ProcessOptimizer, an Open-Source Python Package for Easy Optimization of Real-World Processes Using Bayesian Optimization: Showcase of Features and Example of Use
ProcessOptimizer is a Python package designed to provide easy access to advanced machine learning techniques, specifically Bayesian optimization using, e.g., Gaussian processes. Aimed at experimentalist scientists and applicable to process and product optimizations in various fields, this package simplifies the optimization process, offering features such as benchmarking, noise addition/removal, multiobjective optimization, batch-mode operation, and comprehensive plotting features. The present publication focuses on ease of use by presenting an optimization of a chemical reaction to produce a specific color, such as leaf green.
Authors
- Rolf H. Taaning
- Sigurd Carlsen
- Søren Bertelsen (ORCID: https://orcid.org/0000-0003-2401-3650)
- Aksel Obdrup
- Michael Nielsen (ORCID: https://orcid.org/0000-0001-6883-0674)
- Søren Furbo
Institutions
- Novo Nordisk (Denmark) (DK)
- Danish Technological Institute (DK)
- Digital Science (United States) (US)
- Haldor Topsoe (Denmark) (DK)
Publication Details
- Journal
- Journal of Chemical Information and Modeling
- Published
- 2025-02-07
- DOI
- https://doi.org/10.1021/acs.jcim.4c02240
- Citations
- 6
- Primary Topic
- Time Series Analysis and Forecasting
- Type
- article
- Field-Weighted Citation Impact
- 8.91