Microscale and Segmented Processes Towards A Carbon Neutral Vision of Process Optimisation

这个项目的机会是通过连接发现和开发活动来创建一个范式转换。我们将利用新的工业4.0功能，通过利用微体积脉冲进行化学开发，推动更快地发现和优化化学工艺，以促进实现碳中和愿景。该项目旨在通过开发能够优化整个药物开发管道中的多操作序列（解锁纯化和复杂化学）的工业4.0平台，为下一代药物开发提供微型自动化。当前的自优化平台主要关注连续变量的优化，往往忽略了催化剂、配体和溶剂等离散变量的相互作用效应。WP 1：反应器设计和接口小分子合成自动化的最新发展是由经济和环境效益的结合驱动的。然而，目前的自优化平台在很大程度上限于低复杂性的单步化学过程。该项目将研究分段流程的使用。WP 2：AZ离散变量优化案例研究：RAB小组最近开发了一种新的贝叶斯优化算法，能够同时优化多个目标的连续和离散变量。我们将进一步发展这种方法，并将其应用于一系列工业相关的案例研究。WP 3：自动化合成过程我们将开发一个网络/物理系统，用于2步反应的自我优化。我们将设计物理系统来进行自动化连续流化学反应序列。该项目进一步开发了EPSRC资助“认知化学制造”EP/R 032807/1的技术。它与以下EPSRC研究领域保持紧密联系：人工智能技术，催化，化学反应动力学和机制，信息系统，过程系统，资源效率，传感器和仪器，合成有机化学

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