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Glasgow
United Kingdom
Applied Mathematics
Applied Statistics
Chemical Engineering
Mathematical Modelling
Physical Chemistry
Thermodynamics
About the Project
Process design requires accurate and reliable thermodynamic data for multicomponent mixtures in the system over a wide range conditions (e.g., solvents, impurity concentrations, temperatures, pressures) which are often missing, especially for new compounds. These data can be obtained by performing a multitude of measurements across a broad range of conditions; however, this is time consuming, expensive and requires significant quantities of material which may not be available. Thermodynamic models can be used to estimate these data; however, they have limited accuracy and reliability, especially for compounds and process conditions not previously encountered. In principle, some of these models can be successively improved by incorporating additional experimental measurements. This project aims to develop an automated system that will specify the precise sequence of experiments that will most efficiently provide the information to produce an accurate thermodynamic model of complex, multicomponent mixtures. The proposed work will use machine learning to develop an automated methodology to efficiently construct accurate mathematical representations of the thermodynamics of complex multicomponent mixtures. This will provide engineers and scientists a novel system to determine the optimal experimental measurements that will most effectively improve the thermodynamic model and yield most accurate predictions. It will radically improve our ability to rapidly and efficiently construct accurate thermodynamic models, which is key to the development of any chemical manufacturing process.
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:
www.strath.ac.uk/engineering/chemicalprocessengineering
www.strath.ac.uk/courses/research/chemicalprocessengineering/
Funding Notes
This PhD project is initially offered on a self-funding basis. It is open to applicants with their own funding, or those applying to funding sources. However, excellent candidates will be eligible to be considered for a University scholarship.
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