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

Qihao Jiang, Sohan Seth, Theresa Scharl, Tim Schroeder, Alois Jungbauer, Simone Dimartino*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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, particle size) and qualitative attributes (backbone and functional mode). The machine learning model offered excellent predictive capabilities for the plate height and the
asymmetry (90% and 93%, respectively), with packing quality strongly influenced by backbone (~70% relative importance) and functional mode (~15% relative importance), well above all other quantitative column parameters. The results highlight the ability of machine learning to provide reliable predictions of column performance from simple, generic parameters, including strategic qualitative parameters such as backbone and functionality, usually excluded from quantitative considerations. Our results will guide further efforts in column optimization, e.g. by focusing on improvements of backbone and functional mode to obtain optimised packings.
Original languageEnglish
Pages (from-to)1445-1457
JournalJournal of Separation Science
Issue number8
Early online date9 Mar 2022
Publication statusPublished - Apr 2022


  • Asymmetry
  • Machine learning
  • plate height
  • Porous Media
  • pre-packed columns


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