| People | Locations | Statistics |
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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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Mcafee, Marion
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (22/22 displayed)
- 2024Embedding a surface acoustic wave sensor and venting into a metal additively manufactured injection mould tool for targeted temperature monitoringcitations
- 2024Sensorised metal AM injection mould tools for in-process monitoring of cooling performance with conventional and conformal cooling channel designscitations
- 2024Investigation of the effect of Graphene oxide concentration on the final properties of Aspirin loaded PLA filaments for drug delivery systems
- 2023Enhancement of biodegradability of polylactides by γ-ray irradiation
- 2023Interpretable machine learning methods for monitoring polymer degradation in extrusion of polylactic acidcitations
- 2021Comparison of data summarization and feature selection techniques for in-process spectral datacitations
- 2018A soft sensor for prediction of mechanical properties of extruded PLA sheet using an instrumented slit die and machine learning algorithmscitations
- 2014The application of computational chemistry and chemometrics to developing a method for online monitoring of polymer degradation in the manufacture of bioresorbable medical implants
- 2012Water spray cooling of polymerscitations
- 2012Dynamic grey-box modeling for online monitoring of extrusion viscositycitations
- 2011The inferential monitoring of screw load torque to predict process fluctuations in polymer extrusioncitations
- 2011The inferential monitoring of the screw disturbance torque to predict process fluctuations in polymer extrusioncitations
- 2011Internal cooling in rotational molding-A reviewcitations
- 2011Quantitative characterization of clay dispersion in polymer-clay nanocompositescitations
- 2010Quantitative characterization of clay dispersion in polypropylene-clay nanocomposites by combined transmission electron microscopy and optical microscopy
- 2010Quantitative characterization of clay dispersion in polypropylene-clay nanocomposites by combined transmission electron microscopy and optical microscopycitations
- 2010Structure-property relationships in biaxially deformed polypropylene nanocompositescitations
- 2007Enhancing process insight in polymer extrusion by grey box modellingcitations
- 2007A novel approach to dynamic modelling of polymer extrusion for improved process controlcitations
- 2007A Soft Sensor for viscosity control of polymer extrusioncitations
- 2006Energy efficient extrusion
- 2003Design of a soft sensor for polymer extrusion
Places of action
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article
Energy efficient extrusion
Abstract
<p>The UK-based Queen's University Belfast has developed an intelligent system technology to reduce set-up times and consequently waste and energy usage in polymer extrusion process. The system enables real-time measurement and control of viscosity using a device, based on 'soft sensor' technology that employs non-invasive process measurements within an intelligent system. The technology applies a novel 'grey-box' method that involves integrating process theory, where a genetic algorithm operation optimizes the model structure and parameters. Process models are used to calculate the optimum screw speed and temperature profile to achieve user-defined viscosity/rate constraints, while minimizing energy use. The ability to self-tune and update settings to different material properties is being developed, incorporating information about the material properties available from the soft sensor. Online analysis of process signals will be employed to detect deviations from optimum conditions and automatically take the correct course of action, thus enhancing the efficiency of the process.</p>