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 the University of 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. 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.
The 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 pharmaceutical and nanoparticle producing industrial partners.
