An integrated system of inferential measurement and control of polymer extrusion for self-tuning optimisation and response to disturbances

聚合物颗粒通过在加热桶中旋转的螺杆熔化并输送的挤出过程构成了几乎所有聚合物加工操作的基础。在这个项目中，通过过程建模、推理测量和更智能的过程控制方法的进步，将开发一个改进挤压过程性能的综合框架。这将需要开发透明的在线模型，将第一性原理模型与可观察的在线数据分析结合起来。它将使关键业绩指标的推断测量成为可能；工艺故障的识别；在线优化设置；并能自我调整，以改变饲料材料和条件。最终的结果将是一种新的挤压控制方法，它将为精确、高效和灵活的挤压加工提供一个全面的框架。这种装置将对实现更高的产品质量和更大的回收原料潜力产生重大影响，同时减少停机时间、能源消耗和宝贵聚烯烃资源的浪费。为了开发这一系统，需要在许多领域进行关键研究。这些包括新的理论（开发新的建模技术；对原料变化的鲁棒在线系统识别）；创新的实际测量（实时数据采集和高频信号的处理）和用于解释复杂数据的创新数据分析（数据与非均匀熔化的相关性，材料降解的识别，集成在一个强大的，自适应的控制策略中）。这项研究的技术发展也将为在新兴的复杂加工应用中取得更大的成功提供一个理想的平台，例如在反应挤出、微挤出和纳米复合材料的复合中。

The process of extrusion, whereby polymer granules are melted and conveyed by a screw rotating in a heated barrel, forms the basis of practically all polymer processing operations. In this project an integrated framework for improved performance of extrusion processes will be developed through advances in process modelling, inferential measurement and a more intelligent approach to control of the process. This will require the development of transparent on-line models integrating first-principles' models with analysis of observable on-line data. It will enable inferential measurement of key performance indicators; identification of process faults; on-line optimisation of settings; and an ability to self-tune to changing feed materials and conditions. The end result will be a novel methodology for extrusion control which will provide a comprehensive framework for accurate, efficient and flexible extrusion processing. Such a device would have a major impact on achieving higher product quality and greater potential of recycled feed material while reducing downtime, energy consumption and wastage of valuable polyolefin resources. In order to develop this system, pivotal research is required in a number of areas. These include new theory (development of novel modelling techniques; robust on-line system identification for feed material changes); innovative practical measurements (real time data acquisition and processing of high frequency signals) and innovative data analysis for interpretation of complex data (correlation of data with non-homogenous melting, identification of material degradation, integration in a robust, adaptive control strategy). The development of techniques in this research will also provide an ideal platform for achieving greater success in emerging complex processing applications, for example in reactive extrusion, micro-extrusion and compounding of nanocomposites.