Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Process Modeling for Dynamic Disperse Particle Separation and Deposition Processes Read chapter Read first chapter

2020 | OriginalPaper | Chapter

5. Development of a Dynamic-Physical Process Model for Sieving

Authors : Darius Markauskas, Harald Kruggel-Emden

Published in: Dynamic Flowsheet Simulation of Solids Processes

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

For a broad range of applications sieving/screening is well suited to separate bulk materials according to particle sizes. In the treated bulk materials particles frequently prevail in broad size distributions, with non-spherical shape and sometimes even under moist conditions, complicating the separation process. Therefore, it is inevitable to gain a deeper understanding of the subprocesses of screening (size based stratification, particle passage through the screen surface and possible transport along the screen) under the aforementioned conditions. To gain this knowledge, detailed particle-based simulation approaches like the discrete element method (DEM) are available. Based on the latter method, discontinuous and continuous screening as well as its subprocesses are investigated. Therein, different screen geometries and characteristics are considered along with various mechanical excitations applying model and real particle shapes first under dry conditions and later under the influence of various liquid amounts. In order to perform reliable DEM screening simulations, the exact determination of particle properties like size, shape, material and contact parameters is essential, which is required in advance of the simulations. Besides the DEM, the integral outcome of screening can be represented by various phenomenological process models. Usually, the material-, operating-, and apparatus-specific parameters of the latter process models are empirically determined by experiments, whereas, here, the parameters for screening process models are directly obtained from DEM simulations, which allows their benchmarking under defined conditions. Additionally, suitable process models are successfully extended to represent screening processes under the presence of moisture.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
previous chapter Dynamic Simulation of Technical Precipitation Processes
next chapter Dynamic Process Models for Fine Grinding and Dispersing
Literature
1.
Stieß, M. Mechanische Verfahrenstechnik - Partikeltechnologie 1, Springer, Berlin (2009)
2.
Liu, K.: Some factors affecting sieving performance and efficiency. Powder Technol. 193(2), 208–213 (2009)
3.
Grozubinsky, V., Sultanovitch, E., Lin, I.J.: Efficiency of solid particle screening as a function of screen slot size, particle size, and duration of screening—the theoretical approach. Int. J. Miner. Process. 52(4), 261–272 (1998)
4.
Bunge, R.: Mechanische Aufbereitung - Primär- und Sekundärrohstoffe, 1st edn. Wiley-VCH Verlag, Weinheim (2012)
5.
Kruse, R.: Mechanische Verfahrenstechnik - Grundlagen der Flüssigkeitsförderung und der Partikeltechnologie. Wiley-VCH Verlag, Weinheim (1999)
6.
Schmidt, P., Körber, R., Coppers, M.: Sieben und Siebmaschinen - Grundlagen und Anwendungen. Wiley-VCH Verlag, Weinheim (2003)
7.
Yoshida, Y., Ishikawa, S., Shimosaka, A., Shirakawa, Y., Hidaka, J.: Estimation equation for sieving rate based on the model for undersized particles passing through vibrated particle bed. J. Chem. Eng. Jpn. 46(2), 116–126 (2013)
8.
Cleary, P.W., Sinnott, M.D., Morrison, R.D.: Separation performance of double deck banana screens—Part 1: flow and separation for different accelerations. Miner. Eng. 22(14), 1218–1229 (2009)
9.
Soldinger, M.: Interrelation of stratification and passage in the screening process. Miner. Eng. 12(5), 497–516 (1999)
10.
Soldinger, M.: Influence of particle size and bed thickness on the screening process. Miner. Eng. 13(3), 297–312 (2000)
11.
Cundall, P.A., Strack, O.D.L.: A discrete numerical model for granular assemblies. Géotechnique 29(1), 47–65 (1979)
12.
Walton, O.R., Braun, R.L.: Viscosity, granular-temperature, and stress calculations for shearing assemblies of inelastic, frictional disks. J. Rheol. 30, 949–980 (1986)
13.
