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Using an artificial neural network (ANN) for prediction of thermal degradation from kinetics parameters of vegetable fibers
Vegetal fibers are prominent reinforcements for polymer composite materials, considering their properties and application possibilities. In particular, thermal degradation behavior is crucial for determining an application subjected to a temperature range. Methods to predict properties are a trend i...
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| Published in: | Cellulose (London) 2021-03, Vol.28 (4), p.1961-1971 |
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| Main Authors: | , , |
| Format: | Article |
| Language: | English |
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| cited_by | cdi_FETCH-LOGICAL-c356t-82b92bc4f93d73e9968427bda55b3edc825508e6a8d4a23b789c3f2a5cd0a7b43 |
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| cites | cdi_FETCH-LOGICAL-c356t-82b92bc4f93d73e9968427bda55b3edc825508e6a8d4a23b789c3f2a5cd0a7b43 |
| container_end_page | 1971 |
| container_issue | 4 |
| container_start_page | 1961 |
| container_title | Cellulose (London) |
| container_volume | 28 |
| creator | Monticeli, Francisco M. Neves, Roberta Motta Ornaghi Júnior, Heitor Luiz |
| description | Vegetal fibers are prominent reinforcements for polymer composite materials, considering their properties and application possibilities. In particular, thermal degradation behavior is crucial for determining an application subjected to a temperature range. Methods to predict properties are a trend in materials science and have the main advantage of saving cost and time. For this reason, in the present study, an artificial neural network (ANN) approach was used to predict the thermal degradation curves. The heating rate of 10 °C·min
− 1
was carried out to train the network with 12 hidden layers and optimal training dataset of 60. Other heating rates were simulated and showed an excellent agreement with the experimental data. The coefficient of determination was
R
2
> 0.99 for all sources of biomass, exhibiting appropriate predictive fit with error following the sequence: ramie (1.15 %) |
| doi_str_mv | 10.1007/s10570-021-03684-2 |
| format | article |
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− 1
was carried out to train the network with 12 hidden layers and optimal training dataset of 60. Other heating rates were simulated and showed an excellent agreement with the experimental data. The coefficient of determination was
R
2
> 0.99 for all sources of biomass, exhibiting appropriate predictive fit with error following the sequence: ramie (1.15 %) < kenaf (1.33 %) < curaua (1.83 %) < jute (1.97 %). In conclusion, ANNs can learn from their data and optimize processing, formulations, predict properties, and other input data combinations. The predictive curves present high reliability with the experimental fit allowing the prediction of the mass loss for different temperatures versus the heating rate set.
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− 1
was carried out to train the network with 12 hidden layers and optimal training dataset of 60. Other heating rates were simulated and showed an excellent agreement with the experimental data. The coefficient of determination was
R
2
> 0.99 for all sources of biomass, exhibiting appropriate predictive fit with error following the sequence: ramie (1.15 %) < kenaf (1.33 %) < curaua (1.83 %) < jute (1.97 %). In conclusion, ANNs can learn from their data and optimize processing, formulations, predict properties, and other input data combinations. The predictive curves present high reliability with the experimental fit allowing the prediction of the mass loss for different temperatures versus the heating rate set.
Graphic abstract</description><subject>Artificial neural networks</subject><subject>Bioorganic Chemistry</subject><subject>Ceramics</subject><subject>Chemistry</subject><subject>Chemistry and Materials Science</subject><subject>Composite materials</subject><subject>Composites</subject><subject>Glass</subject><subject>Heating rate</subject><subject>Jute</subject><subject>Kenaf</subject><subject>Materials science</subject><subject>Natural Materials</subject><subject>Neural networks</subject><subject>Optimization</subject><subject>Organic Chemistry</subject><subject>Original Research</subject><subject>Physical Chemistry</subject><subject>Polymer matrix composites</subject><subject>Polymer Sciences</subject><subject>Predictions</subject><subject>Properties (attributes)</subject><subject>Sustainable Development</subject><subject>Thermal degradation</subject><subject>Vegetable fibers</subject><issn>0969-0239</issn><issn>1572-882X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kEtLAzEUhYMoWKt_wFXAjS5GM8lkZrIsxRdI3VhwFzKZZEzbeXiTKuKfN-0I7lxdOPc753IPQucpuU4JKW58SnhBEkLThLC8zBJ6gCYpL2hSlvT1EE2IyEVcM3GMTrxfEUJEQdMJ-l561zVYdVhBcNZppza4M1vYj_DZwxpfzhaLK2x7wAOY2ung-g73Foc3A23katOAqtVettC3eO2i1WmPBwWqNcGA3_EfpjFBVRuDrauidoqOrNp4c_Y7p2h5d_syf0ienu8f57OnRDOeh6SklaCVzqxgdcGMEPE_WlS14rxiptYl5ZyUJldlnSnKqqIUmlmquK6JKqqMTdHFmDtA_741PshVv4UunpSUE5KLNGd5pOhIaei9B2PlAK5V8CVTIncly7FkGUuW-5IljSY2mnyEu8bAX_Q_rh8uqYEe</recordid><startdate>20210301</startdate><enddate>20210301</enddate><creator>Monticeli, Francisco M.</creator><creator>Neves, Roberta Motta</creator><creator>Ornaghi Júnior, Heitor Luiz</creator><general>Springer Netherlands</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>KB.</scope><scope>PDBOC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><orcidid>https://orcid.org/0000-0002-0005-9534</orcidid><orcidid>https://orcid.org/0000-0002-0814-8160</orcidid><orcidid>https://orcid.org/0000-0002-7017-0852</orcidid></search><sort><creationdate>20210301</creationdate><title>Using an artificial neural network (ANN) for prediction of thermal degradation from kinetics parameters of vegetable fibers</title><author>Monticeli, Francisco M. ; 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In particular, thermal degradation behavior is crucial for determining an application subjected to a temperature range. Methods to predict properties are a trend in materials science and have the main advantage of saving cost and time. For this reason, in the present study, an artificial neural network (ANN) approach was used to predict the thermal degradation curves. The heating rate of 10 °C·min
− 1
was carried out to train the network with 12 hidden layers and optimal training dataset of 60. Other heating rates were simulated and showed an excellent agreement with the experimental data. The coefficient of determination was
R
2
> 0.99 for all sources of biomass, exhibiting appropriate predictive fit with error following the sequence: ramie (1.15 %) < kenaf (1.33 %) < curaua (1.83 %) < jute (1.97 %). In conclusion, ANNs can learn from their data and optimize processing, formulations, predict properties, and other input data combinations. The predictive curves present high reliability with the experimental fit allowing the prediction of the mass loss for different temperatures versus the heating rate set.
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| ispartof | Cellulose (London), 2021-03, Vol.28 (4), p.1961-1971 |
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| language | eng |
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| source | Springer Nature:Jisc Collections:Springer Nature Read and Publish 2023-2025: Springer Reading List |
| subjects | Artificial neural networks Bioorganic Chemistry Ceramics Chemistry Chemistry and Materials Science Composite materials Composites Glass Heating rate Jute Kenaf Materials science Natural Materials Neural networks Optimization Organic Chemistry Original Research Physical Chemistry Polymer matrix composites Polymer Sciences Predictions Properties (attributes) Sustainable Development Thermal degradation Vegetable fibers |
| title | Using an artificial neural network (ANN) for prediction of thermal degradation from kinetics parameters of vegetable fibers |
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