Dynamic gray‐box modeling for on‐line monitoring of polymer extrusion viscosity

Abstract

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. © 2012 Society of Plastics Engineers

Low-cost process monitoring for polymer extrusion

“…Alternatively, a model-based soft-sensor approach provides an achievable solution for industrial applications (Liu et al, 2012). The reliability of this method depends on the accuracy of the mathematical model.…”

“…where h represents the viscosity, t is the shear stress, and _ g denotes the shear rate. Under laboratory conditions, shear stress can be determined from the pressure drop along the channel of a slit die or capillary die (Liu et al, 2012), while shear rate can be calculated from the volumetric flow rate through the die. Another development in this area is known as a 'torsional viscometer' (http://www.manscoproducts.com/ index.htm, Venkatraman and Okano, 1990).…”

“…Unfortunately, this method is not applicable to industrial manufacturing lines as it limits the throughput of the melt stream. A low-cost alternative is the previously proposed 'soft-sensor' approach (Liu et al, 2010(Liu et al, , 2012McAfee, 2005). In this paper, the accuracy of the viscosity model is further improved by properly exciting the plan using pseudorandom sequence (PRS) and the integration of differential evolution (DE) into a two-stage selection for radial basis function (RBF) network model construction.…”

Gray-box Soft Sensors in Process Industry: Current Practice, and Future Prospects in Era of Big Data

“…Autoregressive moving average exogenous (ARMAX) structure was used as a BB part of the GB model. Liu et al [49] proposed a serial GB modeling for prediction and control of melt viscosity of product of a polymer extrusion process of a petrochemical plant. Genetic algorithm was integrated with the WB model to develop the GB model.…”

“…Industry Application Category Process GB Type Target BB Type [32][33][34][35][36]92] Iron and steelmaking estimation and control "pickling process", "continuous casting", "hot strip mill" "serial", "parallel", "combined" "concentration of hydrochloric acid", "tundish temperature", "scale breaker entry temperature", "drying rate" "Taylor series", "PLS and RF", "ANN" [37][38][39] Food industry estimation and control 'fish drying process", "milk drying process", "whey separation" serial "drying rate", "moisture contents", "membrane fouling" "ANN", "exponential static membrane resistance function" [45][46][47][48][49][50][51][52][53][54]93] Chemical, biochemical, and pharmaceutical "estimation and optimization", "estimation and control" "fermentation extraction", "twin screw extruder ", "extrusion", "mold cooling", "acetone-butanol ethanol fermentation process", "MP fermentation", "fed-batch fermentation", "evaporation plant"…”