Data-Driven Modelling of Polyethylene Recycling under High-Temperature Extrusion
Two main problems are studied in this article. The first one is the use of the extrusion process for controlled thermo-mechanical degradation of polyethylene for recycling applications. The second is the data-based modelling of such reactive extrusion processes. Polyethylenes (high density polyethyl...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Polymers |
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| Online Access: | https://www.mdpi.com/2073-4360/14/4/800 |
| _version_ | 1797476950189539328 |
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| author | Fanny Castéran Karim Delage Nicolas Hascoët Amine Ammar Francisco Chinesta Philippe Cassagnau |
| author_facet | Fanny Castéran Karim Delage Nicolas Hascoët Amine Ammar Francisco Chinesta Philippe Cassagnau |
| author_sort | Fanny Castéran |
| collection | DOAJ |
| description | Two main problems are studied in this article. The first one is the use of the extrusion process for controlled thermo-mechanical degradation of polyethylene for recycling applications. The second is the data-based modelling of such reactive extrusion processes. Polyethylenes (high density polyethylene (HDPE) and ultra-high molecular weight polyethylene (UHMWPE)) were extruded in a corotating twin-screw extruder under high temperatures (350 °C < <i>T</i> < 420 °C) for various process conditions (flow rate and screw rotation speed). These process conditions involved a decrease in the molecular weight due to degradation reactions. A numerical method based on the Carreau-Yasuda model was developed to predict the rheological behaviour (variation of the viscosity versus shear rate) from the in-line measurement of the die pressure. The results were successfully compared to the viscosity measured from offline measurement assuming the Cox-Merz law. Weight average molecular weights were estimated from the resulting zero-shear rate viscosity. Furthermore, the linear viscoelastic behaviours (Frequency dependence of the complex shear modulus) were also used to predict the molecular weight distributions of final products by an inverse rheological method. Size exclusion chromatography (SEC) was performed on five samples, and the resulting molecular weight distributions were compared to the values obtained with the two aforementioned techniques. The values of weight average molecular weights were similar for the three techniques. The complete molecular weight distributions obtained by inverse rheology were similar to the SEC ones for extruded HDPE samples, but some inaccuracies were observed for extruded UHMWPE samples. The Ludovic<sup>®</sup> (SC-Consultants, Saint-Etienne, France) corotating twin-screw extrusion simulation software was used as a classical process simulation. However, as the rheo-kinetic laws of this process were unknown, the software could not predict all the flow characteristics successfully. Finally, machine learning techniques, able to operate in the low-data limit, were tested to build predicting models of the process outputs and material characteristics. Support Vector Machine Regression (SVR) and sparsed Proper Generalized Decomposition (sPGD) techniques were chosen to predict the process outputs successfully. These methods were also applied to material characteristics data, and both were found to be effective in predicting molecular weights. More precisely, the sPGD gave better results than the SVR for the zero-shear viscosity prediction. Stochastic methods were also tested on some of the data and showed promising results. |
| first_indexed | 2024-03-09T21:11:02Z |
| format | Article |
| id | doaj.art-57894b4a89a149b890e814b90558ffd4 |
| institution | Directory Open Access Journal |
| issn | 2073-4360 |
| language | English |
| last_indexed | 2024-03-09T21:11:02Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Polymers |
