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Integrated RSM and machine learning modelling of Pb(II) adsorption using bio-engineered walnut shells
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DOI：10.1007/s42247-026-01502-8.png)
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Abstract
En 中文
A green, chemical-free method was successfully employed to produce a highly efficient Pb(II) bioadsorbent using Juglans regia shell as a low-cost adsorbent material through Aspergillus niger mediated SSF. The bioadsorbent was found to have a > 10-fold adsorption capacity (40.24 mg g⁻¹) compared to the raw material. Pseudo-second-order kinetics and the Langmuir model are the best fits; the rate-determining step is film diffusion. Adsorption is a spontaneous, endothermic and favourable process. The adsorbent was predicted to have a Pb(II) removal efficiency of 99.50 ± 0.42% at a low dose of 0.66 gL⁻¹ using response surface methodology. Machine learning evaluation of six regression models showed that gradient boosting achieved the highest accuracy, with R² ≈ 0.958. The method was successfully employed to treat battery wastewater with Pb(II) removal. With a low carbon footprint and significant cost reduction, this method is viable for sustainable wastewater treatment.
Keywords:
Fermented Walnut Shell (FWS)
Pseudo-2nd−order
Langmuir model
Journal
E
IF:
4.1
Papers:
503
Citations:
2.8K
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