ADVANCING MULTIPHASE PROCESS DEVELOPMENT WITH HYBRID PHYSICS-BASED – MACHINE LEARNING MODELS
type: CreativeWork
description: Multiphase reaction systems are widely used in the chemical industry for the synthesis of pharmaceuticals, fine chemicals, and bulk intermediates. However, scaling up such systems is challenging due to the coupled effects of mass transfer and intrinsic reaction kinetics, both of which can control overall process behaviour. To develop predictive and generalizable models, it is essential to capture both reaction kinetics and the complex transport phenomena that influence system performance. This thesis presents a hybrid modelling framework for reactive multiphase processes, demonstrated on a liquid-liquid nitration system, and developed using experimental data guided by strategies for informative experiment selection. Understanding hydrodynamic behaviour is critical in such systems, as it determines interfacial area and mass transfer characteristics, both of which impact reaction performance. A workflow is developed to extract hydrodynamic descriptors and estimate interfacial area in a liquid–liquid system in a microreactor. High-speed imaging and computer vision techniques are applied to quantify flow characteristics, and a machine learning model is trained to predict the specific surface area from operating conditions. In addition, accurate estimation of kinetic parameters in multiphase systems often requires extensive experimentation, as parameters may be correlated and not all operating conditions contribute equally to model calibration. To address this, a model-based design of experiments (MBDoE) approach is developed to identify the most informative experimental conditions based on the structure of the mechanistic model. Building on these elements, a hybrid modelling framework is proposed by integrating a mechanistic kinetic model with a machine learning model for predicting the mass transfer coefficient, alongside the specific surface area model. The kinetic model is experimentally calibrated using MBDoE on an automated continuous flow platform, which enables t…
abstract: Multiphase reaction systems are widely used in the chemical industry for the synthesis of pharmaceuticals, fine chemicals, and bulk intermediates. However, scaling up such systems is challenging due to the coupled effects of mass transfer and intrinsic reaction kinetics, both of which can control overall process behaviour. To develop predictive and generalizable models, it is essential to capture both reaction kinetics and the complex transport phenomena that influence system performance. This thesis presents a hybrid modelling framework for reactive multiphase processes, demonstrated on a liquid-liquid nitration system, and developed using experimental data guided by strategies for informative experiment selection. Understanding hydrodynamic behaviour is critical in such systems, as it determines interfacial area and mass transfer characteristics, both of which impact reaction performance. A workflow is developed to extract hydrodynamic descriptors and estimate interfacial area in a liquid–liquid system in a microreactor. High-speed imaging and computer vision techniques are applied to quantify flow characteristics, and a machine learning model is trained to predict the specific surface area from operating conditions. In addition, accurate estimation of kinetic parameters in multiphase systems often requires extensive experimentation, as parameters may be correlated and not all operating conditions contribute equally to model calibration. To address this, a model-based design of experiments (MBDoE) approach is developed to identify the most informative experimental conditions based on the structure of the mechanistic model. Building on these elements, a hybrid modelling framework is proposed by integrating a mechanistic kinetic model with a machine learning model for predicting the mass transfer coefficient, alongside the specific surface area model. The kinetic model is experimentally calibrated using MBDoE on an automated continuous flow platform, which enables t…
author: Katsarou, Anna
language: eng
keywords: Machine learning, Design of experiments, Reaction kinetics
license: https://www.repository.cam.ac.uk/bitstreams/0b751d7a-3ffd-49ab-93ad-b0f08ed923f0/download
license: http://purl.org/NET/rdflicense/allrightsreserved
date published: 2025-04-04
date modified: 2026-07-22T22:24:23Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.121350