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Iron ore identification method using reflectance spectrometer and a deep neural network framework.

Dong Xiao¹, Ba Tuan Le², Thai Thuy Lam Ha³

¹College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China; Key Laboratory of Intelligent Diagnosis and Safety for Metallurgical Industry, Liaoning Province, Northeastern University, Shenyang 110819, China.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy

|November 24, 2020

Summary

This summary is machine-generated.

This study introduces a novel method for iron ore identification using deep learning and visible-infrared spectroscopy. The developed model achieves high accuracy, significantly improving ore classification efficiency.

Keywords:

Convolution neural network Extreme learning machine Identification Iron ore Spectrometer Visible-infrared spectroscopy

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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Area of Science:

Geoscience and Mining Engineering
Artificial Intelligence and Machine Learning

Background:

Accurate iron ore classification is crucial for efficient mineral processing.
Traditional identification methods are time-consuming and costly.

Purpose of the Study:

To develop a rapid and accurate iron ore identification method.
To establish an iron ore classification model using deep learning and spectroscopy.

Main Methods:

Collected iron ore samples from the Anshan iron ore area.
Measured spectral data using a spectrometer.
Developed a deep neural network combining convolutional neural networks and an improved extreme learning machine algorithm.

Main Results:

The proposed model effectively identifies iron ore types.
Achieved an overall classification accuracy of 98.11%.

Conclusions:

The deep learning-based spectroscopic method offers a highly accurate and efficient approach for iron ore identification.
This technique overcomes the limitations of traditional methods in terms of speed and cost.