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ProcessOptimizer, an Open-Source Python Package for Easy Optimization of Real-World Processes Using Bayesian

Søren Bertelsen1, Sigurd Carlsen2, Søren Furbo1

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ProcessOptimizer simplifies advanced machine learning for scientists. This Python package uses Bayesian optimization for efficient process and product development, demonstrated with a chemical reaction example.

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Area of Science:

  • Computational chemistry
  • Chemical engineering
  • Data science

Background:

  • Experimental scientists require accessible tools for complex optimization tasks.
  • Advanced machine learning, particularly Bayesian optimization, offers powerful solutions but can be difficult to implement.
  • A need exists for user-friendly software integrating these techniques for practical applications.

Purpose of the Study:

  • Introduce ProcessOptimizer, a Python package for machine learning-driven optimization.
  • Demonstrate the package's utility in optimizing a chemical reaction for a specific product color (leaf green).
  • Highlight features enhancing ease of use for experimentalists.

Main Methods:

  • Utilized Gaussian processes for Bayesian optimization within the ProcessOptimizer package.
  • Implemented features for benchmarking, noise handling, and multiobjective optimization.
  • Applied the package to optimize parameters of a chemical reaction for achieving a target color.

Main Results:

  • Successfully demonstrated the optimization of a chemical reaction using ProcessOptimizer.
  • Showcased the package's capability to simplify complex Bayesian optimization tasks.
  • Validated the effectiveness of ProcessOptimizer in achieving a specific product characteristic (leaf green color).

Conclusions:

  • ProcessOptimizer provides an accessible interface to advanced Bayesian optimization techniques.
  • The package is well-suited for experimental scientists seeking to optimize processes and products.
  • The successful chemical reaction optimization highlights the practical value and ease of use of ProcessOptimizer.