Cognitive Chemical Manufacturing

将新化工产品的开发时间缩短到可靠的生产和进入市场是一个不断的挑战。任何延迟都会导致公司的收入损失和消费者的延迟受益，而匆忙的开发可能会导致质量问题。这可能会产生重大的社会影响，例如影响对患者至关重要的药物的可得性或关键质量。因此，有必要最大限度地减少识别安全可靠的化学制造过程所需的时间。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值、传热和传质都会影响质量和可扩展性，这使得在制造规模上定义化学过程成为一项非常具有挑战性的任务。变量的绝对数量意味着一个系统的，“一次改变一个因素”的方法实际上是不可能的，并且通常忽略了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的自动模型生成和识别技术，以及利兹过程研究与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器的使用。该研究能力将用于开发基于机器学习的化学过程设计知识生成的新算法，并将这些算法耦合到自动化实验的网络平台。联合的网络物理系统将通过深入的案例研究来验证，这些案例研究与阿斯利康（AstraZeneca）和普罗米修斯颗粒（prometheus Particles）目前面临的制造挑战有关，阿斯利康是英国第五大出口商，普罗米修斯颗粒是一家中小企业，最近开设了他们的第一家纳米颗粒制造工厂。该项目旨在开发一种工业4.0方法，利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不断发展的动力学动机的新算法，合并数据分析和进一步实验的生成。基于云的机器学习服务（hub）将生成实验设定值，并通过云交付给自动化实验室平台（LabBots）。一个关键的新颖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，从而产生数据生成-数据分析闭环。这使得机器学习应用于化学开发：系统将不断学习，随着时间的推移，从以前的迭代中增加信心和知识。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；生产规模的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，提高质量和健壮性，并最大限度地减少开发时间；更快地将产品推向市场，从而使有益效果更快地实现。我们的方法将使基于机械和统计数据模型的制造工艺和技术的设计，选择和评估成为可能。此外，在开发中同样重要的是，中试数量很容易生成，支持后期开发活动（例如，功效和稳定性测试），并且相同的数据分析服务可以协调中试和实验室数据。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世界制造挑战中得到证明。

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.