Prediction and optimization of Rhodamine B removal from water using metal-organic frameworks: RSM-CCD, ANN, non-linear kinetics, and isotherm studies
Abstract This study involved the chemical synthesis of Metal-organic Frameworks (MOFs). The synthesized MOFs were characterized using Scanning Electron Microscopy (SEM), Fourier Transform Infrared (FTIR), and Powder X-ray diffraction (PXRD). Artificial intelligence models such as Response Surface Me...
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2025-07-01
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| Online Access: | https://doi.org/10.1186/s13065-025-01590-3 |
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| author | Simon Bbumba John Ssekatawa Ibrahim Karume Emmanuel Tebandeke Moses Kigozi Solomon Yiga Robert Setekera Joseph Ssebuliba Steven Sekitto Ruth Mbabazi Ivan Kiganda Maximillian Kato Patrick Taremwa Moses Murungi Chinaecherem Tochukwu Arum Collins Yiiki Letibo Geofrey Kaddu Margret Namugwanya John Kusasira Peace Mwesigwa Muhammad Ntale |
| author_facet | Simon Bbumba John Ssekatawa Ibrahim Karume Emmanuel Tebandeke Moses Kigozi Solomon Yiga Robert Setekera Joseph Ssebuliba Steven Sekitto Ruth Mbabazi Ivan Kiganda Maximillian Kato Patrick Taremwa Moses Murungi Chinaecherem Tochukwu Arum Collins Yiiki Letibo Geofrey Kaddu Margret Namugwanya John Kusasira Peace Mwesigwa Muhammad Ntale |
| author_sort | Simon Bbumba |
| collection | DOAJ |
| description | Abstract This study involved the chemical synthesis of Metal-organic Frameworks (MOFs). The synthesized MOFs were characterized using Scanning Electron Microscopy (SEM), Fourier Transform Infrared (FTIR), and Powder X-ray diffraction (PXRD). Artificial intelligence models such as Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) were used to predict and optimize the adsorptive removal of Rhodamine B (RhB) from water. The adsorption process was optimized using RSM with a Central Composite Design (CCD), which predicted a maximum removal efficiency of 95.91% under the following conditions: initial dye concentration (10 mg/L), adsorbent dosage (15 mg), pH (6), and temperature (25 °C). ANN was also optimized using similar conditions and the resulting predictive removal efficiency of 97.18% was obtained. Non-linear isotherm studies strongly correlated with the Freundlich (R² = 0.9987) and Sips (R² = 0.9928) models, indicating multilayer and monolayer adsorption. Non-linear Pseudo-first-order, Pseudo-second-order, and Elovich model correlation coefficients of 0.9644, 0.9998, and 0.952 suggested that the mechanisms were by chemisorption and physisorption on energetically stable heterogeneous surfaces. The findings of this study show a dual approach based on metal-organic framework and machine learning models as efficient alternatives to understanding the removal of RhB from water. |
| format | Article |
| id | doaj-art-c5888139579a4a148fe1e034048ec7a3 |
| institution | Kabale University |
| issn | 2661-801X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Chemistry |
| spelling | doaj-art-c5888139579a4a148fe1e034048ec7a32025-08-20T03:45:40ZengBMCBMC Chemistry2661-801X2025-07-0119112010.1186/s13065-025-01590-3Prediction and optimization of Rhodamine B removal from water using metal-organic frameworks: RSM-CCD, ANN, non-linear kinetics, and isotherm studiesSimon Bbumba0John Ssekatawa1Ibrahim Karume2Emmanuel Tebandeke3Moses Kigozi4Solomon Yiga5Robert Setekera6Joseph Ssebuliba7Steven Sekitto8Ruth Mbabazi9Ivan Kiganda10Maximillian Kato11Patrick Taremwa12Moses Murungi13Chinaecherem Tochukwu Arum14Collins Yiiki Letibo15Geofrey Kaddu16Margret Namugwanya17John Kusasira18Peace Mwesigwa19Muhammad Ntale20Department of Chemistry, College of Natural Sciences, Makerere UniversityDepartment of Chemistry, College of Natural Sciences, Makerere UniversityDepartment of Chemistry, College of Natural Sciences, Makerere UniversityDepartment of Chemistry, College of Natural Sciences, Makerere UniversityDepartment of Chemistry, Busitema UniversityDepartment of Chemistry, College of Natural Sciences, Makerere UniversityDepartment of Science, Faculty of Science and Computing, Ndejje UniversityDepartment of Mathematics, College of Natural Sciences, Makerere