Manyezit triyaj atığı geri dönüşümü için optik ayıklama teknolojisi: Kirlilikleri azaltmak için iki aşamalı bir işlem

Year 2025, Volume: 14 Issue: 4, 1604 - 1611, 15.10.2025

Kemal Bilir

Abstract

Bu çalışmada, triyaj atıklarından manyezitin geri kazanılmasında sensör tabanlı optik ayıklama teknolojisinin etkinliği değerlendirilmiştir. Eskişehir bölgesindeki bir manyezit tesisinin atık sahasının farklı bölgelerinden, %45,9 MgO, %4,7 Fe₂O₃ ve %10,6 SiO₂ içeren yaklaşık bir ton manyezit atık örneği alınmıştır. İki boyut fraksiyonu (-35+15 mm ve -15+8 mm) manyetik ayırma, yıkama ve iki aşamalı optik ayıklamaya tabi tutulmuştur. -35+15 mm fraksiyonu için safsızlık seviyeleri %9,94'ten %1,91 SiO₂'ye, %1,22'den %0,15 Fe₂O₃'e ve %2,41'den %0,81 CaO'a düşürülerek %65,12 konsantre verimi elde edilmiştir. -15+8 mm fraksiyonu da %72 konsantre verimi ile benzer iyileştirmeler göstermiştir. Sonuçlar, optik ayıklama teknolojisinin, atık değerlendirmesi yoluyla ekonomik faydalar sağlamanın yanı sıra sürdürülebilir madencilik uygulamalarını ve döngüsel ekonomi prensiplerini destekleyerek, manyezit atıklarını kabul edilebilir kalite seviyelerine etkili bir şekilde yükseltebileceğini göstermektedir.

Keywords

Manyezit , Optik ayıklama , Manyezit geri kazanımı , Triyaj atığı , Kirlilik azaltımı

Optical sorting technology for magnesite triage waste recycling: A two-stage process for reducing impurities

Abstract

This study evaluated the effectiveness of sensor-based optical sorting technology for recovering magnesite from triage waste. Approximately one ton of magnesite waste samples, containing 45.9% MgO, 4.7% Fe₂O₃, and 10.6% SiO₂, were sampled from different regions of a waste site at a magnesite plant in the Eskişehir region. Two size fractions (-35+15 mm and -15+8 mm) were subjected to magnetic separation, washing, and two-stage optical sorting. For the -35+15 mm fraction, impurity levels were reduced from 9.94% to 1.91% SiO₂, 1.22% to 0.15% Fe₂O₃, and 2.41% to 0.81% CaO, achieving 65.12% concentrate yield. The -15+8 mm fraction showed similar improvements with a 72% concentrate yield. Results demonstrate that optical sorting technology can effectively upgrade magnesite waste to acceptable quality levels, supporting sustainable mining practices and circular economy principles while providing economic benefits through waste evaluation.

Keywords

Magnesite , Optical sorting , Magnesite recovery , Triage waste , Impurity reduction

