Smoluchowski coagulation equation

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This diagram describes the aggregation kinetics of discrete particles according to the Smoluchowski aggregation equation.

In statistical physics, the Smoluchowski coagulation equation is a population balance equation introduced by Marian Smoluchowski in a seminal 1916 publication,[1] describing the time evolution of the number density of particles as they coagulate (in this context "clumping together") to size x at time t.

Simultaneous coagulation (or aggregation) is encountered in processes involving polymerization,[2] coalescence of aerosols,[3] emulsication,[4] flocculation.[5]


The distribution of particle size change in time according to the interrelation of all particles of the system. Therefore, the Smoluchowski coagulation equation is an integrodifferential equation of the particle-size distribution. In the case when the sizes of the coagulated particles are continuous variables, the equation involves an integral:

If dy is interpreted as a discrete measure, i.e. when particles join in discrete sizes, then the discrete form of the equation is a summation:

There exists a unique solution for a chosen kernel function.[6]

Coagulation kernel[edit]

The operator, K, is known as the coagulation kernel and describes the rate at which particles of size coagulate with particles of size . Analytic solutions to the equation exist when the kernel takes one of three simple forms:

known as the constant, additive, and multiplicative kernels respectively.[7]

However, in most practical applications the kernel takes on a significantly more complex form. For example, the free-molecular kernel which describes collisions in a dilute gas-phase system,

Some coagulation kernels account for a specific fractal dimension of the clusters, as in the diffusion-limited aggregation:

or Reaction-limited aggregation:

where are fractal dimensions of the clusters, is the Boltzmann constant, is the temperature, is the Fuchs stability ratio, is the continuous phase viscosity, and is the exponent of the product kernel, usually considered a fitting parameter.[8]

Generally the coagulation equations that result from such physically realistic kernels are not solvable, and as such, it is necessary to appeal to numerical methods. Most of deterministic methods can be used when there is only one particle property (x) of interest, the two principal ones being the method of moments[9][10][11][12][13] and sectional methods.[14] In the multi-variate case, however, when two or more properties (such as size, shape, composition, etc.) are introduced, one has to seek special approximation methods that suffer less from curse of dimensionality. Approximation based on Gaussian radial basis functions has been successfully applied to the coagulation equation in more than one dimension.[15][16]

When the accuracy of the solution is not of primary importance, stochastic particle (Monte Carlo) methods are an attractive alternative.[citation needed]

Condensation-driven aggregation[edit]

In addition to aggregation, particles may also grow in size by condensation, deposition or by accretion. Hassan and Hassan recently proposed a condensation-driven aggregation (CDA) model in which aggregating particles keep growing continuously between merging upon collision;[17][18] the CDA model can be understood by the following reaction scheme

where denotes the aggregate of size at time and is the elapsed time. This reaction scheme can be described by the following generalized Smoluchowski equation

Considering that a particle of size grows due to condensation between collision time equal to inverse of by an amount i.e.

One can solve the generalized Smoluchowski equation for constant kernel to give

which exhibits dynamic scaling. A simple fractal analysis reveals that the condensation-driven aggregation can be best described fractal of dimension

The th moment of is always a conserved quantity which is responsible for fixing all the exponents of the dynamic scaling. Such conservation law has also been found in Cantor set too.

See also[edit]


