Tannery shaving dust-based charcoal blended adsorbent for efficient heavy metal remediation: An experimental and machine learning approach
Tannery shaving dust (TSD) is one of the tannery wastes, poses significant concerns due to its availability and carcinogenic properties. This study has focused on utilizing this waste as adsorbent for heavy metals (HMs) treatment in wastewater. By crosslinking polyvinyl alcohol (PVA) with activated...
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Elsevier
2025-12-01
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| Series: | Case Studies in Chemical and Environmental Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266601642500163X |
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| author | Md. Sabbir Hosen Md. Sahariar Sahen Hasan Ahmed Md. Selim Reza Pranta Bhowmik Farzana Mim Md. Badrul Islam Md. Azizul Haque Khan Naim Mohammad Majibur Rahman Md. Mostafizur Rahman |
| author_facet | Md. Sabbir Hosen Md. Sahariar Sahen Hasan Ahmed Md. Selim Reza Pranta Bhowmik Farzana Mim Md. Badrul Islam Md. Azizul Haque Khan Naim Mohammad Majibur Rahman Md. Mostafizur Rahman |
| author_sort | Md. Sabbir Hosen |
| collection | DOAJ |
| description | Tannery shaving dust (TSD) is one of the tannery wastes, poses significant concerns due to its availability and carcinogenic properties. This study has focused on utilizing this waste as adsorbent for heavy metals (HMs) treatment in wastewater. By crosslinking polyvinyl alcohol (PVA) with activated tannery shaving dust (AsD) from TSD and modified charcoal (MC) powder, a composite PVA-AsD-MC adsorbent (PAsMc) was fabricated to remove As, Cr, Zn and Pb from synthetic wastewater. Here, PVA-AsD (1:10) blended with 2:3 MC has sufficient active sites that were ensured by the FT-IR. As an adsorbent the PAsMc showed more thermal stability than AsD, and surface morphology was observed as highly rough. Moreover, the batch experiments have considered pH, adsorbent dose, and contact time factors, achieving impressive metals removal efficiencies: 98.86 % for As, 99.45 % for Cr, 99.72 % for Zn, and 98.30 % for Pb. The optimal conditions were identified as an adsorbent dosage of 4.0 g/L for 25 minutes, and an agitation speed of 300 rpm at pH 8.0–9.0. The adsorption isotherm and kinetics model provided an auspicious result for chemisorption adsorption on the surface. Notably, these datasets were then enhanced with the machine learning model, specifically the Random Forest (RF), aimed at predicting the removal of HMs. The R2 values for the training and testing dataset within the range of 0.9927–0.9984 and 0.9940–0.9975 with a RMSE value of 0.9622–1.4612 and 1.1125–1.9294, respectively. Ultimately, a predictive model for HMs removal was developed, which will assist in making rational applications of PAsMc in wastewater treatment. |
| format | Article |
| id | doaj-art-fb534353a29f41ada5e1a185bae751b3 |
| institution | Kabale University |
| issn | 2666-0164 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Chemical and Environmental Engineering |
| spelling | doaj-art-fb534353a29f41ada5e1a185bae751b32025-08-20T03:28:25ZengElsevierCase Studies in Chemical and Environmental Engineering2666-01642025-12-011210125610.1016/j.cscee.2025.101256Tannery shaving dust-based charcoal blended adsorbent for efficient heavy metal remediation: An experimental and machine learning approachMd. Sabbir Hosen0Md. Sahariar Sahen1Hasan Ahmed2Md. Selim Reza3Pranta Bhowmik4Farzana Mim5Md. Badrul Islam6Md. Azizul Haque Khan Naim7Mohammad Majibur Rahman8Md. Mostafizur Rahman9Department of Environmental Sciences, Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh; Laboratory of Environmental Health and Ecotoxicology, Department of Environmental Sciences, Jahangirnagar University, Savar, Dhaka, 1342, BangladeshDepartment of Environmental Sciences, Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh; Laboratory of Environmental Health and Ecotoxicology, Department of Environmental Sciences, Jahangirnagar University, Savar, Dhaka, 1342, BangladeshDepartment of Soil, Water and Environment, Dhaka University, Dhaka, 1000, BangladeshDepartment of Biochemistry and Molecular Biology, Jahangirnagar University, Savar, Dhaka, 