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

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.