DEVELOPMENT OF A MACHINE LEARNING-ASSISTED DIGITAL TWIN PLATFORM FOR REAL-TIME OPTIMISATION OF REACTION SYSTEMS UNDER UNCERTAINTY

波音777双引擎喷气式飞机于1995年投入使用，是世界上第一架100%数字设计的飞机。计算机辅助设计被证明比人类工程团队更准确，所有未来计划的物理模型都被取消了。尽管这发生在几十年前的航空业，但这种情况尚未在化工行业复制，尽管化工和制药行业是英国经济的第三大制造业。这就是这个项目渴望为之做出贡献的愿景：以数字方式设计一个化工厂，而不需要物理原型。根据Industry 4.0的范例，该项目旨在开发一个“数字孪生”平台，在该平台中，基于可重新配置的数学模型的化学过程的电子模拟替代物被用于快速探索替代和创新的解决方案，以设计新的可持续过程，并对化学过程进行稳健的模拟、控制和优化，以实现诸如净零排放等可持续发展目标。然而，只有当基础模型提供用于放大模型的反应系统的准确描述时，才能获得适用于探索大范围操作条件的可靠的数字孪生。识别合适的数字孪生模型需要在实验和分析资源方面进行大量投资，以及开发和严格验证预测模型的人力。为了使反应模型研究更便宜、更快速、更适用于工业，我们打算通过开发一种数字双平台技术，在制药和精细化学品制造方面带来可观的变化，其中融合了自动化、人工智能和实验优化设计算法的优点，用于快速识别预测多保真模型，包括基于物理的模型和代理机器学习(ML)模型。该平台将结合数字孪生软件，其中虚拟测试、先进的物理信息最大似然法和最优实验设计算法用于快速决策，与灵活的反应堆(泰勒-涡旋反应堆)相结合，可以确保有效的质量和热量传递以及可调节的流体动力学。使用泰勒-涡流反应堆的动机是这样一个事实，即它是一种反应堆类型，由于缺乏设计指南和记录，在化学工业中的采用有限，尽管它为制造提供了现实的选择。因此，它提供了一个极好的范例来展示数字孪生技术在降低化工过程开发和扩大风险方面的力量。这些算法确定的计算廉价的替代ML模型将推动在线实验设计和实时优化，允许在没有用户干预的情况下操作平台，并能够快速生成信息数据集，快速识别动力学、质量和热传递模型，而对时间、人力和分析资源的影响最小。为了开发这一平台并确保其在工业领域的直接适用性，我们有一家大型制药公司葛兰素史克和两家大型化工公司巴斯夫和强生马泰作为直接合作者，以确保所开发的平台对化学和制药制造的知识转移和直接影响。该团队得到了Autichem(设备提供商)和Quitient Sciences(药物开发和制造加速器)的补充，这两家中小企业在整个药物开发过程中与全球制药公司合作，帮助开发新的制造工艺和方法。公司将为指导研究并确保其结果与工业相关并最终可用于工业研发以及化学和制药工艺制造做出贡献。

The Boeing 777 twin engine jet that entered service in 1995 was the world's first 100% digitally designed aircraft. The computer-aided design was proven to be more accurate than a human engineering team could be and all future planned physical mock-ups were cancelled. Even though this happened decades ago in the airline industry, this has not yet been replicated in the chemical industry, despite the chemicals & pharmaceuticals sector being the 3rd largest manufacturing sector in the UK economy. This is the vision that this project aspires to contribute to: design a chemical plant digitally without the need for physical prototypes. In line with the Industry 4.0 paradigm, this project aims to the development of a "digital twin" platform where in-silico surrogates of chemical processes based on reconfigurable mathematical models are used to quickly explore alternative and innovative solutions for the design of new sustainable processes, and for the robust simulation, control and optimisation of chemical processes, to achieve sustainability targets such as net-zero emissions. However, reliable digital twins, suitable for the exploration of a wide range of operating conditions, can be obtained only if the underlying models provide an accurate description of the reaction systems to be used in scale-up models. The identification of suitable digital twin models requires a significant investment in terms of experimental and analytical resources, as well as manpower to develop and rigorously validate predictive models. To make reaction modelling studies cheaper, faster and more industrially applicable, we intend to bring about a sizable step change in both pharmaceuticals and fine chemicals manufacturing by developing a digital twin platform technology, where the benefits of automation, AI and optimal design of experiments algorithms are merged for the quick identification of predictive multifidelity models, including physics-based models and surrogate machine learning (ML) models. The platform will combine a digital twin software, where virtual testing, advanced physics-informed ML and optimal experimental design algorithms are used for fast decision-making, with flexible reactors (Taylor-vortex reactors) that can guarantee efficient mass and heat transfer and adjustable hydrodynamics. The use of Taylor-vortex reactors is motivated by the fact that it is a reactor type with limited adoption in the chemical industry due to the lack of design guidelines and track record, even though it provides a realistic option for manufacturing. Thus, it provides an excellent exemplar to demonstrate the power of digital twin technology in derisking chemical process development and scale-up.Computationally cheap surrogate ML models identified by these algorithms will drive the online design of experiments and real time optimization, allowing to operate the platform without user intervention and enabling the fast generation of informative data sets and the quick identification of kinetics, mass, and heat transfer models with minimum impact on time, human and analytical resources. In order to develop this platform and ensure its direct applicability in the industrial sector, we have as direct collaborators a large pharmaceutical company, GSK, and two large chemical companies, BASF and Johnson & Matthey, to ensure transfer of knowledge and direct impact of the developed platform on chemical and pharmaceutical manufacturing. The team is complemented by Autichem (equipment provider) and Quotient Sciences (drug development and manufacturing accelerator), two SMEs who work with global pharmaceutical companies across the entire medicine development pathway to assist the development of novel manufacturing processes and approaches. Companies will contribute to directing the research and ensuring its outcomes are industrially relevant and eventually exploitable in industrial R&D and in chemical and pharmaceutical process manufacturing.