Generalized Additive Models to Optimize the Hydrophobicity Process of Kaolinite
The wide industrial use of kaolinite requires that the extraction processes be modeled to determine the appropriate conditions of the benefit. Although classic linear regression models have been used, these have not been appropriate due to the non-compliance with normal distribution for the response...
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| Format: | Online |
| Language: | spa |
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Universidad Pedagógica y Tecnológica de Colombia
2023
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| Online Access: | https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/14424 |
| _version_ | 1801706355270090752 |
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| author | Usuga Manco, Liliana María Hernández Barajas, Freddy Usuga Manco, Olga |
| author_facet | Usuga Manco, Liliana María Hernández Barajas, Freddy Usuga Manco, Olga |
| author_sort | Usuga Manco, Liliana María |
| collection | OJS |
| description | The wide industrial use of kaolinite requires that the extraction processes be modeled to determine the appropriate conditions of the benefit. Although classic linear regression models have been used, these have not been appropriate due to the non-compliance with normal distribution for the response variable. The data analyzed in this study correspond to a kaolinite extraction process by surface physicochemistry carried out in La Unión, Antioquia, Colombia. The response variable was the zeta potential and the explanatory variables were type of collecting solution, concentration, and pH. In this article, the recovery of kaolinite is modeled through generalized additive models, which can choose the statistical distribution and model all the parameters based on explanatory variables. Five distributions were selected for the response variable according to the Akaike information criterion ($AIC$). The model with generalized distribution Beta 2 was the model that presented the best performance according to the metrics used and it was found that the best-operating conditions obtained are the type of oleic acid collector, the concentration of 10 units, and pH 6 |
| format | Online |
| id | oai:oai.revistas.uptc.edu.co:article-14424 |
| institution | Revista Ciencia en Desarrollo |
| language | spa |
| publishDate | 2023 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | ojs |
| spelling | oai:oai.revistas.uptc.edu.co:article-144242024-05-10T00:51:44Z Generalized Additive Models to Optimize the Hydrophobicity Process of Kaolinite Modelos Aditivos Generalizados para optimizar el proceso de hidrofobicidad de la caolinita Usuga Manco, Liliana María Hernández Barajas, Freddy Usuga Manco, Olga Caolinita hidrofobicidad modelos aditivos modelos de regresión potencial zeta additive models, hydrophobicity, kaolinite, regression models, zeta potential The wide industrial use of kaolinite requires that the extraction processes be modeled to determine the appropriate conditions of the benefit. Although classic linear regression models have been used, these have not been appropriate due to the non-compliance with normal distribution for the response variable. The data analyzed in this study correspond to a kaolinite extraction process by surface physicochemistry carried out in La Unión, Antioquia, Colombia. The response variable was the zeta potential and the explanatory variables were type of collecting solution, concentration, and pH. In this article, the recovery of kaolinite is modeled through generalized additive models, which can choose the statistical distribution and model all the parameters based on explanatory variables. Five distributions were selected for the response variable according to the Akaike information criterion ($AIC$). The model with generalized distribution Beta 2 was the model that presented the best performance according to the metrics used and it was found that the best-operating conditions obtained are the type of oleic acid collector, the concentration of 10 units, and pH 6 El amplio uso industrial de la caolinita requiere que los procesos de extracción sean modelados de para determinar las condiciones apropiadas del beneficio. Aunque se han utilizado modelos de regresión lineal clásicos, estos no han sido apropiados debido al incumplimiento de distribución normal para la variable respuesta. Los datos analizados en este estudio corresponden a un proceso de extracción de caolinita mediante fisicoquímica de superficies realizado en La Unión, Antioquia, Colombia. La variable de respuesta fue el potencial zeta y las variables explicativas fueron tipo de solución colectora, concentración y pH. En este artículo se modela la recuperación de caolinita a través de los modelos aditivos generalizados, los cuales permiten elegir la distribución estadística y modelar todos los parámetros en función de variables explicativas. Se seleccionaron cinco distribuciones para la variable respuesta de acuerdo al criterio de información de Akaike ($AIC$). El modelo con distribución generalizada Beta 2 fue el modelo que presentó el mejor desempeño de acuerdo a las métricas utilizadas. A partir de este modelo se encontró que las mejores condiciones de operación obtenidas del análisis de las superficies de respuesta son tipo de colector ácido oleico, concentración 10 unidades y pH de 6 Universidad Pedagógica y Tecnológica de Colombia 2023-07-19 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/14424 10.19053/01217488.v14.n2.2023.14424 Ciencia En Desarrollo; Vol. 14 No. 2 (2023): Vol 14, Núm.2 (2023): Julio-Diciembre; 103-112 Ciencia en Desarrollo; Vol. 14 Núm. 2 (2023): Vol 14, Núm.2 (2023): Julio-Diciembre; 103-112 2462-7658 0121-7488 spa https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/14424/13678 |
| spellingShingle | Caolinita hidrofobicidad modelos aditivos modelos de regresión potencial zeta additive models, hydrophobicity, kaolinite, regression models, zeta potential Usuga Manco, Liliana María Hernández Barajas, Freddy Usuga Manco, Olga Generalized Additive Models to Optimize the Hydrophobicity Process of Kaolinite |
| title | Generalized Additive Models to Optimize the Hydrophobicity Process of Kaolinite |
| title_alt | Modelos Aditivos Generalizados para optimizar el proceso de hidrofobicidad de la caolinita |
| title_full | Generalized Additive Models to Optimize the Hydrophobicity Process of Kaolinite |
| title_fullStr | Generalized Additive Models to Optimize the Hydrophobicity Process of Kaolinite |
| title_full_unstemmed | Generalized Additive Models to Optimize the Hydrophobicity Process of Kaolinite |
| title_short | Generalized Additive Models to Optimize the Hydrophobicity Process of Kaolinite |
| title_sort | generalized additive models to optimize the hydrophobicity process of kaolinite |
| topic | Caolinita hidrofobicidad modelos aditivos modelos de regresión potencial zeta additive models, hydrophobicity, kaolinite, regression models, zeta potential |
| topic_facet | Caolinita hidrofobicidad modelos aditivos modelos de regresión potencial zeta additive models, hydrophobicity, kaolinite, regression models, zeta potential |
| url | https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/14424 |
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