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

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.