Rapid detection of copper ore grade based on visible-infrared spectroscopy and TSVD-IVTELM

Xie, Hongfei; Mao, Zhizhong; Xiao, Dong; Liu, Jingyi

doi:10.1016/j.measurement.2022.112003

Cited by 10 publications

(4 citation statements)

References 36 publications

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“…Figure 9 shows the comparison of spatial mean conductivity distribution σ * under simulation σ * SIM and an experiment using EIT σ * EIT , which represent the relationship between σ * and metal position in the case of different Cu position (5)(6)(7)(8)(9). The σ * represent spatial mean conductivity distribution in the sensor domain.…”

Section: Eit Analysismentioning

confidence: 99%

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Metal Particle Detection by Integration of a Generative Adversarial Network and Electrical Impedance Tomography (GAN-EIT) for a Wet-Type Gravity Vibration Separator

Ibrahim,

Sejati,

Darma

et al. 2023

Sensors

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The minor copper (Cu) particles among major aluminum (Al) particles have been detected by means of an integration of a generative adversarial network and electrical impedance tomography (GAN-EIT) for a wet-type gravity vibration separator (WGS). This study solves the problem of blurred EIT reconstructed images by proposing a GAN-EIT integration system for Cu detection in WGS. GAN-EIT produces two types of images of various Cu positions among major Al particles, which are (1) the photo-based GAN-EIT images, where blurred EIT reconstructed images are enhanced by GAN based on a full set of photo images, and (2) the simulation-based GAN-EIT images. The proposed metal particle detection by GAN-EIT is applied in experiments under static conditions to investigate the performance of the metal detection method under single-layer conditions with the variation of the position of Cu particles. As a quantitative result, the images of detected Cu by GAN-EIT ψ̿GAN in different positions have higher accuracy as compared to σ*EIT. In the region of interest (ROI) covered by the developed linear sensor, GAN-EIT successfully reduces the Cu detection error of conventional EIT by 40% while maintaining a minimum signal-to-noise ratio (SNR) of 60 [dB]. In conclusion, GAN-EIT is capable of improving the detailed features of the reconstructed images to visualize the detected Cu effectively.

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Section: Eit Analysismentioning

confidence: 99%

“…On the other hand, optical detectors that use image processing to identify minor Cu particles have a significant advantage in detecting minor Cu particles [7]. The minor Cu particles in the uppermost layer are detected by image processing techniques.…”

Section: Introductionmentioning

confidence: 99%

Metal Particle Detection by Integration of a Generative Adversarial Network and Electrical Impedance Tomography (GAN-EIT) for a Wet-Type Gravity Vibration Separator

Ibrahim,

Sejati,

Darma

et al. 2023

Sensors

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“…The reflectance in the 350-2500 nm band is negatively correlated with magnetite, and the error of the model prediction result is less than 1% through the model established by the law [3] ；SURAJITP S et al used the EO-1 Hyperion data to study the iron ore grade in the iron ore deposit area of Noamundi, and found that in the wavelength region of 752-773 nm, the position of the near-infrared absorption trough moves to the direction of the longer wavelengths with the decreasing of the iron content [4] . XIE H F et al used the hyperspectral data of different grades of copper ore to analyze, and found that with the increase of copper ore grade, its spectral reflectance decreases, and through the correlation analysis, found that the Cu content in the ore is negatively correlated with the spectral reflectance of the ore [5] ; Mengqian Li used hyperspectral detection technology to determine the iron content of iron ore powder, and the relative error of the inversion was 7.26% [6] ; Xu Yanhui et al used spectral analysis technology to spectroscopically analyze magnetite quartzite, and found that the content of TFe and mFe had an exponential negative correlation with the mean value of the reflectance of 850-900 nm [7] ; He Qun et al used spectral analysis technology to carry out the whole iron grade of BIF iron ore model inversion, the inversion error is about 3.43% on average [8] .…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral magnetite grade based on characteristic band screening inversion study

Bai,

Jia,

Xie

et al. 2024

Preprint

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In order to clarify the relationship between magnetite grade and reflectance spectra, spectral testing of different grades of magnetite was carried out to analyze the effect of iron grade on the reflectance spectra of specimens. The results show that the fluctuation trends of the spectral curves of different grades of magnetite are slightly different. By screening the characteristic bands of magnetite grade, it was found that the characteristic bands of different grades of magnetite are mainly distributed in the range of 350-1000 nm and 1800-2500 nm. Then based on the relationship between magnetite grade and reflectance spectra, two inversion models of magnetite grade, PLSR and MLSR, were established. By comparing and analyzing the inversion models of magnetite grade established by PLSR and MLSR, it was found that the inversion accuracy based on the PLSR model was better than that of the MLSR model, in which the and the RMSE of validation set of the best model was respectively 0.9982 and 0.95%.

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“…Spectroscopy detection enables fast and non-destructive detection, making it widely used in ore detection [5]. Compared with chemical spotting and flame atomic absorption [6], spectroscopy has significant advantages in terms of repeat detection and speed of detection in ores.…”

Section: Introductionmentioning

confidence: 99%

Rapid Detection of Iron Ore and Mining Areas Based on MSSA-BNVTELM, Visible—Infrared Spectroscopy, and Remote Sensing

Xu,

Mao,

Zhang

et al. 2023

Remote Sensing

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The accuracy and rapidity of total iron content (TFE) analysis can accelerate iron ore production. Although the conventional TFE detection methods are accurate, its detection speed presents difficulties in meeting production requirements. Therefore, this paper proposes a method of TFE detection based on reflectance spectroscopy (wavelength range: 340–2500 nm) and remote sensing. Firstly, spectral experiments were conducted on iron ore using the HR SVC-1024 spectrometer to obtain spectral data for each sample. Then, the spectra were smoothed and dimensionally reduced by using wavelet transform and principal component analysis. To improve the detection accuracy of TFE, a two hidden layer extreme learning machine with variable neuron nodes based on an improved sparrow search algorithm and batch normalization optimization (MSSA-BNVTELM) is proposed. According to the experimental results, MSSA-BNVTELM exhibited superior detection accuracy in comparison to other algorithms. In addition, this research established a remote sensing detection model using Sentinel-2 data and MSSA-BNVTEM to detect the distribution of TFE in the mining area. The distribution of TFE in the mine area was plotted based on the detection results. The results show that the remote sensing of the mine area can be useful for detection of the TFE distribution, providing assistance for the mining plan.

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Rapid detection of copper ore grade based on visible-infrared spectroscopy and TSVD-IVTELM

Cited by 10 publications

References 36 publications

Metal Particle Detection by Integration of a Generative Adversarial Network and Electrical Impedance Tomography (GAN-EIT) for a Wet-Type Gravity Vibration Separator

Metal Particle Detection by Integration of a Generative Adversarial Network and Electrical Impedance Tomography (GAN-EIT) for a Wet-Type Gravity Vibration Separator

Hyperspectral magnetite grade based on characteristic band screening inversion study

Rapid Detection of Iron Ore and Mining Areas Based on MSSA-BNVTELM, Visible—Infrared Spectroscopy, and Remote Sensing

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