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<p>This work investigated the fluoride removal efficiency by calcium oxide-based polyaniline nanocomposite (CaO-PAn NC) and optimization study using Response Surface Methodology (RSM) and Artificial Neural Network (ANN). The kinetic and isotherm studies were well explained by pseudo-second-order and Langmuir isotherm model. The maximum fluoride adsorption capacity was 186.58&#xa0;mg/g. The thermodynamics studies indicate the adsorption process was spontaneous and endothermic in nature. The optimal value for fluoride removal by CaO-PAn NC and the interactive effect of input variables pH, dosage, temperature and reaction time was investigated using RSM and ANN. The performances were determined using statistical tool regression coefficient (R<sup>2</sup>), Root mean square error (RMSE), Standard error of prediction (SEP) and Absolute average deviation (AAD). RSM with R<sup>2</sup> (0.9984), AAD (0.0401), RMSE (0.0902), SEP (0.2089) was at higher side of accuracy than ANN with R<sup>2</sup> (0.9877), AAD (0.1223), RMSE (0.5897), SEP (0.6409). The maximum fluoride removal was predicted to be 91.05% and 92.01% by RSM and ANN at (pH ̴ 7, time 65&#xa0;min, temperature 35&#xa0;°C, dose 0.55&#xa0;g/L) respectively. The PAn nanocomposite can be reused up to 6th cycles for defluoridation mechanism.</p>

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Optimization of Multiple Parameters for Adsorption of Fluoride from Aqueous Medium by Ultra-Sonicated Calcium Oxide-Based Polyaniline Nano-Composite

  • 期刊论文

  • 发表日期：

    2025年1月6日

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  • Anjan Kumar Bej,
  • Prakash Chandra Mishra

摘要

This work investigated the fluoride removal efficiency by calcium oxide-based polyaniline nanocomposite (CaO-PAn NC) and optimization study using Response Surface Methodology (RSM) and Artificial Neural Network (ANN). The kinetic and isotherm studies were well explained by pseudo-second-order and Langmuir isotherm model. The maximum fluoride adsorption capacity was 186.58 mg/g. The thermodynamics studies indicate the adsorption process was spontaneous and endothermic in nature. The optimal value for fluoride removal by CaO-PAn NC and the interactive effect of input variables pH, dosage, temperature and reaction time was investigated using RSM and ANN. The performances were determined using statistical tool regression coefficient (R2), Root mean square error (RMSE), Standard error of prediction (SEP) and Absolute average deviation (AAD). RSM with R2 (0.9984), AAD (0.0401), RMSE (0.0902), SEP (0.2089) was at higher side of accuracy than ANN with R2 (0.9877), AAD (0.1223), RMSE (0.5897), SEP (0.6409). The maximum fluoride removal was predicted to be 91.05% and 92.01% by RSM and ANN at (pH ̴ 7, time 65 min, temperature 35 °C, dose 0.55 g/L) respectively. The PAn nanocomposite can be reused up to 6th cycles for defluoridation mechanism.