Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25310%2F21%3A39918021" target="_blank" >RIV/00216275:25310/21:39918021 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/1420-3049/26/12/3727" target="_blank" >https://www.mdpi.com/1420-3049/26/12/3727</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/molecules26123727" target="_blank" >10.3390/molecules26123727</a>

Result language

angličtina

Original language name

Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo

Original language 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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10403 - Physical chemistry

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Molecules

ISSN

1420-3049

e-ISSN

—

Volume of the periodical

26

Issue of the periodical within the volume

12

Country of publishing house

CH - SWITZERLAND

Number of pages

25

Pages from-to

"3727-1"-"3727-25"

UT code for WoS article

000667869100001

EID of the result in the Scopus database

2-s2.0-85108881118