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Abstract

For most precious metal mines, cemented tailings backfill slurry (CTBS) with different cement-sand

ratio and solid concentration are transported into the gobs to keep the stability of the stope and mitigate

environmental pollution by mine tailing. However, transporting several kinds of CTBS through the same

pipeline will increase the risk of pipe plugging. Therefore, the joint impacts of cement-sand ratio and

solid concentration on the rheological characteristics of CTBS need a more in-depth study. Based on the

experiments of physical and mechanical parameters of fresh slurry, the loss of pumping pressure while

transporting CTBS with different cement-sand ratio, flux and solid mass concentration were measured

using pumping looping pipe experiments to investigate the joint impacts of cement-sand ratio and solid

concentration on the rheological characteristics of CTBS. Meanwhile, the effect of different stopped pumping

time on blockage accident was revealed and discussed by the restarting pumping experiments. Furthermore,

Fluent software was applied to calculate the pressure loss and velocity distribution in the pipeline to further

analysis experimental results. The overall trends of the simulation results were good agreement with the

experiment results. Then, the numerical model of the pipeline in the Sanshandao gold mine was conducted

to simulate the characteristics of CTBS pipeline transportation. The results show that the pumping pressure

of the delivery pump can meet the transportation requirements when there is no blockage accident. This

can provide a theoretical method for the parameters optimizing in the pipeline transportation system.

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Authors and Affiliations

Xiao Siyou
Liu Zhixiang
Jiang Yuanjun
Li Cheng
Sun Changning
Su Lijun
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Abstract

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.
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Authors and Affiliations

Zhiyuan Fang
1 2 3
Hao Yang
1 2 3
Cheng Li
1 2 3
Liangliang Cheng
1 2 3
Ming Zhao
1 2
Chenbo Xie
1 2

  1. Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics,Chinese Academy of Sciences, Hefei 230031, China
  2. Science Island Branch of Graduate School, University of Science and Technology of China,Hefei 230026, China
  3. Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, Chin

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