Determination of lead and arsenic in iron ore by X-ray fluorescence spectrometry based on genetic neural network
LI Ying-na1,XU Zhi-bin2,LIU Shuang-long2
1. Department of Environmental and Chemical Engineering,Tangshan College,Tangshan 063000,China; 2.Hebei Entry-exit Inspection and Quarantine Technical Center,Tangshan 063611,China
Abstract：In order to determine the content of trace lead and arsenic in iron ore, the sample was grinded by planetary ball mill. Then it was pre-fused at 800 ℃ followed by fusion at 1 050 °C for 5 min. After cooling, the sample was re-fused for 8 min to prepare the stable fuse pieces with low dilution ratio (the mass ratio of sample and flux was 1∶2). The fluorescence intensity of lead and arsenic in fuse pieces of synthetic sample was scanned with standard-less analysis software. The X-ray fluorescence intensity data obtained under different scanning angles were used as the input of neural network. Meanwhile, the content of lead and arsenic was used as output. The weight and threshold values of network were optimized by genetic algorithm. The spectral overlapping of lead and arsenic in sample of test set was corrected, solving the problem of high background intensity due to low dilution ratio. The root-mean-square error of calibration (RMSEC) for lead and arsenic content in prediction set was 0.39 and 0.42, respectively. The correlation coefficients were both 0.98, which had no significant difference with the theoretical α coefficient regression equation method.
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LI Ying-na,XU Zhi-bin,LIU Shuang-long. Determination of lead and arsenic in iron ore by X-ray fluorescence spectrometry based on genetic neural network. , 2017, 37(10): 22-26.
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