Development of separation sharpness model for hydrocyclone

doi:10.1016/j.cjche.2019.12.014

Chinese Journal of Chemical Engineering ›› 2020, Vol. 28 ›› Issue (3): 785-792.DOI: 10.1016/j.cjche.2019.12.014

• Separation Science and Engineering • Previous Articles Next Articles

Development of separation sharpness model for hydrocyclone

Pakpoom Supachart¹, Thanit Swasdisevi², Pratarn Wongsarivej³, Mana Amornkitbamrung², Naris Pratinthong¹

1 Division of Energy Technology, School of Energy, Environment and Materials, King Mongkut's University of Technology Thonburi, 126 Pracha-Uthit Road, Bang Mod, Thung khru, Bangkok 10140, Thailand;
2 Division of Thermal Technology, School of Energy, Environment and Materials, King Mongkut's University of Technology Thonburi, 126 Pracha-Uthit Road, Bang Mod, Thung khru, Bangkok 10140, Thailand;
3 National Nanotechnology Center, National Science and Technology Development Agency, 111 Thailand Science Park, Phahonyothin Road, Khlong Nueng, Khlong Luang, Pathumthani 12120, Thailand

Received:2019-09-03 Revised:2019-12-09 Online:2020-06-11 Published:2020-03-28
Contact: Pakpoom Supachart
Supported by:
The authors would like to express their sincere thanks to the NSTDA University Industry Research Collaboration (NUI-RC) Thailand for supporting the funding in the research.

Development of separation sharpness model for hydrocyclone

Pakpoom Supachart¹, Thanit Swasdisevi², Pratarn Wongsarivej³, Mana Amornkitbamrung², Naris Pratinthong¹

1 Division of Energy Technology, School of Energy, Environment and Materials, King Mongkut's University of Technology Thonburi, 126 Pracha-Uthit Road, Bang Mod, Thung khru, Bangkok 10140, Thailand;
2 Division of Thermal Technology, School of Energy, Environment and Materials, King Mongkut's University of Technology Thonburi, 126 Pracha-Uthit Road, Bang Mod, Thung khru, Bangkok 10140, Thailand;
3 National Nanotechnology Center, National Science and Technology Development Agency, 111 Thailand Science Park, Phahonyothin Road, Khlong Nueng, Khlong Luang, Pathumthani 12120, Thailand

通讯作者: Pakpoom Supachart
基金资助:
The authors would like to express their sincere thanks to the NSTDA University Industry Research Collaboration (NUI-RC) Thailand for supporting the funding in the research.

Abstract

Abstract: Hydrocyclones are mechanical devices used in classifying and separating many different types of materials. A classification function of the hydrocyclone has been continually developed for solid-liquid separation. In the classification process of solids from liquids, it is desirable to reduce the amount of misplaced material; therefore, the separation sharpness, α (alpha), is a parameter that helps in evaluating misplaced material and has been developed as a model to help the designer predict the performance of the classification. However, the problem with the separation sharpness model is that it cannot be used outside the range of conditions under which it was developed. Therefore, this research aimed to develop the separation sharpness model to predict more accurately and cover a wide range of conditions using the multiple linear regression method. The new regression model of separation sharpness was based on a wide range of both experimental and industrial data-sets of 431 tests collaborating with the additional experiments of 117 tests that were obtained from a total of 548 tests. The new model of separation sharpness can be used in the range of 30-762 mm hydrocyclone body diameters and feed solid concentrations in the range of 0.5wt%-80 wt%. When compared with the experimental separation sharpness, the accuracy of the separation sharpness model prediction has an error of 4.53% and R² of 0.973.

