Research Article
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Predicting screening/classification products via the pseudorandom number selection routine

Year 2022, Volume 61, Issue 1, 25 - 30, 07.03.2022
https://doi.org/10.30797/madencilik.947610

Abstract

Eleme ve sınıflandırma, tanelerin boyuna göre ayrılması için kullanılmaktadır. Eleme/sınıflandırma ürünlerinnin tane boyu dağılımlarını belirlemek için ampirik, yarı-ampirik ve nümerik modeller vardır. Bu makale, aynı amaçla kullanılan, kısmen yarı-ampirik ve nümerik modellere benzeyen bir algoritma sunmaktadır. Algoritma dar tane aralıklarını önceden belirlenmiş olasılıklarla seçmekte; daha sonra seçilen dar tane aralıklarından tane kütlelerini iri veya ince ürüne taşımaktadır. Algoritmanın uygulanabilirliği, bazı endüstriyel ölçekli eleme/sınıflandırma işlemlerinin ürün tane boyutlarına karşı doğrulanmıştır. Sonuçlar, tanelerin seçilme olasılığının taneyi içeren dar tane aralığının kütlesine ve tane çapının bazı kuvvetine orantılı olduğunda algoritmanın doğruya yakın tahmin yapabileceğini göstermektedir. Sonuçlar ayrıca titreşimli eleklerin en keskin tane ayrımını yapabileceğini tavsiye etmektedir.

