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A Neural Network Approach to Reducing the Costs of Parameter-Setting in the Production of Polyethylene Oxide Nanofibers
Nanofibers, which are formed by the electrospinning process, are used in a variety of applications. For this purpose, a specific diameter suited for each application is required, which is achieved by varying a set of parameters. This parameter adjustment process is empirical and works by trial and e...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10386166/ https://www.ncbi.nlm.nih.gov/pubmed/37512721 http://dx.doi.org/10.3390/mi14071410 |
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author | Solis-Rios, Daniel Villarreal-Gómez, Luis Jesús Goyes, Clara Eugenia Fonthal Rico, Faruk Cornejo-Bravo, José Manuel Fong-Mata, María Berenice Calderón Arenas, Jorge Mario Martínez Rincón, Harold Alberto Mejía-Medina, David Abdel |
author_facet | Solis-Rios, Daniel Villarreal-Gómez, Luis Jesús Goyes, Clara Eugenia Fonthal Rico, Faruk Cornejo-Bravo, José Manuel Fong-Mata, María Berenice Calderón Arenas, Jorge Mario Martínez Rincón, Harold Alberto Mejía-Medina, David Abdel |
author_sort | Solis-Rios, Daniel |
collection | PubMed |
description | Nanofibers, which are formed by the electrospinning process, are used in a variety of applications. For this purpose, a specific diameter suited for each application is required, which is achieved by varying a set of parameters. This parameter adjustment process is empirical and works by trial and error, causing high input costs and wasting time and financial resources. In this work, an artificial neural network model is presented to predict the diameter of polyethylene nanofibers, based on the adjustment of 15 parameters. The model was trained from 105 records from data obtained from the literature and was then validated with nine nanofibers that were obtained and measured in the laboratory. The average error between the actual results was 2.29%. This result differs from those taken in an evaluation of the dataset. Therefore, the importance of increasing the dataset and the validation using independent data is highlighted. |
format | Online Article Text |
id | pubmed-10386166 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103861662023-07-30 A Neural Network Approach to Reducing the Costs of Parameter-Setting in the Production of Polyethylene Oxide Nanofibers Solis-Rios, Daniel Villarreal-Gómez, Luis Jesús Goyes, Clara Eugenia Fonthal Rico, Faruk Cornejo-Bravo, José Manuel Fong-Mata, María Berenice Calderón Arenas, Jorge Mario Martínez Rincón, Harold Alberto Mejía-Medina, David Abdel Micromachines (Basel) Article Nanofibers, which are formed by the electrospinning process, are used in a variety of applications. For this purpose, a specific diameter suited for each application is required, which is achieved by varying a set of parameters. This parameter adjustment process is empirical and works by trial and error, causing high input costs and wasting time and financial resources. In this work, an artificial neural network model is presented to predict the diameter of polyethylene nanofibers, based on the adjustment of 15 parameters. The model was trained from 105 records from data obtained from the literature and was then validated with nine nanofibers that were obtained and measured in the laboratory. The average error between the actual results was 2.29%. This result differs from those taken in an evaluation of the dataset. Therefore, the importance of increasing the dataset and the validation using independent data is highlighted. MDPI 2023-07-12 /pmc/articles/PMC10386166/ /pubmed/37512721 http://dx.doi.org/10.3390/mi14071410 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Solis-Rios, Daniel Villarreal-Gómez, Luis Jesús Goyes, Clara Eugenia Fonthal Rico, Faruk Cornejo-Bravo, José Manuel Fong-Mata, María Berenice Calderón Arenas, Jorge Mario Martínez Rincón, Harold Alberto Mejía-Medina, David Abdel A Neural Network Approach to Reducing the Costs of Parameter-Setting in the Production of Polyethylene Oxide Nanofibers |
title | A Neural Network Approach to Reducing the Costs of Parameter-Setting in the Production of Polyethylene Oxide Nanofibers |
title_full | A Neural Network Approach to Reducing the Costs of Parameter-Setting in the Production of Polyethylene Oxide Nanofibers |
title_fullStr | A Neural Network Approach to Reducing the Costs of Parameter-Setting in the Production of Polyethylene Oxide Nanofibers |
title_full_unstemmed | A Neural Network Approach to Reducing the Costs of Parameter-Setting in the Production of Polyethylene Oxide Nanofibers |
title_short | A Neural Network Approach to Reducing the Costs of Parameter-Setting in the Production of Polyethylene Oxide Nanofibers |
title_sort | neural network approach to reducing the costs of parameter-setting in the production of polyethylene oxide nanofibers |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10386166/ https://www.ncbi.nlm.nih.gov/pubmed/37512721 http://dx.doi.org/10.3390/mi14071410 |
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