Zhu, H.P., Zhou, Z.Y., Yang, R.Y., Yu, A.B.: Discrete particle simulation of particulate systems: theoretical developments. Chem. Eng. Sci. 62(13), 3378–3396 (2007)
14.
Zhu, H.P., Zhou, Z.Y., Yang, R.Y., Yu, A.B.: Discrete particle simulation of particulate systems: a review of major applications and findings. Chem. Eng. Sci. 63(23), 5728–5770 (2008)
15.
Cleary, P.W. Large scale industrial DEM modelling. Eng. Computations 21(2/3/4), 169–204 (2004)
16.
Lu, G., Third, J.R., Müller, C.R.: Discrete element models for non-spherical particle systems: from theoretical developments to applications. Chem. Eng. Sci. 127, 425–465 (2015)
17.
Höhner, D., Wirtz, S., Kruggel-Emden, H., Scherer, V.: Comparison of the multi-sphere and polyhedral approach to simulate non-spherical particles within the discrete element method: influence on temporal force evolution for multiple contacts. Powder Technol. 208(3), 643–656 (2011)
18.
Mio, H., Shimosaka, A., Shirakawa, Y., Hidaka, J.: Optimum cell size for contact detection in the algorithm of the discrete element method. J. Chem. Eng. Jpn. 38(12), 969–975 (2005)
19.
Cleary, P.W.: The effect of particle shape on simple shear flows. Powder Technol. 179(3), 144–163 (2008)
20.
Mead, S.R., Cleary, P.W., Robinson, G.K. Characterising the failure and repose angles of irregularly shaped three-dimensional particles using DEM. In: Ninth International Conference on CFD in the Minerals and Process Industries, Melbourne, Australia, pp. 1–6 (2012)
21.
Delaney, G.W., Cleary, P.W. The packing properties of superellipsoids. Europhys. Lett. 89 (3), (2010)
22.
Jensen, R.P., Bosscher, P.J., Plesha, M.E., Edil, T.B.: DEM simulation of granular media-structure interface: effects of surface roughness and particle shape. Int. J. Numer. Anal. Meth. Geomech. 23(6), 531–547 (1999)
23.
Favier, J.F., Abbaspour-Fard, M.H., Kremmer, M., Raji, A.O.: Shape representation of axi-symmetrical, non-spherical particles in discrete element simulation using multielement model particles. Eng. Comput. 16(4), 467–480 (1999)
24.
Favier, J.F., Abbaspour-Fard, M.H., Kremmer, M.: Modeling nonspherical particles using multisphere discrete elements. J. Eng. Mech. 127(10), 971–977 (2001)
25.
Vu-Quoc, L., Zhang, X., Walton, O.R.: A 3-D discrete emelent method for dry granular flows of ellipsoidal particles. Comput. Methods Appl. Mech. Eng. 187, 483–528 (2000)
26.
Kruggel-Emden, H., Rickelt, S., Wirtz, S., Scherer, V.: A study on the validity of the multi-sphere Discrete Element Method. Powder Technol. 188(2), 153–165 (2008)
27.
Markauskas, D., Kačianauskas, R.: Investigation of rice grain flow by multi-sphere particle model with rolling resistance. Granular Matter 13(2), 143–148 (2011)
28.
Munjiza, A., Latham, J.P., John, N.W.M.: 3D dynamics of discrete element systems comprising irregular discrete elements—integration solution for finite rotations in 3D. Int. J. Numer. Meth. Eng. 56(1), 35–55 (2003)
29.
Elskamp, F., Kruggel-Emden, H.: DEM simulations of screening processes under the influence of moisture. Chem. Eng. Res. Des. 136, 593–609 (2018)
30.
Elskamp, F., Kruggel-Emden, H., Hennig, M., Teipel, U.: A strategy to determine DEM parameters for spherical and non-spherical particles. Granular Matter 19(3), 46 (2017)
31.
