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

开发用于流程图优化的新型数据驱动建模策略

• 批准号: 2903759
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2903759
• 关键词: Developing; Novel; Data; driven; Modelling

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.

该项目旨在为新配方创建可扩展和最佳的工艺流程图（PFD）。与配方化学品相关的一个独特特性是其产品寿命短，这意味着战术和积极的产品创新至关重要。由于趋势和消费者的需求变化越来越频繁和季节性，制造商必须适应和快速响应客户不断变化的需求，同时能够制造改进的配方。在配制化学工业中普遍存在的挑战涉及当前的工艺流程图（PFD）开发程序，该程序经历几个重要但耗时的步骤，即从实验室规模的产品形成开始到中试规模的研发工艺设计，最后转变为工厂制造。由于生产线变得越来越复杂，需要进行大量的设计迭代和实验来分析和优化。这些挑战导致PFD开发周期长，劳动力和能源成本高，并产生废物。例如，仅处理这些试验产生的废物就占个人护理产品制造厂总能源成本的10%。为了解决这一挑战，在这个项目中，我们将专注于开发一个有效的反馈回路，使用实时过程数据，以显着提高性能的建议PFD。该项目的创新之处在于研究如何使用最先进的可解释机器学习和混合建模技术来：（1）确定配方特性和PFD之间的关键相关性;（2）为新配方创建可扩展和最佳的PFD;以及（3）使用实时过程数据开发有效的反馈回路，以进一步提高拟议PFD的性能。它与EPSRC在制造未来和数字制造方面的优先事项保持一致。