Machine learning for chemical manufacture

这个令人兴奋的项目结合了IBM在优化算法开发方面的专业知识，以及伦敦大学学院研究人员使用自动模型生成和判别的专业知识，以及利兹过程研究和开发研究所的实验自动化专业知识，以及诺丁汉大学开发的先进水热反应堆的使用。这一研究能力将用于开发基于机器学习的新算法，以生成化学工艺设计知识，并将这些算法耦合到网络平台上进行自动化实验。结合的网络-物理系统将通过与当前制药制造挑战相关的深入案例研究来验证。该项目旨在开发一种行业4.0方法，使用先进的数据丰富和认知计算技术彻底改变从实验室到生产的转移。我们将开发基于贝叶斯优化和进化动力学主题的新算法，将数据分析和进一步实验的生成结合在一起。基于云的机器学习服务(Hub)将生成通过云提供给自动化实验室平台(LabBots)的实验设置点。一个关键的新奇之处在于，分析服务可以接收和分析结果，并将进一步的实验发布到LabBots，从而生成数据生成-数据分析闭环。这使得机器学习能够应用到化学开发中：系统将从以前的迭代中不断学习，随着时间的推移，信心和知识不断增加。目标：-开发结合全局和局部搜索方法的优化算法，以快速优化和定义操作空间-开发模拟工具，用于评估各种化学动力学曲线上的优化算法-对真实化学系统进行案例研究，演示通过基准测试对传统方法进行增强-为非反应过程(如液-液分离和层析)开发优化例程本学员将与认知化学制造grant:http://gow.epsrc.ac.uk/NGBOViewGrant.aspx?GrantRef=EP/R032807/1的PDRA合作

This exciting project combines the expertise of IBM in the development of algorithms for optimisation and the use of automated model generation and discrimination by researchers at UCL with the experimental automation expertise within the Institute of Process Research and Development at Leeds and the use of advanced hydrothermal reactors developed at the University of Nottingham. This research capability will be used to develop new algorithms for machine learning based generation of chemical process design knowledge and coupling these algorithms to a cyber platform for automated experimentation. The combined cyber-physical system will be validated via in-depth case studies related to current pharmaceutical manufacturing challenges.This project aims to develop an Industry 4.0 approach revolutionising the transfer from laboratory to production using advanced data-rich and cognitive computing technologies. We will develop new algorithms based on BayesianOptimisation and evolving Kinetic Motifs that merge data analysis and the generation of further experiments. Cloud based machine learning services (hubs) will generate experiment setpoints delivered through the cloud to automated laboratory platforms (LabBots). A key novelty is that the analysis services can receive and analyse results, and post further experiments to the LabBots, thus generating a data generation - data analysis closed-loop. This enables the application of machine learning to chemical development: the system will continuously learn, increasing in confidence and knowledge over time, from previous iterations.Objectives:- Develop optimisation algorithms that combine global and local search methodologies for rapid optimisation and definition of operating space- Develop simulation tools for evaluation of optimisation algorithms on a variety of chemical kinetic profiles- Perform case studies on real chemical systems demonstrating enhancements via benchmarking against traditional approaches- Develop optimisation routines for non-reactive processes such as liquid-liquid separation and chromatographyThis studentship will work alongside PDRAs from the Cognitive Chemical Manufacture grant:http://gow.epsrc.ac.uk/NGBOViewGrant.aspx?GrantRef=EP/R032807/1