Application Overview of Membrane Separation Technology in Coal Mine Water Resources Treatment in Western China

2021 ◽  
Author(s):  
Fan Wang ◽  
Yu Wang ◽  
Cong Jing
Keyword(s):  
Water Resources ◽  
Coal Mine ◽  
Mine Water ◽  
Membrane Separation ◽  
Western China ◽  
Separation Technology ◽  
Coal Mine Water

scholarly journals Electrodialysis of coal mine water

2021 ◽  
Vol 25 ◽  
pp. 100143
Author(s):  
Krzysztof Mitko ◽  
Aleksandra Noszczyk ◽  
Piotr Dydo ◽  
Marian Turek
Keyword(s):  
Coal Mine ◽  
Mine Water ◽  
Coal Mine Water

scholarly journals Directional Drainage Grouting Technology of Coal Mine Water Damage Treatment

2011 ◽  
Vol 26 ◽  
pp. 264-270 ◽  
Author(s):  
Qiucheng Zhang ◽  
Jinhai Li ◽  
Bingshen Liu ◽  
Xiaoguo Chen
Keyword(s):  
Coal Mine ◽  
Mine Water ◽  
Coal Mine Water ◽  
Water Damage

scholarly journals Effect of Cone-Plate Clarifier Structure Parameters on Flocculation Efficiency

Separations ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 6
Author(s):  
Yulong Zhang ◽  
Peikun Liu ◽  
Linjing Xiao ◽  
Long Chang ◽  
Fangping Yan ◽  
...  
Keyword(s):  
Removal Efficiency ◽  
Coal Mine ◽  
Mine Water ◽  
Regression Models ◽  
Structural Parameters ◽  
Insertion Depth ◽  
Turbidity Removal ◽  
Plate Spacing ◽  
Coal Mine Water ◽  
Flocculation Effect

In this study, a coal mine water flocculation system was established. A series of flocculation tests were carried out at different structural parameters (cylinder height, cone-plate insertion depth and cone-plate spacing) to better investigate the effect of the cone-plate clarifier on coal mine water treatment performance. Sixteen sampling points were set up in the system for data monitoring to generate the required data. The cone-plate clarifier was divided into five zones for flocculation analysis. The increased cylinder height facilitated the flocculation of particles in the micro flocculation zone and the settling of particles in the settlement zone. The chemicals used are polyaluminum chloride (PACl), Fe3O4 and polyacrylamide (PAM), corresponding to doses of 60 mg/L, 40 mg/L and 6 mg/L, respectively. Insufficient insertion depth of the cone-plate will cause the small flocs that have not been fully flocculated to enter the exit pipe zone directly through the cone-plate, while too much insertion depth will cause the large floc in the settlement zone to re-enter the exit pipe zone. The flocculation effect of small flocs increased as the cone-plate spacing decreased, which is consistent with the shallow pool theory. When the cone plate spacing was too narrow, the amount of fluid was reduced and the increase in fluid velocity reduced the flocculation effect. Curve fitting was conducted for Suspended solids(SS) and turbidity removal efficiency under each structural parameter to derive the variation of SS and turbidity removal efficiency under different structural parameters. The regression models of SS and turbidity removal efficiency on the cylinder height, cone-plate insertion depth and cone-plate spacing were established based on the curve fitting results, and the regression models were verified to be well fitted based on the comparison of experimental results. Finally, the optimal values of SS and turbidity removal efficiency were found based on the regression model. The flow rate of the cone-plate clarifier is 0.6 m3/h. The SS removal efficiency reached 96.82% when the cylinder height was 708 mm, the cone-plate insertion depth was 367 mm and the cone-plate spacing was 26 mm. The turbidity removal efficiency reached 86.75% when the cylinder height was 709 mm, the cone-plate insertion depth was 369 mm and the cone-plate spacing was 26 mm.

scholarly journals BIOGEOCHEMICAL INTERACTIONS BETWEEN COAL MINE WATER AND UNCONVENTIONAL RESERVOIR WELL CEMENT

2019 ◽  
Author(s):  
Djuna Gulliver ◽  
◽  
James Gardiner ◽  
Daniel Ross ◽  
Daniel Lipus ◽  
...  
Keyword(s):  
Coal Mine ◽  
Mine Water ◽  
Unconventional Reservoir ◽  
Coal Mine Water ◽  
Well Cement

Identification Method of Coal Mine Water Inrush Spectrum Based on Multilayer Regularization Extreme Learning Machine

2018 ◽  
Vol 38 (7) ◽  
pp. 0730002
Author(s):  
王亚 Wang Ya ◽  
周孟然 Zhou Mengran ◽  
陈瑞云 Chen Ruiyun ◽  
闫鹏程 Yan Pengcheng ◽  
胡锋 Hu Feng ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Coal Mine ◽  
Mine Water ◽  
Water Inrush ◽  
Identification Method ◽  
Coal Mine Water ◽  
Learning Machine
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