DOAJ Open Access 2023

A machine learning tool for future prediction of heat release capacity of in-situ flame retardant hybrid Mg(OH)2-Epoxy nanocomposites

Aurelio Bifulco Angelo Casciello Claudio Imparato Stanislao Forte Sabyasachi Gaan +2 lainnya

Abstrak

In this work, the fire behavior of a sol-gel in-situ hybrid Mg(OH)2-epoxy nanocomposite was investigated and an artificial neural network-based system built on a fully connected feed-forward artificial neural network was developed to predict its heat release capacity. The nanocomposite containing only ∼5 wt% loading of Mg(OH)2 promoted a remarkable decrease in heat release capacity (∼34%) measured by pyrolysis combustion flow calorimetry and in peak of heat release rate (∼37%), and heat release rate (∼29%), as assessed by cone calorimetry, as well as a significant decrease of total smoke release and smoke extinction area about 22 and 5%, respectively, indicating the suitability of Mg(OH)2 as an effective smoke suppressant. The proposed machine learning approach may be used as a promising alternative for a cost- and time-saving prediction of the fire performances of novel flame retardant polymer-based nanocomposites.

Topik & Kata Kunci

Polymers and polymer manufacture

Penulis (7)

Aurelio Bifulco

Angelo Casciello

Claudio Imparato

Stanislao Forte

Sabyasachi Gaan

Antonio Aronne

Giulio Malucelli

Format Sitasi

APA MLA BibTeX

Bifulco, A., Casciello, A., Imparato, C., Forte, S., Gaan, S., Aronne, A. et al. (2023). A machine learning tool for future prediction of heat release capacity of in-situ flame retardant hybrid Mg(OH)2-Epoxy nanocomposites. https://doi.org/10.1016/j.polymertesting.2023.108175

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Informasi Jurnal

Tahun Terbit: 2023
Sumber Database: DOAJ
DOI: 10.1016/j.polymertesting.2023.108175
Akses: Open Access ✓