Pilot scale microwave sorting of porphyry copper ores: Part 1 – Laboratory investigations

Ferrari, Rebecca; Katrib, Juliano; Udoudo, Ofonime; Jones, D.A.; Dodds, Chris; Kingman, S.W.

doi:10.1016/j.mineng.2016.07.007

Cited by 17 publications

(12 citation statements)

References 19 publications

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“…Sensor-based sorting is typically used in the fields of food processing [8], waste management [1], and sorting of industrial minerals [9]. The process can be subdivided into the tasks of feeding, preparation, presentation, examination, discrimination, and physical separation [10,11]. Preparation and presentation are realized by means of certain transport mechanisms, for instance conveyor belts, slides, or chutes.…”

Section: Sensor-based Sortingmentioning

confidence: 99%

Real-time multitarget tracking for sensor-based sorting

Maier

Pfaff

Wagner

et al. 2017

J Real-Time Image Proc

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Utilizing parallel algorithms is an established way of increasing performance in systems that are bound to real-time restrictions. Sensor-based sorting is a machine vision application for which firm real-time requirements need to be respected in order to reliably remove potentially harmful entities from a material feed. Recently, employing a predictive tracking approach using multitarget tracking in order to decrease the error in the physical separation in optical sorting has been proposed. For implementations that use hard associations between measurements and tracks, a linear assignment problem has to be solved for each frame recorded by a camera. The auction algorithm can be utilized for this purpose, which also has the advantage of being well suited for parallel architectures. In this paper, an improved implementation of this algorithm for a graphics processing unit (GPU) is presented. The resulting algorithm is implemented in both an OpenCL and a CUDA based environment. By using an optimized data structure, the presented algorithm outperforms recently proposed implementations in terms of speed while retaining the quality of output of the algorithm. Furthermore, memory requirements are significantly decreased, which is important for embedded systems. Experimental results are provided for two different GPUs and six datasets. It is shown that the proposed approach is of particular interest for applications dealing with comparatively large problem sizes.

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Section: Sensor-based Sortingmentioning

confidence: 99%

Real-time multitarget tracking for sensor-based sorting

Maier

Pfaff

Wagner

et al. 2017

J Real-Time Image Proc

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“…Each pixel in a hyperspectral image has its continuous spectrum, the technique has wide geological applications, including the identification and classification of mineralogical alteration assemblages and correlation of geological units (Gomez et al, 2018;Xie B. et al, 2022;Xie B. S. et al, 2022). For decades, hyperspectral imaging has been widely used in geological prospecting, such as porphyry-type copper deposits (Batchelor et al, 2016;Dalm et al, 2017), hydrothermal gold deposits (Osterloo et al, 2012), sulphide deposits (Iyakwari et al, 2016), ore deposits (Murphy and Monteiro, 2013;Metelka et al, 2015), uranium Ore deposits (Reath and Ramsey, 2013), and rare earth deposits (Riaza et al, 2001). The most popular hyperspectral sensors for mineral exploration from spaceborne and airborne platforms are summarized in Table 1.…”

Section: Introductionmentioning

confidence: 99%

Identifying and Mapping Alteration Minerals Using HySpex Airborne Hyperspectral Data and Random Forest Algorithm

et al. 2022

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Airborne hyperspectral remote sensing data provide rapid, non-destructive, and near laboratory quality reflectance spectra for mineral mapping and lithological discrimination, thereby ushering an innovative era of remote sensing. In this study, NEO HySpex cameras, which comprise 504 spectral channels in the spectral ranges of 0.4–1.0 μm and 1.0–2.5 μm, were mounted on a delta wing XT-912 aircraft. The designed flexibility and modular nature of the HySpex aircraft hyperspectral imaging system made it relatively easy to test, transport, install, and remove the system multiple times before the acquisition flights. According to the design fight plan, including the route distance, length, height, and flight speed, we acquired high spectral and spatial resolutions airborne hyperspectral images of Yudai porphyry Cu (Au, Mo) mineralization in Kalatag District, Eastern Tianshan terrane, Northwest China. By comparing the features of the HySpex hyperspectral data and standard spectra data from the United States Geological Survey database, endmember pixels of spectral signatures for most alteration mineral assemblages (goethite, hematite, jarosite, kaolinite, calcite, epidote, and chlorite) were extracted. After a HySpex data processing workflow, the distribution of alteration mineral assemblages (iron oxide/hydroxide, clay, and propylitic alterations) was mapped using the random forest (RF) algorithm. The experiments demonstrated that the workflow for processing data and RF algorithm is feasible and active, and show a good performance in classification accuracy. The overall classification accuracy and Kappa classification of alteration mineral identification were 73.08 and 65.73%, respectively. The main alteration mineral assemblages were primarily distributed around pits and grooves, consistent with field-measured data. Our results confirm that HySpex airborne hyperspectral data have potential application in basic geology survey and mineral exploration, which provide a viable alternative for mineral mapping and identifying lithological units at a high spatial resolution for large areas and inaccessible terrains.

