EVERGREEN

Joint Journal of Novel Carbon Resource Sciences and Green Asia Strategy

ISSN:2189-0420 (Print until Mar 2020)
ISSN:2432-5953 (Online)

SCImago Journal & Country Rank

Open Access
Scopus
Google Scholar
Crossref
SCImago Journal & Country Rank
4.3
2024CiteScore
 
69th percentile
Powered by Scopus
Metrics by SCOPUS 2024
CiteScore
4.3
SJR
0.391
SNIP
1.192

Multi-Objective Optimization Approach for Optimizing the Performance of Double-Stage and Lapple Cyclone Separator

Aswin Aswin1, Sholahudin Sholahudin1, Ridho Irwansyah1, Nasruddin Nasruddin1,*
1Department of Mechanical Engineering, University of Indonesia, Indonesia
*Author to whom correspondence should be addressed:
E-mail: nasruddin.nasruddin.example@university.edu (NN)
Evergreen, Vol. 12, Iss. 02, pp. 1289–1309, 2025
DOI: 10.5109/7363510
Creative Commons Attribution 4.0 International Creative Commons Attribution 4.0 International
Received: January 23, 2025 | Revised: April 21, 2025 | Accepted: May 02, 2025 | Published: June 2025
Abstract
Cyclone separators play a crucial role in various industrial applications, particularly in particle filtration for environmental and health protection. This study employed Response Surface Methodology (RSM), Artificial Neural Network (ANN), Multi Objective Genetic Algorithm (MOGA), and a novel approach called Multi Objective for Ant Lion optimizer (MOALO) to optimize the performance of double-stage and lapple cyclone separators. Through statistical analysis, this study investigates the impacts of cyclone diameter, inlet velocity, and size of the particle on the total efficiency, grade removal efficiency, and pressure drop. The results indicate that the double-stage cyclone separator is preferable to the lapple cyclone separator. The RSM and ANN models exhibit high predictive accuracy, with R2 values indicating strong correlations with actual data. Optimal operating conditions for the double cyclone have been identified with the cyclone diameter 400 mm, inlet velocity 12 m/s, and particle size 1.68 µm, resulting total efficiency of 80.58%, pressure drop of 689.71 Pa, and grade removal efficiency of 61.59%, providing insights for improving cyclone separator performance. This research highlights the effectiveness of RSM, ANN, MOGA and MOALO in optimizing cyclone separators.
Keywords
artificial neural network ; cyclone separator ; MOALO ; MOGA ; response surface methodology
Available Repositories
Journal DOI Zenodo Archive
Share Article
Article Metrics
--
Views
--
Downloads
--
Citations
Export Citation
BibTeX RIS Semantic Scholar CrossRef Mendeley Google Scholar
Full Text
Download PDF
References
  1. 1) P.Swift, "Dust Control in Industry-2," Steam Heat. Engr., vol. 38, pp. 453-464, 1969 1969. [Online]. Available: https://cir.nii.ac.jp/crid/1572824499754293760
  2. 2) G.Sung, H.-U. Kim, D. Shin, W. G. Shin, and T. Kim, "High Efficiency Axial Wet Cyclone Air Sampler," Aerosol and Air Quality Research, vol. 18, no. 10, pp. 2529-2537, 2018 doi:10.4209/aaqr.2017.12.0596
  3. 3) E.Hosseini, H. Fatahian, and E. Fatahian, "New understanding of the effect of particle mass loading on the performance of a square cyclone at low and high gas temperatures," Korean Journal of Chemical Engineering, vol. 39, no. 12, pp. 3482-3496, 2022/12/01 2022 doi:10.1007/s11814-022-1205-1
  4. 4) J.Song, G. Sun, Z. Chao, Y. Wei, and M. Shi, "Gas flow behavior and residence time distribution in a FCC disengager vessel with different coupling configurations between two-stage separators," Powder Technology, vol. 201, no. 3, pp. 258-265, 2010/08/12/ 2010 doi:10.1016/j.powtec.2010.04.008
  5. 5) Y.Zhu, M. Kim, K. W. Lee, Y. O. Park, and M. Kuhlman, "Design and Performance Evaluation of a Novel Double Cyclone," Aerosol Science and Technology - AEROSOL SCI TECH, vol. 34, pp. 373-380, 04/01 2001 doi:10.1080/02786820151092559
  6. 6) R.B. Xiang and K. W. Lee, "Exploratory Study on Cyclones of Modified Designs," Particulate Science and Technology, vol. 19, no. 4, pp. 327-338, 2001/10/01 2001 doi:10.1080/02726350290057868
  7. 7) X.Yang, J. Yang, S. Wang, and Y. Zhao, "Effects of operational and geometrical parameters on velocity distribution and micron mineral powders classification in cyclone separators," Powder Technology, vol. 407, p. 117609, 2022/07/01/ 2022 doi:10.1016/j.powtec.2022.117609
  8. 8) Z.-W.Zhang, Q. Li, Y.-H. Zhang, and H.-L. Wang, "Simulation and experimental study of effect of vortex finder structural parameters on cyclone separator performance," Separation and Purification Technology, vol. 286, p. 120394, 2022/04/01/ 2022 doi:10.1016/j.seppur.2021.120394
