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Area of Science:

  • Polymer Science
  • Chemical Engineering
  • Materials Science

Background:

  • Solvent-based plastic recycling offers selective polymer dissolution from waste streams.
  • Accurate polymer solubility data is essential for process design but currently limited.
  • Thermodynamic modeling, like SAFT-γ Mie, can reduce experimental costs and time.

Purpose of the Study:

  • To generate extensive polyethylene (PE) solubility data in various solvents across multiple temperatures.
  • To assess the predictive accuracy of the SAFT-γ Mie group-contribution (GC) approach for PE solubility.
  • To identify effective conventional and bioderived solvents for PE dissolution in recycling.

Main Methods:

  • Experimental determination of PE solubility using a polythermal method for four PE samples in ten solvents.
  • Thermodynamic modeling using the SAFT-γ Mie group-contribution (GC) approach to predict PE solubility.
  • Comparative analysis of experimental and modeling results for solid-liquid and solid-liquid-liquid equilibria.

Main Results:

  • High agreement between SAFT-γ Mie predictions and experimental data for PE solubility.
  • Decalin, toluene, p-xylene, and mesitylene showed the highest PE solubility.
  • Bioderived solvents like limonene, p-cymene, and α-pinene exhibited comparable solubility to aromatic solvents.
  • Polymer melting properties significantly influenced PE solubility more than molecular weight.

Conclusions:

  • The SAFT-γ Mie GC approach is validated for predicting polymer solubility in complex systems.
  • Bioderived solvents are viable, environmentally benign alternatives for polyethylene dissolution.
  • This work provides essential data and model validation for advancing solvent-based plastic recycling design.