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Data-driven approaches for in-line monitoring of particle attributes in chemical and pharmaceutical manufacturing processes


   Department of Chemical and Process Engineering

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  Dr Javier Cardona, Dr Y Chen, Dr Christos Tachtatzis  No more applications being accepted  Competition Funded PhD Project (UK Students Only)

About the Project

Particle processing is widely used in chemical and pharmaceutical manufacturing industries. In this context, particle attributes influence processability and are key to the optimisation of product quality. However, despite the high material costs involved, significant process inefficiencies are still common in these sectors. New technologies that improve the monitoring of particle attributes are essential to transform the ability to understand and control pharmaceutical processes and to achieve the reliability and stable operation of other sectors such as aerospace and automotive.

Currently, particle attributes are mainly characterised using off-line techniques that are prone to particle alteration during sampling, transport and analysis. In-line measurements are quickly developing as a fast alternative to overcome these limitations and have the potential to provide a more representative view of the particle population in-situ. However, unsolved challenges still remain in the extraction of quantitative particle attributes due to the complex in-line measurement environment.

The project will use a combination of experiments, data analytics and simulation to provide more accurate representation of particle attributes from in-line measurements. Data will be captured using state-of-the-art Process Analytical Technologies (PAT) available at the Centre for Continuous Manufacturing and Crystallisation (CMAC), including Particle View Microscopy (PVM), Focused-Beam Reflectance Measurement (FBRM) and Raman spectroscopy. These data streams will inform the development of Machine Learning and Deep Learning models to extract more representative particle size, shape and morphology distributions, as well as solution solid loading. Simulations of the measurement environment will contribute to identifying deviations from ideal scenarios and to providing physical meaning to these anomalies. While extracting information from individual sensors is a challenge in itself, the project will aim to implement data fusion approaches to further enhance in-line quantification of particle attributes and inform more advanced process control strategies.

This funded project will be based in the Departments of Electronic & Electrical Engineering and Chemical & Process Engineering. The proposed start date is October 2022. Funding is available for a 'home' student. To be classed as a home student, applicants must meet the following criteria:

· Be a UK national (meeting residency requirements), or

· Have settled status, or

· Have pre-settled status (meeting residency requirements), or

· Have indefinite leave to remain or enter.

Normally to be eligible for a full award a student must have no restrictions on how long they can stay in the UK and have been ordinarily resident in the UK for at least 3 years prior to the start of the studentship (with some further constraint regarding residence for education).

 If a student does not meet the criteria above, they will be classed as an international student. International students are permitted to self-fund the difference between the home and international fee rates.

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.


Funding Notes

This PhD project is fully funded for 42 months. Funding covers stipend and fees for UK students. 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 particle technology, material characterisation, data analytics, machine learning and programming skills (e.g. Python, Matlab, PyTorch/TensorFlow) are desirable.
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