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Multi-Objective, Multiphasic and Multi-Step Self-Optimising Continuous Flow Systems

Clayton, Adam David (2020) Multi-Objective, Multiphasic and Multi-Step Self-Optimising Continuous Flow Systems. PhD thesis, University of Leeds.

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Continuous flow chemistry is currently a vibrant area of research, offering many advantages over traditional batch chemistry. These include: enhanced heat and mass transfer, access to a wider range of reaction conditions, safer use of hazardous reagents, telescoping of multi-step reactions and readily accessible photochemistry. As such, there has been an increase in the adoption of continuous flow processes towards the synthesis of active pharmaceutical ingredients (APIs) in recent years. Advances in the automation of laboratory equipment has transformed the way in which routine experimentation is performed, with the digitisation of research and development (R&D) greatly reducing waste in terms of human and material resources. Self-optimising systems combine algorithms, automated control and process analytics for the feedback optimisation of continuous flow reactions. This provides efficient exploration of multi-dimensional experimental space, and accelerates the identification of optimum conditions. Therefore, this technology directly aligns with the drive towards more sustainable process development in the pharmaceutical industry. Yet the uptake of these systems by industrial R&D departments remains relatively low, suggesting that the capabilities of the current technology are still limited. The work in this thesis aims to improve existing self-optimisation technologies, to further bridge the gap between academic and industrial research. This includes introducing multi-objective optimisation algorithms and applying them towards the synthesis of APIs, developing a new multiphasic CSTR cascade reactor with photochemical capabilities and including downstream work-up operations in the optimisation of multi-step processes.

Item Type: Thesis (PhD)
Related URLs:
Keywords: Automated flow reactor, Environmental chemistry, Machine learning, Reaction engineering, Sustainable chemistry.
Academic Units: The University of Leeds > Faculty of Engineering (Leeds) > School of Chemical and Process Engineering (Leeds)
Identification Number/EthosID: uk.bl.ethos.805358
Depositing User: Mr Adam Clayton
Date Deposited: 27 May 2020 16:47
Last Modified: 11 Jun 2020 09:53
URI: http://etheses.whiterose.ac.uk/id/eprint/26668

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