Rapid Detection of Iron Ore Grades Based on Fractional-Order Derivative Spectroscopy and Machine Learning
Hongfei Xie, Dong Xiao, Zhizhong Mao · IEEE Transactions on Instrumentation and Measurement · 2023
The time-consuming nature of chemical testing techniques makes them lag behind mineral processing. Therefore, this paper combines Visible-infrared reflectance spectroscopy with machine learning (ML) algorithms to achieve rapid detection of iron ore grades and meet the requirements of mining production. Firstly, the standard normal variate and de-trending are used to eliminate noise and baseline drift in the original spectral data. Then, extraneous signals are removed using direct orthogonal signal correction (DOSC). In addition, fractional-order derivative (FOD) is performed on the DOSC spectrum to further amplify the spectral details. To extract spectral features and reduce the spectral dimension, a multilayer incremental extreme learning machine auto-encoder (MIELM-AE) is proposed in this paper. MIELM-AE can automatically match the optimal number of network nodes and network layers to minimize the reconstruction error. The experimental results show that the Pearson correlation coefficient (R2) of the extreme learning machine (ELM) built using MIELM-AE improves from 0.715 to 0.821, compared with the ELM built without the dimensionality reduction method. To increase the measurement accuracy, this paper uses Tikhonov regularization and truncated singular value decomposition to alleviate the ill-conditioned matrix of the hidden layer of the ELM and uses the incremental method to match the optimal network nodes. Finally, double regularization incremental ELM (DRIELM) is proposed in this paper. Experiments show that DRIELM obtained the highest detection accuracy with an R2of 0.932 at an FOD of 0.4.