| spelling | doaj.art-57894b4a89a149b890e814b90558ffd42023-11-23T21:46:05ZengMDPI AGPolymers2073-43602022-02-0114480010.3390/polym14040800Data-Driven Modelling of Polyethylene Recycling under High-Temperature ExtrusionFanny Castéran0Karim Delage1Nicolas Hascoët2Amine Ammar3Francisco Chinesta4Philippe Cassagnau5Centre National de la Recherche Scientifique, Ingénierie des Matériaux Polymères, Université Claude Bernard Lyon 1, 15 Boulevard André Latarjet, 69622 Villeurbanne, FranceCentre National de la Recherche Scientifique, Ingénierie des Matériaux Polymères, Université Claude Bernard Lyon 1, 15 Boulevard André Latarjet, 69622 Villeurbanne, FranceESI Group Chair@PIMM, Arts et Métiers Institute of Technology, 151 Boulevard de l’Hôpital, 75013 Paris, FranceESI Group Chair@LAMPA, Arts et Métiers Institute of Technology, 2 Boulevard du Ronceray, 49035 Angers, FranceESI Group Chair@PIMM, Arts et Métiers Institute of Technology, 151 Boulevard de l’Hôpital, 75013 Paris, FranceCentre National de la Recherche Scientifique, Ingénierie des Matériaux Polymères, Université Claude Bernard Lyon 1, 15 Boulevard André Latarjet, 69622 Villeurbanne, FranceTwo main problems are studied in this article. The first one is the use of the extrusion process for controlled thermo-mechanical degradation of polyethylene for recycling applications. The second is the data-based modelling of such reactive extrusion processes. Polyethylenes (high density polyethylene (HDPE) and ultra-high molecular weight polyethylene (UHMWPE)) were extruded in a corotating twin-screw extruder under high temperatures (350 °C < <i>T</i> < 420 °C) for various process conditions (flow rate and screw rotation speed). These process conditions involved a decrease in the molecular weight due to degradation reactions. A numerical method based on the Carreau-Yasuda model was developed to predict the rheological behaviour (variation of the viscosity versus shear rate) from the in-line measurement of the die pressure. The results were successfully compared to the viscosity measured from offline measurement assuming the Cox-Merz law. Weight average molecular weights were estimated from the resulting zero-shear rate viscosity. Furthermore, the linear viscoelastic behaviours (Frequency dependence of the complex shear modulus) were also used to predict the molecular weight distributions of final products by an inverse rheological method. Size exclusion chromatography (SEC) was performed on five samples, and the resulting molecular weight distributions were compared to the values obtained with the two aforementioned techniques. The values of weight average molecular weights were similar for the three techniques. The complete molecular weight distributions obtained by inverse rheology were similar to the SEC ones for extruded HDPE samples, but some inaccuracies were observed for extruded UHMWPE samples. The Ludovic<sup>®</sup> (SC-Consultants, Saint-Etienne, France) corotating twin-screw extrusion simulation software was used as a classical process simulation. However, as the rheo-kinetic laws of this process were unknown, the software could not predict all the flow characteristics successfully. Finally, machine learning techniques, able to operate in the low-data limit, were tested to build predicting models of the process outputs and material characteristics. Support Vector Machine Regression (SVR) and sparsed Proper Generalized Decomposition (sPGD) techniques were chosen to predict the process outputs successfully. These methods were also applied to material characteristics data, and both were found to be effective in predicting molecular weights. More precisely, the sPGD gave better results than the SVR for the zero-shear viscosity prediction. Stochastic methods were also tested on some of the data and showed promising results.https://www.mdpi.com/2073-4360/14/4/800polyethylene recyclingartificial engineeringpolymer extrusionmachine learning |
| spellingShingle | Fanny Castéran Karim Delage Nicolas Hascoët Amine Ammar Francisco Chinesta Philippe Cassagnau Data-Driven Modelling of Polyethylene Recycling under High-Temperature Extrusion Polymers polyethylene recycling artificial engineering polymer extrusion machine learning |
| title | Data-Driven Modelling of Polyethylene Recycling under High-Temperature Extrusion |
| title_full | Data-Driven Modelling of Polyethylene Recycling under High-Temperature Extrusion |
| title_fullStr | Data-Driven Modelling of Polyethylene Recycling under High-Temperature Extrusion |
| title_full_unstemmed | Data-Driven Modelling of Polyethylene Recycling under High-Temperature Extrusion |
| title_short | Data-Driven Modelling of Polyethylene Recycling under High-Temperature Extrusion |
| title_sort | data driven modelling of polyethylene recycling under high temperature extrusion |
| topic | polyethylene recycling artificial engineering polymer extrusion machine learning |
| url | https://www.mdpi.com/2073-4360/14/4/800 |
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