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Comparing cyclohexyl hydroxamic acid and benzohydroxamic acid in cassiterite flotation under lead nitrate activation

Yang Hao, Ou Leming

Physicochemical Problems of Mineral Processing

2023

Vol. 59, iss. 6

art. no. 174673

This paper investigates the flotation behavior of Cyclohexyl hydroxamic acid (CHA) and benzhydroxamic acid (BHA) on cassiterite under lead nitrate activation conditions and elucidates the adsorption mechanism of CHA on the cassiterite surface. Microflotation experiments were performed to compare the capturing efficiency of CHA and BHA at pH values ranging from 4 to 12. Results showed that CHA exhibited superior capability in capturing cassiterite compared to BHA. The recovery of cassiterite in the hydroxamic acid-based flotation system correlated positively with the adsorption of hydroxamic acid on the cassiterite surface. Adsorption experiments revealed an increase in adsorption quantity with an increase in hydroxamic acid dosage, with CHA exhibiting significantly higher adsorption amount than BHA on the cassiterite surface. To analyze the adsorption mechanism of CHA on the cassiterite surface, both infrared spectroscopy and X-ray photoelectron spectroscopy (XPS) analysis were conducted, both before and after lead nitrate activation. IR spectra and XPS results indicated that lead ion activation enhanced the adsorption of CHA on the cassiterite surface, resulting in an increased number of active sites for CHA interaction. Additionally, chemisorption of CHA occurred on the cassiterite surface.

Prediction of PM2.5 hourly concentrations in Beijing based on machine learning algorithm and ground-based LiDAR

Fang Zhiyuan, Yang Hao, Li Cheng, Cheng Liangliang, Zhao Ming, Xie Chenbo

Archives of Environmental Protection

2021

Vol. 47, no. 3

98--107

The prediction of PM2.5 is important for environmental forecasting and air pollution control. In this study, four machine learning methods, ground-based LiDAR data and meteorological data were used to predict the ground-level PM2.5 concentrations in Beijing. Among the four methods, the random forest (RF) method was the most effective in predicting ground-level PM2.5 concentrations. Compared with BP neural network, support vector machine (SVM), and various linear fitting methods, the accuracy of the RF method was superior by 10%. The method can describe the spatial and temporal variation in PM2.5 concentrations under different meteorological conditions, with low root mean square error (RMSE) and mean square deviation (MD), and the consistency index (IA) reached 99.69%. Under different weather conditions, the hourly variation in PM2.5 concentrations has a good descriptive ability. In this paper, we analyzed the weights of input variables in the RF method, constructed a pollution case to correspond to the relationship between input variables and PM2.5, and analyzed the sources of pollutants via HYSPLIT backward trajectory. This method can study the interaction between PM2.5 and air pollution variables, and provide new ideas for preventing and forecasting air pollution.