References

  • Austin, L. G., Klimpel, R. R., and Luckie, P. T. (1984). Process Engineering of Size Reduction: Ball Milling (Vol. 1). New York: AIME. Camalan, M. (2021a).
  • A computational algorithm to understand the evolution of size distribution with successive breakage events at grinding (In Press). Proceedings of the 2nd International Electronic Conference on Mineral Science. https://doi.org/10.3390/iecms2021-09381
  • Camalan, M. (2021b). Investigating the effects of random sieving losses on particle size distributions. Particulate Science and Technology, 39(1), 108–115. https://doi.org/10.1080/02726351.2019.1669749
  • Coelho, M. A. Z., and Medronho, R. A. (1992). An Evaluation of the Plitt and Lynch & Rao Models for the Hydrocyclones. In L. Svarovsky and T. M. Thew (Eds.), Hydrocyclones Analysis and Applications (pp. 63–72). Springer.
  • Davoodi, A., Asbjörnsson, G., Hulthén, E., and Evertsson, M. (2019). Application of the discrete element method to study the effects of stream characteristics on screening performance. Minerals, 9(12). https://doi.org/10.3390/min9120788
  • Dong, K. J., Wang, B., and Yu, A. B. (2013). Modeling of particle flow and sieving behavior on a vibrating screen: From discrete particle simulation to process performance prediction. Industrial and Engineering Chemistry Research, 52(33), 11333–11343. https://doi.org/10.1021/ie3034637
  • Dong, K. J., and Yu, a. B. (2012). Numerical simulation of the particle flow and sieving behaviour on sieve bend/low head screen combination. Minerals Engineering, 31, 2–9. https://doi.org/10.1016/j.mineng.2011.10.020
  • Dündar, H. (2020). Investigating the benefits of replacing hydrocyclones with high-frequency fine screens in closed grinding circuit by simulation. Minerals Engineering, 148(January), 106212. https://doi.org/10.1016/j.mineng.2020.106212
  • Elskamp, F., and Kruggel-Emden, H. (2015). Review and benchmarking of process models for batch screening based on discrete element simulations. Advanced Powder Technology, 26(3), 679–697. https://doi.org/10.1016/j.apt.2014.11.001
  • Frausto, J. J., Ballantyne, G. R., Runge, K., Powell, M. S., Wightman, E. M., Evans, C. L., … Gomez, S. (2021). The effect of screen versus cyclone classification on the mineral liberation properties of a polymetallic ore. Minerals Engineering, 169(April), 106930. https://doi.org/10.1016/j.mineng.2021.106930
  • Gupta, A., and Yan, D. (2016). Mineral Processing Design and Operations. Amsterdam: Elsevier. Heiskanen, K. G. H. (1996). Developments in wet classifiers. International Journal of Mineral Processing, 44–45(SPEC. ISS.), 29–42. https://doi.org/10.1016/0301-7516(95)00015-1
  • Hogg, R. (2008). Issues in particle size analysis. KONA Powder and Particle Journal, 26(March), 81–93. https://doi.org/10.14356/kona.2008009
  • Kelly, E. G. (1991). The significance of by-pass in mineral separators. Minerals Engineering, 4(1), 1–7. https://doi.org/10.1016/0892-6875(91)90113-A
  • Khoshdast, H., Shojaei, V., and Khoshdast, H. (2017). Combined application of computational fluid dynamics (CFD) and design of experiments (DOE) to hydrodynamic simulation of a coal classifier. International Journal of Mining and Geo-Engineering, 51(1), 9–22. https://doi.org/10.22059/ijmge.2016.218483.594634
  • King, R. P. (2012). Modeling and Simulation of Mineral Processing Systems (C. L. Schneider and E. A. King, eds.). SME. Kruggel-Emden, H., and Elskamp, F. (2014). Modeling of screening processes with the discrete element method involving non-spherical particles. Chemical Engineering and Technology, 37(5), 847–856. https://doi.org/10.1002/ceat.201300649
  • Mangadoddy, N., Vakamalla, T. R., Kumar, M., and Mainza, A. (2020). Computational modelling of particle-fluid dynamics in comminution and classification: a review. Mineral Processing and Extractive Metallurgy: Transactions of the Institute of Mining and Metallurgy, 129(2), 145–156. https://doi.org/10.1080/25726641.2019.1708657
  • Matsumoto, M., and Nishimura, T. (1998). Mersenne Twister: A 623-Dimensionally Equidistributed Uniform Pseudo-Random Number Generator. ACM Transactions on Modeling and Computer Simulation, 8(1), 3–30. https://doi.org/10.1145/272991.272995
  • Merkus, H. G. (2009). Particle Size Measurements: Fundamentals, Practice, Quality. New York: Springer.
  • Mular, A. L. (2009). Size Separation. In M. C. Fuerstenau and K. Han (Eds.), Principles of Mineral Processing (pp. 119–172). SME.
  • Muñoz, D. A., Diaz, J. L., Taborda, S., and Alvarez, H. (2017). Hydrocyclone Phenomenological-Based Model and Feasible Operation Region. International Journal of Mining, Materials, and Metallurgical Engineering, 3, 1–9.
  • Nageswararao, K., Wiseman, D. M., and Napier-Munn, T. J. (2004). Two empirical hydrocyclone models revisited. Minerals Engineering, 17(5), 671–687. https://doi.org/10.1016/j.mineng.2004.01.017
  • Napier-Munn, T. J., and Lynch, A. J. (1992). The modelling and computer simulation of mineral treatment processes - current status and future trends. Minerals Engineering, 5(2), 143–167. https://doi.org/10.1016/0892-6875(92)90039-C
  • Narasimha, M., Brennan, M., and Holtham, P. N. (2007). A Review of CFD Modelling for Performance Predictions of Hydrocyclone. Engineering Applications of Computational Fluid Mechanics, 1(2), 109–125. https://doi.org/10.1080/19942060.2007.11015186
  • Svarovsky, L., and Svarovsky, J. (1992). A New Method of Testing Hydrocyclone Grade Efficiencies. In L. Svarovsky and T. M. Thew (Eds.), Hydrocyclones Analysis and Applications (pp. 68–70). Springer.
  • Tang, Z., Yu, L., Wang, F., Li, N., Chang, L., and Cui, N. (2018). Effect of particle size and shape on separation in a hydrocyclone. Water (Switzerland), 11(1), 1–19. https://doi.org/10.3390/w11010016
  • Wills, B. A., and Finch, J. A. (2016). Wills’ Mineral Processing Technology. Amsterdam: Elsevier.
  • Wong, C. K., and Easton, M. C. (1980). An Efficient Method for Weighted Sampling without Replacement. SIAM Journal on Computing, 9(1), 111–113. https://doi.org/10.1137/0209009
  • Zhao, L., Zhao, Y., Bao, C., Hou, Q., and Yu, A. (2016). Laboratory-scale validation of a DEM model of screening processes with circular vibration. Powder Technology, 303, 269–277. https://doi.org/10.1016/j.powtec.2016.09.034