  1. ^ Smoluchowski, Marian (1916). "Drei Vorträge über Diffusion, Brownsche Molekularbewegung und Koagulation von Kolloidteilchen". Phys. Z. (in German). 17: 557–571, 585–599. Bibcode:1916ZPhy...17..557S.
  2. ^ Blatz, P. J.; Tobolsky, A. V. (1945). "Note on the Kinetics of Systems Manifesting Simultaneous Polymerization-Depolymerization Phenomena". The Journal of Physical Chemistry. 49 (2): 77–80. doi:10.1021/j150440a004. ISSN 0092-7325.
  3. ^ Agranovski, Igor (2011). Aerosols: Science and Technology. John Wiley & Sons. p. 492. ISBN 978-3527632084.
  4. ^ Danov, Krassimir D.; Ivanov, Ivan B.; Gurkov, Theodor D.; Borwankar, Rajendra P. (1994). "Kinetic Model for the Simultaneous Processes of Flocculation and Coalescence in Emulsion Systems". Journal of Colloid and Interface Science. 167 (1): 8–17. Bibcode:1994JCIS..167....8D. doi:10.1006/jcis.1994.1328. ISSN 0021-9797.
  5. ^ Thomas, D.N.; Judd, S.J.; Fawcett, N. (1999). "Flocculation modelling: a review". Water Research. 33 (7): 1579–1592. doi:10.1016/S0043-1354(98)00392-3. ISSN 0043-1354.
  6. ^ Melzak, Z. A. (1957). "A scalar transport equation". Transactions of the American Mathematical Society. 85 (2): 547. doi:10.1090/S0002-9947-1957-0087880-6. ISSN 0002-9947.
  7. ^ Wattis, J. A. D. (2006). "An introduction to mathematical models of coagulation–fragmentation processes: A discrete deterministic mean-field approach" (PDF). Physica D: Nonlinear Phenomena. 222 (1–2): 1–20. Bibcode:2006PhyD..222....1W. doi:10.1016/j.physd.2006.07.024.
  8. ^ Kryven, I.; Lazzari, S.; Storti, G. (2014). "Population Balance Modeling of Aggregation and Coalescence in Colloidal Systems". Macromolecular Theory and Simulations. 23 (3): 170. doi:10.1002/mats.201300140.
  9. ^ Marchisio, D. L.; Fox, R. O. (2005). "Solution of Population Balance Equa- tions Using the Direct Quadrature Method of Moments". J. Aerosol Sci. 36 (1): 43–73. Bibcode:2005JAerS..36...43M. doi:10.1016/j.jaerosci.2004.07.009.
  10. ^ Yu, M.; Lin, J.; Chan, T. (2008). "A New Moment Method for Solving the Coagulation Equation for Particles in Brownian Motion". Aerosol Sci. Technol. 42 (9): 705–713. Bibcode:2008AerST..42..705Y. doi:10.1080/02786820802232972. hdl:10397/9612.
  11. ^ McGraw, R. (1997). "Description of Aerosol Dynamics by the Quadrature Method of Moments". Aerosol Sci. Technol. 27 (2): 255–265. Bibcode:1997AerST..27..255M. doi:10.1080/02786829708965471.
  12. ^ Frenklach, M. (2002). "Method of Moments with Interpolative Closure". Chem. Eng. Sci. 57 (12): 2229–2239. doi:10.1016/S0009-2509(02)00113-6.
  13. ^ Lee, K. W.; Chen, H.; Gieseke, J. A. (1984). "Log-Normally Preserving Size Distribution for Brownian Coagulation in the Free-Molecule Regime". Aerosol Sci. Technol. 3 (1): 53–62. Bibcode:1984AerST...3...53L. doi:10.1080/02786828408958993.
  14. ^ Landgrebe, J. D.; Pratsinis, S. E. (1990). "A Discrete-Sectional Model for Particulate Production by Gas-Phase Chemical Reaction and Aerosol Coagulation in the Free-Molecular Regime". J. Colloid Interface Sci. 139 (1): 63–86. Bibcode:1990JCIS..139...63L. doi:10.1016/0021-9797(90)90445-T.
  15. ^ Kryven, I.; Iedema, P. D. (2013). "Predicting multidimensional distributive properties of hyperbranched polymer resulting from AB2 polymerization with substitution, cyclization and shielding". Polymer. 54 (14): 3472–3484. arXiv:1305.1034. doi:10.1016/j.polymer.2013.05.009.
  16. ^ Kryven, I.; Iedema, P. D. (2014). "Topology Evolution in Polymer Modification". Macromolecular Theory and Simulations. 23: 7–14. doi:10.1002/mats.201300121.
  17. ^ M. K. Hassan and M. Z. Hassan, “Condensation-driven aggregation in one dimension”, Phys. Rev. E 77 061404 (2008),
  18. ^ M. K. Hassan and M. Z. Hassan, “Emergence of fractal behavior in condensation-driven aggregation”, Phys. Rev. E 79 021406 (2009),