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Pyrolysis Study of Mixed Polymers for Non-Isothermal TGA: Artificial Neural Networks Application
Pure polymers of polystyrene (PS), low-density polyethylene (LDPE) and polypropylene (PP), are the main representative of plastic wastes. Thermal cracking of mixed polymers, consisting of PS, LDPE, and PP, was implemented by thermal analysis technique “thermogravimetric analyzer (TGA)” with heating...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269264/ https://www.ncbi.nlm.nih.gov/pubmed/35808683 http://dx.doi.org/10.3390/polym14132638 |
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author | Dubdub, Ibrahim |
author_facet | Dubdub, Ibrahim |
author_sort | Dubdub, Ibrahim |
collection | PubMed |
description | Pure polymers of polystyrene (PS), low-density polyethylene (LDPE) and polypropylene (PP), are the main representative of plastic wastes. Thermal cracking of mixed polymers, consisting of PS, LDPE, and PP, was implemented by thermal analysis technique “thermogravimetric analyzer (TGA)” with heating rate range (5–40 K/min), with two groups of sets: (ratio 1:1) mixture of PS and PP, and (ratio 1:1:1) mixture of PS, LDPE, and PP. TGA data were utilized to implement one of the machine learning methods, “artificial neural network (ANN)”. A feed-forward ANN with Levenberg-Marquardt (LM) as learning algorithm in the backpropagation model was performed in both sets in order to predict the weight fraction of the mixed polymers. Temperature and the heating rate are the two input variables applied in the current ANN model. For both sets, 10-10 neurons in logsig-tansig transfer functions two hidden layers was concluded as the best architecture, with almost (R > 0.99999). Results approved a good coincidence between the actual with the predicted values. The model foresees very efficiently when it is simulated with new data. |
format | Online Article Text |
id | pubmed-9269264 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92692642022-07-09 Pyrolysis Study of Mixed Polymers for Non-Isothermal TGA: Artificial Neural Networks Application Dubdub, Ibrahim Polymers (Basel) Article Pure polymers of polystyrene (PS), low-density polyethylene (LDPE) and polypropylene (PP), are the main representative of plastic wastes. Thermal cracking of mixed polymers, consisting of PS, LDPE, and PP, was implemented by thermal analysis technique “thermogravimetric analyzer (TGA)” with heating rate range (5–40 K/min), with two groups of sets: (ratio 1:1) mixture of PS and PP, and (ratio 1:1:1) mixture of PS, LDPE, and PP. TGA data were utilized to implement one of the machine learning methods, “artificial neural network (ANN)”. A feed-forward ANN with Levenberg-Marquardt (LM) as learning algorithm in the backpropagation model was performed in both sets in order to predict the weight fraction of the mixed polymers. Temperature and the heating rate are the two input variables applied in the current ANN model. For both sets, 10-10 neurons in logsig-tansig transfer functions two hidden layers was concluded as the best architecture, with almost (R > 0.99999). Results approved a good coincidence between the actual with the predicted values. The model foresees very efficiently when it is simulated with new data. MDPI 2022-06-28 /pmc/articles/PMC9269264/ /pubmed/35808683 http://dx.doi.org/10.3390/polym14132638 Text en © 2022 by the author. 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 Dubdub, Ibrahim Pyrolysis Study of Mixed Polymers for Non-Isothermal TGA: Artificial Neural Networks Application |
title | Pyrolysis Study of Mixed Polymers for Non-Isothermal TGA: Artificial Neural Networks Application |
title_full | Pyrolysis Study of Mixed Polymers for Non-Isothermal TGA: Artificial Neural Networks Application |
title_fullStr | Pyrolysis Study of Mixed Polymers for Non-Isothermal TGA: Artificial Neural Networks Application |
title_full_unstemmed | Pyrolysis Study of Mixed Polymers for Non-Isothermal TGA: Artificial Neural Networks Application |
title_short | Pyrolysis Study of Mixed Polymers for Non-Isothermal TGA: Artificial Neural Networks Application |
title_sort | pyrolysis study of mixed polymers for non-isothermal tga: artificial neural networks application |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269264/ https://www.ncbi.nlm.nih.gov/pubmed/35808683 http://dx.doi.org/10.3390/polym14132638 |
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