Boron resources are abundant in Da Qaidam salt lake of Qaidamu Basin. It has been given great attention for the polyborate species present in brine. In this study, the Raman spectroscopy was applied to investigate the existing-form of boron in brine during evaporation. The prepared solutions of MgO·2B2O3-H2O, MgO·2B2O3-MgCl2 -H2O, and MgO·2B2O3-MgSO4-H2O was also evaporated and recorded to study the influence of boron concentration, pH, and electrolytes on the borate speciation in brine. The mononborates of B(OH)3 and B(OH)4- were found to be the only forms present in the original salt lake brine. Brine evaporation promotes the formation of polyborate anions B3O3(OH)4-, B5O6(OH)4-, and B6O7(OH)62- and also disappearance of the B(OH)4- ion in brine with boron concentration of more than 11 g/L in B2O3. The pentaborate ion of B5O6(OH)4- was sensitive to the solution pH and found to be appeared under the pH value of 8.0. While the hexaborate ion of B6O7(OH)62- was observed more dependent on the electrolyte of magnesium chloride due to its special properties, such as promoting boron accumulation, lowering solution pH, and also the strong af?nity for water molecules, which is beneficial to the polymerization of borate ions in brine. The interaction mechanisms among polyborate anions during evaporation had also been proposed. 相似文献
Extracting geochemical anomalies from geochemical exploration data is one of the most important activities in mineral exploration. Geochemical anomaly detection can be regarded as a binary classification problem. The similarity between geochemical samples can be measured by their distance. The key issue of this classification is to find the intrinsic relationship and distance between geochemical samples to separate geochemical anomalies from background. In this paper, a hybrid method that integrates random forest and metric learning (RFML) is used to identify geochemical anomalies related to Fe-polymetallic mineralization in Southwest Fujian Province of China. RFML does not require any specific statistical assumption on geochemical data, nor does it depend on sufficient known mineral occurrences as the prior knowledge. The geochemical anomaly map obtained by the RFML method showed that the known Fe deposits and the generated geochemical anomaly area have strong spatial association. Meanwhile, the receiver operating characteristic curves for the results of RFML and another method, namely maximum margin metric learning, indicated that the RFML method exhibited better performance, suggesting that RFML can be effectively applied to recognize geochemical anomalies.