Optimization via multimodel simulation

A new approach to optimization of cyclone separator geometries

Abstract

Increasing computational power and the availability of 3D printers provide new tools for the combination of modeling and experimentation. Several simulation tools can be run independently and in parallel, e.g., long running computational fluid dynamics simulations can be accompanied by experiments with 3D printers. Furthermore, results from analytical and data-driven models can be incorporated. However, there are fundamental differences between these modeling approaches: some models, e.g., analytical models, use domain knowledge, whereas data-driven models do not require any information about the underlying processes. At the same time, data-driven models require input and output data, but analytical models do not. The optimization via multimodel simulation (OMMS) approach, which is able to combine results from these different models, is introduced in this paper. We believe that OMMS improves the robustness of the optimization, accelerates the optimization-via-simulation process, and provides a unified approach. Using cyclonic dust separators as a real-world simulation problem, the feasibility of this approach is demonstrated and a proof-of-concept is presented. Cyclones are popular devices used to filter dust from the emitted flue gasses. They are applied as pre-filters in many industrial processes including energy production and grain processing facilities. Pros and cons of this multimodel optimization approach are discussed and experiences from experiments are presented.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Notes

  1. 1.

    http://www.freecadweb.org

  2. 2.

    Source code and data for performing experiments from this study are available at http://www.gm.fh-koeln.de/~bartz/bart16e. The open source R software package SPOT can be downloaded from https://cran.r-project.org.

References

  1. Barth W (1956) Berechnung und Auslegung von Zyklonabscheidern aufgrund neuerer Untersuchungen. Brennstoff-Wärme-Kraft 8(1):1–9

    Google Scholar 

  2. Bartz-Beielstein T (2016) Stacked generalization of surrogate models - a practical approach. Technical Report 5/2016, TH Köln, Köln. https://cos.bibl.th-koeln.de/frontdoor/index/index/docId/375. Accessed 31 Oct 2017

  3. Bartz-Beielstein T, Zaefferer M (2017) Model-based methods for continuous and discrete global optimization. Appl Soft Comput 55:154–167

    Article  Google Scholar 

  4. Bartz-Beielstein T, Lasarczyk C, Preuss M (2005) Sequential parameter optimization. In: McKay B et al (eds) Proceedings 2005 congress on evolutionary computation (CEC’05), Edinburgh, Scotland. IEEE Press, Piscataway, pp 773–780

  5. Barzier MK, Perry CJ (1991) An approach to the construction and usage of simulation modeling in the shipbuilding industry. In: Nelson BL, Kelton WD, Clark GM (eds) 1991 winter simulation conference proceedings. IEEE, pp 455–464

  6. Breiman L (1996) Stacked regression. Mach Learn 24:49–64

    MathSciNet  MATH  Google Scholar 

  7. Bucila C, Caruana R, Niculescu-Mizil A (2006) Model compression: making big, slow models practical. In: Proceedings of the 12th international conference on knowledge discovery and data Mining (KDD’06)

  8. Chaudhuri A, Haftka RT, Ifju P, Chang K, Tyler C, Schmitz T (2015) Experimental flapping wing optimization and uncertainty quantification using limited samples. Struct Multidiscip Optim 51(4):957–970

    Article  Google Scholar 

  9. Dempster AP (1968) A generalization of bayesian inference. J R Stat Soc Ser B Methodol 30(2):205–247

    MathSciNet  MATH  Google Scholar 

  10. 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):6048–6058

    Article  Google Scholar 

  11. Elsayed K, Lacor C (2012) CFD modeling and multi-objective optimization of cyclone geometry using desirability function, artificial neural networks and genetic algorithms. Appl Math Model 37(8):5680–5704

    Article  Google Scholar 

  12. Fishwick PA, Zeigler BP (1992) A multimodel methodology for qualitative model engineering. ACM Trans Model Comput Simul 2(1):52–81

    Article  MATH  Google Scholar 

  13. Forrester A, Sóbester A, Keane A (2007) Multi-fidelity optimization via surrogate modelling. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science 463(2088):3251–3269

    MathSciNet  Article  MATH  Google Scholar 

  14. Fu MC (1994) Optimization via simulation: a review. Ann Oper Res 53(1):199–247

    MathSciNet  Article  MATH  Google Scholar 

  15. Goel T, Haftka RT, Shyy W, Queipo NV (2007) Ensemble of surrogates. Struct Multidiscip Optim 33(3):199–216

    Article  Google Scholar 

  16. Haftka RT (2016) Requirements for papers focusing on new or improved global optimization algorithms. Struct Multidiscip Optim 54(1):1–1

    Article  Google Scholar 

  17. Haftka RT, Villanueva D, Chaudhuri A (2016) Parallel surrogate-assisted global optimization with expensive functions—a survey. Struct Multidiscip Optim 54(1):3–13

    MathSciNet  Article  Google Scholar 

  18. Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural Network. arXiv:1503.02531

  19. Hoekstra AJ, Derksen JJ, Van Den Akker HEA (1999) An experimental and numerical study of turbulent swirling flow in gas cyclones. Chem Eng Sci 54(13-14):2055–2065

    Article  Google Scholar 

  20. Hoffmann AC, Stein LE (2007) Gas cyclones and swirl tubes. Springer, Berlin

    Google Scholar 

  21. Jin Y (2003) A comprehensive survey of fitness approximation in evolutionary computation. Soft Comput 9 (1):3–12

    Article  Google Scholar 

  22. Jin R, Chen W, Simpson TW (2001) Comparative studies of metamodelling techniques under multiple modelling criteria. Struct Multidiscip Optim 23(1):1–13

