Microscale and Segmented Processes Towards A Carbon Neutral Vision of Process Optimisation
Lead Research Organisation:
University of Leeds
Department Name: Chemical and Process Engineering
Abstract
The opportunity in this project is to create a paradigm shift by bridging discovery and development activities. We will use new Industry 4.0 capabilities to drive more rapid discovery and optimisation of chemical processes to facilitate the move toward a carbon neutral vision by utilising microvolume pulses for chemical development. This project aims to deliver microscale automation for the next generation of medicine development by developing Industry 4.0 platforms capable of optimising multi-operation sequences (unlocking purification and complex chemistry) throughout drug development pipelines. Current self-optimising platforms have focused on the optimisation of continuous variables, often overlooking the interaction effects of discrete variables such as catalysts, ligands and solvents.
WP 1: Reactor Design and Interfacing The recent uptake in the automation of small molecule synthesis is driven by a combination of economic and environmental benefits. However, current self-optimising platforms are largely limited to low complexity, single-step chemical processes. This project will research the use of segmented flow processes.
WP 2: AZ Case Studies for Discrete Variable Optimisation: The RAB group has recently developed a new Bayesian optimisation algorithm capable of simultaneously optimising continuous and discrete variables for multiple objectives. We will further develop this approach and apply it to a range of industrially relevant case studies.
WP 3: Automated Synthetic Process We will develop a cyber/physical system for self-optimisation of 2-step reactions. We will design the physical system to conduct automated continuous flow chemical reaction sequences.
This project further develops technologies resulting from the EPSRC grant "Cognitive Chemical Manufacturing" EP/R032807/1. It aligns strongly with the following EPSRC research areas: AI technologies, Catalysis, Chemical Reaction Dynamics and Mechanisms, Information Systems, Process Systems, Resource Efficiency, Sensors and Instrumentation, Synthetic Organic Chemistry
WP 1: Reactor Design and Interfacing The recent uptake in the automation of small molecule synthesis is driven by a combination of economic and environmental benefits. However, current self-optimising platforms are largely limited to low complexity, single-step chemical processes. This project will research the use of segmented flow processes.
WP 2: AZ Case Studies for Discrete Variable Optimisation: The RAB group has recently developed a new Bayesian optimisation algorithm capable of simultaneously optimising continuous and discrete variables for multiple objectives. We will further develop this approach and apply it to a range of industrially relevant case studies.
WP 3: Automated Synthetic Process We will develop a cyber/physical system for self-optimisation of 2-step reactions. We will design the physical system to conduct automated continuous flow chemical reaction sequences.
This project further develops technologies resulting from the EPSRC grant "Cognitive Chemical Manufacturing" EP/R032807/1. It aligns strongly with the following EPSRC research areas: AI technologies, Catalysis, Chemical Reaction Dynamics and Mechanisms, Information Systems, Process Systems, Resource Efficiency, Sensors and Instrumentation, Synthetic Organic Chemistry
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/W52217X/1 | 01/10/2021 | 30/09/2026 | |||
| 2598856 | Studentship | EP/W52217X/1 | 01/10/2021 | 30/09/2025 | Zara Arshad |