Cognitive chemical manufacturing
We worked with AstraZeneca to optimise their chemical manufacturing processes, saving time and costs in large scale product production and manufacturing.
As a leading pharmaceutical company, AstraZeneca are always looking to meet the significant market demand for their products. To do so, they need to ensure they use the most efficient chemical manufacturing processes to optimise yield of the products’ constituent ingredients. However, this is a complex problem, as each component is composed of a unique combination of chemicals, and each unique combination has a specific set of optimal reactor parameters for maximising yield. Identifying parameters requires large numbers of experiments, with significant financial and resource implications.
The team developed a solution that utilises Bayesian Optimisation to quickly find the most optimal set of reactor parameters in the fewest experiments. Bayesian Optimisation is a highly economical optimisation algorithm that uses Bayesian statistics to determine whether to ‘explore’ – search for novel sets of reactor parameters, or ‘exploit’ – refine a known set of parameters that perform well. By intelligently balancing exploration and exploitation, Bayesian Optimisation can select the optimal set of reactor parameters in far fewer experiments than traditional methods.
Using Bayesian Optimisation allowed AstraZeneca to quickly discover optimal reactor parameters for their chemical manufacturing processes. This project, completed as part of the Innovation Return on Research (IROR) programme, a collaboration between STFC and IBM Research, has significant impact in that it reduces the amount of time and money invested to find the desired reactor configuration – reducing the costs to the company, and reducing the time required to scale the production process for commercial manufacturing.
“Collaborating with The Hartree Centre and IBM has been a mutually beneficial undertaking, providing valuable insight through the incorporation of a Bayesian Optimisation solution. This work has allowed both parties to gain a better understanding of the potential impact of Bayesian Optimisation within a chemical development setting”Brian Taylor, AstraZeneca
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