Kruggel-Emden, H., Simsek, E., Rickelt, S., Wirtz, S., Scherer, V.: Review and extension of normal force models for the Discrete Element Method. Powder Technol. 171(3), 157–173 (2007)
32.
Di Renzo, A., Di Maio, F.P.: Comparison of contact-force models for the simulation of collisions in DEM-based granular flow codes. Chem. Eng. Sci. 59(3), 525–541 (2004)
33.
Kruggel-Emden, H., Wirtz, S., Scherer, V.: A study on tangential force laws applicable to the discrete element method (DEM) for materials with viscoelastic or plastic behavior. Chem. Eng. Sci. 63(6), 1523–1541 (2008)
34.
Kruggel-Emden, H., Rickelt, S., Wirtz, S., Scherer, V.: A numerical study on the sensitivity of the discrete element method for hopper discharge. J. Pressure Vessel Technol. 131(3), 031211 (2009)
35.
Cleary, P.W., Sawley, M.L.: DEM modelling of industrial granular flows: 3D case studies and the effect of particle shape on hopper discharge. Appl. Math. Model. 26(2), 89–111 (2002)
36.
Höhner, D., Wirtz, S., Scherer, V.: Experimental and numerical investigation on the influence of particle shape and shape approximation on hopper discharge using the discrete element method. Powder Technol. 235, 614–627 (2013)
37.
Di Renzo, A., Di Maio, F.P.: An improved integral non-linear model for the contact of particles in distinct element simulations. Chem. Eng. Sci. 60, 1303–1312 (2005)
38.
Ge, W., Wang, L., Xu, J., Chen, F., Zhou, G., Lu, L., Chang, Q., Li, J.: Discrete simulation of granular and particle-fluid flows: from fundamental study to engineering application. Rev. Chem. Eng. 33(6), 551–623 (2017)
39.
Rabinovich, Y.I., Esayanur, M.S., Moudgil, B.M.: Capillary forces between two spheres with a fixed volume liquid bridge: theory and experiment. Langmuir 21(24), 10992–10997 (2005)PubMed
40.
Willett, C.D., Adams, M.J., Johnson, S.A., Seville, J.P.K.: Capillary bridges between two spherical bodies. Langmuir 16(10), 9396–9405 (2000)
41.
Weigert, T., Ripperger, S.: Calculation of the Liquid bridge volume and bulk saturation from the half-filling angle. Part. Part. Syst. Charact. 16(5), 238–242 (1999)
42.
Adams, M.J., Perchard, V.: The cohesive forces between particles with interstitial liquid. Int. Chem. Eng. Symp. Ser. 91, 147–160 (1985)
43.
Goldman, A.J., Cox, R.G., Brenner, H.: Slow viscous motion of a sphere parall to a plane wall—I Motion through a quiescent fluid. Chem. Eng. Sci. 22(4), 653–660 (1967)
44.
Pitois, O., Moucheront, P., Chateau, X.: Liquid bridge between two moving spheres: an experimental study of viscosity effects. J. Colloid Interface Sci. 231, 26–31 (2000)PubMed
45.
Pitois, O., Moucheront, P., Chateau, X.: Rupture energy of a pendular liquid bridge. Eur. Phys. J. B 23, 79–86 (2001)
46.
Pepin, X., Rossetti, D., Iveson, S.M., Simons, S.J.R.: Modeling the evolution and rupture of pendular liquid bridges in the presence of large wetting hysteresis. J. Colloid Interface Sci. 232, 289–297 (2000)PubMed
47.
Shi, D., McCarthy, J.J.: Numerical simulation of liquid transfer between particles. Powder Technol. 184, 64–75 (2008)
48.
Lian, G., Thornton, C., Adams, M.J.: A theoretical study of the liquid bridge forces between two rigid spherical bodies. J. Colloid Interface Sci. 161, 138–147 (1993)
49.