PREDICTING SCREENING AND CLASSIFICATION PRODUCTS VIA THE PSEUDORANDOM NUMBER SELECTION ROUTINE

Year 2022, Volume 61, Issue 1, 25 - 30, 07.03.2022
https://doi.org/10.30797/madencilik.947610

Abstract

Screening and classification are performed for the separation of particles by their sizes. There are empirical, phenomenological, and numerical models for predicting the size distributions of screening/classification products. This paper introduces a new algorithm for the same purpose, which partially mimics phenomenological and numerical models. The algorithm iteratively selects the monosize fractions with pre-defined probabilities, then carries particle masses from the selected fractions either to the oversize or undersize product. The applicability of the algorithm was validated against the product size distributions of some industrial-scale screening/classification operations provided in the literature. The results show that the algorithm is predictive if each particle has a selection probability proportional to the mass of its monosize fraction and some power of its diameter. Results also suggest that vibrating screens can provide the sharpest size separation.

References

  • Austin, L. G., Klimpel, R. R., and Luckie, P. T. (1984). Process Engineering of Size Reduction: Ball Milling (Vol. 1). New York: AIME. Camalan, M. (2021a).
  • A computational algorithm to understand the evolution of size distribution with successive breakage events at grinding (In Press). Proceedings of the 2nd International Electronic Conference on Mineral Science. https://doi.org/10.3390/iecms2021-09381
  • Camalan, M. (2021b). Investigating the effects of random sieving losses on particle size distributions. Particulate Science and Technology, 39(1), 108–115. https://doi.org/10.1080/02726351.2019.1669749
  • Coelho, M. A. Z., and Medronho, R. A. (1992). An Evaluation of the Plitt and Lynch & Rao Models for the Hydrocyclones. In L. Svarovsky and T. M. Thew (Eds.), Hydrocyclones Analysis and Applications (pp. 63–72). Springer.
  • Davoodi, A., Asbjörnsson, G., Hulthén, E., and Evertsson, M. (2019). Application of the discrete element method to study the effects of stream characteristics on screening performance. Minerals, 9(12). https://doi.org/10.3390/min9120788
  • Dong, K. J., Wang, B., and Yu, A. B. (2013). Modeling of particle flow and sieving behavior on a vibrating screen: From discrete particle simulation to process performance prediction. Industrial and Engineering Chemistry Research, 52(33), 11333–11343. https://doi.org/10.1021/ie3034637
  • Dong, K. J., and Yu, a. B. (2012). Numerical simulation of the particle flow and sieving behaviour on sieve bend/low head screen combination. Minerals Engineering, 31, 2–9. https://doi.org/10.1016/j.mineng.2011.10.020
  • Dündar, H. (2020). Investigating the benefits of replacing hydrocyclones with high-frequency fine screens in closed grinding circuit by simulation. Minerals Engineering, 148(January), 106212. https://doi.org/10.1016/j.mineng.2020.106212
  • Elskamp, F., and Kruggel-Emden, H. (2015). Review and benchmarking of process models for batch screening based on discrete element simulations. Advanced Powder Technology, 26(3), 679–697. https://doi.org/10.1016/j.apt.2014.11.001
  • Frausto, J. J., Ballantyne, G. R., Runge, K., Powell, M. S., Wightman, E. M., Evans, C. L., … Gomez, S. (2021). The effect of screen versus cyclone classification on the mineral liberation properties of a polymetallic ore. Minerals Engineering, 169(April), 106930. https://doi.org/10.1016/j.mineng.2021.106930
  • Gupta, A., and Yan, D. (2016). Mineral Processing Design and Operations. Amsterdam: Elsevier. Heiskanen, K. G. H. (1996). Developments in wet classifiers. International Journal of Mineral Processing, 44–45(SPEC. ISS.), 29–42. https://doi.org/10.1016/0301-7516(95)00015-1
