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Related Concept Videos

Polymer Classification: Architecture01:14

Polymer Classification: Architecture

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Polymers are classified as linear or branched on the basis of their chain architecture. The polymer chains in linear polymers have a long chain-like structure with minimal to no branching at all. Even if a polymer features large substituent groups on the monomer, which appear as branches to the skeleton, it is not considered a branched polymer. A branched polymer contains secondary polymer chains that arise from the main polymer chain. The branching occurs when the polymer growth shifts from...
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The conversion of alkenes to macromolecules called polymers is a reaction of high commercial importance. The structure of the polymer is defined by a repeating unit, while the terminal groups are considered insignificant. The average degree of polymerization represents the number of repeating units in the polymer molecule and is denoted by the subscript n.
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The introduction of polyesters has brought major development to the textile industry. The wrinkle-free behavior of polyester blends has eliminated the need for starching and ironing clothes.
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Polymer Classification: Crystallinity01:21

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Unlike ionic or small covalent molecules, polymers do not form crystalline solids due to the diffusion limitations of their long-chain structures. However, polymers contain microscopic crystalline domains separated by amorphous domains.
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Polymer Classification: Stereospecificity01:26

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Polymerization generates chiral centers along the entire backbone of a polymer chain. Accordingly, the stereochemistry of the substituent group has a significant effect on polymer properties. Polymers formed from monosubstituted alkene monomers feature chiral carbons at every alternate position in the polymer backbone. Relative to the predominant orientation of substituents at the adjacent chiral carbons, the polymer can exist in three different configurations: isotactic, syndiotactic, and...
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Polymers: Molecular Weight Distribution01:10

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For any given polymer, the weight average molecular weight (Mw) is higher than, if not equal to, the number average molecular weight (Mn). The only situation in which the weight average molecular weight and the number average molecular weight are equal is when a polymer consists only of chains with equal molecular weight. However, this never happens in a synthetic polymer, since it is difficult to control the polymerization process up to a molecular level with accuracy to a hundred percent.
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Data-Driven Modelling of Polyethylene Recycling under High-Temperature Extrusion.

Fanny Castéran1, Karim Delage1, Nicolas Hascoët2

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|February 26, 2022
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Summary

This study explores controlled polyethylene degradation via extrusion for recycling, developing data-based models to predict material properties. Machine learning techniques like SVR and sPGD effectively predicted molecular weights, with sPGD showing superior zero-shear viscosity prediction.

Keywords:
artificial engineeringmachine learningpolyethylene recyclingpolymer extrusion

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Area of Science:

  • Polymer Science and Engineering
  • Materials Science
  • Chemical Engineering

Background:

  • Controlled thermo-mechanical degradation of polyethylene (PE) is crucial for recycling applications.
  • Reactive extrusion processes require accurate data-based modeling for predicting material properties.
  • High-density polyethylene (HDPE) and ultra-high molecular weight polyethylene (UHMWPE) were investigated.

Purpose of the Study:

  • To investigate the extrusion process for controlled degradation of PE.
  • To develop data-based models for predicting rheological behavior and molecular characteristics.
  • To compare different modeling techniques, including numerical methods and machine learning.

Main Methods:

  • Extrusion of HDPE and UHMWPE under varying conditions (temperature, flow rate, screw speed).
  • Development of a numerical method using the Carreau-Yasuda model and Cox-Merz law for rheological prediction.
  • Application of inverse rheology and Size Exclusion Chromatography (SEC) for molecular weight distribution analysis.
  • Implementation of machine learning techniques: Support Vector Machine Regression (SVR) and sparsed Proper Generalized Decomposition (sPGD).

Main Results:

  • Extrusion successfully decreased molecular weight, with weight average molecular weights comparable across numerical, inverse rheology, and SEC methods.
  • Inverse rheology accurately predicted molecular weight distributions for HDPE but showed inaccuracies for UHMWPE.
  • Machine learning models (SVR and sPGD) effectively predicted process outputs and material characteristics, with sPGD outperforming SVR for zero-shear viscosity.
  • Classical process simulation software (Ludovic®) faced limitations due to unknown rheo-kinetic laws.

Conclusions:

  • Controlled extrusion is a viable method for polyethylene degradation in recycling.
  • Data-based modeling, particularly machine learning (sPGD), offers effective prediction of material properties.
  • Further research into rheo-kinetic laws is needed for improved classical process simulations.
  • Machine learning shows significant promise for optimizing reactive extrusion processes with limited data.