XRF-based Detection And Quantitative Analysis Of Key Elements In Copper Mines

Copper mines are widely used in modern industry,modern national defense,play an important role in the national economy,and have great economic and strategic significance.In recent years,the demand for copper ore resources has been increasing,and the development efforts have continued to increase.The limited elemental analysis technology of copper ore has caused a series of environmental pollution and resource waste problems.Therefore,accurate analysis of the element content of copper ore is very important to realize scientific mining and efficient utilization.X-ray fluorescence（XRF）elemental analyzer has the advantages of low cost,wide range of analyzed elements,and unconstrained sample morphology,and is widely used in the quantitative analysis of copper ore elements.However,in the actual analysis,factors such as the complex composition of copper ore and the matrix effect between elements lead to the existence of a continuous spectrum background in the measurement spectrum,and the relationship between the intensity of the spectrum and the content is nonlinear,which greatly reduces the quantitative analysis of elements.Accuracy.Therefore,relying on the general project of the National Natural Science Foundation of China,thesis studies the detection and quantitative analysis method of key elements in copper mines based on XRF.The main contents are as follows.（1）The method of spectral background subtraction is studied,and a background subtraction algorithm based on Variational Mode Decomposition（VMD）is proposed.The algorithm reduces the influence of the signal peak above the background by iteratively,until the fitting converges to the true background,and the corrected spectrum is obtained by subtracting the background.In this study,according to the characteristics of copper ore spectrum and a large number of experimental analysis,the selection of parameters such as VMD decomposition layers and modal components is determined.Based on the simulation spectrum,copper ore spectrum,alloy spectrum and other sets of experiments,it is proved that compared with the existing algorithm,the proposed algorithm has a significant effect in removing the complex spectral background of the ore sample,and improves the accuracy of the measured spectrum.（2）A quantitative analysis method for key elements of copper mines based on Bayesian optimization support vector machine regression（BOA-SVR）combined with sensitivity dimension reduction was established.In order to improve the accuracy of quantitative analysis and the generalization ability of the model,the method uses SVR to fit the nonlinear relationship between element content and characteristic peak intensity;uses Bayesian optimization algorithm to find the optimal SVR hyperparameter;Relevant features are modeled to achieve feature dimensionality reduction.The results show that the coefficients of determination of the proposed algorithm for fitting the straight lines of Cu and Fe elements on the test set are 0.98 and 0.87,respectively,which are higher than the accuracy of the unreduced SVR model and partial least squares（PLS）algorithms.（3）A method to improve the accuracy of quantitative analysis of key elements in copper mines based on Monte Carlo（MCNP）was studied.In view of the difficulty of obtaining copper mines and the difficulty of sample preparation,first,a simulated spectrum is generated from the content information of copper ore standard substances,and then the segmented parameter fitting correction method is used for correction,and finally the corrected simulated spectrum is used to assist the SVR training.Improve analysis accuracy.The results show that the coefficient of determination is improved from0.85 to 0.92 by using the simulated spectrum to assist the SVR model to train and predict the Cu element of the real sample,which verifies the effectiveness of the method.To sum up,this study proposes a background subtraction algorithm based on VMD;establishes a BOA-SVR quantitative analysis model based on sensitivity dimensionality reduction,and develops supporting software;and studies methods to improve the accuracy of quantitative analysis based on MCNP.It provides important technical support for the accurate analysis of copper ore element content based on XRF.