About the Project
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.
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 particle technology, material characterisation, data analytics, machine learning and programming skills (e.g. Python, Matlab, PyTorch/TensorFlow) are desirable.
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