Evaluation of the Thermogravimetric Profile of Hybrid Cellulose Acetate Membranes using Machine Learning Approaches
Thermogravimetric analysis (TGA) is a characterization technique routinely used in materials science. In this particular case, TGA determines the variation of weight with temperature. The thermogravimetric analysis of cellulose acetate (CA) hybrid membranes can provide similar results, despite thei...
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| Format: | Article |
| Language: | English |
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Universidade Federal do Rio Grande
2023-06-01
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| Series: | Vetor |
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| Online Access: | https://periodicos.furg.br/vetor/article/view/15167 |
| _version_ | 1797790417685577728 |
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| author | Filipi França dos Santos Kelly Cristina Da Silveira Daniela Herdy Carrielo Gesiane Mendonça Ferreira Guilherme de Melo Baptista Domingues Monica Calixto Andrade |
| author_facet | Filipi França dos Santos Kelly Cristina Da Silveira Daniela Herdy Carrielo Gesiane Mendonça Ferreira Guilherme de Melo Baptista Domingues Monica Calixto Andrade |
| author_sort | Filipi França dos Santos |
| collection | DOAJ |
| description |
Thermogravimetric analysis (TGA) is a characterization technique routinely used in materials science. In this particular case, TGA determines the variation of weight with temperature. The thermogravimetric analysis of cellulose acetate (CA) hybrid membranes can provide similar results, despite their different chemical composition. The present study uses machine learning algorithms to correlate data from thermogravimetric analyses with variations in chemical composition. Experimental points relating to temperature and weight from these analyses were treated in different ways and used to estimate the composition of the membranes. The Extra-Trees Classifier, Random Forest, Decision Tree, and K-Nearest Neighbors (KNN) algorithms were applied to this data and then evaluated using a confusion and accuracy matrix. The decision tree-based algorithms demonstrated a superior capacity for estimating the composition, albeit with negligible disparities in the thermogravimetric profile. The Extra-Trees Classifier algorithm, in particular, stood out for its ability to estimate composition in all tests, achieving 90% accuracy.
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| first_indexed | 2024-03-13T02:04:20Z |
| format | Article |
| id | doaj.art-7c9ef1d537ac448f9df3641bfda89ef3 |
| institution | Directory Open Access Journal |
| issn | 0102-7352 2358-3452 |
| language | English |
| last_indexed | 2024-03-13T02:04:20Z |
| publishDate | 2023-06-01 |
| publisher | Universidade Federal do Rio Grande |
| record_format | Article |
| series | Vetor |
| spelling | doaj.art-7c9ef1d537ac448f9df3641bfda89ef32023-07-01T15:08:39ZengUniversidade Federal do Rio GrandeVetor0102-73522358-34522023-06-0133110.14295/vetor.v33i1.15167Evaluation of the Thermogravimetric Profile of Hybrid Cellulose Acetate Membranes using Machine Learning ApproachesFilipi França dos Santos0Kelly Cristina Da Silveira1Daniela Herdy Carrielo2Gesiane Mendonça Ferreira3Guilherme de Melo Baptista Domingues4Monica Calixto Andrade5Universidade do Estado do Rio de Janeiro, Instituto Politécnico, Nova Friburgo, RJ, BrasilUniversidade do Estado do Rio de Janeiro, Instituto Politécnico, Nova Friburgo, RJ, BrasilUniversidade do Estado do Rio de Janeiro, Instituto Politécnico – Nova Friburgo, RJ, BrasilUniversidade do Estado do Rio de Janeiro, Instituto Politécnico, Nova Friburgo, RJ, BrasilUniversidade do Estado do Rio de Janeiro, Instituto Politécnico, Nova Friburgo, RJ, BrasilUniversidade do Estado do Rio de Janeiro, Instituto Politécnico, Nova Friburgo, RJ, Brasil Thermogravimetric analysis (TGA) is a characterization technique routinely used in materials science. In this particular case, TGA determines the variation of weight with temperature. The thermogravimetric analysis of cellulose acetate (CA) hybrid membranes can provide similar results, despite their different chemical composition. The present study uses machine learning algorithms to correlate data from thermogravimetric analyses with variations in chemical composition. Experimental points relating to temperature and weight from these analyses were treated in different ways and used to estimate the composition of the membranes. The Extra-Trees Classifier, Random Forest, Decision Tree, and K-Nearest Neighbors (KNN) algorithms were applied to this data and then evaluated using a confusion and accuracy matrix. The decision tree-based algorithms demonstrated a superior capacity for estimating the composition, albeit with negligible disparities in the thermogravimetric profile. The Extra-Trees Classifier algorithm, in particular, stood out for its ability to estimate composition in all tests, achieving 90% accuracy. https://periodicos.furg.br/vetor/article/view/15167cellulose acetate membranesmachine learningThermogravimetric analysis (TG) |
| spellingShingle | Filipi França dos Santos Kelly Cristina Da Silveira Daniela Herdy Carrielo Gesiane Mendonça Ferreira Guilherme de Melo Baptista Domingues Monica Calixto Andrade Evaluation of the Thermogravimetric Profile of Hybrid Cellulose Acetate Membranes using Machine Learning Approaches Vetor cellulose acetate membranes machine learning Thermogravimetric analysis (TG) |
| title | Evaluation of the Thermogravimetric Profile of Hybrid Cellulose Acetate Membranes using Machine Learning Approaches |
| title_full | Evaluation of the Thermogravimetric Profile of Hybrid Cellulose Acetate Membranes using Machine Learning Approaches |
| title_fullStr | Evaluation of the Thermogravimetric Profile of Hybrid Cellulose Acetate Membranes using Machine Learning Approaches |
| title_full_unstemmed | Evaluation of the Thermogravimetric Profile of Hybrid Cellulose Acetate Membranes using Machine Learning Approaches |
| title_short | Evaluation of the Thermogravimetric Profile of Hybrid Cellulose Acetate Membranes using Machine Learning Approaches |
| title_sort | evaluation of the thermogravimetric profile of hybrid cellulose acetate membranes using machine learning approaches |
| topic | cellulose acetate membranes machine learning Thermogravimetric analysis (TG) |
| url | https://periodicos.furg.br/vetor/article/view/15167 |
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