1342, BangladeshDepartment of Civil Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1000, BangladeshDepartment of Biochemistry and Molecular Biology, Jahangirnagar University, Savar, Dhaka, 1342, BangladeshNatural Products Research Division, BCSIR Laboratories Rajshahi, Rajshahi, 6205, BangladeshDepartment of Environmental Sciences, Jahangirnagar University, Savar, Dhaka, 1342, BangladeshDepartment of Environmental Sciences, Jahangirnagar University, Savar, Dhaka, 1342, BangladeshDepartment of Environmental Sciences, Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh; Laboratory of Environmental Health and Ecotoxicology, Department of Environmental Sciences, Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh; Corresponding author. Department of Environmental Sciences, Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh.Tannery shaving dust (TSD) is one of the tannery wastes, poses significant concerns due to its availability and carcinogenic properties. This study has focused on utilizing this waste as adsorbent for heavy metals (HMs) treatment in wastewater. By crosslinking polyvinyl alcohol (PVA) with activated tannery shaving dust (AsD) from TSD and modified charcoal (MC) powder, a composite PVA-AsD-MC adsorbent (PAsMc) was fabricated to remove As, Cr, Zn and Pb from synthetic wastewater. Here, PVA-AsD (1:10) blended with 2:3 MC has sufficient active sites that were ensured by the FT-IR. As an adsorbent the PAsMc showed more thermal stability than AsD, and surface morphology was observed as highly rough. Moreover, the batch experiments have considered pH, adsorbent dose, and contact time factors, achieving impressive metals removal efficiencies: 98.86 % for As, 99.45 % for Cr, 99.72 % for Zn, and 98.30 % for Pb. The optimal conditions were identified as an adsorbent dosage of 4.0 g/L for 25 minutes, and an agitation speed of 300 rpm at pH 8.0–9.0. The adsorption isotherm and kinetics model provided an auspicious result for chemisorption adsorption on the surface. Notably, these datasets were then enhanced with the machine learning model, specifically the Random Forest (RF), aimed at predicting the removal of HMs. The R2 values for the training and testing dataset within the range of 0.9927–0.9984 and 0.9940–0.9975 with a RMSE value of 0.9622–1.4612 and 1.1125–1.9294, respectively. Ultimately, a predictive model for HMs removal was developed, which will assist in making rational applications of PAsMc in wastewater treatment.http://www.sciencedirect.com/science/article/pii/S266601642500163XTannery shaving dustAdsorbentReusabilityHeavy metalsMachine learning |
| spellingShingle | Md. Sabbir Hosen Md. Sahariar Sahen Hasan Ahmed Md. Selim Reza Pranta Bhowmik Farzana Mim Md. Badrul Islam Md. Azizul Haque Khan Naim Mohammad Majibur Rahman Md. Mostafizur Rahman Tannery shaving dust-based charcoal blended adsorbent for efficient heavy metal remediation: An experimental and machine learning approach Case Studies in Chemical and Environmental Engineering Tannery shaving dust Adsorbent Reusability Heavy metals Machine learning |
| title | Tannery shaving dust-based charcoal blended adsorbent for efficient heavy metal remediation: An experimental and machine learning approach |
| title_full | Tannery shaving dust-based charcoal blended adsorbent for efficient heavy metal remediation: An experimental and machine learning approach |
| title_fullStr | Tannery shaving dust-based charcoal blended adsorbent for efficient heavy metal remediation: An experimental and machine learning approach |
| title_full_unstemmed | Tannery shaving dust-based charcoal blended adsorbent for efficient heavy metal remediation: An experimental and machine learning approach |
| title_short | Tannery shaving dust-based charcoal blended adsorbent for efficient heavy metal remediation: An experimental and machine learning approach |
| title_sort | tannery shaving dust based charcoal blended adsorbent for efficient heavy metal remediation an experimental and machine learning approach |
| topic | Tannery shaving dust Adsorbent Reusability Heavy metals Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S266601642500163X |
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