A machine learning enhanced digital twin toward sustainable pharmaceutical tablet manufacturing

Project title: A machine learning enhanced digital twin toward sustainable pharmaceutical tablet manufacturing  

Supervisory Team: Dr Xi Yu, Dr Mohamed Hassan-Sayed, Floria Bouchier, Dr Gavin Reynolds

Project description:

The reduction of emission from pharmaceutical tablet manufacturing is urgent and challenging. Rapid identification and quantification of emission sources is an important milestone to set reduction targets and implement corresponding reduction measures. Reliable and accurate in-silico predictions to identify sustainable process opportunities including resource efficiency, pollution prevention, renewable energy and green chemistry would be a game-changing tool.

Process digital twins (PDTs) are powerful tools for improving tablet manufacturing processes by providing a virtual platform for simulation, monitoring, optimization, and decision support. However, PDTs typically focus on optimizing technical aspects of processes, such as energy efficiency, production rates, and product quality. While they cannot fully capture broader sustainability and socio-economic factors. Life cycle analysis (LCA) is used widely in the pharmaceutical industry to evaluate environmental impact in pharmaceutical process. However, the approach suffers from the limitations, such as time consuming for case-by-case comparison and missing data of Life cycle inventory. In this project you will address the challenges of green tableting processes.

In this project you will apply a novel combination of advanced machine learning (ML), LCA and PDT to design and optimize pharmaceutical tablet manufacturing that are not only efficient and cost-effective but also environmentally sustainable across their entire life cycle. You will utilize cutting-edge numerical platform to optimize typical tableting routes including roller compaction and continuous direct compression.

You will spend at least 3 months of this 3 ½ year studentship working at AstraZeneca, where you will learn how computer modelling is applied to pharmaceutical process. At the University of Southampton you will be supervised by Dr Xi Yu and Dr Mohamed Hassan Sayed, with expertise in the development and application of numerical methodology to pharmaceutical and energy sector. You will also be supervised by Flora Bouchier and Dr Gavin Reynolds from AstraZeneca who are experts in the development and application of process system engineering to pharmaceutical process.

Entry Requirements

A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).

Closing date: 31 August 2024.  Applications will be considered in the order that they are received, the position will be considered filled when a suitable candidate has been identified.

Funding: We offer a range of funding opportunities for both UK and international students, including Bursaries and Scholarships.  For more information please visit PhD Scholarships | Doctoral College | University of Southampton  Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.

How To Apply

Apply online:  HERE Select programme type (Research), 2024/25, Faculty of Engineering and Physical Sciences, next page select “PhD Chemical Engineering (Full time)”. In Section 2 of the application form you should insert the name of the supervisor Dr Xi Yu.

Applications should include:

Research Proposal

Curriculum Vitae

Two reference letters

Degree Transcripts/Certificates to date

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