    Article  Google Scholar 

  23. Kazemi P, Khalid MH, Szlek J, Mirtič A, Reynolds GK, Jachowicz R, Mendyk A (2016) Computational intelligence modeling of granule size distribution for oscillating milling. Powder Technol 301 (Supplement C):1252–1258

    Article  Google Scholar 

  24. Kleijnen JPC (2008) Design and analysis of simulation experiments. Springer, New York

    Google Scholar 

  25. Kleijnen JPC (2014) Simulation-optimization via Kriging and bootstrapping: a survey. Journal of Simulation 8(4):241–250

    Article  Google Scholar 

  26. Konan A, Huckaby D (2015) Modeling and simulation of a gas-solid cyclone during an upset event (presentation). OpenFOAM Workshop

  27. Law AM (2007) Simulation modeling and analysis, 4th edn. McGraw-Hill, New York

    Google Scholar 

  28. LeBlanc M, Tibshirani R (1996) Combining estiamates in regression and classification. J Am Stat Assoc 91(436):1641

    MATH  Google Scholar 

  29. Löffler F (1988) Staubabscheiden. Thieme, Stuttgart

    Google Scholar 

  30. Meerschaert MM (2013) Mathematical modeling (fourth edition), 4th edn. Elsevier, Amsterdam

    Google Scholar 

  31. Mothes H, Loeffler F (1984) Bewegung und Abscheidung der Partikel im Zyklon. Chem-Tech-Ing 56:714–715

    Article  Google Scholar 

  32. Müller J, Shoemaker CA (2014) Influence of ensemble surrogate models and sampling strategy on the solution quality of algorithms for computationally expensive black-box global optimization problems. J Glob Optim 60(2):123–144

    MathSciNet  Article  MATH  Google Scholar 

  33. Murphy KP (2012) Machine learning: a probabilistic perspective. MIT Press, Cambridge

    Google Scholar 

  34. Muschelknautz E (1972) Die Berechnung von Zyklonabscheidern für Gase. Chemie Ingenieur Technik 44(1-2):63–71

    Article  Google Scholar 

  35. Nelson BL (1995) Stochastic modeling: analysis and simulation. Dover, New York

    Google Scholar 

  36. OpenFOAM Foundation (2016) OpenFOAM tutorials lagrangian MPPICFoam cyclone. Official OpenFOAM repository. https://github.com/OpenFOAM/. Accessed 16 Nov 2016

  37. Overcamp TJ, Mantha SV (1998) A simple method for estimating cyclone efficiency. Environ Prog 17(2):77–79

    Article  Google Scholar 

  38. Preen R, Bull L (2014) Towards the coevolution of novel vertical-axis wind turbines. IEEE Trans Evol Comput PP(99):284–294

    Google Scholar 

  39. Santner TJ, Williams BJ, Notz WI (2003) The design and analysis of computer experiments. Springer, Berlin

    Google Scholar 

  40. Simpson T, Toropov V, Balabanov V, Viana F (2012) Design and analysis of computer experiments in multidisciplinary design optimization: a review of how far we have come - or not. In: 12th AIAA/ISSMO multidisciplinary analysis and optimization conference. American Institute of Aeronautics and Astronautics, Reston, pp 1–22

  41. Turner AJ, Balestrini-Robinson S, Mavris D (2013) Heuristics for the regression of stochastic simulations. Journal of Simulation 7(4):229–239

    Article  Google Scholar 

  42. Wolpert DH (1992) Stacked generalization. Neural Netw 5(2):241–259

    Article  Google Scholar 

  43. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Article  Google Scholar 

  44. Yang Y (2003) Regression with multiple candidate models: selecting or mixing?. Stat Sin 13(3):783–809

    MathSciNet  MATH  Google Scholar 

  45. Zaefferer M, Breiderhoff B, Naujoks B, Friese M, Stork J, Fischbach A, Flasch O, Bartz-Beielstein T (2014) Tuning multi-objective optimization algorithms for cyclone dust separators. In: Proceedings of the 2014 conference on genetic and evolutionary computation, GECCO ’14. ACM, New York, pp 1223–1230

  46. Zeigler BP, Oren TI (1986) Multifaceted, multiparadigm modeling perspectives: tools for the 90’s. In: Proceedings of the 18th conference on winter simulation. ACM, New York, pp 708–712

  47. Zerpa LE, Queipo NV, Pintos S, Salager J-L (2005) An optimization methodology of alkaline–surfactant–polymer flooding processes using field scale numerical simulation and multiple surrogates. J Pet Sci Eng 47(3):197–208

    Article  Google Scholar 

Download references

Acknowledgements

This work is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 692286. We would like to thank Horst Stenzel, Beate Breiderhoff, Dimitri Gusew, Aylin Mengi, Baris Kabacali, Jerome Tünte, Lukas Büscher, Sascha Wüstlich, and Thomas Friesen for their support.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Thomas Bartz-Beielstein.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bartz-Beielstein, T., Zaefferer, M. & Pham, Q.C. Optimization via multimodel simulation. Struct Multidisc Optim 58, 919–933 (2018). https://doi.org/10.1007/s00158-018-1934-2

Download citation

Keywords

  • Combined simulation
  • Multimodeling
  • Simulation-based optimization
  • Metamodel
  • Multi-fidelity optimization
  • Stacking
  • Response surface methodology
  • 3D printing
  • Computational fluid dynamics