Many non-traditional isotopes, such as chlorine, magnesium, calcium, etc., are widely used as groundwater tracers. A new sample processing protocol of purification and concentration for isotopic analysis is presented to overcome many of the major drawbacks of existing methods. Contemporary sample preparation often requires several laborious off-line procedures in a ultra clean laboratory prior to instrumental determination; additionally, interference ions in real samples are difficult to completely remove, especially when the concentration of those ions is equal to that of the target ions. The new protocol includes the following steps: (i) one-step purification using a newly developed isotopic preparative chromatograph (IPC) with a background suppressed mode to obtain extremely pure components that only have target ions and H2O; (ii) enrichment of the collected pure solution from the previous step using a newly developed ultra clean concentrator filled with high purity nitrogen; (iii) transforming the enriched target ion into suitable speciation inside the ultra clean concentrator; (iv) finally, sending the enriched solutions to a multi-collector inductively coupled-plasma mass-spectrometer (MC-ICP-MS) or thermal ionization mass spectrometer (TIMS). The present method was validated using certified reference materials and real samples for both chlorine and magnesium; the precision of chlorine ratio value was generally below 0.22‰ and that of Mg was below 0.12‰. This processing protocol provides a potential method for isotope sample preparation and analysis in a small number of geological samples with low concentrations of many other elements or compounds such as nitrate, sulfate, lithium, calcium, strontium, etc.
High resolution remote sensing image classification was one of the vital research points in the remote sensing field.Considering the high complementary of spectral angle and spectral distance in the classification,a new method called automatic weighting fusion classification based on spectral angel and spectral distance was proposed.It was an improvement of the strategy to merge the results of different classifiers based on automatic weighting fusion for different classifiers,which promoted the classification accuracy.The experimental results of QuickBird images showed that the classification accuracy of this method obviously exceeded both spectral angel classification and spectral distance classification,and this method could be widely used to classify and recognize various high spatial resolution remote sensing images.
Polymer electrolyte fuel cells have been widely used in automotive applications, in which fast-response and highly accurate fuel cell systems are required to achieve good performance. To fulfill this requirement, an adaptive fuel cell model is developed herein for a polymer electrolyte fuel cell system. The model is established on the basis of a least squares support vector machine. A genetic algorithm is employed to set the initial values of the internal parameters of the model by incorporating existing data from previous experiments. Then, an adaptive process is further conducted to provide an online update of the model's internal parameters. The genetic algorithm can effectively avoid the initial parameters by falling to a local minimum. Moreover, the online updating of the parameters makes the model more adaptive to load changes in the real-time application of the fuel cell system. The proposed model is experimentally-tested on a fuel cell test rig. The results indicate that the proposed model can accurately and effectively predict fuel cell voltage. In addition, two reference models are employed to compare with the online adaptive model, by which the advantages of the genetic algorithm and parameter updating are verified. The model accuracy is improved significantly with the genetic algorithm, indicating the importance of initial parameters setting. The gradient method also benefits the model's accuracy in online modeling and predicting, but its efficiency still depends on the initial parameters. This online adaptive model can easily address frequent load change and the long term operation of fuel cells.
It is difficult to observe metal ore body underground directly because the spatial geometry of which often displays great variability.Detailed three-dimensional(3D) shaping and characterizing of ore body is one of the hot topics and difficulties in the fields of Visualization in Scientific Computing and Geostatistics.The complexity of 3D space often leads Kriging reserves estimation to great uncertainty and empiricism.Fortunately,3D Visualization has since evolved target for improving the estimation accuracy.For anisotropic nested overlap and reserves estimation,this paper proposes a visualized reserves estimation method based on 3D Kriging method.First take 3D CAD technology into account to establish base object,then improve the variogram analysis in a visualized way and propose an improved anisotropic nested overlap algorithm based on Quaternion.After that,carry on reserves estimation incorporated with the Kriging algorithm.According to the results,we make some necessary modifications of decisions which have bigger errors in order to improve the Kriging estimation accuracy and objectivity.We provide instances of Ashele Copper Deposit in Xinjiang Province and Jiama Copper Deposit in Tibet Province to demonstrate the proposed solution.Experimental results show that the calculations of variability are consistent with the actual explorations.The reserves estimations are credible and agree well with the actual reserves with an error between 5% and 10%,which suggests our method effective.
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