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Kinetic Monte Carlo convergence demands for thermochemical recycling kinetics of vinyl polymers with dominant depropagation

Eli Moens (UGent) , Yoshi Marien (UGent) , Alessandro Trigilio (UGent) , Kevin Van Geem (UGent) , Paul Van Steenberge (UGent) and Dagmar D'hooge (UGent)
(2023) PROCESSES. 11(6).
Author
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Abstract
As societal interest in recycling of plastics increases, modeling thermochemical recycling of vinyl polymers, e.g., via pyrolysis or reactive extrusion, becomes increasingly important. A key aspect remains the reliability of the simulation results with fewer evaluation studies regarding convergence as in the polymerization or polymer reaction engineering field. Using the coupled matrix-based Monte Carlo (CMMC) framework, tracking the unzipping of individual chains according to a general intrinsic reaction scheme consisting of fission, & beta;-scission, and termination, it is however illustrated that similar convergence demands as in polymerization benchmark studies can be employed, i.e., threshold values for the average relative error predictions on conversion and chain length averages can be maintained. For this illustration, three theoretical feedstocks are considered as generated from CMMC polymer synthesis simulations, allowing to study the effect of the initial chain length range and the number of defects on the convergence demands. It is shown that feedstocks with a broader chain length distribution and a long tail require a larger Monte Carlo simulation volume, and that the head-head effects play a key role in the type of degradation mechanism and overall degradation rate. A minimal number of chains around 5 x 10(5) is needed to properly reflect the degradation kinetics. A certain degree of noise can be allowed at the higher carbon-based conversions due to the inevitable decrease in number of chains.
Keywords
Process Chemistry and Technology, Chemical Engineering (miscellaneous), Bioengineering, kinetic Monte Carlo, convergence, thermochemical degradation, THERMAL-DEGRADATION, POLY(METHYL METHACRYLATE), RADICAL POLYMERIZATION, OXIDATIVE-DEGRADATION, PYROLYSIS, COPOLYMERIZATION, ACCELERATION, CONVERSION, MODEL

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Citation

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MLA
Moens, Eli, et al. “Kinetic Monte Carlo Convergence Demands for Thermochemical Recycling Kinetics of Vinyl Polymers with Dominant Depropagation.” PROCESSES, vol. 11, no. 6, 2023, doi:10.3390/pr11061623.
APA
Moens, E., Marien, Y., Trigilio, A., Van Geem, K., Van Steenberge, P., & D’hooge, D. (2023). Kinetic Monte Carlo convergence demands for thermochemical recycling kinetics of vinyl polymers with dominant depropagation. PROCESSES, 11(6). https://doi.org/10.3390/pr11061623
Chicago author-date
Moens, Eli, Yoshi Marien, Alessandro Trigilio, Kevin Van Geem, Paul Van Steenberge, and Dagmar D’hooge. 2023. “Kinetic Monte Carlo Convergence Demands for Thermochemical Recycling Kinetics of Vinyl Polymers with Dominant Depropagation.” PROCESSES 11 (6). https://doi.org/10.3390/pr11061623.
Chicago author-date (all authors)
Moens, Eli, Yoshi Marien, Alessandro Trigilio, Kevin Van Geem, Paul Van Steenberge, and Dagmar D’hooge. 2023. “Kinetic Monte Carlo Convergence Demands for Thermochemical Recycling Kinetics of Vinyl Polymers with Dominant Depropagation.” PROCESSES 11 (6). doi:10.3390/pr11061623.
Vancouver
1.
Moens E, Marien Y, Trigilio A, Van Geem K, Van Steenberge P, D’hooge D. Kinetic Monte Carlo convergence demands for thermochemical recycling kinetics of vinyl polymers with dominant depropagation. PROCESSES. 2023;11(6).
IEEE
[1]
E. Moens, Y. Marien, A. Trigilio, K. Van Geem, P. Van Steenberge, and D. D’hooge, “Kinetic Monte Carlo convergence demands for thermochemical recycling kinetics of vinyl polymers with dominant depropagation,” PROCESSES, vol. 11, no. 6, 2023.
@article{01HKSR1DNNER2ZSDKHJG0RTNXZ,
  abstract     = {{As societal interest in recycling of plastics increases, modeling thermochemical recycling of vinyl polymers, e.g., via pyrolysis or reactive extrusion, becomes increasingly important. A key aspect remains the reliability of the simulation results with fewer evaluation studies regarding convergence as in the polymerization or polymer reaction engineering field. Using the coupled matrix-based Monte Carlo (CMMC) framework, tracking the unzipping of individual chains according to a general intrinsic reaction scheme consisting of fission, & beta;-scission, and termination, it is however illustrated that similar convergence demands as in polymerization benchmark studies can be employed, i.e., threshold values for the average relative error predictions on conversion and chain length averages can be maintained. For this illustration, three theoretical feedstocks are considered as generated from CMMC polymer synthesis simulations, allowing to study the effect of the initial chain length range and the number of defects on the convergence demands. It is shown that feedstocks with a broader chain length distribution and a long tail require a larger Monte Carlo simulation volume, and that the head-head effects play a key role in the type of degradation mechanism and overall degradation rate. A minimal number of chains around 5 x 10(5) is needed to properly reflect the degradation kinetics. A certain degree of noise can be allowed at the higher carbon-based conversions due to the inevitable decrease in number of chains.}},
  articleno    = {{1623}},
  author       = {{Moens, Eli and Marien, Yoshi and Trigilio, Alessandro and Van Geem, Kevin and Van Steenberge, Paul and D'hooge, Dagmar}},
  issn         = {{2227-9717}},
  journal      = {{PROCESSES}},
  keywords     = {{Process Chemistry and Technology,Chemical Engineering (miscellaneous),Bioengineering,kinetic Monte Carlo,convergence,thermochemical degradation,THERMAL-DEGRADATION,POLY(METHYL METHACRYLATE),RADICAL POLYMERIZATION,OXIDATIVE-DEGRADATION,PYROLYSIS,COPOLYMERIZATION,ACCELERATION,CONVERSION,MODEL}},
  language     = {{eng}},
  number       = {{6}},
  pages        = {{20}},
  title        = {{Kinetic Monte Carlo convergence demands for thermochemical recycling kinetics of vinyl polymers with dominant depropagation}},
  url          = {{http://doi.org/10.3390/pr11061623}},
  volume       = {{11}},
  year         = {{2023}},
}

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