Neural networks applied in kinetic analysis of complex nucleation-growth processes: Outstanding solution for fully overlapping reaction mechanisms
Článekpeer-reviewedpublished Náhled není k dispozici
Datum publikování
2022
Vedoucí práce
Oponent
Název časopisu
Název svazku
Vydavatel
Elsevier Science BV
Abstrakt
Performance of several neural network architectures (convolutional neural network CNN, multilayer perceptron MLP, CNN/MLP hybrid CDD) was evaluated for kinetic analysis of complex processes with overlapping independent reaction mechanisms based on the nucleation-growth Johnson-Mehl-Avrami (JMA) model. Theoretically simulated data used for the testing covered absolute majority of real-life JMA-JMA solid-state kinetics scenarios. The performance of the tested architectures decreased in the following order: MLP > CDD >> CNN. For partially overlapping processes the CDD and MLP architectures provided accurate estimates of the JMA model kinetic parameters, performing on par with traditional methods of kinetic analysis. For the fully overlapping kinetic processes, the accuracy of the estimates provided by the neural networks significantly worsened, however still largely outperforming the traditional approaches of kinetic analysis based on the standard non-linear optimization, such as mathematic or kinetic deconvolution. The corresponding kinetic predictions were of suitable precision for majority of real-life applications preparation (glass-ceramics).
Rozsah stran
"121640-1"-"121640-11"
ISSN
0022-3093
Trvalý odkaz na tento záznam
Projekt
Zdrojový dokument
Journal of Non-Crystalline Solids, volume 588, issue: July
Vydavatelská verze
https://www.sciencedirect.com/science/article/pii/S0022309322002411
Přístup k e-verzi
pouze v rámci univerzity
Název akce
ISBN
Studijní obor
Studijní program
Signatura tištěné verze
Umístění tištěné verze
Přístup k tištěné verzi
Klíčová slova
CDD, complex process, JMA model, MLP, theoretical kinetic analysis, CDD, komplexní proces, JMA model, MLP, teoretická kinetická analýza