Active learning enabled reactor characterization for mass transfer in aerobic oxidation reactions
Year: 2024
Type: article
Abstract: 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.
Source: Reaction Chemistry & Engineering
DOI: 10.1039/d4re00410h
Institution: Merck & Co., Inc., Rahway, NJ, USA (United States)
Language: English
Topic: Fluid Dynamics and Mixing
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4404483200