Active learning enabled reactor characterization for mass transfer in aerobic oxidation reactions
A generalizable active learning framework enables accurate prediction of mass transfer coefficients ( k L a ), and iterative design of experiments to efficiently characterize new reactor configurations with minimal experimental trials.
Authors
- Keith Mattern (ORCID: https://orcid.org/0000-0002-4693-0698)
- Ajit Vikram (ORCID: https://orcid.org/0000-0002-7397-3431)
- Shane T. Grosser (ORCID: https://orcid.org/0000-0002-9268-7545)
Institutions
- Merck & Co., Inc., Rahway, NJ, USA (United States) (US)
Publication Details
- Journal
- Reaction Chemistry & Engineering
- Published
- 2024-11-18
- DOI
- https://doi.org/10.1039/d4re00410h
- Citations
- 1
- Primary Topic
- Innovative Microfluidic and Catalytic Techniques Innovation
- Type
- article
- Field-Weighted Citation Impact
- 0.18