  • Hogg, R. (2008). Issues in particle size analysis. KONA Powder and Particle Journal, 26(March), 81–93. https://doi.org/10.14356/kona.2008009
  • Kelly, E. G. (1991). The significance of by-pass in mineral separators. Minerals Engineering, 4(1), 1–7. https://doi.org/10.1016/0892-6875(91)90113-A
  • Khoshdast, H., Shojaei, V., and Khoshdast, H. (2017). Combined application of computational fluid dynamics (CFD) and design of experiments (DOE) to hydrodynamic simulation of a coal classifier. International Journal of Mining and Geo-Engineering, 51(1), 9–22. https://doi.org/10.22059/ijmge.2016.218483.594634
  • King, R. P. (2012). Modeling and Simulation of Mineral Processing Systems (C. L. Schneider and E. A. King, eds.). SME. Kruggel-Emden, H., and Elskamp, F. (2014). Modeling of screening processes with the discrete element method involving non-spherical particles. Chemical Engineering and Technology, 37(5), 847–856. https://doi.org/10.1002/ceat.201300649
  • Mangadoddy, N., Vakamalla, T. R., Kumar, M., and Mainza, A. (2020). Computational modelling of particle-fluid dynamics in comminution and classification: a review. Mineral Processing and Extractive Metallurgy: Transactions of the Institute of Mining and Metallurgy, 129(2), 145–156. https://doi.org/10.1080/25726641.2019.1708657
  • Matsumoto, M., and Nishimura, T. (1998). Mersenne Twister: A 623-Dimensionally Equidistributed Uniform Pseudo-Random Number Generator. ACM Transactions on Modeling and Computer Simulation, 8(1), 3–30. https://doi.org/10.1145/272991.272995
  • Merkus, H. G. (2009). Particle Size Measurements: Fundamentals, Practice, Quality. New York: Springer.
  • Mular, A. L. (2009). Size Separation. In M. C. Fuerstenau and K. Han (Eds.), Principles of Mineral Processing (pp. 119–172). SME.
  • Muñoz, D. A., Diaz, J. L., Taborda, S., and Alvarez, H. (2017). Hydrocyclone Phenomenological-Based Model and Feasible Operation Region. International Journal of Mining, Materials, and Metallurgical Engineering, 3, 1–9.
  • Nageswararao, K., Wiseman, D. M., and Napier-Munn, T. J. (2004). Two empirical hydrocyclone models revisited. Minerals Engineering, 17(5), 671–687. https://doi.org/10.1016/j.mineng.2004.01.017
  • Napier-Munn, T. J., and Lynch, A. J. (1992). The modelling and computer simulation of mineral treatment processes - current status and future trends. Minerals Engineering, 5(2), 143–167. https://doi.org/10.1016/0892-6875(92)90039-C
  • Narasimha, M., Brennan, M., and Holtham, P. N. (2007). A Review of CFD Modelling for Performance Predictions of Hydrocyclone. Engineering Applications of Computational Fluid Mechanics, 1(2), 109–125. https://doi.org/10.1080/19942060.2007.11015186
  • Svarovsky, L., and Svarovsky, J. (1992). A New Method of Testing Hydrocyclone Grade Efficiencies. In L. Svarovsky and T. M. Thew (Eds.), Hydrocyclones Analysis and Applications (pp. 68–70). Springer.
  • Tang, Z., Yu, L., Wang, F., Li, N., Chang, L., and Cui, N. (2018). Effect of particle size and shape on separation in a hydrocyclone. Water (Switzerland), 11(1), 1–19. https://doi.org/10.3390/w11010016
  • Wills, B. A., and Finch, J. A. (2016). Wills’ Mineral Processing Technology. Amsterdam: Elsevier.
  • Wong, C. K., and Easton, M. C. (1980). An Efficient Method for Weighted Sampling without Replacement. SIAM Journal on Computing, 9(1), 111–113. https://doi.org/10.1137/0209009
  • Zhao, L., Zhao, Y., Bao, C., Hou, Q., and Yu, A. (2016). Laboratory-scale validation of a DEM model of screening processes with circular vibration. Powder Technology, 303, 269–277. https://doi.org/10.1016/j.powtec.2016.09.034

Details

Primary Language English
Subjects Engineering, Multidisciplinary
Journal Section Research Article
Authors

