- Opens: Wednesday 3 March 2021
- Number of places: One
- Duration: 3 years
OverviewThe project will explore the use of hybrid models that combine the advantages of data-driven and physics-based first principles models to enhance the performance of current models for crystal nucleation and growth in chemical and pharmaceutical manufacturing.
Students applying should have (or expect to achieve) a high 2.1 undergraduate degree in a relevant Engineering/science discipline, and must 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.
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.
This PhD project is initially offered on a self-funding basis. However, excellent candidates will be considered for a University scholarship.
During the application you'll be asked for the following information and evidence uploaded to the application:
- your full contact details
- transcripts and certificates of all degrees
- proof of English language proficiency if you are not from a majority English-speaking country as recognised by UKVI
- two references, one of which must be academic. Please see our guidance on referees
- funding or scholarship information
- international students must declare any previous UK study
By filling these details out as fully as possible, you'll avoid any delay to your application being processed by the University.
Start date: Oct 2021 - Sep 2022
Chemical and Process Engineering
Start date: Oct 2022 - Sep 2023
Chemical and Process Engineering