The behaviour of multi-component particles in a hydrocyclone is not clearly understood in the past, also unaccounted for most mathematical models. In this work, a number of historical experimental data sets were compiled to assess multi-component particles during the classification action. The bi-component classification experiments are carried-out using artificial magnetite and silica mixture in a laboratory hydrocyclone. The insights present evidence of the significant differences between the individual components to overall mixture performance in terms of their cut-size, solids recovery and sharpness of the separation were clearly assessed. An extension of single average density hydrocyclone model has been utilised to propose a model concept for hydrocyclone performance. Specific model equations of cut size, sharpness of separation and solids recovery were formulated to capture the influence of multi-component particle behaviour. The model equations were validated using independent data sets and found close match between experimental and predicted results.
BradleyD. The Hydrocyclone. London: Pergamon Press Ltd, 1965.
2.
Napier-MunnTJMorrellSMorrisonRD,et al.Mineral comminution circuits––their operation and optimisation. In: JKMRC Monograph Series, Julius Kruttschnitt Mineral Research Centre. Indooroopilly, Qld Australia: University of Queensland, 1996, pp.273–319.
3.
LynchAJRaoTC. Modeling and scale-up of hydrocyclone classifiers, In XI IMPC, Cagliari. 1975: 9–25.
4.
SvarovskyL. Hydrocyclones. Holt, Rinehart and Winson Ltd., London. 1984.
5.
KojovicT. The development and application of MODEL – an automated model builder for mineral processing. The University of Queensland, 1988.
6.
NageswararaoK. Further modeling and scale-up of hydrocyclones. JKMRC University of Queensland, 1978.
7.
PlittLR. A mathematical model of the hydrocyclone classifier, In CIM Bull., pp. 114–122. 1976.
8.
NarasimhaMMainzaANHolthamPN, et al. A semi-mechanistic model of hydrocyclones — developed from industrial data and inputs from CFD. Int J Miner Process2014; 133: 1–12.
9.
KingRP. Modelling, and simulation of mineral processing systems. Oxford: Butterworth-Heinemann, 2001, pp.353–393.
10.
WisemanDMRichardsonJM. JKSimMet––the mineral processing simulator. In: Second Conference on Computer Applications for the Mineral Industry (CAMI). Vancouver. 1991.
11.
LynchAJ. Mineral crushing and grinding circuits: their simulation, optimisation, design and control. Elsevier Scientific Publishing Company, 1977, p.34.
12.
MainzaAPowellMSKnopjesB. Differential classification of dense material in a three-product cyclone. Miner Eng2004; 17: 573–579.
KawatraSKEiseleTCWalquiHJ. DOE quarterly report on Optimization of Comminution Circuit Throughput and Product Size Distribution by Simulation and Control. 2005.
15.
WellerKRSternsUJArtoneE,et al.Multi-component models of grinding and classification for scale-up from continuous small or pilot scale circuits. Int J Miner Process1988; 22: 119–147.
16.
AustinLGKlimpelRRLuckiePT. Process engineering of size reduction. SME - AIME, 1984.
17.
NarasimhaM. Improved computational and empirical models of hydrocyclones. University of Queensland, JKMRC, 2010.
18.
NarasimhaMBrennanMMainzaAN,et al.Towards improved hydrocyclone models: Contributions from CFD, (IMPC 2010), 6–10 Sep 2010. In XXV International Mineral Processing Congress. 2010a.
19.
PadhiMMangadoddyNSreenivasT, et al. Study on multi-component particle behaviour in a hydrocyclone classifier using experimental and computational fluid dynamics techniques. Sep Purif Technol2019; 229: 115698.
20.
PadhiMMangadoddyNMainzaAN, et al. Study on the particle interaction in a hydrocyclone classifier with multi-component feed blend at a high solids content. Powder Technol2021; 393: 380–396.
21.
MainzaAN. Contributions to the Understanding of Three Product Cyclones in the Classification of Dual Density Platinum Ores. PhD thesis, The University of Cape Town. 2006.
22.
PlittLRFinchJAFlintoffBC. Modelling the hydrocyclone classifier, In European symposium on particle technology. Amsterdam, NL. 1980.
23.
NarasimhaMDurgaCPBanerjee GuptaVK. Coal rheology effect on dense medium cyclone performance, private communication. Indian School of Mines. 2010b.
24.
AsomahAK. Improved models of hydrocyclones. JKMRC, The University of Queensland, 1996.
25.
MainzaAPowellMSKnopjesB. Differential classification of dense material in a three-product cyclone. Miner Eng2004; 17: 573–579.
26.
MainzaAPowellMSKnopjesB. A comparison of different cyclones in addressing challenges in the classification of dual-density UG2 platinum ore. J S Afr Inst Min Metallur2005; 105: 341–348.
27.
KraipechWChenWParmaFJ,et al.Modelling the fish-hook effect of the flow within hydrocyclones. Int J Miner Process2002; 66: 49–65.
28.
VakamallaTRMangadoddyN. Comprehensive dense slurry CFD model for performance evaluation of industrial hydrocyclones. Ind Eng Chem Res2021; 60: 12403–12418.
29.
FinchJAMatwijenkoO. Individual mineral behavior in a closed grinding circuit, In Annual Meeting of SME of AIME. CIM Bulletin, Atlanta. 1977: 164.
30.
LynchAJRaoTC. Studies on the operating characteristics of hydrocyclone classifiers. Indian J Technol1968; 6: 106–114.
31.
SchubertHNeesseT. A hydrocyclone separation model in consideration of the turbulent multi-phase flow, In Proc. International conferenc on hydrocyclones. BHRA Fluid Engineering, Cambridge, 1980: 23–36.
32.
Jeason John Crasta. Performance of hydrocyclone separating multi component mixture. Indian Institute of Technology Hyderabad, 2014.
33.
NarasimhaMMainzaANHolthamPN, et al.Improved comminution circuit simulations using a new set of equations of hydrocyclone classifier, Comminution- 2012, Cape Town.