Mahmut CAMALAN> (Primary Author)
It is not affiliated with an institution
0000-0001-7071-7910
Türkiye

Publication Date March 7, 2022
Submission Date June 3, 2021
Acceptance Date September 23, 2021
Published in Issue Year 2022, Volume 61, Issue 1

Cite

Bibtex @research article { madencilik947610, journal = {Scientific Mining Journal}, issn = {2564-7024}, eissn = {2587-2613}, address = {Selanik Cad. No: 19/4 06650 Kızılay-Çankaya / ANKARA - TURKEY}, publisher = {Chamber of Mining Engineers of Turkey}, year = {2022}, volume = {61}, number = {1}, pages = {25 - 30}, doi = {10.30797/madencilik.947610}, title = {PREDICTING SCREENING AND CLASSIFICATION PRODUCTS VIA THE PSEUDORANDOM NUMBER SELECTION ROUTINE}, key = {cite}, author = {Camalan, Mahmut} }
APA Camalan, M. (2022). PREDICTING SCREENING AND CLASSIFICATION PRODUCTS VIA THE PSEUDORANDOM NUMBER SELECTION ROUTINE . Scientific Mining Journal , 61 (1) , 25-30 . DOI: 10.30797/madencilik.947610
MLA Camalan, M. "PREDICTING SCREENING AND CLASSIFICATION PRODUCTS VIA THE PSEUDORANDOM NUMBER SELECTION ROUTINE" . Scientific Mining Journal 61 (2022 ): 25-30 <http://www.mining.org.tr/en/pub/issue/68805/947610>
Chicago Camalan, M. "PREDICTING SCREENING AND CLASSIFICATION PRODUCTS VIA THE PSEUDORANDOM NUMBER SELECTION ROUTINE". Scientific Mining Journal 61 (2022 ): 25-30
RIS TY - JOUR T1 - PREDICTING SCREENING AND CLASSIFICATION PRODUCTS VIA THE PSEUDORANDOM NUMBER SELECTION ROUTINE AU - MahmutCamalan Y1 - 2022 PY - 2022 N1 - doi: 10.30797/madencilik.947610 DO - 10.30797/madencilik.947610 T2 - Scientific Mining Journal JF - Journal JO - JOR SP - 25 EP - 30 VL - 61 IS - 1 SN - 2564-7024-2587-2613 M3 - doi: 10.30797/madencilik.947610 UR - https://doi.org/10.30797/madencilik.947610 Y2 - 2021 ER -
EndNote %0 Scientific Mining Journal PREDICTING SCREENING AND CLASSIFICATION PRODUCTS VIA THE PSEUDORANDOM NUMBER SELECTION ROUTINE %A Mahmut Camalan %T PREDICTING SCREENING AND CLASSIFICATION PRODUCTS VIA THE PSEUDORANDOM NUMBER SELECTION ROUTINE %D 2022 %J Scientific Mining Journal %P 2564-7024-2587-2613 %V 61 %N 1 %R doi: 10.30797/madencilik.947610 %U 10.30797/madencilik.947610
ISNAD Camalan, Mahmut . "PREDICTING SCREENING AND CLASSIFICATION PRODUCTS VIA THE PSEUDORANDOM NUMBER SELECTION ROUTINE". Scientific Mining Journal 61 / 1 (March 2022): 25-30 . https://doi.org/10.30797/madencilik.947610
AMA Camalan M. PREDICTING SCREENING AND CLASSIFICATION PRODUCTS VIA THE PSEUDORANDOM NUMBER SELECTION ROUTINE. Mining. 2022; 61(1): 25-30.
Vancouver Camalan M. PREDICTING SCREENING AND CLASSIFICATION PRODUCTS VIA THE PSEUDORANDOM NUMBER SELECTION ROUTINE. Scientific Mining Journal. 2022; 61(1): 25-30.
IEEE M. Camalan , "PREDICTING SCREENING AND CLASSIFICATION PRODUCTS VIA THE PSEUDORANDOM NUMBER SELECTION ROUTINE", Scientific Mining Journal, vol. 61, no. 1, pp. 25-30, Mar. 2022, doi:10.30797/madencilik.947610

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