ProcessOptimizer, an Open-Source Python Package for Easy Optimization of Real-World Processes Using Bayesian Optimization: Showcase of Features and Example of Use
Year: 2025
Type: software-paper
Abstract: 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: Søren Bertelsen, Sigurd Carlsen, Søren Furbo, Morten Bormann Nielsen, Aksel Obdrup, Rolf Taaning
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $4,500
OpenAlex ID: W4407250421