Key words: Model, Hydrocyclone, Separation, Sharpness, Slurry, Viscosity

摘要： Hydrocyclones are mechanical devices used in classifying and separating many different types of materials. A classification function of the hydrocyclone has been continually developed for solid-liquid separation. In the classification process of solids from liquids, it is desirable to reduce the amount of misplaced material; therefore, the separation sharpness, α (alpha), is a parameter that helps in evaluating misplaced material and has been developed as a model to help the designer predict the performance of the classification. However, the problem with the separation sharpness model is that it cannot be used outside the range of conditions under which it was developed. Therefore, this research aimed to develop the separation sharpness model to predict more accurately and cover a wide range of conditions using the multiple linear regression method. The new regression model of separation sharpness was based on a wide range of both experimental and industrial data-sets of 431 tests collaborating with the additional experiments of 117 tests that were obtained from a total of 548 tests. The new model of separation sharpness can be used in the range of 30-762 mm hydrocyclone body diameters and feed solid concentrations in the range of 0.5wt%-80 wt%. When compared with the experimental separation sharpness, the accuracy of the separation sharpness model prediction has an error of 4.53% and R² of 0.973.

关键词: Model, Hydrocyclone, Separation, Sharpness, Slurry, Viscosity

Pakpoom Supachart, Thanit Swasdisevi, Pratarn Wongsarivej, Mana Amornkitbamrung, Naris Pratinthong. Development of separation sharpness model for hydrocyclone[J]. Chinese Journal of Chemical Engineering, 2020, 28(3): 785-792.

Pakpoom Supachart, Thanit Swasdisevi, Pratarn Wongsarivej, Mana Amornkitbamrung, Naris Pratinthong. Development of separation sharpness model for hydrocyclone[J]. 中国化学工程学报, 2020, 28(3): 785-792.

