Spatial-Temporal Distribution, Geological Characteristics and Ore-Formation Controlling Factors of Major Types of Rare Metal Mineral Deposits in China

Rare metals including Lithium(Li),Beryllium(Be),Rubidium(Rb),Cesium(Cs),Zirconium(Zr),Hafnium(Hf),Niobium(Nb),Tantalum(Ta),Tungsten(W)and Tin(Sn)are important critical mineral resources.In China,rare metal mineral deposits are spatially distributed mainly in the Altay and Southern Great Xingán Range regions in the Central Asian orogenic belt;in the Middle Qilian,South Qinling and East Qinling mountains regions in the Qilian-Qinling-Dabie orogenic belt;in the Western Sichuan and Bailongshan-Dahongliutan regions in the Kunlun-Songpan-Garze orogenic belt,and in the Northeastern Jiangxi,Northwestern Jiangxi,and Southern Hunan regions in South China.Major ore-forming epochs include Indosinian(mostly 200-240 Ma,in particular in western China)and the Yanshanian(mostly 120-160 Ma,in particular in South China).In addition,Bayan Obo,Inner Mongolia,northeastern China,with a complex formation history,hosts the largest REE and Nb deposits in China.There are six major rare metal mineral deposit types in China:Highly fractionated granite;Pegmatite;Alkaline granite;Carbonatite and alkaline rock;Volcanic;and Hydrothermal types.Two further types,namely the Leptynite type and Breccia pipe type,have recently been discovered in China,and are represented by the Yushishan Nb-Ta-(Zr-Hf-REE)and the Weilasituo Li-Rb-Sn-W-Zn-Pb deposits.Several most important controlling factors for rare metal mineral deposits are discussed,including geochemical behaviors and sources of the rare metals,highly evolved magmatic fractionation,and structural controls such as the metamorphic core complex setting,with a revised conceptual model for the latter.

Factors affecting appresorium formation of Magnaporthe grisea

M. grisea causes one of the major diseases on rice, rice blast. Appresoriumformation is the key for the pathogen to penetrate into host tissue. So, it is im-portant to study the effect of environmental factors on the appresorinm formation

Geohydraulic Investigation of Aquifer Parameters in Abak, Southern Nigeria

A geophysical investigation was carried out to characterize aquifer parameters and assess the groundwater condition in Abak, southern Nigeria. Vertical electrical sounding (VES) was carried out using Schlumberger configuration in 10 locations within the area. Both manual and computer interpretation of the resistivity data reveals three to four geoelectric units (laterite topsoil, medium-grained sand, coarse-grained sand, and sandy clay sand) which agrees with the lithologic log from existing boreholes. The aquifer layer was identified along the third formation with resistivity values ranging from 1239 - 5719 Ωm and aquifer depth ranging from 30.2 - 54.8 m. The aquifer thickness ranged from 24.2 - 43.7 m. Hydraulic conductivity ranged from 0.1206 m/day - 0.5026 m/day with an average value of 0.29403 m/day. Formation factor ranged from 14.55 - 16.64. Porosity ranged from 15.98% - 22.40%, with an average value of 19.64%. The aquiferous zone falls within the medium/coarse-grained sand. The aquifer is shallow, unconfined, and prolific with a little overburden formation of 13.5 m. The area shows good prospects for groundwater development.

Origin and controlling factors of chlorite coatings—an example from the reservoir of T_3x Group of the Baojie area,Sichuan Basin,China

The study of the chlorite coatings always attracts scholars in China and other countries because the chlorite coatings play an important role in the preservation of residual primary pores in sandstone reservoirs.At present,the study of the origin and the controlling factors is relatively few.The occurrence,time of formation,genesis,controlling factors,and the mechanism of chlorite coatings inhibiting quartz overgrowths were studied in detail with thin section and SEM analysis.Samples were from the sandstone reservoirs of the T3x Group in the Baojie area,the transitional zone from the middle to the south of Sichuan Basin.The results indicate that the chlorite coatings on the walls of the pore spaces are oriented perpendicular to grain surfaces in the form of isopachous(even-thickness) grain-coating,while the chlorite coatings at the contacts between adjacent detrital grains are arranged with a preferred orientation tangential to the surface of detrital grains.The chlorite coatings were formed in the eogenetic stage.They were formed by recrystallization of Fe-rich clay films during the syndepositional period,and chlorite cements would be recrystallized after the coatings’ formation.The formation of chlorite coatings was mainly controlled by the depositional environment,provenance conditions,and diagenetic environment.The presence of chlorite coatings could result in the preservation of primary pores in deeply buried sandstone reservoirs by effectively inhibiting quartz overgrowths and the development of compaction and pressure solution.

