Automatic Design of Experiments in Chemical Micro-reactors

化学微反应器实验的自动设计

• 批准号: 2605895
• 金额: --
• 依托单位: Imperial College London
• 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-2605895
• 关键词: Automatic; Design; Experiments; Chemical; Micro

The area of experimental design concerns trying to maximise (or minimise) a certain objective function by selecting different experimental inputs. Bayesian Optimisation has proven to provide solutions to such problems. Query data is used to calculate a posterior from a surrogate model, which we can use to select the next experiment (by optimising a function called the acquisition function). However, in the classical setting, Bayesian Optimisation assumes we select a single experiment and immediately obtain an observation.Micro-reactors are changing laboratory chemistry as they allow us to carry out many experiments on the micro-scale and as such, they require automatic experimental design. Micro-droplets travel through the reactor and each can be considered to be a single experiment. However, the problem brings many complications. A very important part of the problem concerns time-delay. We will have to choose many new experiments before we receive the results from previous ones. We will also be receiving observations from multiple sources, some will be quick but inaccurate, while others will be accurate but slowand expensive. We will have restrictions on how much we want to vary our inputs, as we try to maintain the chemical reaction in steady-state. Other important challenges include multi-objective optimisation, input delay, and safety constraints.Many of these complications have been studied in isolated environments. In particular, there is extensive literature on multi-fidelity and asynchronous Bayesian Optimisation. The objective of the project is to propose methods that can take into account as many complications as possible, at the same time. To provide such a method we will have to combine and fundamentally change previouslyproposed methods, in novel ways. Having an effective way of designing such experiments would allow us to have more efficient production of chemicals, and reduce waste. The impact of the project would not be restricted tochemical production. The setting of experimental design is important in many areas, from food manufacturing to the optimisation of machine learning hyper-parameters. The research project falls in an intersection of ESPRC's themes of Artificial Intelligence and Robotics, Engineering, and Mathematical Sciences.The research is being done in collaboration with chemical manufacturer BASF, whom are providing funding for the project. The collaboration will hopefully allow us to test any developed methods in real life settings, involving micro-reactors and other chemical experiments. It should also help us bridge the gap between the mathematical nature of the project, and the chemical applications it is trying to tackle.

实验设计的领域涉及试图通过选择不同的实验输入来最大化（或最小化）某个目标函数。贝叶斯优化已被证明可以为这些问题提供解决方案。查询数据用于计算代理模型的后验，我们可以使用它来选择下一个实验（通过优化称为采集函数的函数）。然而，在经典环境中，贝叶斯优化假设我们选择一个单一的实验，并立即获得一个观察.微型反应器正在改变实验室化学，因为它们允许我们进行许多实验上的微观尺度，因此，他们需要自动实验设计.微液滴穿过反应器，每个微液滴都可以被认为是一个单独的实验。然而，这个问题带来了许多复杂性。这个问题的一个非常重要的部分涉及时间延迟。在我们收到以前的实验结果之前，我们必须选择许多新的实验。我们还将接收来自多个来源的观测结果，有些将是快速但不准确的，而另一些将是准确但缓慢和昂贵的。当我们试图保持化学反应处于稳定状态时，我们会限制我们想要改变我们的输入的程度。其他重要的挑战包括多目标优化、输入延迟和安全约束。特别是，有大量的文献多保真度和异步贝叶斯优化。该项目的目标是提出能够同时考虑到尽可能多的复杂性的方法。为了提供这样一种方法，我们将不得不联合收割机，并以新的方式从根本上改变以前提出的方法。如果有一种有效的方法来设计这样的实验，将使我们能够更有效地生产化学品，并减少浪费。该项目的影响将不仅限于化学生产。实验设计的设置在许多领域都很重要，从食品制造到机器学习超参数的优化。该研究项目福尔斯属于ESPRC的人工智能和机器人、工程和数学科学主题的交叉点。该研究是与化学制造商巴斯夫合作完成的，巴斯夫为该项目提供资金。这项合作将有望使我们能够在真实的生活环境中测试任何开发的方法，包括微反应器和其他化学实验。它还应该帮助我们弥合项目的数学性质和它试图解决的化学应用之间的差距。