Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo
Artificial neural networks (ANNs) are a method of machine learning (ML) that is now widely used in physics, chemistry, and material science. ANN can learn from data to identify nonlinear trends and give accurate predictions. ML methods, and ANNs in particular, have already demonstrated their worth i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/26/12/3727 |
| _version_ | 1797529647522512896 |
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| author | Nikita V. Muravyev Giorgio Luciano Heitor Luiz Ornaghi Roman Svoboda Sergey Vyazovkin |
| author_facet | Nikita V. Muravyev Giorgio Luciano Heitor Luiz Ornaghi Roman Svoboda Sergey Vyazovkin |
| author_sort | Nikita V. Muravyev |
| collection | DOAJ |
| description | Artificial neural networks (ANNs) are a method of machine learning (ML) that is now widely used in physics, chemistry, and material science. ANN can learn from data to identify nonlinear trends and give accurate predictions. ML methods, and ANNs in particular, have already demonstrated their worth in solving various chemical engineering problems, but applications in pyrolysis, thermal analysis, and, especially, thermokinetic studies are still in an initiatory stage. The present article gives a critical overview and summary of the available literature on applying ANNs in the field of pyrolysis, thermal analysis, and thermokinetic studies. More than 100 papers from these research areas are surveyed. Some approaches from the broad field of chemical engineering are discussed as the venues for possible transfer to the field of pyrolysis and thermal analysis studies in general. It is stressed that the current thermokinetic applications of ANNs are yet to evolve significantly to reach the capabilities of the existing isoconversional and model-fitting methods. |
| first_indexed | 2024-03-10T10:17:44Z |
| format | Article |
| id | doaj.art-9047cc0944384767914d0c137156ae2e |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-10T10:17:44Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-9047cc0944384767914d0c137156ae2e2023-11-22T00:42:42ZengMDPI AGMolecules1420-30492021-06-012612372710.3390/molecules26123727Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status QuoNikita V. Muravyev0Giorgio Luciano1Heitor Luiz Ornaghi2Roman Svoboda3Sergey Vyazovkin4N.N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 4 Kosygina Str., 119991 Moscow, RussiaCNR, Istituto di Scienze e Tecnologie Chimiche “Giulio Natta”, Via De Marini 6, 16149 Genova, ItalyDepartment of Materials, Federal University for Latin American Integration (UNILA), Silvio Américo Sasdelli Avenue, 1842, Foz do Iguaçu-Paraná 85866-000, BrazilDepartment of Physical Chemistry, Faculty of Chemical Technology, University of Pardubice, Studentská 95, CZ-53210 Pardubice, Czech RepublicDepartment of Chemistry, University of Alabama at Birmingham, 901 S. 14th Street, Birmingham, AL 35294, USAArtificial neural networks (ANNs) are a method of machine learning (ML) that is now widely used in physics, chemistry, and material science. ANN can learn from data to identify nonlinear trends and give accurate predictions. ML methods, and ANNs in particular, have already demonstrated their worth in solving various chemical engineering problems, but applications in pyrolysis, thermal analysis, and, especially, thermokinetic studies are still in an initiatory stage. The present article gives a critical overview and summary of the available literature on applying ANNs in the field of pyrolysis, thermal analysis, and thermokinetic studies. More than 100 papers from these research areas are surveyed. Some approaches from the broad field of chemical engineering are discussed as the venues for possible transfer to the field of pyrolysis and thermal analysis studies in general. It is stressed that the current thermokinetic applications of ANNs are yet to evolve significantly to reach the capabilities of the existing isoconversional and model-fitting methods.https://www.mdpi.com/1420-3049/26/12/3727artificial neural networksconversion degreekineticsmachine learningpyrolysisthermal analysis |
| spellingShingle | Nikita V. Muravyev Giorgio Luciano Heitor Luiz Ornaghi Roman Svoboda Sergey Vyazovkin Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo Molecules artificial neural networks conversion degree kinetics machine learning pyrolysis thermal analysis |
| title | Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo |
| title_full | Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo |
| title_fullStr | Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo |
| title_full_unstemmed | Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo |
| title_short | Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo |
| title_sort | artificial neural networks for pyrolysis thermal analysis and thermokinetic studies the status quo |
| topic | artificial neural networks conversion degree kinetics machine learning pyrolysis thermal analysis |
| url | https://www.mdpi.com/1420-3049/26/12/3727 |
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