Research progress in the causes of intracranial aneurysms

Subaracoid hemorrhage caused by intracranial aneurysms is characterized by high morbidity and mortality.There is no definite conclusion regarding the mechanism of formation,development,and rupture of intracranial aneurysms.It is generally believed to be related to congenital hereditary connective tissue diseases and acquired hemodynamic factors,vascular inflammation,and oral pathogens.In addition,gender,age,hypertension,and psychological status are also important factors.Relevant studies show a significantly lower quality of life in patients with intracranial aneurysms,and psychological factors should be studied in more depth.Neurological complications are considered an important factor in the decrease in quality of life.In conclusion,the formation,development,and rupture of intracranial aneurysms should result from a combination of congenital and acquired factors.

Investigation of effects of clay content on F-Ф relationship by Lattice gas automation using digital rock model

The Lattice Gas Automation （LGA） method, which is improved by introducing a reflection coefficient for the border between phases to show its effect on current path, is used in this paper to simulate the current flow in digital rock for investigating the effects of clay content and clay distribution types on the relationship between formation factor（F） and rock porosity（φ）. The digital rock model is constructed by simulating a natural deposit of matrix particles with different shapes and radius. Based on the simulation results, it was found that both dispersed clay and laminated clay can lead to a non-Archie relationship of F-φ. The non-Archie effect of laminated clay on the F-φ relationship was more significant than that of dispersed clay. Moreover, a realistic model is developed in this work for quantitatively describing the effect of clay content （Denoted as Vsh） on parameters a and m. These study results have further demonstrated the validity of LGA in study of electrical transport properties at a pore scale.

Development of new correlations for the oil formation volume factor in oil reservoirs using artificial intelligent white box technique

Oil formation volume factor(OFVF)is considered one of the main parameters required to characterize the crude oil.OFVF is needed in reservoir simulation and prediction of the oil reservoir performance.Existing correlations apply for specific oils and cannot be extended to other oil types.In addition,big errors were obtained when we applied existing correlations to predict the OFVF.There is a massive need to have a global OFVF correlation that can be used for different oils with less error.The objective of this paper is to develop a new empirical correlation for oil formation volume factor(OFVF)prediction using artificial intelligent techniques(AI)such as;artificial neural network(ANN),adaptive neuro-fuzzy inference system(ANFIS),and support vector machine(SVM).For the first time we changed the ANN model to a white box by extracting the weights and the biases from AI models and form a new empirical equation for OFVF prediction.In this paper we present a new empirical correlation extracted from ANN based on 760 experimental data points for different oils with different compositions.The results obtained showed that the ANN model yielded the highest correlation coefficient(0.997)and lowest average absolute error(less than 1%)for OFVF prediction as a function of the specific gravity of gas,the dissolved gas to oil ratio,the oil specific gravity,and the temperature of the reservoir compared with ANFIS and SVM.The developed empirical equation from the ANN model outperformed the previous empirical correlations and AI models for OFVF prediction.It can be used to predict the OFVF with a high accuracy.

Apparent Formation Factor for Leachate-Saturated Waste and Sediments: Examples from the USA and China

The formation factor relates bulk resistivity to pore fluid resistivity in porous materials. Understanding the formation factor is essential in using electrical and electromagnetic methods to monitor ieachate accumulations and movements both within and around landfills. Specifically, the formation factor allows leachate resistivity, the degree of saturation, and, possibly, even the hydraulic conductivity of the waste to be estimated from non-invasive surface measurements. In this study, apparent formation factors are computed for three landfills with different types of waste as well as sediments contaminated by landfill leachate. Resistivity soundings at the closed Mallard North landfill in suburban Chicago （Illinois, USA） mapped leachate surfaces that were confirmed by monitoring wells. The resistivity of leachate-saturated waste from resistivity sounding inversions was then divided by the leachate resistivity values measured in-situ to compute apparent formation factors （Fa） ranging from 1.6 to 4.9. A global Fa of 3.0±1.9 was computed for the entire monitored portion of this landfill. At a nearby mixed laboratory waste landfill, a 2D inverted resistivity section was used to compute an Fa of 2.9. Finally, a distinctly different Fa value of 10.6±2.8 was computed for leachate-saturated retorted oil shale wastes north of Maoming （茂名）, Guangdong （广东） Province, China. Shallow aquifers in the Laohuling （老虎岭） Formation near this landfill are polluted by acidic leachate containing heavy metals and organic compounds. The Fa for aquifers containing contaminated groundwater fall in the same range as aquifers with normal groundwater, 1.7-3.9. However, models from inverted sounding curves over these contaminated areas exhibit unusually low resistivity layers, which may be diagnostic of contamination.

