A machine learning tool for future prediction of heat release capacity of in-situ flame retardant hybrid Mg(OH)2-Epoxy nanocomposites
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 on...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-10-01
|
| Series: | Polymer Testing |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142941823002556 |
| _version_ | 1797683179160600576 |
|---|---|
| author | Aurelio Bifulco Angelo Casciello Claudio Imparato Stanislao Forte Sabyasachi Gaan Antonio Aronne Giulio Malucelli |
| author_facet | Aurelio Bifulco Angelo Casciello Claudio Imparato Stanislao Forte Sabyasachi Gaan Antonio Aronne Giulio Malucelli |
| author_sort | Aurelio Bifulco |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T00:11:44Z |
| format | Article |
| id | doaj.art-0ac31323dd5a400a926152cadcd6c0e0 |
| institution | Directory Open Access Journal |
| issn | 0142-9418 |
| language | English |
| last_indexed | 2024-03-12T00:11:44Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Polymer Testing |
| spelling | doaj.art-0ac31323dd5a400a926152cadcd6c0e02023-09-16T05:28:06ZengElsevierPolymer Testing0142-94182023-10-01127108175A machine learning tool for future prediction of heat release capacity of in-situ flame retardant hybrid Mg(OH)2-Epoxy nanocompositesAurelio Bifulco0Angelo Casciello1Claudio Imparato2Stanislao Forte3Sabyasachi Gaan4Antonio Aronne5Giulio Malucelli6Department of Chemical Materials and Production Engineering (DICMaPI), University of Naples Federico II, Piazzale V. Tecchio 80, 80125, Naples, ItalyDepartment of Chemical Materials and Production Engineering (DICMaPI), University of Naples Federico II, Piazzale V. Tecchio 80, 80125, Naples, ItalyDepartment of Chemical Materials and Production Engineering (DICMaPI), University of Naples Federico II, Piazzale V. Tecchio 80, 80125, Naples, ItalySoftware Care Srl, Via Servio Tullio, 106, 80126, Naples, ItalyLaboratory for Advanced Fibers, Empa Swiss Federal Laboratories for Materials Science and Technology, Lerchenfeldstrasse 5, 9014, St. Gallen, SwitzerlandDepartment of Chemical Materials and Production Engineering (DICMaPI), University of Naples Federico II, Piazzale V. Tecchio 80, 80125, Naples, ItalyDepartment of Applied Science and Technology, Politecnico di Torino, Viale Teresa Michel 5, Alessandria, 15121, Italy; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S0142941823002556Magnesium hydroxideSol-gelEpoxyPrediction of fire parametersArtificial neural networksMachine learning |
| spellingShingle | Aurelio Bifulco Angelo Casciello Claudio Imparato Stanislao Forte Sabyasachi Gaan Antonio Aronne Giulio Malucelli A machine learning tool for future prediction of heat release capacity of in-situ flame retardant hybrid Mg(OH)2-Epoxy nanocomposites Polymer Testing Magnesium hydroxide Sol-gel Epoxy Prediction of fire parameters Artificial neural networks Machine learning |
| title | A machine learning tool for future prediction of heat release capacity of in-situ flame retardant hybrid Mg(OH)2-Epoxy nanocomposites |
| title_full | A machine learning tool for future prediction of heat release capacity of in-situ flame retardant hybrid Mg(OH)2-Epoxy nanocomposites |
| title_fullStr | A machine learning tool for future prediction of heat release capacity of in-situ flame retardant hybrid Mg(OH)2-Epoxy nanocomposites |
| title_full_unstemmed | A machine learning tool for future prediction of heat release capacity of in-situ flame retardant hybrid Mg(OH)2-Epoxy nanocomposites |
| title_short | A machine learning tool for future prediction of heat release capacity of in-situ flame retardant hybrid Mg(OH)2-Epoxy nanocomposites |
| title_sort | machine learning tool for future prediction of heat release capacity of in situ flame retardant hybrid mg oh 2 epoxy nanocomposites |
| topic | Magnesium hydroxide Sol-gel Epoxy Prediction of fire parameters Artificial neural networks Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S0142941823002556 |
| work_keys_str_mv | AT aureliobifulco amachinelearningtoolforfuturepredictionofheatreleasecapacityofinsituflameretardanthybridmgoh2epoxynanocomposites AT angelocasciello amachinelearningtoolforfuturepredictionofheatreleasecapacityofinsituflameretardanthybridmgoh2epoxynanocomposites AT claudioimparato amachinelearningtoolforfuturepredictionofheatreleasecapacityofinsituflameretardanthybridmgoh2epoxynanocomposites AT stanislaoforte amachinelearningtoolforfuturepredictionofheatreleasecapacityofinsituflameretardanthybridmgoh2epoxynanocomposites AT sabyasachigaan amachinelearningtoolforfuturepredictionofheatreleasecapacityofinsituflameretardanthybridmgoh2epoxynanocomposites AT antonioaronne amachinelearningtoolforfuturepredictionofheatreleasecapacityofinsituflameretardanthybridmgoh2epoxynanocomposites AT giuliomalucelli amachinelearningtoolforfuturepredictionofheatreleasecapacityofinsituflameretardanthybridmgoh2epoxynanocomposites AT aureliobifulco machinelearningtoolforfuturepredictionofheatreleasecapacityofinsituflameretardanthybridmgoh2epoxynanocomposites AT angelocasciello machinelearningtoolforfuturepredictionofheatreleasecapacityofinsituflameretardanthybridmgoh2epoxynanocomposites AT claudioimparato machinelearningtoolforfuturepredictionofheatreleasecapacityofinsituflameretardanthybridmgoh2epoxynanocomposites AT stanislaoforte machinelearningtoolforfuturepredictionofheatreleasecapacityofinsituflameretardanthybridmgoh2epoxynanocomposites AT sabyasachigaan machinelearningtoolforfuturepredictionofheatreleasecapacityofinsituflameretardanthybridmgoh2epoxynanocomposites AT antonioaronne machinelearningtoolforfuturepredictionofheatreleasecapacityofinsituflameretardanthybridmgoh2epoxynanocomposites AT giuliomalucelli machinelearningtoolforfuturepredictionofheatreleasecapacityofinsituflameretardanthybridmgoh2epoxynanocomposites |