THE USE OF CHELATING RESIN FOR THE PRE-CONCENTRATION OF TRACE RARE EARTH ELEMENTS IN BIOLOGICAL SAMPLES AND THEIR DETERMINATION

Macroporous amino-carboxvlic chelating resin of acrylic acid series may concentrate trace rare earth in the presence of 0.1 mol/l α-hydroxy-isobutyric acid at pH 4.5.With 0.10 mol/l HCl as eluant rare earth may be eluted quantitatively and separated from Ca,Mg,Fe and Cu.This method is used to determine trace amount of rare earth in human hair,stone (in kidney and bladder),peanut,grape,strawberry,cucumber,pig liver and other biological samples.

Numerical analysis of the effects of downhole dynamic conditions on formation testing while drilling

Formation testing while drilling is an innovative technique that is replacing conventional pressure testing in which the fluid sampling is conducted in a relatively short time following the drilling. At this time, mud invasion has just started, mudcake has not formed entirely and the formation pressure is not stable. Therefore, it is important to study the influence of the downhole dynamic environment on pressure testing and fluid sampling. This paper applies an oil-water two phase finite element model to study the influence of mudcake quality and mud filtrate invasion on supercharge pressure, pretest and sampling in the reservoirs of different permeability. However, the study is only for the cases with water based mud in the wellbore. The results illustrate that the mudcake quality has a significant influence on the supercharge pressure and fluid sampling, while the level of mud filtrate invasion has a strong impact on pressure testing and sampling. In addition, in-situ formation pressure testing is more difficult in low permeability reservoirs as the mud filtrate invasion is deeper and therefore degrades the quality of fluid sampling. Finally, a field example from an oil field on the Alaskan North Slope is presented to validate the numerical studies of the effects of downhole dynamic conditions on formation testing while drilling.

An Artificial Neural Network-Based Response Surface Method for Reliability Analyses of c-φ Slopes with Spatially Variable Soil

This paper presents an artificial neural network（ANN）-based response surface method that can be used to predict the failure probability of c-φslopes with spatially variable soil.In this method,the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model;the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties;and finally,an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables.The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability.As a result,the obtained approximate function can be used as an alternative to the specific analysis process in c-φslope reliability analyses.

Representative elementary volume analysis of polydisperse granular packings using discrete element method

The representative elementary volume （REV） for three-dimensional polydisperse granular packings was determined using discrete element method simulations. Granular mixtures of various sizes and particle size distributions were poured into a cuboid chamber and subjected to uniaxial compression, Findings showed that the minimum REV for porosity was larger compared with the REV for parameters such as coordination number, effective elastic modulus, and pressure ratio. The minimum REV for porosity and other parameters was found to equal 15,10, and 5 times the average grain diameter, respectively. A study of the influence of sample size on energy dissipation in random packing of spheres has also confirmed that the REV size is about 15 times the average grain diameter. The heterogeneity of systems was found to have no effect on the REV for the parameters of interest for the narrow range of coefficient of uniformity analyzed in this paper. As the REV approach is commonly applied in both experimental and numerical studies, determining minimum REV size for polydisperse granular packings remains a crucial issue.

Effect of sample size on the response of DEM samples with a realistic grading

This paper shows that for DEM simulations of triaxial tests using samples with a grading that is repre- sentative of a real soil, the sample size significantly influences the observed material response. Four DEM samples with identical initial states were produced： three cylindrical samples bounded by rigid wails and one bounded by a cubical periodic cell, When subjected to triaxial loading, the samples with rigid boundaries were more dilative, stiffer and reached a higher peak stress ratio than the sample enclosed by periodic boundaries. For the rigid-wall samples, dilatancy increased and stiffness decreased with increasing sample size, The periodic sample was effectively homogeneous, The void ratio increased and the contact density decreased close to the rigid walls, This heterogeneity reduced with increasing sample size. The positions of the critical state lines （CSLs） of the overall response in e-log p＇ space were sensitive to the sample size, although no difference was observed between their slopes. The critical states of the interior regions of the rigid-wall-bounded samples approached that of the homogeneous periodic sample with increasing sample size. The ultimate strength of the material at the critical state is independent of sample size.