In-line near-infrared spectroscopy for measuring the composition, compositional fluctuation and mixedness of polymer blends in a twin screw extruder
•Development of an in-line near-infrared spectroscopy (NIR) system capable of measuring in real time the composition of polymer blends at various locations of a twin screw extruder.•Assessment of accuracy and robustness of the in-line NIR system under various operating conditions.•Use of the NIR sys...
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Veröffentlicht in: | Chemical engineering science 2024-07, Vol.294, p.120098, Article 120098 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | •Development of an in-line near-infrared spectroscopy (NIR) system capable of measuring in real time the composition of polymer blends at various locations of a twin screw extruder.•Assessment of accuracy and robustness of the in-line NIR system under various operating conditions.•Use of the NIR system to study the effects of the geometry of mixing screw elements on the amplitude of compositional fluctuation and mixedness of polymer blends.
Polymer blending is often conducted in the molten state using a twin screw extruder because the atom efficiency can reach nearly 100%. One of the challenges it faces is that the composition of a polymer blend at the die exit may fluctuate to an unacceptable extent. The amplitude of compositional fluctuation depends very much on mixing inside the twin screw extruder. This work aims to develop an in-line near-infrared spectroscopy (NIR) system to measure in real time the composition of polystyrene (PS) and polyolefin elastomer (POE) blends at various locations of the twin screw extruder. The accuracy and robustness of the in-line NIR system are examined under various operating conditions (POE content, barrel temperature, screw rotation speed and feeding rate). It is then used to study the effects of the geometry of mixing screw elements on the amplitude of compositional fluctuation and mixedness (the state of dispersion and distribution of one phase in another) of the polymer blend. |
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ISSN: | 0009-2509 1873-4405 |
DOI: | 10.1016/j.ces.2024.120098 |