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Rapid additive and solvent screening for morphology control in continuous crystallisation via machine learning


Project Description

The Centre for Sustainable Chemical Technologies (CSCT) at the University of Bath focuses on developing new molecules, materials, processes and systems from the lab right through to industrial application, with an emphasis on practical sustainability. We train scientists and engineers to work together with industry to meet the needs of current and future generations. For more information on the CSCT, see http://www.csct.ac.uk/study-with-us/.

All PhD students in the Centre will have the opportunity to take part in an extensive training programme including public engagement, sustainable technology, clean technology, design of experiments, patents and entrepreneurship training sessions. Students will also have the opportunity to take part in Student Symposia, the CSCT Summer Showcase and public engagement opportunities such as the popular Festival of Nature.

We are now accepting applications to work with our industrial partner, CMAC, on this exciting PhD project. You may find further information about CMAC here: https://www.cmac.ac.uk/.

Particle morphology significantly contributes to the bulk properties of crystalline solid-state materials, influencing downstream processing in pharmaceutical manufacturing, such as flow, compaction, disintegration, bioavailability, etc. Particle morphology is the result of the crystal different growth rates of individual crystal faces during crystallisation. This growth rate can be activated or deactivated by introducing solvents and/or additives that contain molecules and functional groups that interact with the crystal. However, the design of such growth modifiers often relies on trial-and-error and requires extensive screening that consumes time, resources and that are restricted by experimental conditions. As a smarter approach for solvent and additive screening, this project will utilise machine learning to rapidly predict the performance of crystal modifiers. In machine learning, (with enough data and a learning algorithm) the rules that underlie the behaviour of molecules and physical phenomena can be identified by assessing a portion of a dataset and building models to make predictions. As a consequence, the aim of this project is to investigate the effect of additives and solvents for the control of morphology in continuous crystallisation. This will be achieved by experimentally screening various active pharmaceutical ingredients (APIs), solvents and additives, while simultaneously developing a machine-learning platform that can be used as the baseline to predict the morphology of materials crystallised using modifiers. The PhD project will require generating, collecting and processing the data needed required by the machine-learning algorithm. The platform will be developed in MATLAB’s Statistics and Machine Learning Toolbox.

Using machine learning to predict the performance of crystal modifiers will make the manufacturing process more efficient and therefore more sustainable. Also, predicting the performance of crystal modifiers will enable the pharmaceutical industry to screen them in a more environmental and sustainable manner, significantly reducing solvent/material usage as well as human resources.

We invite applications from Science and Engineering graduates who have, or expect to obtain, a first or upper second class degree and have a strong interest in Sustainable Chemical Technologies.

Informal enquiries about the research project should be directed to Dr Bernardo Castro Dominguez on .

Enquiries about the application process should be sent to .

Formal applications should be made via the University of Bath’s online application form for a PhD in Chemical Engineering:
https://samis.bath.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RDUCE-FP01&code2=0013

On the application form, please ensure that you quote CSCT in the Finance section and the supervisor’s name and project title in the ‘Your research interests’ section.

More information about applying for a PhD at Bath may be found here:
http://www.bath.ac.uk/guides/how-to-apply-for-doctoral-study/

Anticipated start date: 30 September 2019.

Funding Notes

UK and EU citizens applying for this project will be considered for a studentship covering UK/EU tuition fees and maintenance in line with the UKRI Doctoral Stipend rate (£15,009 in 2019/20) for a period of up to 3.5 years.

Candidates who are classed as Overseas for tuition fee purposes are unfortunately not eligible for funding.

How good is research at University of Bath in Aeronautical, Mechanical, Chemical and Manufacturing Engineering?

FTE Category A staff submitted: 61.00

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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