Optimisation of Discrete and Continuous Parameters for the Manufacture of Pharmaceuticals

Continuous flow processing has revolutionised the field of chemistry by enabling enhanced heat and mass transfer, safer handling of hazardous reagents, end-to-end processing of telescoped reactions and access to a broader range of reaction conditions compared to traditional batch methodologies. The desire to reduce the labour and material demands in research and development (R&D) processes has emphasised the need for automation in chemical synthesis. Flow platforms have played a pivotal role in enabling the automation of chemical systems, offering enhanced control over reaction parameters. The implementation of algorithms in feedback loops on automated flow platforms has expanded the potential of these systems, facilitating efficient exploration and optimisation of chemical processes. This synergy has resulted in the development of proficient self-optimisation systems that adeptly navigate experimental domains, expediting the discovery of optimal conditions and enhancing process understanding. The work in this thesis aims to unlock the potential of self-optimisation flow platforms, extending the capabilities into previously unexploited areas within this field. This involves introducing discrete variables into automated self-optimisation processes, applying them for the synthesis of APIs and extending the approach to incorporate telescoped flow reactions with consideration of multiple objectives to highlight the effectiveness of end-to-end optimisations.