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Multi-Objective Surrogate Based Optimization of Gas Cyclones Using Support Vector Machines and CFD Simulations

Part of the Springer Tracts in Mechanical Engineering book series (SPTME)

Abstract

In order to accurately predict the complex nonlinear relationships between the cyclone performance parameters (The Euler and Stokes numbers) and the four significant geometrical dimensions (the inlet section height and width, the vortex finder diameter and the cyclone total height), the support vector machines approach has been used. Two support vector regression surrogates (SVR) have been trained and tested by CFD datasets. The result demonstrates that SVR can offer an alternative and powerful approach to model the performance parameters. The SVR model parameters have been optimized to obtain the most accurate results from the cross validation steps. SVR (with optimized parameters) can offer an alternative and powerful approach to model the performance parameters better than Kriging. SVR surrogates have been employed to study the effect of the four geometrical parameters on the cyclone performance. The genetic algorithms optimization technique has been applied to obtain a new geometrical ratio for minimum Euler number and for minimum Euler and Stokes numbers. New cyclones over-perform the standard Stairmand design performance. Pareto optimal solutions have been obtained and a new correlation between the Euler and Stokes numbers is fitted.

Keywords

Cyclone separator Multi-objective optimization Support vector machines Surrogate models 

References

  1. 1.
    Zhao B (2009) Modeling pressure drop coefficient for cyclone separators a support vector machine approach. Chem Eng Sci 64:4131–4136CrossRefGoogle Scholar
  2. 2.
    Elsayed K, Lacor C (2011) The effect of cyclone inlet dimensions on the flow pattern and performance. Appl Math Model 35(4):1952–1968CrossRefGoogle Scholar
  3. 3.
    Elsayed K, Lacor C (2010) Optimization of the cyclone separator geometry for minimum pressure drop using mathematical models and CFD simulations. Chem Eng Sci 65(22):6048–6058CrossRefGoogle Scholar
  4. 4.
    Elsayed K, Lacor C (2011) Numerical modeling of the flow field and performance in cyclones of different cone-tip diameters. Comput Fluids 51(1):48–59CrossRefGoogle Scholar
  5. 5.
    Elsayed K, Lacor C (2011) Modeling, analysis and optimization of aircyclones using artificial neural network, response surface methodology and CFD simulation approaches. Powder Technol 212(1):115–133CrossRefGoogle Scholar
  6. 6.
    Elsayed K, Lacor C (2012) Modeling and pareto optimization of gas cyclone separator performance using RBF type artificial neural networks and genetic algorithms. Powder Technol 217:84–99CrossRefGoogle Scholar
  7. 7.
    Elsayed K, Lacor C (2013) CFD modeling and multi-objective optimization of cyclone geometry using desirability function, artificial neural networks and genetic algorithms. Appl Math Model 37(8):5680–5704CrossRefGoogle Scholar
  8. 8.
    Elsayed K, Lacor C (2013) The effect of cyclone vortex finder dimensions on the flow pattern and performance using LES. Comput Fluids 71:224–239MathSciNetCrossRefGoogle Scholar
  9. 9.
    Elsayed K, Lacor C (2014) CFD-based analysis and optimization of gas cyclones performance. In: International energy and environment foundation (IEEF), Chap 8, pp 223–276. ISBN 13: 978-1-49487-575-6Google Scholar
  10. 10.
    Hoffmann AC, Stein LE (2008) Gas cyclones and swirl tubes: principle, design and operation, 2nd edn. Springer, BerlinGoogle Scholar
  11. 11.
    Derksen JJ, Sundaresan S, van den Akker HEA (2006) Simulation of mass-loading effects in gas–solid cyclone separators. Powder Technol 163:59–68CrossRefGoogle Scholar
  12. 12.
    Xiang R, Park SH, Lee KW (2001) Effects of cone dimension on cyclone performance. J Aerosol Sci 32(4):549–561CrossRefGoogle Scholar
  13. 13.
    Stairmand CJ (1951) The design and performance of cyclone separators. Ind Eng Chem 29:356–383Google Scholar
  14. 14.
    Ramachandran G, Leith D, Dirgo J, Feldman H (1991) Cyclone optimization based on a new empirical model for pressure drop. Aerosol Sci Technol 15:135–148CrossRefGoogle Scholar
  15. 15.
    Liu S, Xu L, Li D, Li Q, Jiang Y, Tai H, Zeng L (2013) Prediction of dissolved oxygen content in river crab culture based on least squares support vector regression optimized by improved particle swarm optimization. Comput Electron Agric 95:82–91CrossRefGoogle Scholar
  16. 16.
    Suykens JAK, Gestel TV, Brabanter JD, Moor BD, Vandewalle J (2002) Least squares support vector machines. World Scientific, SingaporeMATHCrossRefGoogle Scholar
  17. 17.
    Baylar A, Hanbay D, Batan M (2009) Application of least square support vector machines in the prediction of aeration performance of plunging overfall jets from weirs. Expert Syst Appl 36(4):8368–8374CrossRefGoogle Scholar
  18. 18.
    Singh KP, Basant N, Gupta S, Sinha S (2011) Support vector machines in water quality management: a case study. Anal Chim Acta 703:152–162CrossRefGoogle Scholar
  19. 19.
    Samui P (2011) Application of least square support vector machine (lssvm) for determination of evaporation losses in reservoirs. Engineering 3(4):431–434CrossRefGoogle Scholar
  20. 20.
    Elsayed K, Lacor C (2013) Comparison of Kriging, RBFNN, RBF and polynomial regression surrogates in design optimization. In: Eleventh international conference of fluid dynamics (ICFD11), AlexandriaGoogle Scholar
  21. 21.
    Elsayed K, Lacor C (2014) Robust parameter design optimization using Kriging, RBF and RBFNN with gradient-based and evolutionary optimization techniques. Appl Math Comput 236:325–344MathSciNetCrossRefGoogle Scholar
  22. 22.
    Nelder JA, Mead R (1965) A simplex method for function minimization. Comput J 7(4):308–313MATHCrossRefGoogle Scholar
  23. 23.
    Holland JH (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann ArborGoogle Scholar
  24. 24.
    Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182–197CrossRefGoogle Scholar
  25. 25.
    Fortin F-A, De Rainville F-M, Gardner M-A, Parizeau M, Gagne C (2012) Deap: evolutionary algorithms made easy. J Mach Learn Res 13:2171–2175MATHMathSciNetGoogle Scholar
  26. 26.
    Bressert E (2012) SciPy and NumPy: an overview for developers. O’Reilly Media, SebastopolGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringVrije Universiteit BrusselBrusselsBelgium
  2. 2.Faculty of Engineering at El-Mattaria, Mechanical Power Engineering DepartmentHelwan UniversityCairoEgypt

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