Experimental Studies of Surface Tensions for Binary and Ternary Systems of Benzyl Alcohol, N-Hexanol and Water. Modeling with Neural Networks
The design of installations in the chemical industry requires knowledge of the thermodynamic properties of liquid mixtures. In the absence of experimental data, accurate predictive methods are needed. In this work, the refractive index and the surface tension are experimentally determined at differe...
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| Format: | Article |
| Language: | English |
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Iranian Association of Chemical Engineering (IAChE)
2024-02-01
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| Series: | Iranian Journal of Chemical Engineering |
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| Online Access: | https://www.ijche.com/article_194251_124f68f3b433537139db27001f6fb5fa.pdf |
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| author | Iuliana Bîrgăuanu Cătălin Lisa Alexandra Bargan Silvia Curteanu Gabriela Lisa |
| author_facet | Iuliana Bîrgăuanu Cătălin Lisa Alexandra Bargan Silvia Curteanu Gabriela Lisa |
| author_sort | Iuliana Bîrgăuanu |
| collection | DOAJ |
| description | The design of installations in the chemical industry requires knowledge of the thermodynamic properties of liquid mixtures. In the absence of experimental data, accurate predictive methods are needed. In this work, the refractive index and the surface tension are experimentally determined at different temperatures and atmospheric pressure, for the binary and ternary systems of benzyl alcohol, n-hexanol and water, less studied in the literature. Two models were developed for the correlation of excess surface tension with composition, normalized temperature and refractive index. The statistical processing of the experimental data with the multiple linear regression method allowed the development of a model for which, in the validation stage, the correlation coefficient was 0.9086 and the standard deviation was 4.36. With the best performing neural model, a correlation coefficient of 0.9727 and a standard deviation of 2.14 were obtained in the validation stage. |
| format | Article |
| id | doaj-art-5069c2ff2f9a449db50235ae8f30a84d |
| institution | OA Journals |
| issn | 1735-5397 2008-2355 |
| language | English |
| publishDate | 2024-02-01 |
| publisher | Iranian Association of Chemical Engineering (IAChE) |
| record_format | Article |
| series | Iranian Journal of Chemical Engineering |
| spelling | doaj-art-5069c2ff2f9a449db50235ae8f30a84d2025-08-20T02:18:57ZengIranian Association of Chemical Engineering (IAChE)Iranian Journal of Chemical Engineering1735-53972008-23552024-02-0121131610.22034/ijche.2024.446235.1524194251Experimental Studies of Surface Tensions for Binary and Ternary Systems of Benzyl Alcohol, N-Hexanol and Water. Modeling with Neural NetworksIuliana Bîrgăuanu0Cătălin Lisa1Alexandra Bargan2Silvia Curteanu3Gabriela Lisa4Gheorghe Asachi Technical University, Faculty of Chemical Engineering and Environmental Protection “Cristofor Simionescu”, 73 Prof.dr.doc. D. Mangeron Street, 700050 Iaşi, RomaniaGheorghe Asachi Technical University, Faculty of Chemical Engineering and Environmental Protection “Cristofor Simionescu”, 73 Prof.dr.doc. D. Mangeron Street, 700050 Iaşi, RomaniaPetru Poni Institute of Macromolecular Chemistry of Iaşi, 41A Aleea Gr. Ghica Voda, 700487 Iasi, Romania1Gheorghe Asachi Technical University, Faculty of Chemical Engineering and Environmental Protection “Cristofor Simionescu”, 73 Prof.dr.doc. D. Mangeron Street, 700050 Iaşi, RomaniaGheorghe Asachi Technical University of Iasi, Faculty of Chemical EngineeringThe design of installations in the chemical industry requires knowledge of the thermodynamic properties of liquid mixtures. In the absence of experimental data, accurate predictive methods are needed. In this work, the refractive index and the surface tension are experimentally determined at different temperatures and atmospheric pressure, for the binary and ternary systems of benzyl alcohol, n-hexanol and water, less studied in the literature. Two models were developed for the correlation of excess surface tension with composition, normalized temperature and refractive index. The statistical processing of the experimental data with the multiple linear regression method allowed the development of a model for which, in the validation stage, the correlation coefficient was 0.9086 and the standard deviation was 4.36. With the best performing neural model, a correlation coefficient of 0.9727 and a standard deviation of 2.14 were obtained in the validation stage.https://www.ijche.com/article_194251_124f68f3b433537139db27001f6fb5fa.pdfexcess thermodynamic propertiesbenzyl alcoholn-hexanolwatersurface tensionrefractive indexmultiple linear regressionmultilayer perceptron |
| spellingShingle | Iuliana Bîrgăuanu Cătălin Lisa Alexandra Bargan Silvia Curteanu Gabriela Lisa Experimental Studies of Surface Tensions for Binary and Ternary Systems of Benzyl Alcohol, N-Hexanol and Water. Modeling with Neural Networks Iranian Journal of Chemical Engineering excess thermodynamic properties benzyl alcohol n-hexanol water surface tension refractive index multiple linear regression multilayer perceptron |
| title | Experimental Studies of Surface Tensions for Binary and Ternary Systems of Benzyl Alcohol, N-Hexanol and Water. Modeling with Neural Networks |
| title_full | Experimental Studies of Surface Tensions for Binary and Ternary Systems of Benzyl Alcohol, N-Hexanol and Water. Modeling with Neural Networks |
| title_fullStr | Experimental Studies of Surface Tensions for Binary and Ternary Systems of Benzyl Alcohol, N-Hexanol and Water. Modeling with Neural Networks |
| title_full_unstemmed | Experimental Studies of Surface Tensions for Binary and Ternary Systems of Benzyl Alcohol, N-Hexanol and Water. Modeling with Neural Networks |
| title_short | Experimental Studies of Surface Tensions for Binary and Ternary Systems of Benzyl Alcohol, N-Hexanol and Water. Modeling with Neural Networks |
| title_sort | experimental studies of surface tensions for binary and ternary systems of benzyl alcohol n hexanol and water modeling with neural networks |
| topic | excess thermodynamic properties benzyl alcohol n-hexanol water surface tension refractive index multiple linear regression multilayer perceptron |
| url | https://www.ijche.com/article_194251_124f68f3b433537139db27001f6fb5fa.pdf |
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