Agile Self-Optimisation for High Pressure Flow Chemistry

高压流体化学的敏捷自我优化

• 批准号: 2445548
• 金额: --
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2445548
• 关键词: Agile; Self; Optimisation; Pressure; Flow

In flow chemistry, optimising the parameters (concentration, temperature, pressure etc.) is important in ensuring a high-quality product. The standard procedure to do this is manual optimisation in which adjustment to one parameter at a time is done. This approach can be very time consuming; it can deplete resources very quickly and produces considerable amounts of waste which can be a major issue with expensive starting materials.The concept of self-optimisation involves machine learning algorithms and process analytical technology (PAT) working together to reduce the consumption of reagents needed whilst simultaneously accelerating the optimisation process. Self-optimisation facilitates process development as it allows more precise control over reaction parameters and is becoming increasingly popular for flow chemistry. However its application to high pressure continuous flow reactions has been rather more limited. High pressure is important because, for example, it permits the use of supercritical solvents which have particular advantages for reactions involving gaseous reagents such as hydrogenation with hydrogen or oxidation with molecular oxygen both of which can lead to highly atom efficient and more sustainable reactions. Optimising such reactions can be complicated because many of the reactor parameters are highly correlated - changing one can affect several others. This project is supervised by an interdisciplinary team involving computational chemistry, mathematics, spectroscopy and process chemistry. Initially a flexible high pressure flow reactor will be constructed with facilities for using a variety of process analytical techniques for monitoring thermal catalytic reactions. Then this equipment will be applied to reaction self-optimisation. The aim is to build up a library of algorithms that can manipulate data from the reaction monitoring to identify the optimum reaction parameters. Understanding the advantages and disadvantages of each algorithm will enable reactions to be optimised as efficiently as possible, thereby minimizing the use of chemicals during process development and delivering sustainable processes.

在流动化学中，优化参数（浓度、温度、压力等）对于确保高质量的产品非常重要。这样做的标准程序是手动优化，其中一次调整一个参数。这种方法可能非常耗时；它可以非常迅速地耗尽资源，并产生相当数量的废物，这可能是昂贵的起始材料的主要问题。自我优化的概念涉及机器学习算法和过程分析技术（PAT）共同工作，以减少所需试剂的消耗，同时加速优化过程。自优化有助于工艺开发，因为它可以更精确地控制反应参数，并且在流动化学中越来越受欢迎。然而，它在高压连续流反应中的应用相当有限。高压是很重要的，因为，例如，它允许使用超临界溶剂，超临界溶剂在涉及气体试剂的反应中具有特别的优势，例如用氢加氢或用分子氧氧化，这两种反应都可以导致高原子效率和更可持续的反应。优化这类反应可能很复杂，因为许多反应堆参数是高度相关的——改变一个参数可能会影响到其他几个参数。该项目由一个跨学科团队监督，包括计算化学、数学、光谱学和过程化学。首先，将建造一个灵活的高压流动反应器，并配备各种过程分析技术来监测热催化反应。然后将该设备应用于反应自优化。目的是建立一个算法库，该算法库可以处理来自反应监测的数据，以确定最佳反应参数。了解每种算法的优缺点将使反应尽可能有效地优化，从而在工艺开发过程中最大限度地减少化学品的使用，并提供可持续的工艺。