References

[1] M. Narasimha, M.S. Brennan, P.N. Holtham, CFD modeling of hydrocyclones:Prediction of particle size segregation, Miner. Eng. 39(2012) 173-183.
[2] D. Bradley, The Hydrocyclone, 1st ed. Pergamon, London, U.K., 196552-55.
[3] K.J. Hwang, Y.W. Hwang, H. Yoshida, Design of novel hydrocyclone for improving fine particle separation using computational fluid dynamics, Chem. Eng. Sci. 85(2013) 62-68.
[4] X. Lü, S. Liu, Evaluation of fractionation of softwood pulp in a cylindrical hydrocyclone, Chin. J. Chem. Eng. 14(4) (2006) 537-541.
[5] M. Saidi, R. Maddahian, B. Farhanieh, H. Afshin, Modeling of flow field and separation efficiency of a deoiling hydrocyclone using large eddy simulation, Int. J. Miner. Proc. 112-113(2012) 84-93.
[6] L.Y. Chu, Q. Luo, Hydrocyclone with high separation sharpness, Filtr. Separat. 31(1994) 733-736.
[7] L. Svarovsky, Solid-liquid separation, 4th ed. Butterworth-Heinemann, Holt, Rinehart&Winston, London, Oxford, 200073.
[8] P.H. Fahlstrom, Studies of the hydrocyclone as a classifier, 6th IMPC, Cannes 1963, pp. 87-114.
[9] A.J. Lynch, Matrix Models of Certain Mineral Dressing Processes, Ph.D Thesis, University of Queensland, 1964.
[10] A.J. Lynch, T.C. Rao, Studies on the operating characteristics of hydrocyclone classifiers, Indian J. Tech. 6(4) (1968) 106-114.
[11] T.C. Rao, The Characteristics of Hydrocyclones and their Application as Control Units in Comminution Circuits, Ph.D Thesis, University of Queensland, 1966.
[12] L.R. Plitt, The analysis of solid-solid separations in classifiers, CIM Bulletin, April 64(708) (1971) 42-47.
[13] K. Nageswararao, Reduced efficiency curves of industrial hydrocyclones-An analysis for plant practice, Miner. Eng. 12(5) (1999) 517-544.
[14] L.R. Plitt, A mathematical model of the hydrocyclone classifier, CIM bulletin, December (1976) 114-123.
[15] A.C. Apling, D. Montaldo, P.A. Young, Hydrocyclone models in an ore-grinding context, Processing of the International Conference on Hydrocyclones, Cambridge, BHRA 113-126(1980).
[16] B.C. Flintoff, L.R. Plitt, A.A. Turak, Cyclone modeling:A review of present technology, CIM Bulletin, 80(905) (1987) 39-50.
[17] T. Kojovic, The Development and Application of Model-An Automated Model Builder for Mineral Processing, Ph.D Thesis, University of Queensland, 1988.
[18] K. Nageswararao, Further Developments in the Modelling and Scale-up of Industrial Hydrocyclones, Ph.D Thesis, University of Queensland, 1978.
[19] I.K. Asomah, Improved Models of Hydrocyclone, Ph.D Thesis, University of Queensland, 1996.
[20] I.K. Asomah, T.J. Napier-munn, An empirical model of hydrocyclones, incorporating angle of cyclone inclination, Miner. Eng. 10(1996) 339-347.
[21] J. Xiao, Extensions of Model Building Techniques and their Applications in Mineral Processing, Ph.D Thesis, University of Queensland, 1997.
[22] M. Narasimha, A.N. Mainza, P.N. Holtham, M.S. Powell, M.S. Brennan, A semi-mechanistic model of hydrocyclones-Developed from industrial data and inputs from CFD, Int. J. Miner. Proc. 133(2014) 1-12.
[23] M. Narasimha, Improved Computational and Empirical Models of Hydrocyclones, Ph.D Thesis, University of Queensland, 2009.
[24] A.L. Hinde, Classification and concentration of heavy minerals in grinding circuits, chamber of mines of South Africa, Auckland Park, South Africa (1985) 1-15.
[25] O. Castro, An Investigation of Pulp Rheology Effects and their Application to the Dimensionless Type Hydrocyclone Models, M.Eng.Sc Thesis, University of Queensland, 1990.
[26] A.N. Mainza, Contribution to the Understanding of the Three-Product Cyclone on the Classification of a Dual Density Platinum Ore, Ph.D Thesis, University of Cape Town, 2006.
[27] A.J. Lynch, T.C. Rao, Modelling and scale up of hydrocyclone classifiers, XI IMPC, Cagliari 1975, pp. 9-25.
[28] A.L. Hinde, G.R. Jennery, J.G. Mackay, The classification performance of a hydrocyclone, chamber of mines of South Africa research, Organization (1979) 1-61.
[29] J.G. Mackay, A.L. Hinde, P.J.D. Lloyd, A mathematical model of the hydrocyclone classifier, Chamber of Mines of South Africa (1983) 1-15.
[30] A.N. Mainza, T. Olson, M.S. Powell, T. Stewart, P9N Private Communication, 2006.
[31] P. Wongsarivej, W. Tanthapanichakoon, H. Yoshida, Classification of silica fine particles using a novel electric hydrocyclone, Sci. Technol. Adv. Mat. 6(2005) 364-369.
[32] T. Neesse, The hydrocyclone as a turbulence classifier, ChemTech 23(1971) 146-152.
[33] T. Neesse, W. Dallman, D. Espig, Effect of turbulence on the efficiency of separation in hydrocyclones at high feed solids concentrations, Aufbereitungs-Technik, Miner. Proc. 919(1986) 6-14.
[34] M. Ishii, K. Mishima, Two-fluid model and hydrodynamic constitutive relations, Nucl. Eng. Des. 82(1984) 107-126.
[35] H.H. Steinour, Rate of sedimentation suspensions of uniform size angular particles, Ind. Eng. Chem. 36(1944) 840-847.
[36] K. Heiskanen, Particle classification, 1st ed. Chapman & Hall, London, 19935.