Lian, G., Seville, J.: The capillary bridge between two spheres: new closed-form equations in a two century old problem. Adv. Coll. Interface. Sci. 227, 53–62 (2016)
50.
Gladkyy, A., Schwarze, R.: Comparison of different capillary bridge models for application in the discrete element method. Granular Matter 16(6), 911–920 (2014)
51.
Mikami, T., Kamiya, H., Horio, M.: Numerical simulation of cohesive powder behavior in a fluidized bed. Chem. Eng. Sci. 53(10), 1927–1940 (1998)
52.
Washino, K., Chan, E.L., Miyazaki, K., Tsuji, T., Tanaka, T.: Time step criteria in DEM simulation of wet particles in viscosity dominant systems. Powder Technol. 302, 100–107 (2016)
53.
Schmelzle, S., Nirschl, H.: DEM simulations: mixing of dry and wet granular material with different contact angles. Granular Matter 20, 19 (2018)
54.
Radl, S., Kalvoda, E., Glasser, B.J., Khinast, J.G.: Mixing characteristics of wet granular matter in a bladed mixer. Powder Technol. 200(3), 171–189 (2010)
55.
Scholtès, L., Chareyre, B., Nicot, F., Darve, F.: Discrete modelling of capillary mechanisms in multi-phase granular media. Comput. Model. Eng. Sci. 52(3), 297–318 (2009)
56.
Melnikov, K., Mani, R., Wittel, F.K., Thielmann, M., Herrmann, H.J.: Grain scale modeling of arbitrary fluid saturation in random packings. Phys. Rev. E 92(2), 022206 (2015)
57.
Wang, J., Gallo, E., François, B., Gabrieli, F., Lambert, P.: Capillary force and rupture of funicular liquid bridges between three spherical bodies. Powder Technol. 305, 89–98 (2017)
58.
Butt, H.-J., Kappl, M.: Surface and Interfacial Forces. Wiley-VCH Verlag, Weinheim (2010)
59.
Coetzee, C.J.: Review: calibration of the discrete element method. Powder Technol. 310, 104–142 (2017)
60.
Marigo, M., Stitt, E.H.: Discrete element method (DEM) for industrial applications: comments on calibration and validation for the modelling of cylindrical pellets. KONA Powder Part. J. 32(32), 236–252 (2015)
61.
Simons, T.A.H., Weiler, R., Strege, S., Bensmann, S., Schilling, M., Kwade, A.: A ring shear tester as calibration experiment for DEM simulations in agitated mixers—a sensitivity study. Procedia Eng. 102, 741–748 (2015)
62.
Grima, A.P., Wypych, P.W.: Investigation into calibration of discrete element model parameters for scale-up and validation of particle-structure interactions under impact conditions. Powder Technol. 212(1), 198–209 (2011)
63.
Barrios, G.K.P., de Carvalho, R.M., Kwade, A., Tavares, L.M.: Contact parameter estimation for DEM simulation of iron ore pellet handling. Powder Technol. 248, 84–93 (2013)
64.
Markauskas, D., Kačianauskas, R., Džiugys, A., Navakas, R.: Investigation of adequacy of multi-sphere approximation of elliptical particles for DEM simulations. Granular Matter 12(1), 107–123 (2010)
65.
Pasha, M., Hare, C., Ghadiri, M., Gunadi, A., Piccione, P.M.: Effect of particle shape on flow in discrete element method simulation of a rotary batch seed coater. Powder Technol. 296, 29–36 (2016)
66.
Williams, K.C., Chen, W., Weeger, S., Donohue, T.J.: Particle shape characterisation and its application to discrete element modelling. Particuology 12(1), 80–89 (2014)
67.
Mollon, G., Zhao, J.: Generating realistic 3D sand particles using Fourier descriptors. Granular Matter 15(1), 95–108 (2013)
68.
Just, S., Toschkoff, G., Funke, A., Djuric, D., Scharrer, G., Khinast, J., Knop, K., Kleinebudde, P.: Experimental analysis of tablet properties for discrete element modeling of an active coating process. AAPS PharmSciTech 14(1), 402–411 (2013)PubMedPubMedCentral
69.
