Application of computational approach in plastic pyrolysis kinetic modelling: a review
| Autor: | Syieluing Wong, Bemgba Bevan Nyakuma, Sabino Armenise, J. M. Ramírez-Velasquez, Franck Launay, Marta Muñoz, Joaquín Rams, Daniel Wuebben |
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| Přispěvatelé: | Universidad Rey Juan Carlos [Madrid] (URJC), Yachay Tech University, Laboratoire de Réactivité de Surface (LRS), Institut de Chimie du CNRS (INC)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Benue State University, Makurdi. |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computational model
Computer science Scale (chemistry) Reaction pathways Plastic pyrolysis 02 engineering and technology 010402 general chemistry 021001 nanoscience & nanotechnology Reactor design 01 natural sciences Quantum mechanics Catalysis 0104 chemical sciences Kinetics Machine learning Plastic waste Relevance (information retrieval) Biochemical engineering Physical and Theoretical Chemistry 0210 nano-technology Pyrolysis Plastic Pyrolysis Machine Learning Quantum Mechanics Kinetics Reaction Pathways [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology |
| Zdroj: | Reaction kinetics, mechanisms and catalysis Reaction kinetics, mechanisms and catalysis, Springer, 2021, ⟨10.1007/s11144-021-02093-7⟩ |
| ISSN: | 1878-5190 1878-5204 |
| Popis: | During the past decade, pyrolysis routes have been identified as one of the most promising solutions for plastic waste management. However, the industrial adoption of such technologies has been limited and several unresolved blind spots hamper the commercial application of pyrolysis. Despite many years and efforts to explain pyrolysis models based on global kinetic approaches, recent advances in computational modelling such as machine learning and quantum mechanics offer new insights. For example, the kinetic and mechanistic information about plastic pyrolysis reactions necessary for scaling up processes is unravelling. This selective literature review reveals some of the foundational knowledge and accurate views on the reaction pathways, product yields, and other features of pyrolysis created by these new tools. Pyrolysis routes mapped by machine learning and quantum mechanics will gain more relevance in the coming years, especially studies that combine computational models with different time and scale resolutions governed by “first principles.” Existing research suggests that, as machine learning is further coupled to quantum mechanics, scientists and engineers will better predict products, yields, and compositions, as well as more complicated features such as ideal reactor design. |
| Databáze: | OpenAIRE |
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