References

• E. Tzamos, M. Bussolesi, G. Grieco, P. Marescotti, L. Crispini, A. Kasinos, ... and A. Zouboulis, Mineralogy and geochemistry of ultramafic rocks from rachoni magnesite mine, Gerakini (Chalkidiki, Northern Greece). Minerals, 10(11), 934, 2020. https://doi.org/1 0.3390/min10110934
• E. Pagona, E. Tzamos, G. Grieco, A. Zouboulis and M. Mitrakas, Characterization and evaluation of magnesite ore mining by-products of Gerakini mines (Chalkidiki, N. Greece). Science of the Total Environment, 732, 139279, 2020. https://doi.org/10.1016/j.scitotenv.2020 .139279
• G.M. Bimpilas and G.N. Anastassakis, Magnesite beneficiation methods: a review. Sustain. Extr. Process. Raw Mater, 1, 14-20, 2020. https://doi.org/10.5281/ze nodo.4269873
• A. Yılmaz and M. Kusçu, Formation, classification, applications and quality classification of magnesite deposits. ERU. J. Inst. Sci. Technol, 28, 65-72, 2012.
• M. Hojamberdiev, P. Arifov, K. Tadjiev and Y.H. Xu, Processing of refractory materials using various magnesium sources derived from Zinelbulak talc-magnesite. International Journal of Minerals, Metallurgy, and Materials, 18(1), 105-114, 2011. https://doi.org/10.1007/s12613-011-0408-y
• E. Pagona, K. Kalaitzidou, A. Zouboulis and M. Mitrakas, Effects of additives on the physical properties of magnesite ore mining by-products for the production of refractories. Minerals Engineering, 174, 107247, 2021. https://doi.org/10.1016/j.mineng.2021.107247
• Y. Tan, C. Wu, H. Yu, Y. Li and J. Wen, Review of reactive magnesia-based cementitious materials: Current developments and potential applicability. Journal of Building Engineering, 40, 102342, 2021. https://doi.org/10.1016/j.jobe.2021.102342
• G. A. Khater, M. Romero, A. López-Delgado, I. Padilla, A.A. El-Kheshen, M.M Farag, ... and H. Shendy, Novel ceramic materials based on industrial wastes within the CaO–MgO–Al2O3–SiO2 system. Materials Chemistry and Physics, 331, 130178, 2025. https://doi.org/10.1016/j.matchemphys.2024.130178
• Q. Q. Wang, X. A. Li, D. Z. Wei and S. J. Dai, The application of magnesite processing technics. Applied Mechanics and Materials, 71, 2323-2326, 2011. https://doi.org/10.4028/www.scientific.net/AMM.71-78.2323
• L. G. V. F. Von Ketelhodt, Viability of optical sorting of gold waste rock dumps. In World Gold Conference (pp. 271-278), 2009.
• B. A. Wills, and J. Finch, Wills' mineral processing technology: an introduction to the practical aspects of ore treatment and mineral recovery. Butterworth-Heinemann, 2015.
• H. Knapp, K. Neubert, C. Schropp, and H. Wotruba, Viable applications of sensor‐based sorting for the processing of mineral resources. ChemBioEng Reviews, 1(3), 86-95, 2014. https://doi.org/10.1002/c ben.201400011
• R. A. Bearman, D. J. Bowman, and R. Dunne, Decision support for ore sorting and preconcentration in gold applications. Mineral Processing and Extractive Metallurgy, 129(1), 12-23, 2020. https://doi.org/10.10 80/25726641.2019.1652488
• J. D. Salter, and N. P. G. Wyatt, Sorting in the minerals industry: past, present and future. Minerals Engineering, 4 (7-11), 779-796, 1991. https://doi.org/1 0.1016/0892-6875(91)90065-4
• E. G. D. Santos, I. A. S. D. Brum and W. M. Ambrós, Techniques of Pre-Concentration by Sensor-Based Sorting and Froth Flotation Concentration Applied to Sulfide Ores-A Review. Minerals, 15(4), 350, 2025. https://doi.org/10.3390/min15040350
• T. P. De Jong, M. B. Mesina and W. Kuilman, Electromagnetic De‐Shaling of Coal. Physical Separation in Science and Engineering, 12(4), 223-236, 2003. https://doi.org/10.1080/14786470410001665802
• M. Dehler, Optical sorting of ceramic raw material. Tile & brick international, 19(4), 248-251, 2003.
• K. Friedrich, G. Koinig, T. Fritz, R. Pomberger and D. Vollprecht, Sensor-based and robot sorting processes and their role in achieving European recycling goals-a review, 2022. https://doi.org/10.19080/AJOP.2022.05. 555668
• Y. Liu, Z. Zhang, X. Liu, L. Wang and X. Xia, Deep learning-based image classification for online multi-coal and multi-class sorting. Computers & Geosciences, 157, 104922, 2021. https://doi.org/10.10 16/j.cageo.2021.104922
• D. G. Shatwell, V. Murray and A. Barton, Real-time ore sorting using color and texture analysis. International Journal of Mining Science and Technology, 33(6), 659-674, 2023. https://doi.org/10.1 016/j.ijmst.2023.03.004
• G. Özbayoğlu, A. M. Özbayoğlu and M. E Özbayoğlu, Estimation of Hardgrove grindability index of Turkish coals by neural networks. International Journal of Mineral Processing, 85(4), 93-100, 2008. https://doi. org/10.1016/j.minpro.2007.08.003
• C. W. Liao and Y. S. Tarng, On-line automatic optical inspection system for coarse particle size distribution. Powder Technology, 189(3), 508-513, 2009. https:// doi.org/10.1016/j.powtec.2008.08.013
• F. Nakhaei, M. R. Mosavi, A. Sam and Y. Vaghei, Recovery and grade accurate prediction of pilot plant flotation column concentrate: Neural network and statistical techniques. International Journal of Mineral Processing, 110, 140-154, 2012. https://doi.org/10.10 16/j.minpro.2012.03.003
• F. Ahmadzadeh and J. Lundberg, Remaining useful life prediction of grinding mill liners using an artificial neural network. Minerals Engineering, 53, 1-8, 2013. https://doi.org/10.1016/j.mineng.2013.05.026
• A. Jahedsaravani, M. H. Marhaban and M. Massinaei, Prediction of the metallurgical performances of a batch flotation system by image analysis and neural networks. Minerals Engineering, 69, 137-145, 2014. https://doi.org/10.1016/j.mineng.2014.08.003
• A. Jahedsaravani, M. H. Marhaban, M. Massinaei, M. I. Saripan and S. M. Noor, Froth-based modeling and control of a batch flotation process. International Journal of Mineral Processing, 146, 90-96, 2016. https://doi.org/10.1016/j.minpro.2015.12.002
• S. S. Matin, J. C. Hower, L. Farahzadi and S. C. Chelgani, Explaining relationships among various coal analyses with coal grindability index by Random Forest. International Journal of Mineral Processing, 155, 140-146, 2016 https://doi.org/10.1016/j.minpro. 2016.08.015
• X. Yang, T. Ren, & L. Tan. (2020). Size distribution measurement of coal fragments using digital imaging processing. Measurement, 160, 107867. https://doi.org /10.1016/j.measurement.2020.107867
• H. Wotruba, Sensor Sorting Technology - is the Minerals Industry Missing a Chance? Plenary lecture. In XXIII International Mineral Processing Congress, Istanbul. 2006;
• L. Von Ketelhodt and C. Bergmann, Dual energy X-ray transmission sorting of coal. Journal of the southern African Institute of Mining and Metallurgy, 110(7), 371-378, 2010.
• K. Bilir and H. Akdaş, Evaluation of magnesite wastes using optical sorting machine. In Proceedings of the XIII. International Mineral Processing Symposium, Bodrum, 2012.