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Hybrid data-driven and first principles models for crystal nucleation and growth

Department of Chemical and Process Engineering

About the Project

The application of Artificial Intelligence (AI) in manufacturing is quickly developing in many fields. In Chemical and Pharmaceutical manufacturing, AI is seen as an opportunity to correct the significant differential in terms of process efficiency, agility and reliability with respect to other manufacturing sectors such as aerospace and automotive. Nevertheless, one of the main limitations for the uptake of Machine Learning and Deep Learning approaches is still the large amounts of data required in processes characterised by high variability and complex non-linear interactions between multiple variables.

At present, purely data-driven approaches are limited to material design, process operation and fault detection and diagnosis. However, hybrid models can extend the applicability of these approaches by incorporating first principles knowledge to constrain the search space, therefore compensating for the lack of data. Furthermore, Machine Learning methods are useful in finding hidden correlations in complex datasets from which unknown physical meaning can be derived to enhance the predictive ability of first principle models.

The project aims to develop data-driven and first principles hybrid models to improve current nucleation and growth models in crystallisation processes. Extensive datasets captured using state-of-the-art Process Analytical Technologies (PAT) available at the Centre for Continuous Manufacturing and Crystallisation (CMAC) will inform the design of these hybrid models to incorporate currently unaccounted phenomena such as unexpected phase transitions into population balance modelling. The project will expand the physical understanding of nucleation and growth phenomena to increase process efficiency and reliability in pharmaceutical manufacturing, while ensuring the relevance of the research through continuous engagement with CMAC industrial partners.

In addition to undertaking cutting edge research, students are also registered for the Postgraduate Certificate in Researcher Development (PGCert), which is a supplementary qualification that develops a student’s skills, networks and career prospects.

Information about the host department can be found by visiting:

Funding Notes

This PhD project is initially offered on a self-funding basis. It is open to applicants with their own funding, or those applying to funding sources. However, excellent candidates may be considered for a University scholarship.
Students applying should have (or expect to achieve) a minimum 2.1 undergraduate degree in a relevant engineering/science discipline, and be highly motivated to undertake multidisciplinary research.

Knowledge and/or experience in process modelling, particle technology, material characterisation, data analytics, machine learning and programming skills (e.g. Python, Matlab, PyTorch/TensorFlow) are desirable

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