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Fuzzy Markov chains modeling of aggregation processes

Year 2019, Volume: 68 Issue: 2, 2050 - 2063, 01.08.2019
https://doi.org/10.31801/cfsuasmas.586071

Abstract

In this paper, the fuzzy Markov chain method is proposed as a new discrete solution of a population balance equation for an aggregation process. In order to validate the proposed method, analytical solution of an aggregation equation is compared with the fuzzy Markov chain method for the constant aggregation kernel. According to the results, if the size range of the system is divided into a sufficient number of states and an appropriate transition time step is chosen, then the fuzzy Markov chain method displays a good approximation for the particle size distribution(PSD) while the main equation is driven by a constant aggregation kernel.

References

  • Hidy, G. M., and Lilly, D.K., Solutions to the Equations for the Kinetics of Coagulation, Journal of Colloid Science. 20 (1965), 867.
  • Gelbard, F.M., and Seinfeld, J.H., Simulation of Multicomponent Aerosol Dynamics, Journal of Colloid and Interface Science.78 (1980), 485.
  • Hounslow, M.J., The population balance as a tool for understanding particle rate processes, Kona. 16 (1998). 179-193.
  • Hill, P. J., and Ka M. Ng., New discretization procedure for the agglomeration equation, AIChE journal. 42.3 (1996), 727-741.
  • Vanni, M., Approximate Population Balance Equations for Aggregation-Breakage Processes, Journal of Colloid and Interface Science, 221 (2000), 143-160.
  • Ramkrishna, D., Population Balances Theory and applications to Particulate Systems in Engineering, Academic Press, USA, 2000.
  • Snow, R.H., Terry, A., Ennis, B., J., and Lister, J.D., Size reduction and size en-largement. Perry's chemical engineering's' handbook, 7th edition, McGraw-Hill, USA, 1997.
  • Berthiaux, H., and Mizonov, V., Applications of Markov Chains in Particulate Process Engineering: A Review, The Canadian Journal of Chemical Engineering. 82, (2004),1143-1168.
  • Norris, J.R.,Cambridge Series in Statistical and Probabilistic Mathematics, Cambridge University Press, Cambridge, 1997.
  • Farina, L., and Rinaldi, S., Positive Linear Systems: Theory and Application, Wiley, 2000.
  • Starczewski, J. T., Defuzzification of uncertain fuzzy sets in Advanced Concepts in Fuzzy Logic and Systems with Membership Uncertainty, Springer, 2013.
Year 2019, Volume: 68 Issue: 2, 2050 - 2063, 01.08.2019
https://doi.org/10.31801/cfsuasmas.586071

Abstract

References

  • Hidy, G. M., and Lilly, D.K., Solutions to the Equations for the Kinetics of Coagulation, Journal of Colloid Science. 20 (1965), 867.
  • Gelbard, F.M., and Seinfeld, J.H., Simulation of Multicomponent Aerosol Dynamics, Journal of Colloid and Interface Science.78 (1980), 485.
  • Hounslow, M.J., The population balance as a tool for understanding particle rate processes, Kona. 16 (1998). 179-193.
  • Hill, P. J., and Ka M. Ng., New discretization procedure for the agglomeration equation, AIChE journal. 42.3 (1996), 727-741.
  • Vanni, M., Approximate Population Balance Equations for Aggregation-Breakage Processes, Journal of Colloid and Interface Science, 221 (2000), 143-160.
  • Ramkrishna, D., Population Balances Theory and applications to Particulate Systems in Engineering, Academic Press, USA, 2000.
  • Snow, R.H., Terry, A., Ennis, B., J., and Lister, J.D., Size reduction and size en-largement. Perry's chemical engineering's' handbook, 7th edition, McGraw-Hill, USA, 1997.
  • Berthiaux, H., and Mizonov, V., Applications of Markov Chains in Particulate Process Engineering: A Review, The Canadian Journal of Chemical Engineering. 82, (2004),1143-1168.
  • Norris, J.R.,Cambridge Series in Statistical and Probabilistic Mathematics, Cambridge University Press, Cambridge, 1997.
  • Farina, L., and Rinaldi, S., Positive Linear Systems: Theory and Application, Wiley, 2000.
  • Starczewski, J. T., Defuzzification of uncertain fuzzy sets in Advanced Concepts in Fuzzy Logic and Systems with Membership Uncertainty, Springer, 2013.
There are 11 citations in total.

Details

Primary Language English
Journal Section Review Articles
Authors

Nursin Bas Catak This is me 0000-0002-2386-726X

Publication Date August 1, 2019
Submission Date March 21, 2018
Acceptance Date June 27, 2019
Published in Issue Year 2019 Volume: 68 Issue: 2

Cite

APA Catak, N. B. (2019). Fuzzy Markov chains modeling of aggregation processes. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, 68(2), 2050-2063. https://doi.org/10.31801/cfsuasmas.586071
AMA Catak NB. Fuzzy Markov chains modeling of aggregation processes. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. August 2019;68(2):2050-2063. doi:10.31801/cfsuasmas.586071
Chicago Catak, Nursin Bas. “Fuzzy Markov Chains Modeling of Aggregation Processes”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 68, no. 2 (August 2019): 2050-63. https://doi.org/10.31801/cfsuasmas.586071.
EndNote Catak NB (August 1, 2019) Fuzzy Markov chains modeling of aggregation processes. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 68 2 2050–2063.
IEEE N. B. Catak, “Fuzzy Markov chains modeling of aggregation processes”, Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat., vol. 68, no. 2, pp. 2050–2063, 2019, doi: 10.31801/cfsuasmas.586071.
ISNAD Catak, Nursin Bas. “Fuzzy Markov Chains Modeling of Aggregation Processes”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 68/2 (August 2019), 2050-2063. https://doi.org/10.31801/cfsuasmas.586071.
JAMA Catak NB. Fuzzy Markov chains modeling of aggregation processes. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. 2019;68:2050–2063.
MLA Catak, Nursin Bas. “Fuzzy Markov Chains Modeling of Aggregation Processes”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, vol. 68, no. 2, 2019, pp. 2050-63, doi:10.31801/cfsuasmas.586071.
Vancouver Catak NB. Fuzzy Markov chains modeling of aggregation processes. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. 2019;68(2):2050-63.

Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics.

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