Experimental study of the dust-removal performance of a wet scrubber

A variety of dust control methods are often applied in coal mines,among which the application of wet scrubbers has proven to be an efficient technology for the removal of dust in airstreams,rather than diluting or confining the dust.In this paper,a wet scrubber design was developed.Based on a self-designed experimental test platform,the total dust concentration,respirable dust concentration,air volume,and average pressure drops of wet scrubbers with 12,16,20,and 24 blades were measured under different water intake conditions.The results show that the different water intake levels have only minimal effects on the air volume of the wet scrubbers.However,increased water intake had improved the dust removal efficiency of the wet scrubbers with the same number of blades.The wet scrubber with 16 blades was found to have the best dust removal efficiency at a water intake level of 1.35 m^(3)/h.Its total dust and respirable dust removal efficiency reached 96.81%and 95.59%,respectively.The air volume was 200.4 m^(3)/min,and the average pressure drop was determined to be 169.4 Pa.In addition,when the wet scrubber with 16 blades was applied in a coal preparation plant in China's Shanxi Province,it was observed that the total dust concentration had fallen below 8.1 mg/m^(3),and the respirable dust concentration had fallen below 5.9 mg/m^(3).Therefore,the results obtained in this research investigation provide important references for the use of wet scrubbers to improve coal production environmental conditions.

Performance prediction of gravity concentrator by using artificial neural network-a case study

In conventional chromite beneficiation plant, huge quantity of chromite is used to loss in the form of tailing. For recovery these valuable mineral, a gravity concentrator viz. wet shaking table was used.Optimisation along with performance prediction of the unit operation is necessary for efficient recovery.So, in this present study, an artificial neural network(ANN) modeling approach was attempted for predicting the performance of wet shaking table in terms of grade(%) and recovery(%). A three layer feed forward neural network(3:3–11–2:2) was developed by varying the major operating parameters such as wash water flow rate(L/min), deck tilt angle(degree) and slurry feed rate(L/h). The predicted value obtained by the neural network model shows excellent agreement with the experimental values.