Identification of Valuable Minerals or Metals in Ores Using Microwave Imaging

Duan, Beichen; Bobicki, Erin R.; Hum, Sean V.

doi:10.1109/tap.2022.3209711

Cited by 5 publications

(5 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Sensor fusion, the integration of data from several sensing systems, has the potential to enhance the characterization of the detected material and improve sorting capability [4]. Microwave imaging (MWI) is a promising technique that can penetrate deeply into rock particles and serve as an additional approach for analyzing ores with significant differences in electromagnetic characteristics [9]. The efficacy of MWI in ore sorting has been validated by simulations and tests, affirming its capability to segregate valuable minerals or metals from unproductive particles.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Okada,

Nozaki,

Nakamura

et al. 2024

Preprint

View full text Add to dashboard Cite

Arsenic contamination not only complicates mineral processing but also poses environmental and health risks. To address these challenges, this research investigates the feasibility of utilizing Hyperspectral (HS) imaging combined with machine learning techniques for the identification of arsenic-containing minerals in copper ore samples, with a focus on practical application in sorting and processing operations. Through experimentation with various copper sulfide ores, Neighborhood Component Analysis (NCA) was employed to select essential wavelength bands from Hyperspectral data, subsequently used as inputs for machine learning algorithms to identify arsenic concentrations. Results demonstrate that by selecting a subset of informative bands using NCA, accurate mineral identification can be achieved with a significantly reduced the size of dataset, enabling efficient processing and analysis. Comparison with other band selection methods highlights the superiority of NCA in optimizing classification accuracy. Specifically, the identification accuracy exceeded 90% when utilizing 8 or more bands selected by NCA and was comparable to hyperspectral data analysis with 204 bands. The findings suggest potential for cost-effective implementation of multispectral cameras in mineral processing operations. Future research directions include refining machine learning algorithms, exploring broader applications across diverse ore types, and integrating hyperspectral imaging with emerging sensor technologies for enhanced mineral processing capabilities.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Okada,

Nozaki,

Nakamura

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Sensor fusion, the integration of data from several sensing systems, has the potential to enhance the characterization of the detected material and improve sorting capability 4 . Microwave imaging (MWI) is a promising technique that can penetrate deeply into rock particles and serve as an additional approach for analyzing ores with significant differences in electromagnetic characteristics 10 . The efficacy of MWI in ore sorting has been validated by simulations and tests, affirming its capability to segregate valuable minerals or metals from unproductive particles.…”

Section: Introductionmentioning

confidence: 99%

“…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 .…”

Section: Introductionmentioning

confidence: 99%

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

Okada,

Nozaki,

Nakamura

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Arsenic contamination not only complicates mineral processing but also poses environmental and health risks. To address these challenges, this research investigates the feasibility of utilizing Hyperspectral imaging combined with machine learning techniques for the identification of arsenic-containing minerals in copper ore samples, with a focus on practical application in sorting and processing operations. Through experimentation with various copper sulfide ores, Neighborhood Component Analysis (NCA) was employed to select essential wavelength bands from Hyperspectral data, subsequently used as inputs for machine learning algorithms to identify arsenic concentrations. Results demonstrate that by selecting a subset of informative bands using NCA, accurate mineral identification can be achieved with a significantly reduced the size of dataset, enabling efficient processing and analysis. Comparison with other wavelength selection methods highlights the superiority of NCA in optimizing classification accuracy. Specifically, the identification accuracy showed 91.9% or more when utilizing 8 or more bands selected by NCA and was comparable to hyperspectral data analysis with 204 bands. The findings suggest potential for cost-effective implementation of multispectral cameras in mineral processing operations. Future research directions include refining machine learning algorithms, exploring broader applications across diverse ore types, and integrating hyperspectral imaging with emerging sensor technologies for enhanced mineral processing capabilities.

show abstract

“…Recently some review articles have published in the same area [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20] creating an exchange platform for cost effective engineering solutions in sensor based ore sorting. More beneficiation and advanced techniques for minerals like copper, coal, and diamond have also been proposed in [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], and [36], but none of these approaches intently reviews a generalized approach to sensor based ore sorting through the application of the interaction of electromagnetic waves with matter.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Identification of Valuable Minerals or Metals in Ores Using Microwave Imaging

Cited by 5 publications

References 48 publications

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

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

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

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

Contact Info

Product

Resources

About