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<p>The study investigates the use of hemp (H) and hemp-derived biochar (HBC) as adsorbents for removing Cr(VI) from wastewater. The adsorbents were synthesized through pyrolysis and characterized using FTIR, SEM, XRD, BET and EDX before and after adsorption. Response surface methodology (RSM)&#xa0;was utilized to optimize key parameters including initial Cr(VI) concentration, adsorbent dose, and adsorption time. The batch experiments were performed using Cr(VI) concentrations (1–20&#xa0;mg/L) over time intervals of (5–240&#xa0;min). The adsorption mechanism was studied using non-linear adsorption isotherm and kinetics and validating the removal efficiency of H and HBC using an artificial neural network (ANN). The analysis shows that the adsorbent H achieved the maximum Cr(VI) removal rate of 97.9%, while the adsorbent HBC achieved a removal rate of 99.168%. These results were obtained by using the optimum parameters of 1&#xa0;mg/L Cr(VI) concentration, 0.1&#xa0;g adsorbent dosage, and 240&#xa0;min adsorption period. The Freundlich isotherm and pseudo-first-order kinetics best described the adsorption behaviour. The maximum adsorption capacities were 9.77&#xa0;mg/g for H and 11.29&#xa0;mg/g for HBC. The ANN analysis identified the best-fit models for predicting adsorption performance: a multilayer perceptron (MLP) with configurations MLP-2-3-1 for H and MLP-2-8-1 for HBC.</p>

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Enhancing Cr(VI) remediation efficiency using hemp-derived biochar: insights into RSM optimization and adsorption kinetics using ANN modelling

  • 期刊论文

  • 发表日期：

    2025年7月8日

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  • M. ul-Ain,
  • H. M. Abd-ur-Rehman,
  • A. H. Khoja,
  • R. Naeem,
  • A. A. Khan,
  • J. Gul,
  • H. Kanwal,
  • A. H. Kamboh,
  • I. Ud Din,
  • S. Shakir

摘要

The study investigates the use of hemp (H) and hemp-derived biochar (HBC) as adsorbents for removing Cr(VI) from wastewater. The adsorbents were synthesized through pyrolysis and characterized using FTIR, SEM, XRD, BET and EDX before and after adsorption. Response surface methodology (RSM) was utilized to optimize key parameters including initial Cr(VI) concentration, adsorbent dose, and adsorption time. The batch experiments were performed using Cr(VI) concentrations (1–20 mg/L) over time intervals of (5–240 min). The adsorption mechanism was studied using non-linear adsorption isotherm and kinetics and validating the removal efficiency of H and HBC using an artificial neural network (ANN). The analysis shows that the adsorbent H achieved the maximum Cr(VI) removal rate of 97.9%, while the adsorbent HBC achieved a removal rate of 99.168%. These results were obtained by using the optimum parameters of 1 mg/L Cr(VI) concentration, 0.1 g adsorbent dosage, and 240 min adsorption period. The Freundlich isotherm and pseudo-first-order kinetics best described the adsorption behaviour. The maximum adsorption capacities were 9.77 mg/g for H and 11.29 mg/g for HBC. The ANN analysis identified the best-fit models for predicting adsorption performance: a multilayer perceptron (MLP) with configurations MLP-2-3-1 for H and MLP-2-8-1 for HBC.