Hybrid modeling of adsorption process using mass transfer and machine learning techniques for concentration prediction
Abstract This study presents a comprehensive hybrid modeling framework that integrates computational fluid dynamics (CFD) with machine learning (ML) techniques to predict chemical concentration distributions during the adsorption of organic compounds onto porous materials. The primary goal is to imp...
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| Format: | Article |
| Language: | English |
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Springer
2025-07-01
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| Series: | Journal of Saudi Chemical Society |
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| Online Access: | https://doi.org/10.1007/s44442-025-00016-y |
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| author | Jing Lv Lei Wang |
| author_facet | Jing Lv Lei Wang |
| author_sort | Jing Lv |
| collection | DOAJ |
| description | Abstract This study presents a comprehensive hybrid modeling framework that integrates computational fluid dynamics (CFD) with machine learning (ML) techniques to predict chemical concentration distributions during the adsorption of organic compounds onto porous materials. The primary goal is to improve the understanding and prediction of mass transfer behavior in adsorption processes through a data-driven approach. Data points, including x and y coordinates and corresponding solute concentrations (C), were generated through CFD simulations by solving mass transfer equations under varying conditions. Three supervised regression models of Kernel Ridge Regression (KRR), Decision Tree Regression (DT), and Radial Basis Function Support Vector Machine (RBF-SVM) were developed to map spatial coordinates to solute concentrations. Prior to model training, the dataset underwent rigorous preprocessing including outlier removal using the z-score method and normalization. To improve model performance, hyperparameters were optimized using the bio-inspired Barnacles Mating Optimizer (BMO) algorithm. Model evaluation based on R2, root mean square error (RMSE), and mean absolute error (MAE) demonstrated that RBF-SVM outperformed the other models, achieving an R2 of 0.9537, RMSE of 3.5136, and MAE of 1.5326. DT and KRR also turned out strong performance. These findings confirm the effectiveness of ML, particularly RBF-SVM, in analyzing complex spatial dependencies in solute transport processes. |
| format | Article |
| id | doaj-art-5fbf89dfa0df4f9e81e7904eae2c1cf3 |
| institution | Kabale University |
| issn | 1319-6103 2212-4640 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of Saudi Chemical Society |
| spelling | doaj-art-5fbf89dfa0df4f9e81e7904eae2c1cf32025-08-20T03:42:37ZengSpringerJournal of Saudi Chemical Society1319-61032212-46402025-07-0129411210.1007/s44442-025-00016-yHybrid modeling of adsorption process using mass transfer and machine learning techniques for concentration predictionJing Lv0Lei Wang1Intelligence and Manufacture College, Qingdao Huanghai UniversityCeyear Technologies Company LimitedAbstract This study presents a comprehensive hybrid modeling framework that integrates computational fluid dynamics (CFD) with machine learning (ML) techniques to predict chemical concentration distributions during the adsorption of organic compounds onto porous materials. The primary goal is to improve the understanding and prediction of mass transfer behavior in adsorption processes through a data-driven approach. Data points, including x and y coordinates and corresponding solute concentrations (C), were generated through CFD simulations by solving mass transfer equations under varying conditions. Three supervised regression models of Kernel Ridge Regression (KRR), Decision Tree Regression (DT), and Radial Basis Function Support Vector Machine (RBF-SVM) were developed to map spatial coordinates to solute concentrations. Prior to model training, the dataset underwent rigorous preprocessing including outlier removal using the z-score method and normalization. To improve model performance, hyperparameters were optimized using the bio-inspired Barnacles Mating Optimizer (BMO) algorithm. Model evaluation based on R2, root mean square error (RMSE), and mean absolute error (MAE) demonstrated that RBF-SVM outperformed the other models, achieving an R2 of 0.9537, RMSE of 3.5136, and MAE of 1.5326. DT and KRR also turned out strong performance. These findings confirm the effectiveness of ML, particularly RBF-SVM, in analyzing complex spatial dependencies in solute transport processes.https://doi.org/10.1007/s44442-025-00016-yAdsorptionSeparationMachine learningMass transferModeling |
| spellingShingle | Jing Lv Lei Wang Hybrid modeling of adsorption process using mass transfer and machine learning techniques for concentration prediction Journal of Saudi Chemical Society Adsorption Separation Machine learning Mass transfer Modeling |
| title | Hybrid modeling of adsorption process using mass transfer and machine learning techniques for concentration prediction |
| title_full | Hybrid modeling of adsorption process using mass transfer and machine learning techniques for concentration prediction |
| title_fullStr | Hybrid modeling of adsorption process using mass transfer and machine learning techniques for concentration prediction |
| title_full_unstemmed | Hybrid modeling of adsorption process using mass transfer and machine learning techniques for concentration prediction |
| title_short | Hybrid modeling of adsorption process using mass transfer and machine learning techniques for concentration prediction |
| title_sort | hybrid modeling of adsorption process using mass transfer and machine learning techniques for concentration prediction |
| topic | Adsorption Separation Machine learning Mass transfer Modeling |
| url | https://doi.org/10.1007/s44442-025-00016-y |
| work_keys_str_mv | AT jinglv hybridmodelingofadsorptionprocessusingmasstransferandmachinelearningtechniquesforconcentrationprediction AT leiwang hybridmodelingofadsorptionprocessusingmasstransferandmachinelearningtechniquesforconcentrationprediction |