Department of Civil and Environmental Engineering, Indian Institute of Technology Patna, Bihar, 801 106, India.
Department of Civil and Environmental Engineering, Indian Institute of Technology Patna, Bihar, 801 106, India.
Chemosphere. 2024 Jul;360:142397. doi: 10.1016/j.chemosphere.2024.142397. Epub 2024 May 21.
Removal of perfluorooctanoic acid (PFOA) from water matrices is crucial owing to its pervasiveness and adverse ecological and human health effects. This study investigates the adsorptive removal of PFOA using magnetic biochar (MBC) derived from FeCl-treated peanut husk at different temperatures (300, 600, and 900 °C). Preliminary experiments demonstrated that MBC exhibited superior performance, with its characterization confirming the presence of γ-FeO. However, efficient PFOA removal from water matrices depends on determining the optimum combination of inputs in the treatment approaches. Therefore, optimization and predictive modeling of the PFOA adsorption were investigated using the response surface methodology (RSM) and the artificial intelligence (AI) models, respectively. The central composite design (CCD) of RSM was employed as the design matrix. Further, three AI models, viz. artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS) were selected to predict PFOA adsorption. The RSM-CCD model applied to optimize three input process parameters, namely, adsorbent dose (100-400 mg/L), pH (3-10), and contact time (20-60 min), showed a statistically significant (p < 0.05) effect on PFOA removal. Maximum PFOA removal of about 98.3% was attained at the optimized conditions: adsorbent dose: 400 mg/L, pH: 3.4, and contact time: 60 min. Non-linear analysis showed PFOA adsorption was best fitted by pseudo-second-order kinetics (R = 0.9997). PFOA adsorption followed Freundlich isotherm (R = 0.9951) with a maximum adsorption capacity of ∼307 mg/g. Thermodynamics and spectroscopic analyses revealed that PFOA adsorption is a spontaneous, exothermic, and physical phenomenon, with electrostatic interaction, hydrophobic interaction, and hydrogen bonding governing the process. A comparative analysis of the statistical and AI models for PFOA adsorption demonstrated high R (>0.99) for RSM-CCD, ANN, and ANFIS. This research demonstrates the applicability of the statistical and AI models for efficient prediction of PFOA adsorption from water matrices using MBC (MBC).
由于全氟辛酸 (PFOA) 的普遍性及其对生态和人类健康的不利影响，从水基质中去除 PFOA 至关重要。本研究使用不同温度（300、600 和 900°C）下由 FeCl 处理的花生壳制成的磁性生物炭 (MBC) 研究了 PFOA 的吸附去除。初步实验表明，MBC 表现出优异的性能，其特性证实存在γ-FeO。然而，要从水基质中有效去除 PFOA，取决于确定处理方法中输入的最佳组合。因此，分别使用响应面法 (RSM) 和人工智能 (AI) 模型对 PFOA 的吸附进行了优化和预测建模。RSM 的中心复合设计 (CCD) 用作设计矩阵。此外，选择了三种 AI 模型，即人工神经网络 (ANN)、支持向量机 (SVM) 和自适应神经模糊推理系统 (ANFIS) 来预测 PFOA 的吸附。应用 RSM-CCD 模型优化三个输入过程参数，即吸附剂剂量（100-400 mg/L）、pH（3-10）和接触时间（20-60 min），对 PFOA 去除有统计学意义（p < 0.05）。在最佳条件下，PFOA 的去除率约为 98.3%：吸附剂剂量：400 mg/L，pH：3.4，接触时间：60 min。非线性分析表明，PFOA 吸附最适合伪二级动力学（R = 0.9997）。PFOA 吸附遵循 Freundlich 等温线（R = 0.9951），最大吸附容量约为 307 mg/g。热力学和光谱分析表明，PFOA 吸附是一种自发、放热和物理现象，静电相互作用、疏水相互作用和氢键控制着该过程。PFOA 吸附的统计和 AI 模型的比较分析表明，RSM-CCD、ANN 和 ANFIS 的 R 均较高（>0.99）。这项研究证明了统计和 AI 模型在使用 MBC（MBC）从水基质中高效预测 PFOA 吸附中的适用性。
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