Grey Box Modeling For On Line Monitoring Of Polymer Extrusio

Project Title:

Grey-box Modelling for On-line Monitoring of Polymer Extrusion Viscosity

Project Description:

Extrusion is a widespread practical method in polymer processing, but controlling the quality of an extrude material and hence the final product, presents various problems. The feed materials can be highly variable and unpredictable in nature, and selection of appropriate operating conditions for each material, to obtain a desired extrude quality is a complex task. This results in large amounts of energy and material being wasted during long set-up times, by using non-optimum operating conditions. Real-time monitoring of the quality of the extrude material during the extrusion process is therefore desirable to achieve reduced set-up times and improved operation of the extrusion system. In comparison with melt temperature and pressure, melt viscosity is largely recognized as the most relevant indicator of melt quality as it is directly related to the aesthetic/dimensional properties of the melt and the molecular orientation relating to the functional properties of a polymeric extrude. However, on-line viscosity measurement to a required standard has proved difficult to achieve due to the highly nonlinear and significant time delay behaviours of the process.
In our research, a novel soft sensor approach (Fig 1) based on dynamic grey-box modelling is proposed.
soft%20sensor%20structure.png
Fig.1 The soft sensor structure
The soft sensor involves a non-linear finite impulse response model with adaptable linear parameters for real-time prediction of the melt viscosity based on the process inputs; the model output is then used as an input of a model with a simple fixed structure to predict the barrel pressure which can be measured online. Finally, the predicted pressure is compared to the measured value and the corresponding error is used as a feedback signal to correct the viscosity estimate. This novel feedback structure enables the online adaptability of the viscosity model in response to modelling errors and disturbances, hence producing a reliable viscosity estimate.