Determination of total iron in iron ore based on ensembleneural network-X-ray fluorescence spectrometry
LI Ying-na1, XU Zhi-bin*2
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 explore the application of artificial intelligence in rapid quality test of iron ore, the rapid determination method of total iron content in iron ore was studied by machine learning algorithm, chemometrics and X-ray fluorescence spectrometry (XRF). Total 1098 iron ore samples from different regions (the main phases were hematite, limonite, magnetite, goethite and multi-phase mixture structure) were used as the sample set. The fuse pieces of iron ore samples were scanned by X-ray fluorescence spectrometer. The data points extracted from the spectra were used as the input of neural network, while the content of total ion was used as the output result. Then, the self-organization mapping (SOM) networks were optimized according to the phase structure obtained by X-ray diffraction (XRD). The XRF spectra of all samples were classified. The regression submodel of each subset was established based on back propagation (BP) and radial basis function (RBF) network. The prediction results of each submodel were integrated. Finally the prediction model of total iron content in iron ore based on neural network integration and XRF was established. The model experience certificate test results indicated that the accuracy was up to 89.1%. After model establishment, the additional standard substances were not required to establish calibration curve for the classification and total iron content output of unknown samples.
李颖娜, 徐志彬. 基于神经网络集成-X射线荧光光谱法的铁矿石中全铁含量测定[J]. 冶金分析, 2019, 39(1): 35-41.
LI Ying-na, XU Zhi-bin. Determination of total iron in iron ore based on ensembleneural network-X-ray fluorescence spectrometry. , 2019, 39(1): 35-41.
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