Predicting the Adsorption Efficiency Using Machine Learning Framework on a Carbon-Activated Nanomaterial
Due to the excessive use of paracetamol (PCM), a significant amount of its metabolite has been released into the surroundings, and its removal from the surroundings must happen quickly and sustainably. Multicomponent adsorption modelling is difficult because it is challenging to anticipate the relat...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2023-01-01
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| Series: | Adsorption Science & Technology |
| Online Access: | http://dx.doi.org/10.1155/2023/4048676 |
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| _version_ | 1841562083857006592 |
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| author | Kalapala Prasad V. Ravi Kumar R. Suresh Kumar A. S. Rajesh Anjani Kumar Rai Essam A. Al-Ammar Saikh Mohammad Wabaidur Amjad Iqbal Dawit Kefyalew |
| author_facet | Kalapala Prasad V. Ravi Kumar R. Suresh Kumar A. S. Rajesh Anjani Kumar Rai Essam A. Al-Ammar Saikh Mohammad Wabaidur Amjad Iqbal Dawit Kefyalew |
| author_sort | Kalapala Prasad |
| collection | DOAJ |
| description | Due to the excessive use of paracetamol (PCM), a significant amount of its metabolite has been released into the surroundings, and its removal from the surroundings must happen quickly and sustainably. Multicomponent adsorption modelling is difficult because it is challenging to anticipate the relationships among the adsorbates in this artificial intelligence-based modelling, a choice among different algorithms. Utilizing various algorithms, many studies assessed the single and binary adsorption of paracetamol on activated carbon. The present study implements that the effectiveness of PCM adsorption on a carbon-activated nanomaterial was predicted using an artificial neural network, a machine learning technology. As a factor of adsorbent particle size, adsorbent dosage, training time, and starting concentrations, the adsorption capacity for each medicinal ingredient was examined. SEM was used to analyze a nanomaterial that had been chemically altered with orthophosphoric acid (FTIR). To determine the residual proportion of PCM in solvent, batch adsorption of PCM was then carried out at various operation conditions, including contact time, temperatures, and initial dosage. The adsorption effectiveness of paracetamol on carbon-activated nanoparticle was calculated using experimental results. Thus, by using machine learning framework, the adsorption efficiency of paracetamol on a carbon-activated nanomaterial was predicted. |
| format | Article |
| id | doaj-art-5382137fb5f941f09ac8bcf9ac9f4084 |
| institution | Kabale University |
| issn | 2048-4038 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Adsorption Science & Technology |
| spelling | doaj-art-5382137fb5f941f09ac8bcf9ac9f40842025-01-03T01:10:03ZengSAGE PublishingAdsorption Science & Technology2048-40382023-01-01202310.1155/2023/4048676Predicting the Adsorption Efficiency Using Machine Learning Framework on a Carbon-Activated NanomaterialKalapala Prasad0V. Ravi Kumar1R. Suresh Kumar2A. S. Rajesh3Anjani Kumar Rai4Essam A. Al-Ammar5Saikh Mohammad Wabaidur6Amjad Iqbal7Dawit Kefyalew8Department of Mechanical EngineeringDepartment of Mechanical EngineeringDepartment of Mechanical EngineeringDepartment of Mechanical EngineeringDepartment of Computer Engineering and ApplicationsDepartment of Electrical EngineeringChemistry DepartmentDepartment of Advanced Materials & TechnologiesDepartment of Automotive EngineeringDue to the excessive use of paracetamol (PCM), a significant amount of its metabolite has been released into the surroundings, and its removal from the surroundings must happen quickly and sustainably. Multicomponent adsorption modelling is difficult because it is challenging to anticipate the relationships among the adsorbates in this artificial intelligence-based modelling, a choice among different algorithms. Utilizing various algorithms, many studies assessed the single and binary adsorption of paracetamol on activated carbon. The present study implements that the effectiveness of PCM adsorption on a carbon-activated nanomaterial was predicted using an artificial neural network, a machine learning technology. As a factor of adsorbent particle size, adsorbent dosage, training time, and starting concentrations, the adsorption capacity for each medicinal ingredient was examined. SEM was used to analyze a nanomaterial that had been chemically altered with orthophosphoric acid (FTIR). To determine the residual proportion of PCM in solvent, batch adsorption of PCM was then carried out at various operation conditions, including contact time, temperatures, and initial dosage. The adsorption effectiveness of paracetamol on carbon-activated nanoparticle was calculated using experimental results. Thus, by using machine learning framework, the adsorption efficiency of paracetamol on a carbon-activated nanomaterial was predicted.http://dx.doi.org/10.1155/2023/4048676 |
| spellingShingle | Kalapala Prasad V. Ravi Kumar R. Suresh Kumar A. S. Rajesh Anjani Kumar Rai Essam A. Al-Ammar Saikh Mohammad Wabaidur Amjad Iqbal Dawit Kefyalew Predicting the Adsorption Efficiency Using Machine Learning Framework on a Carbon-Activated Nanomaterial Adsorption Science & Technology |
| title | Predicting the Adsorption Efficiency Using Machine Learning Framework on a Carbon-Activated Nanomaterial |
| title_full | Predicting the Adsorption Efficiency Using Machine Learning Framework on a Carbon-Activated Nanomaterial |
| title_fullStr | Predicting the Adsorption Efficiency Using Machine Learning Framework on a Carbon-Activated Nanomaterial |
| title_full_unstemmed | Predicting the Adsorption Efficiency Using Machine Learning Framework on a Carbon-Activated Nanomaterial |
| title_short | Predicting the Adsorption Efficiency Using Machine Learning Framework on a Carbon-Activated Nanomaterial |
| title_sort | predicting the adsorption efficiency using machine learning framework on a carbon activated nanomaterial |
| url | http://dx.doi.org/10.1155/2023/4048676 |
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