Gaussian Process Regression and Machine Learning Methods for Carbon-Based Material Adsorption
Antibiotics have received a lot of attention as promising contaminants because of their ecotoxicological and long-term chemical stability in the atmosphere. Antibiotic adsorption on carbon-based materials (CBMs) such as charcoal and activated carbon has been identified as mainly effective for treati...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2022-01-01
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| Series: | Adsorption Science & Technology |
| Online Access: | http://dx.doi.org/10.1155/2022/3901608 |
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| author | Manar Ahmed Hamza Maha M. Althobaiti Fahd N. Al-Wesabi Rana Alabdan Hany Mahgoub Anwer Mustafa Hilal Abdelwahed Motwakel Mesfer Al Duhayyim |
| author_facet | Manar Ahmed Hamza Maha M. Althobaiti Fahd N. Al-Wesabi Rana Alabdan Hany Mahgoub Anwer Mustafa Hilal Abdelwahed Motwakel Mesfer Al Duhayyim |
| author_sort | Manar Ahmed Hamza |
| collection | DOAJ |
| description | Antibiotics have received a lot of attention as promising contaminants because of their ecotoxicological and long-term chemical stability in the atmosphere. Antibiotic adsorption on carbon-based materials (CBMs) such as charcoal and activated carbon has been identified as mainly effective for treating the wastewater strategies. Machine learning (ML) approaches were used to create generalized computation methods for tetracycline (TC) and sulfamethoxazole (SMX) adsorption in CBMs in this investigation. In the existing system, random forest and ANN methods were used for TC and SMX for predicting the quantities of antibiotics in the CBMs. For reducing the antibiotics from the industrial wastewater, the broadcast efforts of the experiments are a little complicated. In the proposed method, Gaussian process regression (GPR), active learning (AL), and ANN are used for predicting the antibiotic levels in the industrial wastewater. Below a variety of environmental parameters (e.g., warmth, solution pH) and adsorbent varieties, the created Ml algorithms outperformed classic isotherm models in conditions of generalisation. To evaluate TC and SMX adsorption on CBMs, we used comparative significance investigation and partial trust plots based on ML models. The proposed GPR reduces the antibiotics in wastewater; minimal experimental screening and the comparative significance and partial trust plot help in the treatment of wastewater. |
| format | Article |
| id | doaj-art-6d67c56a1936491db86897b02c0876f4 |
| institution | Kabale University |
| issn | 2048-4038 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Adsorption Science & Technology |
| spelling | doaj-art-6d67c56a1936491db86897b02c0876f42025-02-03T10:12:50ZengSAGE PublishingAdsorption Science & Technology2048-40382022-01-01202210.1155/2022/3901608Gaussian Process Regression and Machine Learning Methods for Carbon-Based Material AdsorptionManar Ahmed Hamza0Maha M. Althobaiti1Fahd N. Al-Wesabi2Rana Alabdan3Hany Mahgoub4Anwer Mustafa Hilal5Abdelwahed Motwakel6Mesfer Al Duhayyim7Department of Computer and Self DevelopmentDepartment of Computer ScienceDepartment of Computer ScienceDepartment of Information SystemsDepartment of Computer ScienceDepartment of Computer and Self DevelopmentDepartment of Computer and Self DevelopmentDepartment of Natural and Applied SciencesAntibiotics have received a lot of attention as promising contaminants because of their ecotoxicological and long-term chemical stability in the atmosphere. Antibiotic adsorption on carbon-based materials (CBMs) such as charcoal and activated carbon has been identified as mainly effective for treating the wastewater strategies. Machine learning (ML) approaches were used to create generalized computation methods for tetracycline (TC) and sulfamethoxazole (SMX) adsorption in CBMs in this investigation. In the existing system, random forest and ANN methods were used for TC and SMX for predicting the quantities of antibiotics in the CBMs. For reducing the antibiotics from the industrial wastewater, the broadcast efforts of the experiments are a little complicated. In the proposed method, Gaussian process regression (GPR), active learning (AL), and ANN are used for predicting the antibiotic levels in the industrial wastewater. Below a variety of environmental parameters (e.g., warmth, solution pH) and adsorbent varieties, the created Ml algorithms outperformed classic isotherm models in conditions of generalisation. To evaluate TC and SMX adsorption on CBMs, we used comparative significance investigation and partial trust plots based on ML models. The proposed GPR reduces the antibiotics in wastewater; minimal experimental screening and the comparative significance and partial trust plot help in the treatment of wastewater.http://dx.doi.org/10.1155/2022/3901608 |
| spellingShingle | Manar Ahmed Hamza Maha M. Althobaiti Fahd N. Al-Wesabi Rana Alabdan Hany Mahgoub Anwer Mustafa Hilal Abdelwahed Motwakel Mesfer Al Duhayyim Gaussian Process Regression and Machine Learning Methods for Carbon-Based Material Adsorption Adsorption Science & Technology |
| title | Gaussian Process Regression and Machine Learning Methods for Carbon-Based Material Adsorption |
| title_full | Gaussian Process Regression and Machine Learning Methods for Carbon-Based Material Adsorption |
| title_fullStr | Gaussian Process Regression and Machine Learning Methods for Carbon-Based Material Adsorption |
| title_full_unstemmed | Gaussian Process Regression and Machine Learning Methods for Carbon-Based Material Adsorption |
| title_short | Gaussian Process Regression and Machine Learning Methods for Carbon-Based Material Adsorption |
| title_sort | gaussian process regression and machine learning methods for carbon based material adsorption |
| url | http://dx.doi.org/10.1155/2022/3901608 |
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