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Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo
Authors: Muravyev Nikita V | Luciano Giorgio | Ornaghi Heitor Luiz, Jr | Svoboda Roman | Vyazovkin Sergey
Year: 2021
Type of publication: článek v odborném periodiku
Name of source: Molecules
Publisher name: MDPI
Place: BASEL
Page from-to: "3727-1"-"3727-25"
Titles:
Language Name Abstract Keywords
cze Umělé neurální sítě pro pyrolýzu, termální analýzu a termokinetické studie: Status Quo Umělé neurální sítě jsou metody strojového učení, které jsou nyní široce využívané v řadě oborů jako jsou fyzika, chemie a materiální věda. Tyto sítě se mohou učit z dat a dávat přesné predikce. Jejich aplikace v pyrolýze, termické analýze a zejména v termokinetických studiích jsou však v počátečním stádiu. umělá neurální síť; stupeň konverze; kinetika; strojové učení; pyrolýza; termická analýza
eng 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 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. artificial neural networks; conversion degree; kinetics; machine learning; pyrolysis; thermal analysis

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