Li Longjiang, Wang Yalan, Wang Wenyuan
Mining College, Guizhou University, Guiyang 550025, China.
National & Local Joint Laboratory of Engineering for Effective Utilization of Regional Mineral Resources from Karst Areas, Guiyang 550025, China.
Molecules. 2024 Feb 22;29(5):970. doi: 10.3390/molecules29050970.
Red mud (RM), a bauxite residue, contains hazardous radioactive wastes and alkaline material and poses severe surface water and groundwater contamination risks, necessitating recycling. Pretreated RM can be used to make adsorbents for water treatment. However, its performance is affected by many factors, resulting in a nonlinear correlation and coupling relationship. This study aimed to identify the best formula for an RM adsorbent using a mathematical model that examines the relationship between 11 formulation types (e.g., pore-assisting agent, component modifier, and external binder) and 9 properties (e.g., specific surface area, wetting angle, and Zeta potential). This model was built using a back-propagation neural network (BP) based on single-factor experimental data and orthogonal experimental data. The model trained and predicted the established network structure to obtain the optimal adsorbent formula. The RM particle adsorbents had a pH of 10.16, specific surface area (BET) of 48.92 m·g, pore volume of 2.10 cm·g, compressive strength (ST) of 1.12 KPa, and 24 h immersion pulverization rate () of 3.72%. In the removal of total phosphorus in flotation tailings backwater, it exhibited a good adsorption capacity (Q) and total phosphorous removal rate () of 48.63 mg·g and 95.13%, respectively.
赤泥（RM）是一种铝土矿残渣，含有有害放射性废物和碱性物质，对地表水和地下水构成严重污染风险，因此需要进行回收利用。经过预处理的赤泥可用于制造水处理吸附剂。然而，其性能受多种因素影响，呈现出非线性的相关和耦合关系。本研究旨在使用数学模型确定赤泥吸附剂的最佳配方，该模型考察了11种配方类型（如造孔剂、组分改性剂和外部粘结剂）与9种性能（如比表面积、润湿角和zeta电位）之间的关系。该模型基于单因素实验数据和正交实验数据，采用反向传播神经网络（BP）构建。通过对训练和预测的既定网络结构进行分析，得出了最佳吸附剂配方。赤泥颗粒吸附剂的pH值为10.16，比表面积（BET）为48.92 m·g，孔容为2.10 cm·g，抗压强度（ST）为1.12 KPa，24小时浸泡粉化率为3.72%。在去除浮选尾矿回水中的总磷时，它表现出良好的吸附容量（Q）和总磷去除率，分别为48.63 mg·g和95.13%。
