As part of CEISAM’s lecture series, Richard BOURNE from the University of Leed will be presenting his work on Friday May 23 at 09:30 a.m. in the Marie Curie room.
Industry 4.0: Self-optimising Flow Reactors for Rapid Process Development
Prof. Richard Bourne
Royal Academy of Engineering Research Chair, University of Leeds
Richard A. Bourne is currently a Professor of Digital Chemical Manufacturing at the University of Leeds. He completed a PhD under the supervision of Prof. Martyn Poliakoff, CBE, FRS. He is now a Royal Academy of Engineering Research Chair working on the development of new sustainable processes with focus on continuous flow routes to pharmaceutical and fine chemical products. His group is based within the Institute of Process Research and Development (IPRD) at the University of Leeds, a joint institute between Chemical Engineering and Chemistry. Research is supported by EPSRC, Catalysis Hub, Dial-A-Molecule, Horizon 2020 Funding (ProPAT and IbD), AstraZeneca, UCB Pharma, Syngenta, Royal Academy of Engineering and Dr. Reddy’s Laboratories.
Abstract
This talk will focus on the development of automated continuous flow systems. In particular recent research on self-optimising systems where the reactor and its process control instrumentation become an autonomous unit into which the reactants are pumped, and from which products emerge with optimized. This presentation will outline the recent grant ‘Cognitive Chemical Manufacturing’ and the new approaches to synthesis of fine chemicals and pharmaceutical compounds. These automated systems work without human intervention and are capable of very robust experimentation and rapid optimisation of challenging processes.
This talk will focus on optimisation of multiple unit operations including optimisation of telescoped reactions and reaction followed by continuous work-up. I will also explore the use of algorithms capable of optimising the trade-off between conflicting objectives such as yield and reactor productivity.
References
Bayesian Self-Optimization for Telescoped Continuous Flow Synthesis, Angewandte Chemie International Edition, 2022, 10.1002/anie.202214511
Automated stopped-flow library synthesis for rapid optimisation and machine learning directed experimentation, Chemical Science, 2022, 13,1208
Machine learning directed multi-objective optimization of mixed variable chemical systems, Chemical Engineering Journal, 2023, 451, 138443