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

目前用于聚合物挤出过程中的过程监测的硬件传感器存在许多挑战，例如缺乏耐用性、对熔体流动的干扰、测量延迟等。熔体热均匀性是聚合物挤出过程中的一个关键质量参数，它决定了最终产品的质量。然而，现有的硬件传感器由于其局限性而不能实时测量跨熔体流的熔体温度分布，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很有前途的替代方案，可用于取代硬件传感器，以实现实时过程监控。然而，现有的软测量在文献中报道遭受各种限制。基于物理的软测量模型无法捕捉挤出过程中真实的过程动态。此外，它们不能适应变化的工艺条件，因此当工艺条件改变时，它们的性能会劣化。这些模型仅使用模拟进行了测试，尚未在真实的工业挤出机上进行测试。现有的控制机制只试图保持在预定义的公差范围内的过程参数，而不是保持所需的质量水平，由于缺乏实时的过程监控技术，这导致产品质量差。本研究涉及的软测量的发展，以预测模具熔体温度分布的熔体流动的聚合物挤出过程中的实时，并且该软传感器应该能够适应变化的工艺条件，例如不同的材料、机器和模具设计。此外，将开发反馈控制机制，其将把由软传感器估计的值作为输入来调整过程参数（即，螺杆速度和机筒设定温度），以便将最终产品质量保持在所需范围内。首先，将开发软传感器以基于工艺参数（即，螺杆速度和机筒设定温度）。然后，该模型将配备自适应能力，以便该模型可以适应不同的工艺条件，如不同的聚合物材料，机器和模具设计，而不会降低性能。在开发自适应软测量时，将过程知识和基于人工智能的数据驱动技术相结合。最后，所开发的软测量将被纳入智能反馈控制机制，以适当地调整过程参数（即，螺杆速度和机筒设定温度），以使熔体流中的熔体温度变化最小化。这项研究涉及多个工程学科，包括聚合物加工，聚合物物理，统计学，人工智能，机器学习和控制系统工程。

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.