Reaction Chemistry and Engineering

Active learning enabled reactor characterization for mass transfer in aerobic oxidation reactions

Ajit Vikram 1
, 
Keith A Mattern 1
, 
Shane T Grosser 1
1
 
Process Research and Development, Merck & Co., Inc., Rahway, New Jersey 07065, USA | 
Publication type: Journal Article
Publication date: 2025-01-01
scimago Q1
SJR: 0.850
CiteScore: 6.6
Impact factor: 3.4
ISSN: 20589883
Abstract

A generalizable active learning framework enables accurate prediction of mass transfer coefficients (kLa), and iterative design of experiments to efficiently characterize new reactor configurations with minimal experimental trials.

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