LAPSE:2023.36168
Published Article

LAPSE:2023.36168
Kinetic Monte Carlo Convergence Demands for Thermochemical Recycling Kinetics of Vinyl Polymers with Dominant Depropagation
July 4, 2023
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, β-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 × 105 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.
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, β-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 × 105 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.
Record ID
Keywords
convergence, kinetic Monte Carlo, thermochemical degradation
Subject
Suggested Citation
Moens EKC, Marien YW, Trigilio AD, Van Geem KM, Van Steenberge PHM, D’hooge DR. Kinetic Monte Carlo Convergence Demands for Thermochemical Recycling Kinetics of Vinyl Polymers with Dominant Depropagation. (2023). LAPSE:2023.36168
Author Affiliations
Moens EKC: Laboratory for Chemical Technology, Ghent University, Technologiepark 125, 9052 Ghent, Belgium
Marien YW: Laboratory for Chemical Technology, Ghent University, Technologiepark 125, 9052 Ghent, Belgium [ORCID]
Trigilio AD: Laboratory for Chemical Technology, Ghent University, Technologiepark 125, 9052 Ghent, Belgium [ORCID]
Van Geem KM: Laboratory for Chemical Technology, Ghent University, Technologiepark 125, 9052 Ghent, Belgium [ORCID]
Van Steenberge PHM: Laboratory for Chemical Technology, Ghent University, Technologiepark 125, 9052 Ghent, Belgium [ORCID]
D’hooge DR: Laboratory for Chemical Technology, Ghent University, Technologiepark 125, 9052 Ghent, Belgium; Centre for Textile Science and Engineering, Ghent University, Technologiepark 70a, 9052 Ghent, Belgium [ORCID]
Marien YW: Laboratory for Chemical Technology, Ghent University, Technologiepark 125, 9052 Ghent, Belgium [ORCID]
Trigilio AD: Laboratory for Chemical Technology, Ghent University, Technologiepark 125, 9052 Ghent, Belgium [ORCID]
Van Geem KM: Laboratory for Chemical Technology, Ghent University, Technologiepark 125, 9052 Ghent, Belgium [ORCID]
Van Steenberge PHM: Laboratory for Chemical Technology, Ghent University, Technologiepark 125, 9052 Ghent, Belgium [ORCID]
D’hooge DR: Laboratory for Chemical Technology, Ghent University, Technologiepark 125, 9052 Ghent, Belgium; Centre for Textile Science and Engineering, Ghent University, Technologiepark 70a, 9052 Ghent, Belgium [ORCID]
Journal Name
Processes
Volume
11
Issue
6
First Page
1623
Year
2023
Publication Date
2023-05-26
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr11061623, Publication Type: Journal Article
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LAPSE:2023.36168
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https://doi.org/10.3390/pr11061623
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[v1] (Original Submission)
Jul 4, 2023
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Record Owner
Calvin Tsay
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