Feedback control for polymer extrusion processes based on virtual die melt temperature profile predictions

基于虚拟模具熔体温度分布预测的聚合物挤出过程的反馈控制

• 批准号: 2625300
• 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: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2625300
• 关键词: Feedback; control; polymer; extrusion; processes

The hardware sensors currently used for process monitoring in polymer extrusion processes present numerous challenges such as lack of durability, disturbance to the melt flow, measurement delays and so forth. The melt thermal homogeneity is a key quality parameter in polymer extrusion which defines the final product quality. However, the existing hardware sensors cannot measure the melt temperature profile across the melt flow in real-time due to their limitations and this inhibits the implementation of real-time quality control strategies. Soft sensors or software-based sensors are a promising alternative which can be used to replace hardware sensors, to enable real-time process monitoring. However, the existing soft sensors reported in the literature suffer from various limitations. The physics based soft sensor models fail to capture the real process dynamics in the extrusion processes. Moreover, they are not able to adapt to varying process conditions and hence their performance deteriorate when the process conditions change. These models have only been tested using simulation and they have not been tested on real industrial extruders. The existing control mechanisms only attempt to maintain the process parameters within a pre-defined tolerance rather than maintaining the desired quality level due to the absence of real-time process monitoring techniques, and this leads to products with poor quality.This research involves the development of a soft sensor to predict the die melt temperature profile across the melt flow of polymer extrusion processes in real-time, and this soft sensor should be able to adapt to varying process conditions such as different materials, machines and die designs. Moreover, a feedback control mechanism will be developed, which will take the values estimated by the soft sensor as inputs to adjust the process parameters (i.e., screw speed and barrel set temperatures) in order to maintain the final product quality within the desired range. Firstly, a soft sensor will be developed to predict the melt temperature profile in real-time, based on process parameters (i.e., screw speed and barrel set temperatures). Then, the model will be equipped with adaptive capabilities so that the model can adapt to varying process conditions such as different polymeric materials, machines and die designs without deteriorating the performance. Both the process knowledge and artificial intelligence based data driven techniques will be incorporated when developing the adaptive soft sensor. Finally, the developed soft sensor will be incorporated in an intelligent feedback control mechanism to appropriately adjust the process parameters (i.e., screw speed and barrel set temperatures), in order to minimize the melt temperature variations across the melt flow. This research involves multiple engineering disciplines including polymer processing, polymer physics, statistics, artificial intelligence, machine learning, and control systems engineering.

目前用于聚合物挤出过程中的过程监测的硬件传感器提出了许多挑战，例如缺乏耐用性，对熔体流量的干扰，测量延迟等。熔体热均匀性是聚合物挤出的关键质量参数，该参数定义了最终产品质量。但是，由于其局限性，现有的硬件传感器无法实时测量熔体流量的熔体温度曲线，这抑制了实时质量控制策略的实施。软传感器或基于软件的传感器是一种有希望的替代方案，可用于替换硬件传感器，以实现实时过程监视。但是，文献中报道的现有软传感器都有各种局限性。基于物理的软传感器模型无法捕获挤出过程中的实际过程动力学。此外，他们无法适应不同的过程条件，因此当过程条件发生变化时，其性能恶化。这些模型仅使用模拟进行了测试，并且尚未对实际工业挤出机进行测试。现有的控制机制仅试图在预定的公差内维持过程参数，而不是由于缺乏实时过程监控技术而导致所需的质量水平，这会导致质量较差的产品，这涉及软传感器的开发，以开发软传感器，以预测该材料在类似的材料中，这些材料在实时挤出的情况下进行了融合，并且在实时挤出的情况下进行了反应，并且是在实时挤出的情况下进行的。机器和模具设计。此外，将开发出反馈控制机制，该机制将以软传感器估计的值作为输入，以调整过程参数（即螺丝速度和枪管设置温度），以维持所需范围内的最终产品质量。首先，将开发一个软传感器，以根据过程参数（即螺丝速度和枪管设置温度）实时预测熔体温度曲线。然后，该模型将配备自适应能力，以便模型可以适应不同的过程条件，例如不同的聚合物材料，机器和模具设计，而不会降低性能。开发自适应软传感器时，将合并过程知识和基于人工智能的数据驱动技术。最后，开发的软传感器将被合并到智能反馈控制机制中，以适当调整工艺参数（即螺丝速度和枪管设置温度），以最大程度地减少整个熔体流量的熔体温度变化。这项研究涉及多个工程学科，包括聚合物处理，聚合物物理，统计，人工智能，机器学习和控制系统工程。