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Prediction of colligatives effects in the system Water + NaCl through Machine Learning
dc.rights.license | Atribución-NoComercial 4.0 Internacional |
dc.contributor.advisor | Carrero, Javier Ignacio |
dc.contributor.author | Londoño Arango, Jorge Eduardo |
dc.date.accessioned | 2023-10-02T16:13:26Z |
dc.date.available | 2023-10-02T16:13:26Z |
dc.date.issued | 2023 |
dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/84737 |
dc.description | graficas, tablas |
dc.description.abstract | The use of traditional models such asthemodifiedDebye-Hückelmodel, the Pitzer model, MSE (MixedSolvent Electrolyte), or e-NRTL (Non-Random Two Liquid - Electrolyte) for predicting colligative effects in the Water + NaCl system is challenging. While these models have shown good results in terms of predictions, their statistical and computational implementation has required significant effort. On the other hand, certain Machine Learning algorithms have been studied for phase equilibrium prediction in systems with dissolved electrolytes. In this study, the implementation of three Machine Learning algorithms (Neural Networks, Least Squares Support Vector Machines, and Regression Decision Trees) was evaluated for predicting the decrease in melting temperature and saturation pressure of the Water + NaCl system. The results were compared with the prediction provided by an empirical variant of the Debye-Hückel model. Zero mean, normality, and residual independence tests were conducted for all models to statistically evaluate the regression results. It was found that machine learning models have the potential to predict colligative effects in electrolyte solutions, particularly the Regression Decision Tree model, which met all the assumptions studied for both effects and proved to be a reliable prediction tool. Finally, it was demonstrated that computationally, the implementation of machine learning models was straightforward, and their implementation for new studies in property prediction is a promising research area. (Texto tomado de la fuente) |
dc.description.abstract | El uso de los modelos tradicionales como el modelo modificado de Debye-Hückel, el modelo de Pitzer, MSE (Mixed-Solvent Electrolyte) o e-NRTL (Non-Random Two Liquid - Electrolyte) para la predicción de los efectos coligativos del sistema Agua + NaC es díficil porque aunque han tenido buenos resultados en términos predicciones, su implementación de forma estadística y computacional ha requerido diferentes esfuerzos. Por otro lado, se ha estudiado la aplicación de algoritmos de Machine Learning para la predicción de equilibrios de fase en sistemas con electrolitos disueltos. En este trabajo se evaluó la implementación de 3 algoritmos de Machine Learning (Redes Neuronales, Máquinas de Soporte de Vectores de Mínimos Cuadrados y Árboles de Decisión de Regresión) para la predicción de la disminución en la temperatura de fusión y la presión de saturación del sistema Agua + NaCl. Los resultados se compararon con la predicción dada por una variante empírica del modelo de Debye-Hückel. Para todos los modelos se realizaron pruebas de media cero, normalidad e independencia de residuales con el objetivo de evaluar estadísticamente los resultados de regresión. Se comprobó que los modelos de aprendizaje de máquina tienen potencial para la predicción de los efectos coligativos de soluciones de electrolitos; especialmente se encontró que el modelo árbol de decisión de regresión cumplio con todos los supuestos estudiados para ambos efectos, y es una herramienta de precisión fiable. Finalmente, se mostró que computacionalmente los modelos de aprendizaje automático fueron sencillos de implementar y que su implementación para nuevos estudios en la predicción de propiedades es un área de estudios prometedora. |
dc.format.extent | xi, 52 páginas |
dc.format.mimetype | application/pdf |
dc.language.iso | eng |
dc.publisher | Universidad Nacional de Colombia |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ |
dc.subject.ddc | 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores |
dc.title | Prediction of colligatives effects in the system Water + NaCl through Machine Learning |
dc.type | Trabajo de grado - Maestría |
dc.type.driver | info:eu-repo/semantics/masterThesis |
dc.type.version | info:eu-repo/semantics/acceptedVersion |
dc.publisher.program | Manizales - Ingeniería y Arquitectura - Maestría en Ingeniería - Ingeniería Química |
dc.contributor.researchgroup | Grupo de Fisicoquímica Computacional |
dc.description.degreelevel | Maestría |
dc.description.degreename | Magíster en Ingeniería - Ingeniería Química |
dc.identifier.instname | Universidad Nacional de Colombia |
dc.identifier.reponame | Repositorio Institucional Universidad Nacional de Colombia |
dc.identifier.repourl | https://repositorio.unal.edu.co/ |
dc.publisher.faculty | Facultad de Ingeniería y Arquitectura |
dc.publisher.place | Manizales, Caldas |
dc.publisher.branch | Universidad Nacional de Colombia - Sede Manizales |
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dc.rights.accessrights | info:eu-repo/semantics/openAccess |
dc.subject.proposal | Prediction |
dc.subject.proposal | Colligative effects |
dc.subject.proposal | Cryoscopic effect |
dc.subject.proposal | Boiling point elevation |
dc.subject.proposal | Water + NaCl |
dc.subject.proposal | Empirical Model |
dc.subject.proposal | Debue-Hückel model |
dc.subject.proposal | Machine Learning |
dc.subject.proposal | Neural networks |
dc.subject.proposal | Least-Squares Support Vector Machine |
dc.subject.proposal | Regression Decision Trees |
dc.subject.proposal | Electrolyte solution |
dc.subject.proposal | Melting temperature |
dc.subject.proposal | Saturation pressure |
dc.subject.proposal | Predicción |
dc.subject.proposal | Efectos coligativos |
dc.subject.proposal | Efecto crioscópico |
dc.subject.proposal | Efecto ebulloscópico |
dc.subject.proposal | Agua + NaCl |
dc.subject.proposal | Modelos empíricos |
dc.subject.proposal | Modelo de Debye-Hückel |
dc.subject.proposal | Redes neuronales |
dc.subject.proposal | Máquinas de soporte de vectores de mínimos cuadrados |
dc.subject.proposal | Árboles de decisión de regresión |
dc.subject.proposal | Solución electrolítica |
dc.subject.proposal | Temperatura de fusión |
dc.subject.proposal | Presión de saturación |
dc.title.translated | Predicción de los efectos coligativos en el sistema Agua + NaCl mediante Machine Learning |
dc.type.coar | http://purl.org/coar/resource_type/c_bdcc |
dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa |
dc.type.content | Text |
oaire.accessrights | http://purl.org/coar/access_right/c_abf2 |
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