Enhancing selenium removal using pre-treated natural clay: experimental investigation and predictive modeling
A heat treatment methodology was adopted as a pretreatment strategy, altering the porous structure of the clay to minimize leaching for selenium adsorption in an aqueous system. Rigorous experiments were carried out in batch mode to determine optimal parameters across various variables, including co...
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| Format: | Article |
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IOP Publishing
2024-01-01
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| Series: | Environmental Research Communications |
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| Online Access: | https://doi.org/10.1088/2515-7620/ad8a23 |
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| author | Gude Ramesh Biswajit Ruj Bhaskar Bishayee Rishya Prava Chatterjee Ramesh Kumar Moonis Ali Khan Byong-Hun Jeon Jayato Nayak Sankha Chakrabortty |
| author_facet | Gude Ramesh Biswajit Ruj Bhaskar Bishayee Rishya Prava Chatterjee Ramesh Kumar Moonis Ali Khan Byong-Hun Jeon Jayato Nayak Sankha Chakrabortty |
| author_sort | Gude Ramesh |
| collection | DOAJ |
| description | A heat treatment methodology was adopted as a pretreatment strategy, altering the porous structure of the clay to minimize leaching for selenium adsorption in an aqueous system. Rigorous experiments were carried out in batch mode to determine optimal parameters across various variables, including contact time, adsorbent dosages, selenium concentrations, pH, temperature, and stirring speed during selenium removal using natural clay. Investigating several kinetic and isotherm models revealed the best fitting for the pseudo-second-order and the Langmuir isotherm. Endothermic and spontaneous characteristics of the adsorption process were shown during thermodynamic analysis. In this study, a predictive model for the efficiency of selenium separation was established using Response Surface Methodology (RSM). Additionally, an Artificial Neural Network (ANN), a data-driven model, was employed for comparative analysis. The predictive model exhibited a high degree of agreement with experimental data, demonstrated by a low relative error of <0.10, a high regression coefficient of >0.97), and a substantial Willmott-d index of >0.95. Moreover, the efficacy of pre-activated clay in selenium removal was assessed, revealing the superior performance of ANN models over RSM models in forecasting the efficiency of the adsorption process. This research significantly advances an effective and sustainable material for selenium removal, providing valuable insights into predictive modeling techniques applicable to similar contexts to boost scale-up confidence during industrial implementation in affected regions. |
| format | Article |
| id | doaj-art-fcb324a507794e2a8a3d7ce080926181 |
| institution | Kabale University |
| issn | 2515-7620 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Communications |
| spelling | doaj-art-fcb324a507794e2a8a3d7ce0809261812024-11-21T15:49:02ZengIOP PublishingEnvironmental Research Communications2515-76202024-01-0161111501010.1088/2515-7620/ad8a23Enhancing selenium removal using pre-treated natural clay: experimental investigation and predictive modelingGude Ramesh0https://orcid.org/0000-0002-2388-1109Biswajit Ruj1Bhaskar Bishayee2Rishya Prava Chatterjee3Ramesh Kumar4https://orcid.org/0000-0002-7150-4404Moonis Ali Khan5Byong-Hun Jeon6https://orcid.org/0000-0002-5478-765XJayato Nayak7https://orcid.org/0000-0001-7678-2019Sankha Chakrabortty8https://orcid.org/0000-0001-7719-8586National Institute of Advanced Manufacturing Technology NIAMT , Hatia, Ranchi, Jharkhand - 834003, IndiaMaterial Structure and Evaluation Group, CSIR-Central Mechanical Engineering Research Institute , Durgapur-713209, West Bengal, IndiaMaterial Structure and Evaluation Group, CSIR-Central Mechanical Engineering Research Institute , Durgapur-713209, West Bengal, IndiaMaterial Structure and Evaluation Group, CSIR-Central Mechanical Engineering Research Institute , Durgapur-713209, West Bengal, IndiaDepartment of Earth Resources & Environmental Engineering, Hanyang University , 222-Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of KoreaDepartment of Chemistry, College of Science, King Saud University , Riyadh 11451, Saudi ArabiaDepartment of Earth Resources & Environmental Engineering, Hanyang University , 222-Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of KoreaCentre for Life Science, Mahindra University , Hyderabad, Telangana 500043, IndiaSchool of Chemical Technology, Kalinga Institute of Industrial Technology , Bhubaneswar-751024, Odisha, IndiaA heat treatment methodology was adopted as a pretreatment strategy, altering the porous structure of the clay to minimize leaching for selenium adsorption in an aqueous system. Rigorous experiments were carried out in batch mode to determine optimal parameters across various variables, including contact time, adsorbent dosages, selenium concentrations, pH, temperature, and stirring speed during selenium removal using natural clay. Investigating several kinetic and isotherm models revealed the best fitting for the pseudo-second-order and the Langmuir isotherm. Endothermic and spontaneous characteristics of the adsorption process were shown during thermodynamic analysis. In this study, a predictive model for the efficiency of selenium separation was established using Response Surface Methodology (RSM). Additionally, an Artificial Neural Network (ANN), a data-driven model, was employed for comparative analysis. The predictive model exhibited a high degree of agreement with experimental data, demonstrated by a low relative error of <0.10, a high regression coefficient of >0.97), and a substantial Willmott-d index of >0.95. Moreover, the efficacy of pre-activated clay in selenium removal was assessed, revealing the superior performance of ANN models over RSM models in forecasting the efficiency of the adsorption process. This research significantly advances an effective and sustainable material for selenium removal, providing valuable insights into predictive modeling techniques applicable to similar contexts to boost scale-up confidence during industrial implementation in affected regions.https://doi.org/10.1088/2515-7620/ad8a23Clay materialoptimizationnatural available waste materialpre-treatmentselenium adsorption |
| spellingShingle | Gude Ramesh Biswajit Ruj Bhaskar Bishayee Rishya Prava Chatterjee Ramesh Kumar Moonis Ali Khan Byong-Hun Jeon Jayato Nayak Sankha Chakrabortty Enhancing selenium removal using pre-treated natural clay: experimental investigation and predictive modeling Environmental Research Communications Clay material optimization natural available waste material pre-treatment selenium adsorption |
| title | Enhancing selenium removal using pre-treated natural clay: experimental investigation and predictive modeling |
| title_full | Enhancing selenium removal using pre-treated natural clay: experimental investigation and predictive modeling |
| title_fullStr | Enhancing selenium removal using pre-treated natural clay: experimental investigation and predictive modeling |
| title_full_unstemmed | Enhancing selenium removal using pre-treated natural clay: experimental investigation and predictive modeling |
| title_short | Enhancing selenium removal using pre-treated natural clay: experimental investigation and predictive modeling |
| title_sort | enhancing selenium removal using pre treated natural clay experimental investigation and predictive modeling |
| topic | Clay material optimization natural available waste material pre-treatment selenium adsorption |
| url | https://doi.org/10.1088/2515-7620/ad8a23 |
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