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AI/Machine Learning for chemical process monitoring and design in the manufacture of green chemicals


   School of Engineering


About the Project

At Aberdeen, we have several lines of research around the manufacture of green chemicals. For example, we are building plasma catalysis reactors to reform biomethane and/or carbon dioxide into more valuable chemicals as part of a Leverhulme trust sponsored doctoral training centre. We also continue to work on low-carbon cements produced via sulfur combustion sponsored by the GORD institute. Finally, we’re developing “electronic noses” which can monitor fermentation processes (and plasma reactors) using combinations of cheap commercial gas sensors. All of these systems have huge numbers of variables, and we need to utilise “big data” approaches combined with AI/Machine Learning to characterise and model them.

Machine learning, and more generally Artificial Intelligence, are finding wider applications in the field of Chemical/Process Engineering. The key challenge is to maintain physical correctness and validity of the trained models outside their region of training, thus ensuring safety, when embedding these models into process control, or process design. They must capture the underlying rules such as the laws of thermodynamics, and elemental constraints, thus we’re embedding the Machine Learning/AI at a higher level, layered above the thermodynamic and kinetic models.

The research you undertake here will focus on developing new process models using open-source process modelling frameworks. These can be existing modelling frameworks such as pyomo, or custom in-house solutions. You can work in close collaboration with any of our experimental projects, even taking over aspects of them, depending on your own interests and motivations. The project can be developed together with you; however, at its core will be the modelling work combined with modern engineering principles of rapid iteration and fail fast design which requires comparison against experimental results.

Selection will be made on the basis of academic merit. The successful candidate should have, or expect to obtain, a UK Honours degree at 2.1 or above (or equivalent) in Chemical Engineering, physics, chemistry, or a related field with computational modelling experience modelling.

Knowledge of:

Any background in process modelling and a programming language such as python, Julia, or C++ is highly desirable.

APPLICATION PROCEDURE:

Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php

• Apply for Degree of Doctor of Philosophy in Engineering

• State name of the lead supervisor as the Name of Proposed Supervisor

• State ‘Self-funded’ as Intended Source of Funding

• State the exact project title on the application form

When applying please ensure all required documents are attached:

• All degree certificates and transcripts (Undergraduate AND Postgraduate MSc-officially translated into English where necessary)

• Detailed CV, Personal Statement/Motivation Letter and Intended source of funding

Informal inquiries can be made to Dr M Campbell Bannerman () with a copy of your curriculum vitae and cover letter. All general enquiries should be directed to the Postgraduate Research School ()


Funding Notes

This PhD project has no funding attached and is therefore available to students (UK/International) who are able to seek their own funding or sponsorship. Supervisors will not be able to respond to requests to source funding. Details of the cost of study can be found by visiting View Website

References

T. Hanein, J. L. Galvez-Martos, and M. N. Bannerman, “Carbon footprint of calcium sulfoaluminate clinker production,” J. Clean. Prod., 172, 2278–2287 (2018)
T. Hanein, MS.-E Imbabi, F. P. Glasser, and M. N. Bannerman (2016). Lowering the carbon footprint and energy consumption of cement production: A novel Calcium SulfoAluminate cement production process. In IGCMat 2016, Los Angeles.
Leverhulme centre: https://www.abdn.ac.uk/engineering/research/leverhulme-centre-for-doctoral-training-in-sustainable-production-of-chemicals-and-materials-625.php

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