Characterization of N-nitrosodimethylamine formation from the ozonation of ranitidine

N-nitrosodimethylamine（NDMA） is an emerging disinfection by-product which is formed during water disinfection in the presence of amine-based precursors. Ranitidine, as one kind of amine-based pharmaceuticals, has been identified as NDMA precursor with high NDMA molar conversion during chloramination. This study focused on the characterization of NDMA formation during ozonation of ranitidine. Influences of operational variables（ozone dose, pH value） and water matrix on NDMA generation as well as ranitidine degradation were evaluated. The results indicate high reactivity of ranitidine with ozone.Dimethylamine（DMA） and NDMA were generated due to ranitidine oxidation. High pH value caused more NDMA accumulation. NDMA formation was inhibited under acid conditions（pH ≤ 5） mainly due to the protonation of amines. Water matrix such as HCO-3and humic acid impacted NDMA generation due to UOH scavenging. Compared with UOH,ozone molecules dominated the productions of DMA and NDMA. However, UOH was a critical factor in NDMA degradation. Transformation products of ranitidine during ozonation were identified using gas chromatography–mass spectrometry. Among these products, just DMA and N,N-dimethylformamide could contribute to NDMA formation due to the DMA group in the molecular structures. The NDMA formation pathway from ranitidine ozonation was also proposed.

Data driven prediction of oil reservoir fluid properties

Accuracy of the fluid property data plays an absolutely pivotal role in the reservoir computational processes.Reliable data can be obtained through various experimental methods,but these methods are very expensive and time consuming.Alternative methods are numerical models.These methods used measured experimental data to develop a representative model for predicting desired parameters.In this study,to predict saturation pressure,oil formation volume factor,and solution gas oil ratio,several Artificial Intelligent(AI)models were developed.582 reported data sets were used as data bank that covers a wide range of fluid properties.Accuracy and reliability of the model was examined by some statistical parameters such as correlation coefficient(R2),average absolute relative deviation(AARD),and root mean square error(RMSE).The results illustrated good accordance between predicted data and target values.The model was also compared with previous works and developed empirical correlations which indicated that it is more reliable than all compared models and correlations.At the end,relevancy factor was calculated for each input parameters to illustrate the impact of different parameters on the predicted values.Relevancy factor showed that in these models,solution gas oil ratio has greatest impact on both saturation pressure and oil formation volume factor.In the other hand,saturation pressure has greatest effect on solution gas oil ratio.

On-board measurements of gaseous pollutant emission characteristics under real driving conditions from light-duty diesel vehicles in Chinese cities

A total of 15 light-duty diesel vehicles（LDDVs） were tested with the goal of understanding the emission factors of real-world vehicles by conducting on-board emission measurements. The emission characteristics of hydrocarbons（HC） and nitrogen oxides（NOx） at different speeds, chemical species profiles and ozone formation potential（OFP） of volatile organic compounds（VOCs） emitted from diesel vehicles with different emission standards were analyzed. The results demonstrated that emission reductions of HC and NOxhad been achieved as the control technology became more rigorous from Stage I to Stage IV. It was also found that the HC and NOxemissions and percentage of O2 dropped with the increase of speed, while the percentage of CO2 increased. The abundance of alkanes was significantly higher in diesel vehicle emissions, approximately accounting for 41.1%–45.2%, followed by aromatics and alkenes. The most abundant species were propene,ethane, n-decane, n-undecane, and n-dodecane. The maximum incremental reactivity（MIR）method was adopted to evaluate the contributions of individual VOCs to OFP. The results indicated that the largest contributors to O3 production were alkenes and aromatics, which accounted for 87.7%–91.5%. Propene, ethene, 1,2,4-trimethylbenzene, 1-butene, and1,2,3-trimethylbenzene were the top five VOC species based on their OFP, and accounted for 54.0%-64.8% of the total OFP. The threshold dilution factor was applied to analyze the possibility of VOC stench pollution. The majority of stench components emitted from vehicle exhaust were aromatics, especially p-diethylbenzene, propylbenzene, m-ethyltoluene, and p-ethyltoluene.