Peer-Reviewed Journal Details
Mandatory Fields
Liu, XQ,Li, K,McAfee, M,Nguyen, BK,McNally, GM
2012
June
Polymer Engineering and Science
Dynamic gray-box modeling for on-line monitoring of polymer extrusion viscosity
Published
Optional Fields
NEURAL-NETWORKS IDENTIFICATION EXTRUDER
52
1332
1341
Melt viscosity is a key indicator of product quality in polymer extrusion processes. However, real time monitoring and control of viscosity is difficult to achieve. In this article, a novel soft sensor approach based on dynamic gray-box modeling is proposed. The soft sensor involves a nonlinear 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 modeling errors and disturbances, hence producing a reliable viscosity estimate. The experimental results on different material/die/extruder confirm the effectiveness of the proposed soft sensor method based on dynamic gray-box modeling for real-time monitoring and control of polymer extrusion processes. POLYM. ENG. SCI., 2012. (C) 2012 Society of Plastics Engineers
DOI 10.1002/pen.23080
Grant Details