Mining Science and Technology (china) 

About: Mining Science and Technology (china) is an academic journal. The journal publishes majorly in the area(s): Coal mining & Coal. It has an ISSN identifier of 1674-5264. Over the lifetime, 462 publications have been published receiving 6411 citations.

Environmental issues from coal mining and their solutions

Abstract: The environmental challenges from coal mining include coal mine accidents, land subsidence, damage to the water environment, mining waste disposal and air pollution. These are either environmental pollution or landscape change. A conceptual framework for solving mine environmental issues is proposed. Clean processes, or remediation measures, are designed to address environmental pollution. Restoration measures are proposed to handle landscape change. The total methane drainage from 56 Chinese high methane concentration coal mines is about 101.94 million cubic meters. Of this methane, 19.32 million, 35.58 million and 6.97 million cubic meters are utilized for electricity generation, civil fuel supplies and other industrial purposes, respectively. About 39% of the methane is emitted into the atmosphere. The production of coal mining wastes can be decreased 10% by reuse of mining wastes as underground fills, or by using the waste as fuel for power plants or for raw material to make bricks or other infrastructure materials. The proper use of mined land must be decided in terms of local physical and socio-economical conditions. In European countries more than 50% of previously mined lands are reclaimed as forest or grass lands. However, in China more than 70% of the mined lands are reclaimed for agricultural purposes because the large population and a shortage of farmlands make this necessary. Reconstruction of rural communities or native residential improvement is one environmental problem arising from mining. We suggest two ways to reconstruct a farmer's house in China.

Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks

Abstract: Uniaxial Compressive Strength (UCS) and modulus of elasticity (E) are the most important rock parameters required and determined for rock mechanical studies in most civil and mining projects In this study, two mathematical methods, regression analysis and Artificial Neural Networks (ANNs), were used to predict the uniaxial compressive strength and modulus of elasticity The P-wave velocity, the point load index, the Schmidt hammer rebound number and porosity were used as inputs for both meth-ods The regression equations show that the relationship between P-wave velocity, point load index, Schmidt hammer rebound number and the porosity input sets with uniaxial compressive strength and modulus of elasticity under conditions of linear rela-tions obtained coefficients of determination of (R2) of 064 and 056, respectively ANNs were used to improve the regression re-sults The generalized regression and feed forward neural networks with two outputs (UCS and E) improved the coefficients of determination to more acceptable levels of 086 and 092 for UCS and to 077 and 082 for E The results show that the proposed ANN methods could be applied as a new acceptable method for the prediction of uniaxial compressive strength and modulus of elasticity of intact rocks

Nanobubble generation and its application in froth flotation (part I): nanobubble generation and its effects on properties of microbubble and millimeter scale bubble solutions

Abstract: A special nanobubble generation system has been developed for evaluating the effect of nanobubble on froth flotation. In this study, an eight-factor five-level Central Composite Experimental Design was conducted for investigating eight important parameters governing the median size and the volume of nanobubbles. These process parameters included surfactant concentration, dissolved oxygen (O2) content, dissolved carbon dioxide gas (CO2) content, pressure drop in cavitation tube nozzle, <50 nm hydrophobic particle concentration, <50 nm hydrophilic particle concentration, slurry temperature and the time interval after nanobubble generation. The properties, stability and uniformity of nanobubbles were investigated. The study of the produced nanobubble’s effects on the characteristics of microbubble solutions and millimeter scale bubble solutions was performed in a 50.8 mm column.

Nanobubble generation and its applications in froth flotation (part II): fundamental study and theoretical analysis

Abstract: Froth flotation is a commonly employed technology to improve the quality of raw coal and minerals. Coal and minerals particle size and surface hydrophobicity are two main parameters that affect three key steps in froth flotation process: particle-bubble collision, adhesion, and detachment. This paper fundamentally investigated the effects of nanobubble on coal and phosphate flotation. It has been found that the presence of nanobubble in flotation pulp could widen the coal and phosphate flotation particle size range, increase the particle surface hydrophobicity, and thus improve the coal and phosphate froth flotation recovery.

Evaluation and prediction of blast induced ground vibration using support vector machine

Abstract: We present the application of Support Vector Machine (SVM) for the prediction of blast induced ground vibration by taking into consideration of maximum charge per delay and distance between blast face to monitoring point. To investigate the suitability of this approach, the predictions by SVM have been compared with conventional predictor equations. Blast vibration study was carried out at Magnesite mine of Pithoragarh, India. Total 170 blast vibrations data sets were recorded at different strategic and vulnerable locations in and around to mine. Out of 170 data sets, 150 were used for the training of the SVM network as well as to determine site constants of different conventional predictor equations, whereas, 20 new randomly selected data sets were used to compare the prediction capability of SVM network with conventional predictor equations. Results were compared based on Coefficient of Determination (CoD) and Mean Absolute Error (MAE) between monitored and predicted values of Peak Particle Velocity (PPV). It was found that SVM gives closer values of predicted PPV as compared to conventional predictor equations. The coefficient of determination between measured and predicted PPV by SVM was 0.955, whereas it was 0.262, 0.163, 0.337 and 0.232 by USBM, Langefors-Kihlstrom, Ambraseys-Hendron and Bureau of Indian Standard equations, respectively. The MAE for PPV was 11.13 by SVM, whereas it was 0.973, 1.088, 0.939 and 1.292 by USBM, Langefors-Kihlstrom, Ambraseys-Hendron and Bureau of Indian Standard equations respectively.