Development of separation sharpness model for hydrocyclone

Development of separation sharpness model for hydrocyclone

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Pan Wang, Mengdei Zhou, Zhuangxin Wei, Lu Liu, Tao Cheng, Xiaohua Tian, Jianming Pan. Preparation of bowl-shaped polydopamine surface imprinted polymer composite adsorbent for specific separation of 2′-deoxyadenosine [J]. Chinese Journal of Chemical Engineering, 2023, 60(8): 69-79.
[2]	Wenwen Zhang, Zhigang Xue, Liyun Cui, Haoliang Gao, Di Zhao, Rongfei Zhou, Weihong Xing. Synthesis of an IMF zeolite membrane for the separation of xylene isomer [J]. Chinese Journal of Chemical Engineering, 2023, 60(8): 205-211.
[3]	Xiaolin Guo, Zhaoyang Zhang, Pengfei Xing, Shuai Wang, Yibing Guo, Yanxin Zhuang. Kinetic mechanism of copper extraction from methylchlorosilane slurry residue using hydrogen peroxide as oxidant [J]. Chinese Journal of Chemical Engineering, 2023, 60(8): 228-234.
[4]	Huiqi Wang, Jianpo Ren, Shihao Zhang, Jiayu Dai, Yue Niu, Ketao Shi, Qiuxiang Yin, Ling Zhou. Measurement and correlation of solubility of 9-fluorenone in 11 pure organic solvents from T = 283.15 to 323.15 K [J]. Chinese Journal of Chemical Engineering, 2023, 60(8): 235-241.
[5]	Hammad Saulat, Jianhua Yang, Tao Yan, Waseem Raza, Wensen Song, Gaohong He. Tungsten incorporated mobil-type eleven zeolite membranes: Facile synthesis and tuneable wettability for highly efficient separation of oil/water mixtures [J]. Chinese Journal of Chemical Engineering, 2023, 60(8): 242-252.
[6]	Jindong Dai, Chi Zhai, Jiali Ai, Guangren Yu, Haichao Lv, Wei Sun, Yongzhong Liu. A cellular automata framework for porous electrode reconstruction and reaction-diffusion simulation [J]. Chinese Journal of Chemical Engineering, 2023, 60(8): 262-274.
[7]	Yuan Liu, Hanting Xiong, Jingwen Chen, Shixia Chen, Zhenyu Zhou, Zheling Zeng, Shuguang Deng, Jun Wang. One-step ethylene separation from ternary C₂ hydrocarbon mixture with a robust zirconium metal-organic framework [J]. Chinese Journal of Chemical Engineering, 2023, 59(7): 9-15.
[8]	Borui Liu, Tao Zhang, Yi Zheng, Kailong Li, Hui Pan, Hao Ling. A dynamic control structure of liquid-only transfer stream distillation column [J]. Chinese Journal of Chemical Engineering, 2023, 59(7): 135-145.
[9]	Zhonghao Li, Yuanyuan Yang, Huanong Cheng, Yun Teng, Chao Li, Kangkang Li, Zhou Feng, Hongwei Jin, Xinshun Tan, Shiqing Zheng. Measurement and model of density, viscosity, and hydrogen sulfide solubility in ferric chloride/trioctylmethylammonium chloride ionic liquid [J]. Chinese Journal of Chemical Engineering, 2023, 59(7): 210-221.
[10]	Yaran Bu, Changchun Wu, Lili Zuo, Qian Chen. The calculation and optimal allocation of transmission capacity in natural gas networks with MINLP models [J]. Chinese Journal of Chemical Engineering, 2023, 59(7): 251-261.
[11]	Yafei Su, Xuke Zhang, Hui Li, Donglai Peng, Yatao Zhang. In-situ incorporation of halloysite nanotubes with 2D zeolitic imidazolate framework-L based membrane for dye/salt separation [J]. Chinese Journal of Chemical Engineering, 2023, 58(6): 103-111.
[12]	Junhao Wang, Shugang Ma, Peng Chen, Zhipeng Li, Zhengming Gao, J. J. Derksen. Mixing of miscible shear-thinning fluids in a lid-driven cavity [J]. Chinese Journal of Chemical Engineering, 2023, 58(6): 112-123.
[13]	Weikai Ren, Runsong Dai, Ningde Jin. Modeling of liquid film thickness around Taylor bubbles rising in vertical stagnant and co-current slug flowing liquids [J]. Chinese Journal of Chemical Engineering, 2023, 58(6): 179-194.
[14]	Hongwei Liang, Wenling Li, Zisheng Feng, Jianming Chen, Guangwen Chu, Yang Xiang. Numerical simulation of gas-liquid flow in the bubble column using Wray-Agarwal turbulence model coupled with population balance model [J]. Chinese Journal of Chemical Engineering, 2023, 58(6): 205-223.
[15]	Yun-Zhang Liu, Lu-Yao Zhang, Dan He, Li-Zhen Chen, Zi-Shuai Xu, Jian-Long Wang. Solubility measurement, correlation and thermodynamic properties of 2, 3, 4-trichloro-1, 5-dinitrobenzene in fifteen mono-solvents at temperatures from 278.15 to 323.15 K [J]. Chinese Journal of Chemical Engineering, 2023, 58(6): 224-233.