UniversityDepartment of Science, Faculty of Science and Computing, Ndejje UniversityDepartment of Chemistry, College of Natural Sciences, Makerere UniversityDepartment of Chemistry, College of Natural Sciences, Makerere UniversityDepartment of Science, Faculty of Science and Computing, Ndejje UniversityDepartment of Science, Faculty of Science and Computing, Ndejje UniversityDepartment of Chemistry, College of Natural Sciences, Makerere UniversityDepartment of Material Science and Explosives, Faculty of Science, Nigerian Defence AcademyDepartment of Chemistry, College of Natural Sciences, Makerere UniversityDepartment of Computer Science, Faculty of Science, Technology and Innovations, Mountains of the Moon UniversityFaculty of Education, Ndejje UniversityFaculty of Education, Ndejje UniversityFaculty of Education, Ndejje UniversityDepartment of Chemistry, College of Natural Sciences, Makerere UniversityAbstract This study involved the chemical synthesis of Metal-organic Frameworks (MOFs). The synthesized MOFs were characterized using Scanning Electron Microscopy (SEM), Fourier Transform Infrared (FTIR), and Powder X-ray diffraction (PXRD). Artificial intelligence models such as Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) were used to predict and optimize the adsorptive removal of Rhodamine B (RhB) from water. The adsorption process was optimized using RSM with a Central Composite Design (CCD), which predicted a maximum removal efficiency of 95.91% under the following conditions: initial dye concentration (10 mg/L), adsorbent dosage (15 mg), pH (6), and temperature (25 °C). ANN was also optimized using similar conditions and the resulting predictive removal efficiency of 97.18% was obtained. Non-linear isotherm studies strongly correlated with the Freundlich (R² = 0.9987) and Sips (R² = 0.9928) models, indicating multilayer and monolayer adsorption. Non-linear Pseudo-first-order, Pseudo-second-order, and Elovich model correlation coefficients of 0.9644, 0.9998, and 0.952 suggested that the mechanisms were by chemisorption and physisorption on energetically stable heterogeneous surfaces. The findings of this study show a dual approach based on metal-organic framework and machine learning models as efficient alternatives to understanding the removal of RhB from water.https://doi.org/10.1186/s13065-025-01590-3AdsorptionKineticsIsothermsMetal-organic frameworksArtificial intelligence |
| spellingShingle | Simon Bbumba John Ssekatawa Ibrahim Karume Emmanuel Tebandeke Moses Kigozi Solomon Yiga Robert Setekera Joseph Ssebuliba Steven Sekitto Ruth Mbabazi Ivan Kiganda Maximillian Kato Patrick Taremwa Moses Murungi Chinaecherem Tochukwu Arum Collins Yiiki Letibo Geofrey Kaddu Margret Namugwanya John Kusasira Peace Mwesigwa Muhammad Ntale Prediction and optimization of Rhodamine B removal from water using metal-organic frameworks: RSM-CCD, ANN, non-linear kinetics, and isotherm studies BMC Chemistry Adsorption Kinetics Isotherms Metal-organic frameworks Artificial intelligence |
| title | Prediction and optimization of Rhodamine B removal from water using metal-organic frameworks: RSM-CCD, ANN, non-linear kinetics, and isotherm studies |
| title_full | Prediction and optimization of Rhodamine B removal from water using metal-organic frameworks: RSM-CCD, ANN, non-linear kinetics, and isotherm studies |
| title_fullStr | Prediction and optimization of Rhodamine B removal from water using metal-organic frameworks: RSM-CCD, ANN, non-linear kinetics, and isotherm studies |
| title_full_unstemmed | Prediction and optimization of Rhodamine B removal from water using metal-organic frameworks: RSM-CCD, ANN, non-linear kinetics, and isotherm studies |
| title_short | Prediction and optimization of Rhodamine B removal from water using metal-organic frameworks: RSM-CCD, ANN, non-linear kinetics, and isotherm studies |
| title_sort | prediction and optimization of rhodamine b removal from water using metal organic frameworks rsm ccd ann non linear kinetics and isotherm studies |
| topic | Adsorption Kinetics Isotherms Metal-organic frameworks Artificial intelligence |
| url | https://doi.org/10.1186/s13065-025-01590-3 |
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