Having studied some of the factors that influence the results of iron grade analysis during the iron ore production process, we established an online analytical system based on energy dispersive X-ray fluorescence. The system can determine the iron ore grade within 5 minutes. Compared with similar systems, this online analytical system has the ability to deal with the problems of poor sampling, poor on-site conditions for online iron ore analysis, variation in the moisture content of samples, the matrix effect and spectral drift. The system has been applied to iron ore grade evaluation in the Panzhihua Iron and Steel Company, China. Practical tests illustrated that the system can effectively improve iron ore grade evaluation.
We present the design and optimization of a prompt T-ray neutron activation analysis （PGNAA） thermal neutron output setup based on Monte Carlo simulations using MCNP5 computer code. In these simulations, the moderator materials, reflective materials, and structure of the PCNAA 2526f neutrons of thermal neutron output setup are optimized. The simulation results reveal that the thin layer paraffin and the thick layer of heavy water moderating effect work best for the 252Cf neutron spectrum. Our new design shows a significantly improved per- formance of the thermal neutron flux and flux rate, that are increased by 3.02 times and 3.27 times, respectively, compared with the conventional neutron source design.
The distribution characteristics of the neutron field in cement was simulated using the MCNP code to comply with the requirements of an online Prompt Gamma Neutron Activation Analysis system.Simulation results showed that the neutron relative flux proportion reduced with increasing cement thickness.When the cement thickness remains unchanged,the reduced proportion of thermal neutrons increases to a small extent,but the epithermal, intermediate,and fast neutrons will decrease according to the geometric progression.H element in the cement mainly affects the reduction of fast neutrons and other single-substance elements,e.g.,O,Ca,56Fe,Si,and Al.It also slows down the reduction of the fast neutrons via inelastic scattering.O contributes more than other elements in the reduction of fast neutrons.Changing the H content affects the thermal,epithermal,intermediate,and fast neutrons, while changing the Ca,Fe,and Si contents only influences the thermal,epithermal,and intermediate neutrons;hence, there is little effect on the reduction of fast neutrons.
YANG JianboYANG YigangLI YuanjingTUO XianguoLI ZheLIU MingzheCHENG YiMU KeliangWANG Lei
A semi-empirical detector response function(DRF)model is established to fit characteristic X-ray peaks recorded in Si-PIN spectra,which is mainly composed of four components:a truncated step function,a Gaussian-shaped full-energy peak,a Gaussian-shaped Si escape peak and an exponential tail.A simple but useful statistical distribution-based analytic method(SDA)is proposed to achieve accurate values of standard deviation for characteristic X-ray peaks.And the values of the model parameters except for the standard deviation are obtained by weighted least-squares fitting of the pulse-height spectra from a number of pure-element samples.A Monte Carlo model is also established to simulate the X-ray measurement setup.The simulated flux spectrum can be transformed by Si-PIN detector response function to real pulse height spectrum as studied in this work.Finally,the fitting result for a copper alloy sample was compared with experimental spectra,and the validity of the present method was demonstrated.
A semi-empirical detector response function （DRF） model of Si （PIN） detector is proposed to fit element Kα and Kβ X-ray spectra, which is based on statistical distribution analytic （SDA） method. The model for each single peak contains a step function, a Gaussian function and an exponential tail function. Parameters in the model are obtained by weighted nonlinear least-squares fitting method. In the application, six kinds of elements＇ characteristic X-ray spectra are obtained by Si （PIN） detector, and fitted out by the established DRF model. Reduced chi-square values are at the interval of 1.11-1.25. Other applications of the method are also discussed.
A new statistical fitting approach, named Statistical Distribution-Based Analytic （SDA） method, is proposed to fit single Gaussian-shaped Ka and KI3 X-ray peaks recorded by Si（PIN） and silicon drift detector （SDD）. In this method, we use the dis- crete distribution theory to calculate standard deviation of energy resolution a. The calibration of cr and energy （E） for two de- tectors between the energy ranges of 4.5-26 keV are also completed by measuring characteristic X-ray spectra of nineteen types of pure elements. With the spectrum fraction （SF） parameter proposed in this paper, the SDA method can be used to re- solve overlapping peaks. In measured spectra, the Gaussian part of X-ray peaks can be fitted by a Gaussian function with two parameters, ~ and SF. This new fitting approach is simpler than traditional methods and it achieves relatively good results when fitting the complex X-ray spectra of national standard alloy samples detected by Si（PIN） and SDD detectors. The 3（2 values are obtained for each spectrum to assess fitting results, and the SDA fitting method gives a preferable fit for the SDD detector.
A totally asymmetric simple exclusion process (TASEP) has become an essential tool in modeling and analyzing non-equilibrium systems.A wide variety of TASEP models have been developed that are motivated by real-world traffic,biological transport and by the dynamics of the process itself.This paper provides an overview of recent developments in TASEP with inhomogeneity.Some important generalizations and extensions of inhomogeneous TASEP models are reviewed,and several popular mean-field techniques used to analyze the inhomogeneous TASEP models are summarized.A comparison between similar TASEP models under different updating procedures is given.Phase separations in such disordered systems have been identified.The present status of the inhomogeneous TASEP models and areas for future investigations are also described.
