A highly resourceful, environmentally benign, and recyclable magnetic montmorillonite composite (MMT/CF) was obtained through a simple one-step hydrothermal method and exhibited excellent Pb (II) removal. The as-synthesized adsorbent was then characterized by XRD, SEM–EDX, FTIR, BET, and TGA-DTA. The operating parameters including adsorbent dosage, initial Pb (II) concentration, solution pH, and time were studied. Also, a comparative approach was formed between response surface methodology (RSM) and artificial neural network (ANN) to optimize and model the removal efficiency of Pb (II) by MMT/CF. The results indicated that the ANN model was more precise and quite trusted optimization tool than RSM in consideration of its higher correlation coefficient (R² = 0.998) and lower prediction errors (RMSE = 0.851 and ADD = 0.505). Langmuir isotherm provided the best fit to the experimental data, and the maximum adsorption capacity was 101.01 mg/g. Additionally, the kinetic studies showed that the pseudo-second-order model fitted well with the experimental data. The magnetic MMT/CF composite possesses high adsorption capacity and is suitable for reuse. Therefore, this study shows that MMT/CF composite can be a potential adsorbent in Pb (II) uptake from aqueous media.

通过简单的一步水热法获得了一种资源丰富、环境友好、可回收的磁性蒙脱石复合材料（MMT/CF），并表现出优异的 Pb（II）去除效果。然后通过 XRD、SEM-EDX、FTIR、BET 和 TGA-DTA 对合成的吸附剂进行表征。研究了吸附剂用量、初始 Pb (II) 浓度、溶液 pH 值和时间等操作参数。此外，还建立了响应面法 (RSM) 和人工神经网络 (ANN) 之间的比较方法，以优化和模拟 MMT/CF 对 Pb (II) 的去除效率。结果表明，考虑到较高的相关系数（R ²  = 0.998）和较低的预测误差（RMSE = 0.851 和 ADD = 0.505），ANN 模型是比 RSM 更精确且值得信赖的优化工具。 Langmuir 等温线与实验数据最吻合，最大吸附容量为 101.01 mg/g。此外，动力学研究表明伪二阶模型与实验数据吻合良好。磁性MMT/CF复合材料具有高吸附能力，适合重复使用。因此，本研究表明，MMT/CF 复合材料可以作为从水介质中吸收 Pb (II) 的潜在吸附剂。