Application of random forest regression in modeling the adsorption of methylene blue onto clays.
Saved in:
| Title: | Application of random forest regression in modeling the adsorption of methylene blue onto clays. |
|---|---|
| Authors: | Aguiar, Leandro G.1 (AUTHOR) leandroaguiar@usp.br, Kasemodel, Mariana C.1 (AUTHOR) |
| Source: | Neural Computing & Applications. Jun2026, Vol. 38 Issue 12, p1-16. 16p. |
| Abstract: | The prediction of adsorption equilibrium in heterogeneous systems remains challenging due to the variability of experimental conditions and the lack of generalizable models across independent studies. In this work, a machine learning approach based on random forest regression was developed to predict the equilibrium adsorption (qe) of methylene blue (MB) onto clays, considering both material properties and operational conditions. The model was built using a compiled dataset from 38 independent studies, comprising 1,098 adsorption experiments. Due to incomplete reporting of key variables, multiple models were constructed using different subsets of features. Under conventional cross-validation, the models achieved high predictive performance (R² up to 0.99), indicating a strong ability to reproduce observed data within the available dataset. However, a more rigorous evaluation based on group-based cross-validation, which accounts for correlations among data from the same study, resulted in a significant reduction in performance (R² ≈ 0.66; MAE ≈ 48; RMSE ≈ 69), providing a more realistic assessment of model generalization. Among the evaluated models, the formulation using a reduced set of variables and the largest dataset (Model M5, 726 experiments from 23 studies) showed the most consistent performance under this framework. The model successfully captured key adsorption trends, including the influence of pH, initial dye concentration, and clay activation. The results highlight that, while conventional validation may overestimate predictive performance, more stringent validation strategies are essential for assessing model robustness across heterogeneous datasets. This study provides a step toward the development of more generalizable predictive tools for adsorption systems. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 194652011 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Application of random forest regression in modeling the adsorption of methylene blue onto clays. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Aguiar%2C+Leandro+G%2E%22">Aguiar, Leandro G.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> leandroaguiar@usp.br</i><br /><searchLink fieldCode="AR" term="%22Kasemodel%2C+Mariana+C%2E%22">Kasemodel, Mariana C.</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. Jun2026, Vol. 38 Issue 12, p1-16. 16p. – Name: Abstract Label: Abstract Group: Ab Data: The prediction of adsorption equilibrium in heterogeneous systems remains challenging due to the variability of experimental conditions and the lack of generalizable models across independent studies. In this work, a machine learning approach based on random forest regression was developed to predict the equilibrium adsorption (qe) of methylene blue (MB) onto clays, considering both material properties and operational conditions. The model was built using a compiled dataset from 38 independent studies, comprising 1,098 adsorption experiments. Due to incomplete reporting of key variables, multiple models were constructed using different subsets of features. Under conventional cross-validation, the models achieved high predictive performance (R² up to 0.99), indicating a strong ability to reproduce observed data within the available dataset. However, a more rigorous evaluation based on group-based cross-validation, which accounts for correlations among data from the same study, resulted in a significant reduction in performance (R² ≈ 0.66; MAE ≈ 48; RMSE ≈ 69), providing a more realistic assessment of model generalization. Among the evaluated models, the formulation using a reduced set of variables and the largest dataset (Model M5, 726 experiments from 23 studies) showed the most consistent performance under this framework. The model successfully captured key adsorption trends, including the influence of pH, initial dye concentration, and clay activation. The results highlight that, while conventional validation may overestimate predictive performance, more stringent validation strategies are essential for assessing model robustness across heterogeneous datasets. This study provides a step toward the development of more generalizable predictive tools for adsorption systems. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194652011 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00521-026-12200-1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 1 Titles: – TitleFull: Application of random forest regression in modeling the adsorption of methylene blue onto clays. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Aguiar, Leandro G. – PersonEntity: Name: NameFull: Kasemodel, Mariana C. IsPartOfRelationships: – BibEntity: Dates: – D: 21 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 09410643 Numbering: – Type: volume Value: 38 – Type: issue Value: 12 Titles: – TitleFull: Neural Computing & Applications Type: main |
| ResultId | 1 |