Chung, Y.-C., Ooi, J.Y.: A study of influence of gravity on bulk behaviour of particulate solid. Particuology 6(6), 467–474 (2008)
70.
González-Montellano, C., Fuentes, J.M., Ayuga-Téllez, E., Ayuga, F.: Determination of the mechanical properties of maize grains and olives required for use in DEM simulations. J. Food Eng. 111(4), 553–562 (2012)
71.
Senetakis, K., Coop, M.R., Todisco, M.C.: The inter-particle coefficient of friction at the contacts of Leighton Buzzard sand quartz minerals. Soils Found. 53(5), 746–755 (2013)
72.
Ucgul, M., Fielke, J.M., Saunders, C.: Defining the effect of sweep tillage tool cutting edge geometry on tillage forces using 3D discrete element modelling. Inf. Process. Agric. 2(2), 130–141 (2015)
73.
Chung, Y.C., Lin, C.K., Ai, J.: Mechanical behaviour of a granular solid and its contacting deformable structure under uni-axial compression—Part II: multi-scale exploration of internal physical properties. Chem. Eng. Sci. 144, 421–443 (2016)
74.
Kretz, D., Callau-Monje, S., Hitschler, M., Hien, A., Raedle, M., Hesser, J.: Discrete element method (DEM) simulation and validation of a screw feeder system. Powder Technol. 287, 131–138 (2016)
75.
Ucgul, M., Fielke, J.M., Saunders, C.: Three-dimensional discrete element modelling of tillage: determination of a suitable contact model and parameters for a cohesionless soil. Biosys. Eng. 121, 105–117 (2014)
76.
Li, Y., Xu, Y., Thornton, C.: A comparison of discrete element simulations and experiments for ‘sandpiles’ composed of spherical particles. Powder Technol. 160(3), 219–228 (2005)
77.
Wong, C.X., Daniel, M.C., Rongong, J.A.: Energy dissipation prediction of particle dampers. J. Sound Vib. 319(1–2), 91–118 (2009)
78.
Alonso-Marroquín, F., Ramírez-Gómez, Á., González-Montellano, C., Balaam, N., Hanaor, D.A.H., Flores-Johnson, E.A., Gan, Y., Chen, S., Shen, L.: Experimental and numerical determination of mechanical properties of polygonal wood particles and their flow analysis in silos. Granular Matter 15(6), 811–826 (2013)
79.
Dong, H., Moys, M.H.: Measurement of impact behaviour between balls and walls in grinding mills. Miner. Eng. 16(6), 543–550 (2003)
80.
Gorham, D.A., Kharaz, A.H.: The measurement of particle rebound characteristics. Powder Technol. 112(3), 193–202 (2000)
81.
Höhner, D., Wirtz, S., Scherer, V.: A study on the influence of particle shape and shape approximation on particle mechanics in a rotating drum using the discrete element method. Powder Technol. 253, 256–265 (2014)
82.
Hastie, D.B.: Experimental measurement of the coefficient of restitution of irregular shaped particles impacting on horizontal surfaces. Chem. Eng. Sci. 101, 828–836 (2013)
83.
Wang, L., Zhou, W., Ding, Z., Li, X., Zhang, C.: Experimental determination of parameter effects on the coefficient of restitution of differently shaped maize in three-dimensions. Powder Technol. 284, 187–194 (2015)
84.
Chou, H.T., Lee, C.F., Chung, Y.C., Hsiau, S.S.: Discrete element modelling and experimental validation for the falling process of dry granular steps. Powder Technol. 231, 122–134 (2012)
85.
Coetzee, C.J., Els, D.N.J.: Calibration of discrete element parameters and the modelling of silo discharge and bucket filling. Comput. Electron. Agric. 65(2), 198–212 (2009)
86.
