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The average grade of iron ore is low in China, and it is significant for the effective utilization of iron ore to obtain grades. In the paper, the high-precision model was constructed based on the spectrum of hematite and the multiple hidden layers extreme learning machine with random values and dense connection, which took the output of all previous layers as the input of each hidden layer. It has achieved high precision during the detection of the total iron content for hematite, which meets the requirements for the construction and development of new green and intelligent mines. [ABSTRACT FROM AUTHOR]

Through remote sensing inversion, the evolution and treatment of saline and alkaline land can be monitored on a large scale in real time. The spectral data and salt content of samples for saline and alkaline land are obtained, and the inversion model is an improvement to the extreme learning machine, which is based on the activation function and the seagull optimization algorithm (SOA). First, the paper proposes the two-hidden-layer extreme learning machine (TELM) based on the hyperparameter activation function and the seagull optimization algorithm (HS-TELM). Then, opposition-based learning (OBL), the Cauchy distribution, and the inverse S-shaped functions (Anti-softsign, Anti-tanh, Anti-sigmoid) are used to improve the SOA, resulting in an improved seagull optimization algorithm (ISOA). At the same time, a series of improved algorithms based on the ISOA and the TELM, including the TELM based on the improved seagull optimization algorithm (ISOA-TELM), the TELM with different hidden layers containing different activation functions (D-TELM) based on the improved seagull optimization algorithm (ISOA-D-TELM), the TELM based on the hyperparameter activation function and the improved seagull optimization algorithm (ISOA-HS-TELM), and the TELM with different hidden nodes containing different activation functions (ND-TELM) based on the improved seagull optimization algorithm (ISOA-ND-TELM), are proposed. The experimental results show that the accuracy of the distribution map of salt content is 2.203 g/kg after adding the spectral correction model, which yields the expected high-precision inversion. [ABSTRACT FROM AUTHOR]

Iron ore is an important raw material for the steel industry, so it is of great economic significance to determine the grade of the iron ore quickly and accurately. And the TFe content is the main indicator that determines the grade of the iron ore and whether the iron ore can be smelted directly. Unlike manual methods and methods for chemical analysis, the paper uses the selection of band for the near-infrared spectrum based on the pruning method and the two-hidden-layer extreme learning machine based on LU decomposition and seagull optimization algorithm (LU-TELM-SOA) to identify the TFe content. First of all, the paper proposes the selection of band based on the pruning method to retain the sensitive band of the near-infrared spectrum. Aiming at the problems of poor stability and low accuracy of a single LU-TELM (the two-hidden-layer extreme learning machine based on LU decomposition) model, the paper proposes LU-TELM-SOA. The experimental results show that LU-TELM-SOA has the advantages of high accuracy and strong stability. [ABSTRACT FROM AUTHOR]