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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Thompson, Stephen
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (9/9 displayed)
- 2024Electron Beam and Thermal Stabilities of MFM-300(M) Metal-Organic Frameworkscitations
- 2019Sub-lattice polarization states in anti-ferroelectrics and their relaxation processcitations
- 2017New synchrotron powder diffraction facility for long-duration experimentscitations
- 2015Advances in synchrotron x-ray diffraction and transmission electron microscopy techniques for the investigation of microstructure evolution in proton- and neutron-irradiated zirconium alloyscitations
- 2014The fabrication of a bifunctional oxygen electrode without carbon components for alkaline secondary batteriescitations
- 2011High-resolution synchrotron X-ray diffraction studies of size and strain effects in a complex Al–Fe–Cr–Ti alloy
- 2007A novel approach to dynamic modelling of polymer extrusion for improved process controlcitations
- 2007A Soft Sensor for viscosity control of polymer extrusioncitations
- 2004The structure and thermal expansion behaviour of ikaite, CaCO3. 6H2O, from T = 114 to T = 293 Kcitations
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document
A Soft Sensor for viscosity control of polymer extrusion
Abstract
Viscosity represents a key indicator of product quality in polymer extrusion but has traditionally been difficult to measure in-process in real-time. An innovative, yet simple, solution to this problem is proposed by a Prediction-Feedback observer mechanism. A `Prediction' model based on the operating conditions generates an open-loop estimate of the melt viscosity; this estimate is used as an input to a second, `Feedback' model to predict the pressure of the system. The pressure value is compared to the actual measured melt pressure and the error used to correct the viscosity estimate. The Prediction model captures the relationship between the operating conditions and the resulting melt viscosity and as such describes the specific material behavior. The Feedback model on the other hand describes the fundamental physical relationship between viscosity and extruder pressure and is a function of the machine geometry. The resulting system yields viscosity estimates within 1% error, shows excellent disturbance rejection properties and can be directly applied to model-based control. This is of major significance to achieving higher quality and reducing waste and set-up times in the polymer extrusion industry.