Morphology optimization of ordered chromatography stationary phases - a workflow designed by machine learning and computational fluid dynamics

Aug 3, 2026

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Chromatography is a key separation and analysis method in analytical chemistry and biochemistry. A system couples a mobile phase with a stationary phase, and ordered stationary morphologies can enhance separation performance, though only limited ordered types have been evaluated due to the high cost of experiments and simulations. This thesis establishes correlations between column performance and ordered morphologies using machine learning and computational fluid dynamics. It trains models on roughly 25,000 experiments and demonstrates prediction accuracy with MAPE near 10% for reduced plate height and 7% for peak asymmetry, highlighting column backbones as most influential.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages255

Reading Time10 h 43 min

AccessFree to download

Abstract

Machine learning for packing quality and prediction

Algorithm for generating ordered 2D morphologies with constraints

CFD-based performance analysis and homogeneity metrics

This thesis has been submitted in fulfillment of the requirements for a postgraduate degree(e.g. PhD, MPhil, DClinPsychol) atthe University of Edinburgh. Please note the following terms and conditions of use: - This work is protected by copyright and other intellectual property rights, which are retained by the thesis author, unless otherwise stated. - A copy can be downloaded for personal non-commercial research or study, withoutprior permission or charge. - This thesis cannot be reproduced or quoted extensively from without first obtaining permission in writing from the author. - The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the author. - When referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given. Morphology optimization of ordered chromatography stationary phases: a workflow designed by machine learning and computational fluid dynamics by Qihao Jiang A thesis submitted for the degree of DOCTOR OF PHILOSOPHY The School of Engineering The University of Edinburgh May 2022 Declaration I declare that the thesis has been composed by myself and that the work has not been submitted for any other degree or professional qualification. I confirm that the work submitted is my own, except where work which has formed part of jointly-authored publications has been included. My contribution and those of the other authors to this work have been explicitly indicated below. I confirm that appropriate credit has been given within this thesis where reference has been made to the work of others. Parts of the work presented in chapter 4 were previously published in the Journal of Separation Science as "Prediction of the performance of pre-packed purification columns through machine learning" by Qihao Jiang, Sohan Seth, Theresa Scharl, Tim Schroeder, Alois Jungbauer, and Simone Dimartino. This study was conceived by all of the authors. I carried out all simulation and modeling work, data analysis and paper writing. Parts of the work presented in chapter 5 were previously published as"Permeability in two-dimensional periodic topologies: Combinatorial analysis and CFD simulations" by Stefano Rocca for his master thesis. This study was conceived by all of the authors. I carried out the conceptualisation and supervision of coding work, as well as paper review and editing. Edinburgh, 2022.05.23 Qihao Jiang Abstract Chromatography is a significant separation and analysis method widely used in analytical chemistry and biochemistry. A chromatographic system contains a mobile phase and a stationary phase, where the commonly utilized stationary phase is constructed by randomly packed particles. It has been verified by experiments that the usage of ordered structure for the stationary phase, would improve separation performance. However, due to the long time required for experiments and simulation procedures, there were only a few types of ordered structures evaluated for the chromatography system. This work is to investigate the correlation between column separation performance and ordered morphologies, for figuring out ordered structures with optimal chromatographic performance. Machine learning (ML) technology was applied in this work for finding optimal structures. This method was firstly employed for packing quality analysis of around 25000 experiments of pre-packed columns manufactured for a period of over 10 years. The capability of the ML model was validated to offer predictions of column performance with mean absolute percentage error (MAPE) equal to around 10% for reduced height equivalent to a theoretical plate (ℎ) and 7% for peak asymmetry ( 􀜣􀯦) . Also, the model quantitatively indicated that column backbones were the most influential factor for pre-packed column quality. This work proved the capability of ML to evaluate and predict column performan

FAQs

What problem does the thesis address in ordered chromatography stationary phases?

It tackles the limited number of ordered structures evaluated in chromatography due to long experimental and simulation times, aiming to link ordered morphology with separation performance.

How is machine learning used in the workflow?

Machine learning is applied to analyze packing quality from about 25,000 experiments and to predict column performance with reported errors around 10% MAPE for reduced plate height and 7% for peak asymmetry.

How does the thesis generate large sets of ordered morphologies?

It develops a 2D algorithm that represents morphologies as discrete mobile/stationary elements converted to a binary matrix, then uses principal pathway, symmetry, and porosity constraints to reduce the number of candidate topologies by 97%.

Aug 3, 2026

Upload by Jiven