Prediction of the performance of pre-packed purification columns through machine learning.

ABSTRACT: Pre-packed columns have been increasingly used in process development and biomanufacturing thanks to their ease of use and consistency. Traditionally, packing quality is predicted through rate models, which require extensive calibration efforts through independent experiments to determine relevant mass transfer and kinetic rate constants. Here we propose machine learning as a complementary predictive tool for column performance. A machine learning algorithm, extreme gradient boosting, was applied to a large data set of packing quality (plate height and asymmetry) for pre-packed columns as a function of quantitative parameters (column length, column diameter, and particle size) and qualitative attributes (backbone and functional mode). The machine learning model offered excellent predictive

SUBMITTER: Jiang Q 

PROVIDER: S-EPMC9310636 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature