Hybrid modelling based digital technology for process flow diagram development

智能制造是第四次工业革命时代兴起的新概念之一。为了在区域和全球范围内保持竞争力，公司必须有效地开发更具可持续性和个性化的产品，以实现过程利润的显著增加，减少能源成本和废物产生，并提高客户满意度。然而，目前的工业流程图开发方法通常经历几个必要但耗时的步骤，从实验室规模的产品形成到中试规模的研发工艺设计，最终转化为工厂制造，导致产品开发周期长。鉴于变革性人工智能技术的发展和过程工业中积累的大量数据，将基于机器学习的数据驱动模型与计算流体动力学（CFD）等第一原理模型以及统计分析相结合，将为未来制造业中产品配方和工艺开发的规划和执行方式提供范式变革。需要一种预测和混合数字建模技术的新方法来有效地估计不同成分下产品配方的关键属性，并促进不同生产线的工艺设计、操作、诊断和知识转移。这种数字驱动的工艺流程图开发方法也将允许敏感性和不确定性分析，以考虑外部因素，如原材料可变性、数据测量噪声和站点到站点的变化。基于曼彻斯特大学和联合利华最近的成功和合作文化，该博士项目的主要目标是研究最先进的混合建模和迁移学习策略，以加速流程流程图的开发和自动化流程操作。特别是，我们将探索物理信息混合模型，以最大限度地利用过程数据，并将严格的物理模型与高保真机器学习技术相结合。开发的混合模型将用于预测和优化控制基本单元操作的性能，并确定个人护理产品制造过程中的关键操作活动，我们将测试开发模型在不同地点的可转移性和稳健性。

Smart manufacturing is one of the novel concepts arising from the era of the 4th Industrial Revolution. To remain competitive both regionally and globally, it is critical for companies to efficiently develop more sustainable and personalised products in order to achieve a significant increase in process profit, reduction in energy cost and waste generation, and promotion of customer satisfaction. However, the present industrial process flow diagram development approach usually undergoes several essential but time-consuming steps, ranging from lab-scale product formation to pilot-scale R&D process design, and eventually transformation to factory manufacturing, causing a long product development cycle. Given the development of transformative artificial intelligence technology and the large amount of data accumulated in the process industry, combining machine learning based data-driven models with first-principle models such as computational fluid dynamics (CFD), as well as statistical analysis, offers a paradigm change in the way that product formulation and process development will be planned and executed in future manufacturing. A novel approach to predictive and hybrid digital modelling technology will be required to effectively estimate key properties for product formulation under different ingredients and to facilitate process design, operation, diagnosis and knowledge transfer across different manufacturing lines. This digital driven process flow diagram development approach will also allow sensitivity and uncertainty analyses to account for external factors such as raw material variability, data measurement noise, and site-to-site variation.Building upon recent success and collaborative culture between the University of Manchester and Unilever, the main objective of this PhD project is to investigate state-of-the-art hybrid modelling and transfer learning strategies to accelerate process flow diagram development and automate process operation. Particularly, we will explore physics-informed hybrid models to maximise process data utilisation and combine rigorous physical models with high-fidelity machine learning techniques. The developed hybrid models will be applied to predict and optimally control performance of essential unit operations and identify key operational activities within personal care product manufacturing processes, and we will test the transferability and robustness of the developed models across different sites.