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Journals / Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji / 2018 / Cilt: 6 - Sayı: 2

Enrichment of 8-125 mm Colemanite Ores via NIR/CCD Optical Sorter

8-125 mm Kolemanit Cevherinin NIR/CCD Optik Ayırıcı ile Zenginleştirilmesi

Nurtaç K. KARABULUT
Fazlı C. METİN
Fatih ÖZYÜCEL
Mustafa BARIŞ
Pages
414–425
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

After enrichment made with over 93% yield, waste was found to be in the range of 2.20-11.10% B2O3 grade.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, 8-125 mm Simav Ana Damar (SAD), Simav Tali Damar (STD) and Tülü Sarı (TS) raw colemanite ores which extracted together with the minerals such as clay, tuff, limestone from the quarries of Bigadiç Boron Works Management of Eti Maden enriched by optical sorters used with near-infrared (NIR) and visible light (CCD) sources.
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Key Findings & Data:In these works, the ore passing through the crushing, sieving and washing processes was fed to two optical sorters of 8-25 mm and 25-125 mm grain sizes and the 3.90-9.10 units enrichment was obtained in the ores fed at 22.10-39.30% B2O3 content.
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Conclusion & Impact:After enrichment made with over 93% yield, waste was found to be in the range of 2.20-11.10% B2O3 grade.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

In this study, 8-125 mm Simav Ana Damar (SAD), Simav Tali Damar (STD) and Tülü Sarı (TS) raw colemanite ores which extracted together with the minerals such as clay, tuff, limestone from the quarries of Bigadiç Boron Works Management of Eti Maden enriched by optical sorters used with near-infrared (NIR) and visible light (CCD) sources. In these works, the ore passing through the crushing, sieving and washing processes was fed to two optical sorters of 8-25 mm and 25-125 mm grain sizes and the 3.90-9.10 units enrichment was obtained in the ores fed at 22.10-39.30% B2O3 content. After enrichment made with over 93% yield, waste was found to be in the range of 2.20-11.10% B2O3 grade.

Özet

Bu çalışmada Eti Maden İşletmeleri Genel Müdürlüğü, Bigadiç Bor İşletme Müdürlüğüocaklarından 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. Çalışmalardakırma, eleme ve yıkama işlemlerinden geçirilen tuvönan cevher 8-25 mm ve 25-125 mm taneboyutlarında zenginleştirme yapan iki optik ayırıcıya beslenmiş ve %22,10-39,30 B2O3tenör aralığında beslenen cevherlerde 3,90-9,10 birim zenginleştirme sağlanmıştır. %93’ün üzerindeverimle yapılan zenginleştirme sonrası %2,20-11,10 B2O3tenör aralığında atık açığa çıkmıştır.

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Cite This Article

KARABULUT, N. K., METİN, F. C., ÖZYÜCEL, F., BARIŞ, M. (2022). Enrichment of 8-125 mm Colemanite Ores via NIR/CCD Optical Sorter. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, Cilt: 6 - Sayı: 2, 414–425. https://ajindex.com/articles/522137

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References (17)

  1. A. W. Barry, N. M. Tim, Ore Sorting, Wills' Mineral Processing Technology 7th Edition,
  2. Burlington, A.B.D., 2006.
  3. C. Çelik, Cevher zenginleştirmede gelişen teknolojiler: Optik zenginleştirme, Madencilik Türkiye, 4 (2010) 40-43.
  4. T. Petra, W. Markus, P. Thomas, Industrial application for inline material sorting using hyperspectral imaging in the NIR range, Real-Time Imaging, 11 (2005) 99-107.
  5. P. Williams, K. Norris, Near-infrared technology in the agricultural and food industries, American Association of Cereal Chemists, Wisconsin, A.B.D., 1987.
  6. P. G. Sathish, H. Subrata, T. Atul, A review on automated sorting of source-separated municipal solid waste for recycling, Waste Manage., 60 (2017) 56-74.
  7. J. Blasco, N. Aleixos, J. Gomez, E. Molto, Citrus sorting by identification of the most common defects using multispectral computer vision, J. Food Eng., 83 (2007) 384-393.
  8. P. Razieh, R. G. Hamid, S. Hadi, Z. N. Fariborz, M. V. Mohammad, Study on an automatic sorting system for date fruits, J. Saudi Soc. Agric. Sci., 14 (2015) 83-90.
  9. S. Silvia, C. Daniela, M. Federico, B. Giuseppe, Classification of oat and groat kernels using NIR hyperspectral imaging, Talanta, 103 (2013) 276-284.
  10. I. Mage, J. P. Wol, F. Bjerke, V. Segtnan, On-line sorting of meat trimmings into targeted fat categories, J. Food Eng., 115 (2013) 306-313.
  11. B. Murphy, J. Zyl, G. Domingo, Underground preconcentration by ore sorting and coarse gravity separation, Narrow Vein Mining Conference, Perth-West Aust., 26-27 Mart, 2012.
  12. A. R. Batchelor, R. S. Ferrari-John, J. Katrib, O. Udoudo, D. A. Jones, C. Dodds, S. W. Kingman, Pilot scale microwave sorting of porphyry copper ores: Part 1-Laboratory investigations, Miner. Eng., 98 (2016) 303-327.
  13. S. Veerendra, S. M. Rao, Application of image processing and radial basis neural network techniques for ore sorting and ore classification, Miner. Eng., 18 (2005)1412-1420.
  14. J. Tessier, C. Duchesne, G. Bartolacci, A machine vision approach to on-line estimation of run-of-mine ore composition on conveyor belts, Miner. Eng., 20 (2007)1129-1144.
  15. G. R. Lane, C. Martin, E. Pirard, Techniques and applications for predictive metallurgy and ore characterization using optical image analysis, Miner. Eng., 21 (2008) 568-577.
  16. C. Snehamoy, B. Ashis, S. Biswajit, K. P. Samir, Image-based quality monitoring system of limestone ore grades, Comput. Ind., 61 (2010) 391-408.
  17. L. Sophie, D. Godefroid, B. David, Eric P., Optical analysis of particle size and chromite liberation from pulp