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“…Optik ayırma teknolojisinde alternatif akımdan (10 4 m) gama ışımasına (10 -12 m) kadar geniş elektromanyetik dalgaboyu spektrumunda tanımlama yapabilen sensörlerin tekli veya çoklu olarak kullanılabilmesi farklı birçok malzemenin tanımlanabilmesini imkanlı hale getirmektedir [10,11]. Özellikle madencilik sektöründe fiziksel yolla tanımlanması oldukça zor olan girift yapıdaki minerallerin ayrıştırılması işleminde geleneksel yollara iyi bir alternatif olmuştur.…”

Section: Gi̇ri̇ş (Introduction)unclassified

“…Elmas kazanımında ayrıca xışını altında görünür ışınıma dayalı XRF teknlojisi de kullanılmaktadır. Endüstriyel minerallerin zenginleştirilmesinde en yaygın kullanılan optik ayırma sensörleri ise minerallerin ışığı emme/yansıtma/geçirgenlik karakteristiklerine dayalı görünür ışık CCD sensörü, monokromatik (tek renkli) emme/yansıtma karakteristiklerine bağlı fotometrik sensörler ve emme/yansıtma karakteristiklerine bağlı yakın kızılötesi sensörlerdir[10,11].Çalışmada kolemanitin zenginleştirilmesinde en etkin sensörün belirlenmesi amacıyla, sabit hat üzerinde farklı filtreler yardımıyla ayarlanabilen laboratuvar ölçekli ışık kaynakları altında denemeler yapılmıştır. Denemelerde her bir cevherden 5 kg numune kullanılmıştır.…”

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8-125 Mm Kolemani̇t Cevheri̇ni̇n Nir/CCD Opti̇k Ayirici İle Zengi̇nleşti̇ri̇lmesi̇

Barış¹,

Metin²,

Karabulut³

et al. 2018

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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Bu çalışmada Eti Maden İşletmeleri Genel Müdürlüğü, Bigadiç Bor İşletme Müdürlüğü ocaklarından kil, tüf, kireç taşı gibi minerallerle birlikte çıkan 8-125 mm Simav Ana Damar (SAD), Simav Tali Damar (STD) ve Tülü Sarı (TS) kolemanit cevherleri, yakın kızılötesi (NIR) ve görünür ışık (CCD) kaynakları kullanılan optik ayıcılarla zenginleştirilmiştir. Çalışmalarda kırma, eleme ve yıkama işlemlerinden geçirilen tuvönan cevher 8-25 mm ve 25-125 mm tane boyutlarında zenginleştirme yapan iki optik ayırıcıya beslenmiş ve %22,10-39,30 B 2 O 3 tenör aralığında beslenen cevherlerde 3,90-9,10 birim zenginleştirme sağlanmıştır. %93'ün üzerinde verimle yapılan zenginleştirme sonrası %2,20-11,10 B 2 O 3 tenör aralığında atık açığa çıkmıştır.

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Pilot scale microwave sorting of porphyry copper ores: Part 1 – Laboratory investigations

Cited by 17 publications

References 19 publications

Real-time multitarget tracking for sensor-based sorting

Real-time multitarget tracking for sensor-based sorting

Identifying and Mapping Alteration Minerals Using HySpex Airborne Hyperspectral Data and Random Forest Algorithm

8-125 Mm Kolemani̇t Cevheri̇ni̇n Nir/CCD Opti̇k Ayirici İle Zengi̇nleşti̇ri̇lmesi̇

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