Study on the removal of iron (II) and manganese (II) in acidic mine drainage by red mud: Performance and mechanism.

Red mud is an environmental burden during the alumina production process. To mitigate the hazards associated with red mud storage, this study investigated the utilization of alkaline red mud as a treatment agent for acidic mine drainage (AMD) with high concentrations of Fe(II) and Mn(II). This study explored the influence of reaction times, addition amounts of red mud, and pH values on the removal efficiency of Fe (II) and Mn(II) from high-concentration AMD. Various parameters such as suspended solids levels, effluent pH, and zeta potentials were measured to meet discharge standards. The adsorption mechanism of red mud was examined using SEM, XRD, EDX, XPS, and 3D-EEM analysis. Optimal conditions were determined as a reaction time of 2 h, pH value of 5.01 and the addition of 100 g/L red mud, achieving effective removal of Fe(II) (reduced from 1000 to 0.224 mg/L) and Mn (II) (reduced from 20 to 1.03 mg/L). The treated AMD meets discharge standards with reduced suspended matter content of 37.4 mg/L. These findings provided valuable insights for the utilization of red mud waste in engineering applications.

[Optimization method of MOS sensor array for identification of traditional Chinese medicine based on electronic nose].

Optimization of sensor array is a significant topic in the application of electronic nose (EN). Stepwise discriminant analysis and cluster analysis combining with screening of typical index were employed to optimize the original array in the classification of 100 samples from 10 kinds of traditional Chinese medicine based on alpha-FOX3000 EN. And the identification ability was evaluated by three algorithm including principle component analysis, Fisher discriminant analysis and random forest. The results showed that the identification ability of EN was improved since not only the effective information was maintained but also the redundant one was eliminated by the optimized array. The optimized method was eventually established, it was accurate and efficient. And the optimized array was built up, that is, S1, S2, S5, S6, S8, S12.