查看更多>>摘要：This study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction(LSP).To illustrate various study area scales,Ganzhou City in China,its eastern region(Ganzhou East),and Ruijin County in Ganzhou East were chosen.Different mapping unit scales are represented by grid units with spatial resolution of 30 and 60 m,as well as slope units that were extracted by multi-scale segmentation method.The 3855 landslide locations and 21 typical environmental factors in Ganzhou City are first determined to create spatial datasets with input-outputs.Then,landslide susceptibility maps(LSMs)of Ganzhou City,Ganzhou East and Ruijin County are pro-duced using a support vector machine(SVM)and random forest(RF),respectively.The LSMs of the above three regions are then extracted by mask from the LSM of Ganzhou City,along with the LSMs of Ruijin County from Ganzhou East.Additionally,LSMs of Ruijin at various mapping unit scales are generated in accordance.Accuracy and landslide suscepti-bility indexes(LSIs)distribution are used to express LSP uncertainties.The LSP uncertainties under grid units significantly decrease as study area scales decrease from Ganzhou City,Ganzhou East to Ruijin County,whereas those under slope units are less affected by study area scales.Of course,attentions should also be paid to the broader representativeness of large study areas.The LSP accuracy of slope units increases by about 6％-10％compared with those under grid units with 30 m and 60 m resolution in the same study area's scale.The significance of environmental factors exhibits an averaging trend as study area scale increases from small to large.The importance of environmental factors varies greatly with the 60 m grid unit,but it tends to be consistent to some extent in the 30 m grid unit and the slope unit.
查看更多>>摘要：The residual subsidence caused by underground mining in mountain area has a long subsidence duration time and great potential harm,which seriously threatens the safety of people's production and life in the mining area.Therefore,it is necessary to use appropriate monitoring methods and mathematical models to effectively monitor and predict the residual subsidence caused by underground mining.Compared with traditional level survey and InSAR(Interferometric Synthetic Aperture Radar)technology,GNSS(Global Navigation Satellite System)online monitoring technology has the advantages of long-term monitoring,high precision and more flexible monitoring methods.The empirical equation method of residual subsidence in mining subsidence is effectively combined with the rock creep equation,which can not only describe the residual subsidence process from the mechanism,but also predict the residual subsidence.Therefore,based on GNSS online monitoring technology,combined with the mining subsidence model of mountain area and adding the correlation coefficient of the compaction degree of caving broken rock and the Kelvin model of rock mechanics,this paper constructs the residual subsidence time series model of arbitrary point on the ground in mountain area.Through the example,the predicted results of the model in the inversion parameter phase and the dynamic prediction phase are compared with the measured data sequence.The results show that the model can carry out effective numerical calculation according to the GNSS monitoring data of any point on the ground,and the model prediction effect is good,which provides a new method for the prediction of residual subsidence in mountain mining.
查看更多>>摘要：The cyclic hydrogenation technology in a direct coal liquefaction process relies on the dissolved hydrogen of the solvent or oil participating in the hydrogenation reaction.Thus,a theoretical basis for process optimization and reactor design can be established by analyzing the solubility of hydrogen in liquefaction solvents.Experimental studies of hydrogen solubility in liquefaction solvents are challenging due to harsh reaction conditions and complex solvent compositions.In this study,the composition and content of liquefied solvents were analyzed.As model compounds,hexadecane,toluene,naphthalene,tetrahydronaphthalene,and phenanthrene were chosen to represent the liquefied solvents in chain alkanes and monocyclic,bicyclic,and tricyclic aromatic hydrocarbons.The solubility of hydrogen X(mol/mol)in pure solvent components and mixed solvents(alkanes and aromatics mixed in proportion to the chain alkanes+bicyclic aromatic hydrocarbons,bicyclic saturated aromatic hydrocarbons+bicyclic aromatic hydrocarbons,and bicyclic aromatic hydrocarbons+compounds containing het-eroatoms composed of mixed components)are determined using Aspen simulation at temperature and pressure conditions of 373-523 K and 2-10 MPa.The results demonstrated that at high temperatures and pressures,the solubility of hydrogen in the solvent increases with the increase in temperature and pressure,with the pressure having a greater impact.Further-more,the results revealed that hydrogen is more soluble in straight-chain alkanes than in other solvents,and the solubility of eicosanoids reaches a maximum of 0.296.The hydrogen solubility in aromatic ring compounds decreased gradually with an increase in the aromatic ring number.The influence of chain alkanes on the solubility of hydrogen predominates in a mixture of solvents with different mixing ratios of chain alkanes and aromatic hydrocarbons.The solubility of hydrogen in mixed aromatic solvents is less than that in the corresponding single solvents.Hydrogen is less soluble in solvent compounds containing heteroatoms than in compounds without heteroatoms.
查看更多>>摘要：Utilizing energy storage in depleted oil and gas reservoirs can improve productivity while reducing power costs and is one of the best ways to achieve synergistic development of"Carbon Peak-Carbon Neutral"and"Underground Resource Utiliza-tion".Starting from the development of Compressed Air Energy Storage(CAES)technology,the site selection of CAES in depleted gas and oil reservoirs,the evolution mechanism of reservoir dynamic sealing,and the high-flow CAES and injection technology are summarized.It focuses on analyzing the characteristics,key equipment,reservoir construction,application scenarios and cost analysis of CAES projects,and sorting out the technical key points and existing difficulties.The devel-opment trend of CAES technology is proposed,and the future development path is scrutinized to provide reference for the research of CAES projects in depleted oil and gas reservoirs.
查看更多>>摘要：Exposure to respirable coal mine dust(RCMD)can cause chronic and debilitating lung diseases.Real-time monitoring capabilities are sought which can enable a better understanding of dust components and sources.In many underground mines,RCMD includes three primary components which can be loosely associated with three major dust sources:coal dust from the coal seam itself,silicates from the surrounding rock strata,and carbonates from the inert'rock dust'products that are applied to mitigate explosion hazards.A monitor which can reliably partition RCMD between these three components could thus allow source apportionment.And tracking silicates,specifically,could be valuable since the most serious health risks are typically associated with this component-particularly if abundant in crystalline silica.Envisioning a monitoring concept based on field microscopy,and following up on prior research using polarized light,the aim of the current study was to build and test a model to classify respirable-sized particles as either coal,silicates,or carbonates.For model development,composite dust samples were generated in the laboratory by successively depositing dust from high-purity materials onto a sticky transparent substrate,and imaging after each deposition event such that the identity of each particle was known a priori.Model testing followed a similar approach,except that real geologic materials were used as the source for each dust component.Results showed that the model had an overall accuracy of 86.5％,indicating that a field-microscopy based moni-tor could support RCMD source apportionment and silicates tracking in some coal mines.
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