Coetzee, C.J., Els, D.N.: Calibration of granular material parameters for DEM modelling and numerical verification by blade-granular material interaction. J. Terrramech. 46, 15–26 (2009)
87.
Coetzee, C.J., Els, D.N.J., Dymond, G.F.: Discrete element parameter calibration and the modelling of dragline bucket filling. J. Terrramech. 47(1), 33–44 (2010)
88.
Coetzee, C.J.: Calibration of the discrete element method and the effect of particle shape. Powder Technol. 297, 50–70 (2016)
89.
Wensrich, C.M., Katterfeld, A.: Rolling friction as a technique for modelling particle shape in DEM. Powder Technol. 217, 409–417 (2012)
90.
Frankowski, P., Morgeneyer, M.: Calibration and validation of DEM rolling and sliding friction coefficients in angle of repose and shear measurements. AIP Conf. Proc. 1542, 851–854 (2013)
91.
Combarros, M., Feise, H.J., Zetzener, H., Kwade, A.: Segregation of particulate solids: experiments and DEM simulations. Particuology 12(1), 25–32 (2014)
92.
Santos, D.A., Barrozo, M.A.S., Duarte, C.R., Weigler, F., Mellmann, J.: Investigation of particle dynamics in a rotary drum by means of experiments and numerical simulations using DEM. Adv. Powder Technol. 27(2), 692–703 (2016)
93.
Cabiscol, R., Finke, J.H., Kwade, A.: Calibration and interpretation of DEM parameters for simulations of cylindrical tablets with multi-sphere approach. Powder Technol. 327, 232–245 (2018)
94.
Chung, Y.C., Liao, H.H., Hsiau, S.S.: Convection behavior of non-spherical particles in a vibrating bed: discrete element modeling and experimental validation. Powder Technol. 237, 53–66 (2013)
95.
Skorych, V., Dosta, M., Hartge, E.-U., Heinrich, S.: Novel system for dynamic flowsheet simulation of solids processes. Powder Technol. 314, 665–679 (2017)
96.
Dimian, A., Bildea, C., Kiss, A.: Integrated design and simulation of chemical processes. Elsevier 13, 73–156 (2014)
97.
Dosta, M., Heinrich, S., Werther, J.: Fluidized bed spray granulation: analysis of the system behaviour by means of dynamic flowsheet simulation. Powder Technol. 204(1), 71–82 (2010)
98.
Marquardt, W. Dynamic process simulation—recent progress and future challenges. In: Chemical Process Control CPC-IV, CACHE Publications, pp. 131–180 (1991)
99.
Schwier, D., Hartge, E.U., Werther, J., Gruhn, G.: Global sensitivity analysis in the flowsheet simulation of solids processes. Chem. Eng. Process. 49(1), 9–21 (2010)
100.
Hartge, E.U., Pogodda, M., Reimers, C., Schwier, D., Gruhn, G., Werther, J.: Flowsheet simulation of solids processes. KONA 24, 146–158 (2006)
101.
Reimers, C., Werther, J., Gruhn, G.: Flowsheet simulation of solids processes. Data reconciliation and adjustment of model parameters. Chem. Eng. Process.: Process Intensification 47(1), 138–158 (2008)
102.
Dosta, M., Antonyuk, S., Hartge, E.-U., Heinrich, S.: Parameter estimation for the flowsheet simulation of solids processes. Chem. Ing. Tec. 86(7), 1073–1079 (2014)
103.
Dehghani, A., Monhemius, A.J., Gochin, R.J. Evaluating the Nakajima et al. model for rectangular-aperture screens. Minerals Eng. 15, 1089–1094 (2002)
104.
Hatch, C.C., Mular, A.L. Simulation of the Brenda Mines Ltd. secondary crusher. In: SME-AIME Annual Meeting, pp. 54–79 (1979)
105.
Plitt, L.R.: The analysis of solid—solid separations in classifiers. CIM Bull. 64, 42–47 (1971)
106.
