PMID- 35215711
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220301
IS  - 2073-4360 (Electronic)
IS  - 2073-4360 (Linking)
VI  - 14
IP  - 4
DP  - 2022 Feb 18
TI  - Data-Driven Modelling of Polyethylene Recycling under High-Temperature Extrusion.
LID - 10.3390/polym14040800 [doi]
LID - 800
AB  - 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  degrees C < T < 420  degrees 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((R)) (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.
FAU - Casteran, Fanny
AU  - Casteran F
AUID- ORCID: 0000-0002-2600-5198
AD  - Centre National de la Recherche Scientifique, Ingenierie des Materiaux Polymeres, 
      Universite Claude Bernard Lyon 1, 15 Boulevard Andre Latarjet, 69622 
      Villeurbanne, France.
FAU - Delage, Karim
AU  - Delage K
AD  - Centre National de la Recherche Scientifique, Ingenierie des Materiaux Polymeres, 
      Universite Claude Bernard Lyon 1, 15 Boulevard Andre Latarjet, 69622 
      Villeurbanne, France.
FAU - Hascoet, Nicolas
AU  - Hascoet N
AD  - ESI Group Chair@PIMM, Arts et Metiers Institute of Technology, 151 Boulevard de 
      l'Hopital, 75013 Paris, France.
FAU - Ammar, Amine
AU  - Ammar A
AD  - ESI Group Chair@LAMPA, Arts et Metiers Institute of Technology, 2 Boulevard du 
      Ronceray, 49035 Angers, France.
FAU - Chinesta, Francisco
AU  - Chinesta F
AD  - ESI Group Chair@PIMM, Arts et Metiers Institute of Technology, 151 Boulevard de 
      l'Hopital, 75013 Paris, France.
FAU - Cassagnau, Philippe
AU  - Cassagnau P
AD  - Centre National de la Recherche Scientifique, Ingenierie des Materiaux Polymeres, 
      Universite Claude Bernard Lyon 1, 15 Boulevard Andre Latarjet, 69622 
      Villeurbanne, France.
LA  - eng
PT  - Journal Article
DEP - 20220218
PL  - Switzerland
TA  - Polymers (Basel)
JT  - Polymers
JID - 101545357
PMC - PMC8877389
OTO - NOTNLM
OT  - artificial engineering
OT  - machine learning
OT  - polyethylene recycling
OT  - polymer extrusion
COIS- The authors declare no conflict of interest.
EDAT- 2022/02/27 06:00
MHDA- 2022/02/27 06:01
PMCR- 2022/02/18
CRDT- 2022/02/26 01:06
PHST- 2021/11/11 00:00 [received]
PHST- 2022/02/10 00:00 [revised]
PHST- 2022/02/15 00:00 [accepted]
PHST- 2022/02/26 01:06 [entrez]
PHST- 2022/02/27 06:00 [pubmed]
PHST- 2022/02/27 06:01 [medline]
PHST- 2022/02/18 00:00 [pmc-release]
AID - polym14040800 [pii]
AID - polymers-14-00800 [pii]
AID - 10.3390/polym14040800 [doi]
PST - epublish
SO  - Polymers (Basel). 2022 Feb 18;14(4):800. doi: 10.3390/polym14040800.