DigiScale: Digitally driven scale up of chemical processes

DigiScale：数字化驱动的化学工艺放大

• 批准号: EP/X024237/1
• 负责人: Thomas Chamberlain
• 金额: $ 123.71万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX024237%2F1
• 关键词: DigiScale; Digitally; driven; scale; up

Chemical synthesis underpins our society in general - whether in the production of medicines, household or personal care items or for heat and power. The process of discovering a new molecule and taking it through to production scale remains a challenge, despite centuries of experience. The conditions of small scale discovery can be very different to those at the scales required to bring the new chemical product to wider society. Reducing this time may mean bringing drugs to market earlier, or providing more environmentally friendly alternatives to existing products. A digital twin is a representation of a physical process within a computational framework, allowing virtual experimentation to be carried out to assess the best set of conditions under which to run processes. These conditions can be selected and weighted towards (for example) the most environmentally friendly process, or the cheapest - depending on the wider demands of society. A digital twin also allows this to be addressed dynamically.This project develops the framework for building digital twins. As all chemical processes are different, and depend very much on the particular synthesis, we have to first learn something about our specific reaction. This will be carried out at the small scale under computer control, to narrow down the wide range of conditions (e.g. temperatures, concentrations, choice of catalysts used to speed up the reaction) for the process. This information then informs our digital twin model. At larger scale conditions vary through the reactor - dealing with many thousands of litres of material is very different to dealing with the small quantities at the discovery scale. Now we have gradients of temperature, material is much more poorly mixed - these can all influence the reaction. So we take our knowledge of these variations, which includes running flow simulations, and map onto it the information from our small scale tests. We can select which reactors we want to use, and their conditions that they are run under and make decisions about the overall process - just as before, driving it through questions of sustainability, cost, material purity.This gives us a framework that spans the physical development of material with a digital representation. We can explore a much wider parameter space than ever possible through experimentation, and assess the entire process digitally. This brings unprecedented agility to the manufacturing of products.The project builds on the world-class research that exists in the UK in automated tools used for self-optimising reactor systems and has a range of industrial partners spanning pharmaceuticals; AstraZeneca, Pfizer and UCB Pharma, and reactor control software experts; Perceptive Engineering, ensuring the barriers to adoption by industry of the approach developed in this project are minimised.

化学合成是我们整个社会的基础——无论是药品、家庭或个人护理用品的生产，还是热能和电力的生产。尽管已有数百年的经验，但发现新分子并将其规模化生产的过程仍然是一个挑战。小规模发现的条件可能与将新化学产品推向更广泛社会所需的规模有很大不同。减少这个时间可能意味着更早地将药物推向市场，或者为现有产品提供更环保的替代品。数字孪生是计算框架内物理过程的表示，允许进行虚拟实验来评估运行过程的最佳条件集。这些条件可以选择并权衡（例如）最环保的工艺或最便宜的工艺 - 取决于社会的更广泛需求。数字孪生还可以动态解决这个问题。该项目开发了构建数字孪生的框架。由于所有化学过程都是不同的，并且很大程度上取决于特定的合成，因此我们必须首先了解有关特定反应的一些信息。这将在计算机控制下小规模进行，以缩小该过程的广泛条件范围（例如温度、浓度、用于加速反应的催化剂的选择）。然后，这些信息将告知我们的数字孪生模型。在较大规模的情况下，整个反应堆的条件各不相同——处理数千升的材料与处理发现规模的少量材料有很大不同。现在我们有温度梯度，材料混合得更差——这些都会影响反应。因此，我们利用对这些变化的了解，包括运行流动模拟，并将小规模测试中的信息映射到其中。我们可以选择要使用的反应器及其运行条件，并对整个过程做出决策 - 就像以前一样，通过可持续性、成本、材料纯度等问题推动整个过程。这为我们提供了一个涵盖材料物理发展和数字表示的框架。我们可以通过实验探索比以往更广泛的参数空间，并以数字方式评估整个过程。这为产品制造带来了前所未有的敏捷性。该项目建立在英国用于自优化反应器系统的自动化工具的世界级研究基础上，并拥有一系列工业合作伙伴，涵盖制药业、制药业、医疗保健业、医疗保健业、医疗保健业、医疗保健业、医疗保健业等领域。阿斯利康、辉瑞和 UCB Pharma 以及反应堆控制软件专家；感知工程，确保行业采用该项目开发的方法的障碍最小化。