Rogers, R.S.C.: A classification function for vibrating screens. Powder Technol. 31, 135–137 (1982)
107.
Molerus, O., Hoffmann, H. Darstellung von Windsichtertrennkurven durch ein stochastisches Modell, Chemie Ingenieur Technik 41 (5+6), 340–344 (1969)
108.
Trawinski, H.: Die mathematische Formulierung der Tromp-Kurve. Aufbereitungstechnik 17(248–254), 449–459 (1976)
109.
Elskamp, F., Kruggel-Emden, H., Hennig, M., Teipel, U.: Benchmarking of process models for continuous screening based on discrete element simulations. Miner. Eng. 83, 78–96 (2015)
110.
Standish, N.: The kinetics of batch sieving. Powder Technol. 41, 57–67 (1985)
111.
Standish, N., Meta, I.A.: Some kinetic aspects of continuous screening. Powder Technol. 41, 165–171 (1985)
112.
Trumic, M., Magdalinovic, N.: New model of screening kinetics. Miner. Eng. 24, 42–49 (2011)
113.
Andreev, S.E., Perov, V.A., Zverevic, V.V.: Droblenie izmelcenie i grohocenie poleznyh iskopaemyh. Nedra, Moscow (1980)
114.
Subasinghe, G.K.N.S., Schaap, W., Kelly, E.G.: Modelling screening as a conjugate rate process. Int. J. Miner. Process. 28, 289–300 (1990)
115.
Subasinghe, G.K.N.S., Schaap, W., Kelly, E.G.: Modelling the screening process: a probabilistic approach. Powder Technol. 59, 37–44 (1989)
116.
Nakajima, Y., Whiten, W.J.: Behaviour of non-spherical particles in screening. Trans. Inst. Min. Metall. 88, C88–C92 (1979)
117.
Ferrara, G., Preti, U., Schena, G.D. Computer-aided use of a screening process model. In: Twentieth International Symposium on the Application of Computers and Mathematics in the Mineral Industries, pp. 153–166 (1987)
118.
Shimosaka, A., Higashihara, S., Hidaka, J.: Estimation of the sieving rate of powders using computer simulation. Adv. Powder Technol. 11(4), 487–502 (2000)
119.
Li, J., Webb, C., Pandiella, S.S., Campbell, G.M.: Discrete particle motion on sieves—a numerical study using the DEM simulation. Powder Technol. 133, 190–202 (2003)
120.
Gaudin, A.M.: Principles of mineral dressing. McGraw-Hill, New York, USA (1939)
121.
Soldinger, M.: Transport velocity of a crushed rock material bed on a screen. Miner. Eng. 15, 7–17 (2002)
122.
Elskamp, F., Kruggel-Emden, H.: Review and benchmarking of process models for batch screening based on discrete element simulations. Adv. Powder Technol. 26, 679–697 (2015)
123.
Elskamp, F., Kruggel-Emden, H.: Extension of process models to predict batch screening results under the influence of moisture based on DEM simulations. Powder Technol. 342, 698–713 (2019)
124.
Dong, K.J., Wang, B., Yu, A.B.: Modeling of particle flow and sieving behavior on a vibrating screen: from discrete particle simulation to process performance prediction. Ind. Eng. Chem. Res. 52(33), 11333–11343 (2013)
125.
Delaney, G.W., Cleary, P.W., Hilden, M., Morrison, R.D.: Testing the validity of the spherical DEM model in simulating real granular screening processes. Chem. Eng. Sci. 68(1), 215–226 (2012)
126.
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Longman, Boston, Massachusetts (1989)
Metadata
Title
Development of a Dynamic-Physical Process Model for Sieving
Authors
Darius Markauskas
Harald Kruggel-Emden
Copyright Year
2020
Book
Dynamic Flowsheet Simulation of Solids Processes

Print ISBN: 978-3-030-45167-7

Electronic ISBN: 978-3-030-45168-4

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-45168-4_5