Sensor-Based Ore Sorting—A Review of Current Use of Electromagnetic Spectrum in Sorting

Abstract

Sensor-based sorting has had a wide range of industrial use in automating and speeding up the process which requires substances or objects to be segregated from each other. The high demand for goods including raw materials, food, minerals, and waste recycling has increased the pressure for high-speed sorting. The first part of this paper presents a comprehensive theoretical and practical survey and comparison of current sorting methods relying on the use of the electromagnetic spectrum. Sorting methods are classified among other things, which portion of the EM spectrum is it, background noise rejection capacity, sample size limitations, sample chemistry limitations, sample surface cleanliness, spatial resolution capacity, spectral resolution, and feed rate limitations. The analysis focuses on color or visible light sorting, gamma-ray sorting, infra-red sorting, x-ray transmission-based sorting and x-ray fluorescence sorting, coupling the findings to the classification criteria outlined. We see a need to define a universal sorting scheme that will in general be applied to most sorting tasks. To do this, the final part of this paper re-looks at the x-ray transmission and x-ray fluorescence sorting scheme in line with the established limitations and proposes a dual x-ray transmission and fluorescence method to mitigate the challenge affecting the different schemes.

Optimizing Multi-Spectral Ore Sorting: Incorporating Wavelength Selection Utilizing Neighborhood Component Analysis for Effective Arsenic Mineral Detection

“…In recent years, more and more equipment has been installed that can withstand larger production scales [23]. SBOS methods have utilized a range of sensing technologies, including X-ray transmission, X-ray fluorescence, optical sensing, and inductive sensing [9], [26], [27]. Furthermore, the utilization of area-scan cameras and predictive tracking systems that rely on machine learning approaches have demonstrated potential in minimizing characterization and separation errors [28].…”

“…Sensor-based ore sorting methods and technologies have the potential to significantly improve ore grades and reduce waste in mineral processing [29]. These methods, which rely on the electromagnetic spectrum, can be classified based on their characteristics and limitations [27]. An example of a successful method for sorting complicated ores is the utilization of hyperspectral short-wave infrared sensors in conjunction with machine learning, as demonstrated by Tusa [30].…”

Optimizing multi-spectral ore sorting incorporating wavelength selection utilizing neighborhood component analysis for effective arsenic mineral detection

“…In recent years, more and more equipment has been installed that can withstand larger production scales 25 . SBOS methods have utilized a range of sensing technologies, including X-ray transmission, X-ray fluorescence, optical sensing, and inductive sensing 9 , 10 , 28 . Furthermore, the utilization of area-scan cameras and predictive tracking systems that rely on machine learning approaches have demonstrated potential in minimizing characterization and separation errors 29 .…”

“…Sensor-based ore sorting methods and technologies have the potential to significantly improve ore grades and reduce waste in mineral processing 6 . These methods, which rely on the electromagnetic spectrum, can be classified based on their characteristics and limitations 28 . An example of a successful method for sorting complicated ores is the utilization of hyperspectral short-wave infrared sensors in conjunction with machine learning, as demonstrated by Tusa 32 .…”