Methods and Algorithms for Implementation of Imitation Approach to Modeling of Physicochemical Processes
This article describes the key methods and algorithms to organize the study of complex physicochemical processes using the imitation approach to modeling. The imitation approach to modeling is based on running a series of statistical tests and subsequent digital processing of all elementary particle...
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| Veröffentlicht in: | 2024 International Russian Smart Industry Conference (SmartIndustryCon) S. 829 - 833 |
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| Hauptverfasser: | , , |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
IEEE
25.03.2024
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| Schlagworte: | |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | This article describes the key methods and algorithms to organize the study of complex physicochemical processes using the imitation approach to modeling. The imitation approach to modeling is based on running a series of statistical tests and subsequent digital processing of all elementary particle interactions. Using this approach for the integration of digital technology helps review complex molecular interactions and reaction dynamics on a level that cannot be attained in experimentation. The authors describe the main idea of the imitation approach to modeling based on the representation of the kinetic mechanism of elementary reactions. They present a step-by-step algorithm for the reproduction of the molecular weight distribution curve based on the idea of the fractionation of the established data array by molecular weight. The software implementation of this algorithm adapted for the operation with large numbers helped solve the problem of assessing the molecular parameters of the isoprene polymerization product over a neodymium catalyst. The obtained molecular weight distribution curve is aligned with the results of Flory's model distribution. Additionally calculated values of average molecular weights showed sufficient alignment with the result of kinetic approach organization and the results of laboratory experiments. |
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| DOI: | 10.1109/SmartIndustryCon61328.2024.10515621 |