Developing a Novel Data-driven Modelling Strategy for Process Flow Diagram Optimisation

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This project aims to create scalable and optimal Process Flow Diagrams (PFDs) for new formulations. A unique characteristic associated with formulated chemicals is their short product life, meaning that tactical and aggressive product innovation is crucial. As both trends and consumers' desires change more frequently and seasonally, manufacturers must adapt and respond quickly to the everchanging needs of the customer, whilst being able to manufacture improved formulations. A prevalent challenge within the formulated chemical industries involves the current process flow diagram (PFD) development procedure which undergoes several essential, but time-consuming steps, i.e. starting from lab-scale product formation to pilot-scale R&D process design, and finally transformation to factory manufacturing. Numerous design iterations and experiments are required to be carried out during upscaling due to the production line becoming more complex to analyse and optimise. These challenges result in a long PFD development cycle with substantial labour and energy costs and waste generation. For example, disposing wastes generated through these trials alone accounts for 10% of total energy costs for a personal care product manufacturing plant. To resolve this challenge, in this project we will focus on developing an efficient feedback loop using real-time process data to significantly improve the performance of a proposed PFD. The innovation of this project is investigating how state-of-the-art interpretable machine learning and hybrid modelling techniques can be used to: (1) identify key correlations between formulation properties and PFDs; (2) create scalable and optimal PFDs for new formulations; and (3) develop an efficient feedback loop using real-time process data to further improve the performance of a proposed PFD. It aligns well with EPSRC's priority in Manufacturing the Future and Digital Manufacturing.