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.

The CDT in Molecules to Product addresses an overarching concern articulated by industry operating in the area of complex chemical products. It centres on the lack of a pipeline of doctoral graduates who understand the cross-scale issues that need to be addressed within the chemicals continuum. Translating their concern into a vision, the focus of the CDT is to train a new generation of research leaders with the skills and expertise to navigate the journey from a selected molecule or molecular system through to the final product that delivers the desired structure and required performance. To address this vision, three inter-related Themes form the foundation of the CDT - Product Functionalisation and Performance, Product Characterisation, and Process Modelling between Scales. More specifically, industry has identified a real need to recruit PGR graduates with the interdisciplinary skills covered by the CDT research and training programme. As future leaders they will be instrumental in delivering enhanced process and product understanding, and hence the manufacture of a desired end effect such as taste, dissolution or stability. For example, if industry is better informed regarding the effect of the manufacturing process on existing products, can the process be made more efficient and cost effective through identifying what changes can be made to the current process? Alternatively, if there is an enhanced understanding of the effect of raw materials, could stages in the process be removed, i.e. are some stages simply historical and not needed. For radically new products that have been developed, is it possible through characterisation techniques to understand (i) the role/effect of each component/raw material on the final product; and (ii) how the product structure is impacted by the process conditions both chemical and mechanical? Finally, can predictive models be developed to realise effective scale up? Such a focus will assist industry to mitigate against wasted development time and costs allowing them to focus on products and processes where the risk of failure is reduced. Although the ethos of the CDT embraces a wide range of sectors, it will focus primarily on companies within speciality chemicals, home and personal care, fast moving consumer goods, food and beverage, and pharma/biopharma sectors. The focus of the CDT is not singular to technical challenges: a core element will be to incorporate the concept of 'Education for Innovation' as described in The Royal Academy of Engineering Report, 'Educating engineers to drive the innovation economy'. This will be facilitated through the inclusion of innovation and enterprise as key strands within the research training programme. Through the combination of technical, entrepreneurial and business skills, the PGR students will have a unique set of skills that will set them apart from their peers and ultimately become the next generation of leaders in industry/academia. The training and research agendas are dependent on strong engagement with multi-national companies, SMEs, start-ups and stakeholders. Core input includes the offering, and supervision of research projects; hosting of students on site for a minimum period of 3 months; the provision of mentoring to students; engagement with the training through the shaping and delivery of modules and the provision of in-house courses. Additional to this will be, where relevant, access to materials and products that form the basis of projects, the provision of software, access to on-site equipment and the loan of equipment. In summary, the vision underpinning the CDT is too big and complex to be tackled through individual PhD projects - it is only through bringing academia and industry together from across multiple disciplines that a solution will be achievable. The CDT structure is the only route to addressing the overarching vision in a structured manner to realise delivery of the new approach to product development.