Cognitive Chemical Manufacturing

如何将新化工产品的开发时间缩短到可靠的生产和进入市场的程度，是一个不断的挑战。任何拖延都将导致公司收入损失和消费者受益延迟，而仓促开发可能会导致质量问题。这可能会产生重大的社会影响，例如影响患者的关键药物的可获得性或关键质量。因此，确实需要最大限度地减少识别安全可靠的化学制造工艺所需的时间。然而，这些过程是复杂的，过程结果受到大量化学和物理参数的影响；例如，温度、压力、试剂化学计量、pH、热和质量传递影响质量和可扩展性，这使得在生产规模上定义化学过程是一项非常具有挑战性的任务。变量的数量之多意味着，一次只改变一个因素的系统性方法实际上是不可能的，而且通常会忽略一些因素可能高度相关的事实。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-物理系统将通过深入的案例研究得到验证，这些挑战与阿斯利康和Promethean粒子目前面临的制造挑战有关。阿斯利康是一家总部位于英国的大型制药制造商，是英国第五大出口商，Promethean粒子是一家中小企业，最近开设了第一家纳米粒子制造工厂。该项目旨在开发一种工业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变。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：系统将不断学习，随着时间的推移，信心和知识将从先前的迭代中增加。使用相同的基于云的平台，这种过程理解可以快速转移到PilotBots；生产规模制造机器人使用相同的数据传输协议，但规模要大102-105倍。这种完全自动化的方法能够降低成本、提高质量和稳定性，并最大限度地缩短开发时间；更快地将产品推向市场，从而更快地实现效益。我们的方法将使基于机械和统计数据模型的制造过程和技术的设计、选择和评估成为可能。此外，在开发中同样重要的是，很容易生成试验量，以支持后期开发活动(例如，有效性和稳定性测试)，并且相同的数据分析服务可以协调试验量和实验室数据。这种方法的预期影响将在我们的制药和纳米颗粒生产工业合作伙伴面临的现实制造挑战中得到展示。

It is an unceasing challenge to reduce the time scale for development of new chemical products to the point of reliable manufacture and entrance into the market place. Any delays will result in both the loss of revenue for companies, and delayed benefit to the consumers, whilst rushed development might lead to quality issues. This can have significant societal implications, for example impacting the availability, or critical quality of crucial medicines to patients. Therefore, there is a real need to minimise the time taken to identify safe and robust chemical manufacturing processes. These processes however, are complex with process outcome being affected by a vast number of chemical and physical parameters; e.g. temperature, pressure, reagent stoichiometry, pH, heat and mass transfer affect quality and scalability making the definition of a chemical process at manufacturing scale a very challenging task. The sheer number of variables means that a systematic, 'change one factor at a time' approach is practically impossible and generally disregards the fact that some factors might be heavily correlated. 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 manufacturing challenges faced by AstraZeneca, a large UK based manufacturer of Pharmaceuticals who are the UK's fifth largest exporter and Promethean Particles, a SME who have recently opened their first nanoparticle manufacturing facility. 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 Bayesian Optimisation 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.Using the same cloud based platform, this process understanding can be rapidly transferred to PilotBots; production scale manufacturing robots that use the same data transfer protocols, but on a 102-105 times larger scale. This fully automated approach has the power to reduce the cost, improve quality and robustness and minimise development time; bringing products to market faster and therefore enabling the beneficial effects to be realised more rapidly.Our approach will enable the design, selection and evaluation of manufacturing process and technology based on mechanistic and statistical data models. Further, and not less important in development, pilot quantities are easily generated, supporting late stage development activities (e.g. efficacy and stability testing) and the same data analysis services can reconcile the pilot and lab data. The anticipated impact of this approach will be demonstrated on real world manufacturing challenges faced by our pharmaceutical and nanoparticle producing industrial partners.