Chemistry

Background:

  • Stochastic variability in environmental variables poses challenges for robust process optimization.
  • Multi-objective optimization in chromatography requires methods that account for uncertainty.
  • Existing methods may not adequately address process variability under dynamic conditions.

Purpose of the Study:

  • To introduce a novel algorithm for robust multi-objective process optimization under stochastic environmental variability.
  • To apply this algorithm to gradient elution chromatography.
  • To provide a method for estimating the expected Pareto front and its variability for robust decision-making.

Main Methods:

  • Simultaneous optimization of multiple scenarios with random environmental variables.
  • Experiment design maximizing cumulative expected hypervolume improvement using Gaussian process regression models.
  • Estimation of the expected Pareto front and its variability with traceability to process parameters.

Main Results:

  • Successful application of the algorithm to gradient elution chromatography.
  • Development of a method for estimating the expected Pareto front and its variability.
  • Demonstration of robust process optimization by identifying Pareto optimal processes meeting specific criteria with confidence.

Conclusions:

  • The novel algorithm enables robust multi-objective process optimization under stochastic variability.
  • The method provides essential information for determining reliable process parameters.
  • The approach is applicable to both in silico and wet lab experiments, though it may increase experimental effort.