Competitive Voting-based Multi-class Prediction for Ore Selection

Zhang, Zelin; Liu, Ying; Hu, Qi; Zhang, Zhiwei; Liu, Yang

doi:10.1109/case48305.2020.9217017

Cited by 9 publications

(3 citation statements)

References 24 publications

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“…Numerous studies of sorting using optical/infrared imaging were identified within the review [19,22,24,[28][29][30][31][32][33][34][35]. It was found that optical and infrared imaging were suitable for sorting a wide range of ores including coal, copper, gold, lead, marble, mineral sands, rare earth elements, tin and zinc.…”

Section: Optical and Hyperspectral Imagingmentioning

confidence: 99%

A Review of Sensor-Based Sorting in Mineral Processing: The Potential Benefits of Sensor Fusion

Peukert

Dowd

2022

Minerals

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Sensor-based sorting techniques offer the potential to improve ore grades and reduce the amount of waste material processed. Previous studies show that sensor-based sorting can reduce energy, water and reagent consumption and fine waste production by discarding waste prior to further processing. In this literature review, recent investigations of sensor-based sorting and the fundamental mechanisms of the main sorting techniques are evaluated to inform optimal sensor selection. Additionally, the fusing of data from multiple sensing techniques to improve characterization of the sensed material and hence sorting capability is investigated. It was found that the key to effective implementation of sensor-based sorting is the selection of a sensing technique which can sense a characteristic capable of separating ore from waste with a sampling distribution sufficient for the considered sorting method. Classes of potential sensor fusion sorting applications in mineral processing are proposed and illustrated with example cases. It was also determined that the main holdup for implementing sensor fusion is a lack of correlatable data on the response of multiple sensing techniques for the same ore sample. A combined approach of experimental testing supplemented by simulations is proposed to provide data to enable the evaluation and development of sensor fusion techniques.

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Section: Optical and Hyperspectral Imagingmentioning

confidence: 99%

A Review of Sensor-Based Sorting in Mineral Processing: The Potential Benefits of Sensor Fusion

Peukert

Dowd

2022

Minerals

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“…In the field of mineral processing, Sensor-based ore sorting is a vital component as it enhances ore grades and minimizes the amount of waste material that is processed [5]. Evidence demonstrates that it effectively decreases the usage of energy, water, and reagents, while also minimizing the formation of fine waste, by disposing of trash before undergoing additional processing [6], [7]. To successfully apply sensor-based sorting, it is crucial to select a sensing approach that can efficiently distinguish between ore and waste [8].…”

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

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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.

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“…In the field of mineral processing, Sensor-based ore sorting is a vital component as it enhances ore grades and minimizes the amount of waste material that is processed 6 . Evidence demonstrates that it effectively decreases the usage of energy, water, and reagents, while also minimizing the formation of fine waste, by disposing of trash before undergoing additional processing 7,8 . To successfully apply sensor-based sorting, it is crucial to select a sensing approach that can efficiently distinguish between ore and waste 9 .…”

mentioning

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

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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.

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Competitive Voting-based Multi-class Prediction for Ore Selection

Cited by 9 publications

References 24 publications

A Review of Sensor-Based Sorting in Mineral Processing: The Potential Benefits of Sensor Fusion

A Review of Sensor-Based Sorting in Mineral Processing: The Potential Benefits of Sensor Fusion

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

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