About the Project
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:
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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