  9. 9) F.Parvaz et al., "Influence of gas exhaust geometry on flow pattern, performance, and erosion rate of a gas cyclone," Korean Journal of Chemical Engineering, vol. 40, no. 7, pp. 1587-1597, 2023/07/01 2023 doi:10.1007/s11814-023-1430-2
  10. 10) Z.Wang, G. Sun, Z. Song, S. Yuan, and Z. Qian, "Effect of inlet volute wrap angle on the flow field and performance of double inlet gas cyclones," Particuology, vol. 77, pp. 29-36, 2023/06/01/ 2023 doi:10.1016/j.partic.2022.08.006
  11. 11) E.Dehdarinejad, F. Parvaz, S. H. Hosseini, G. Ahmadi, and K. Elsayed, "Performance analysis of a gas cyclone with a dustbin inverted hybrid solid cone," Aerosol Science and Technology, vol. 57, no. 9, pp. 911-924, 2023/09/02 2023 doi:10.1080/02786826.2023.2217873
  12. 12) R.A. F. Oliveira, V. G. Guerra, and G. C. Lopes, "Improvement of collection efficiency in a cyclone separator using water nozzles: A numerical study," Chemical Engineering and Processing - Process Intensification, vol. 145, p. 107667, 2019/11/01/ 2019 doi:10.1016/j.cep.2019.107667
  13. 13) J.-J.Wang, L.-Z. Wang, and C.-W. Liu, "Effect of a Stick on the Gas Turbulence Structure in a Cyclone Separator," Aerosol Science and Technology, vol. 39, no. 8, pp. 713-721, 2005/08/01 2005 doi:10.1080/02786820500182404
  14. 14) A.-N.Huang, K. Ito, T. Fukasawa, H. Yoshida, H.-P. Kuo, and K. Fukui, "Classification performance analysis of a novel cyclone with a slit on the conical part by CFD simulation," Separation and Purification Technology, vol. 190, pp. 25-32, 2018/01/08/ 2018 doi:10.1016/j.seppur.2017.08.047
  15. 15) J.-H.Lim, S.-I. Park, H.-J. Lee, M. Z. Zahir, and S.-J. Yook, "Performance evaluation of a tangential cyclone separator with additional inlets on the cone section," Powder Technology, vol. 359, pp. 118-125, 2020/01/01/ 2020 doi:10.1016/j.powtec.2019.09.056
  16. 16) M.Dasar and R. S. Patil, "Hydrodynamic characteristics of a 2D2D cyclone separator with a finned cylindrical body," Powder Technology, vol. 363, pp. 541-558, 2020/03/01/ 2020 doi:10.1016/j.powtec.2020.01.021
  17. 17) G.Liu, W. Wang, J. Yu, and X. Li, "Effect of extra inlets structure on cyclone wall erosion," Powder Technology, vol. 411, p. 117926, 2022/10/01/ 2022 doi:10.1016/j.powtec.2022.117926
  18. 18) L.Wang et al., "Numerical simulation and experimental study of gas cyclone–liquid jet separator for fine particle separation," Chinese Journal of Chemical Engineering, vol. 51, pp. 43-52, 2022/11/01/ 2022 doi:10.1016/j.cjche.2021.12.015
  19. 19) J.Duan, S. Gao, C. Hou, W. Wang, P. Zhang, and C. Li, "Effect of cylinder vortex stabilizer on separator performance of the Stairmand cyclone," Powder Technology, vol. 372, pp. 305-316, 2020/07/15/ 2020 doi:10.1016/j.powtec.2020.06.031
  20. 20) W.I. Mazyan, A. Ahmadi, J. Brinkerhoff, H. Ahmed, and M. Hoorfar, "Enhancement of cyclone solid particle separation performance based on geometrical modification: Numerical analysis," Separation and Purification Technology, vol. 191, pp. 276-285, 2018/01/31/ 2018 doi:10.1016/j.seppur.2017.09.040
  21. 21) F.Zhou, G. Sun, X. Han, Y. Zhang, and W. Bi, "Experimental and CFD study on effects of spiral guide vanes on cyclone performance," Advanced Powder Technology, vol. 29, no. 12, pp. 3394-3403, 2018/12/01/ 2018 doi:10.1016/j.apt.2018.09.022
  22. 22) S.Pandey et al., "CFD Investigations of Cyclone Separators with Different Cone Heights and Shapes," Applied Sciences, vol. 12, p. 4904, 05/12 2022 doi:10.3390/app12104904
  23. 23) A.J. Frimpong et al., "Experimental investigation supported by artificial neural networks (ANNs) for predicting the heating performance of a cyclone separator coupled with induction heating coil," Process Safety and Environmental Protection, vol. 180, pp. 451-474, 2023/12/01/ 2023 doi:10.1016/j.psep.2023.10.020
  24. 24) D.Park, J. Cha, M. Kim, and J. S. Go, "Multi-objective optimization and comparison of surrogate models for separation performances of cyclone separator based on CFD, RSM, GMDH-neural network, back propagation-ANN and genetic algorithm," Engineering Applications of Computational Fluid Mechanics, vol. 14, no. 1, pp. 180-201, 2020/01/01 2020 doi:10.1080/19942060.2019.1691054
  25. 25) F.Yulia, I. Chairina, A. Zulys, and Nasruddin, "Multi-objective genetic algorithm optimization with an artificial neural network for CO2/CH4 adsorption prediction in metal–organic framework," Thermal Science and Engineering Progress, vol. 25, p. 100967, 2021/10/01/ 2021 doi:10.1016/j.tsep.2021.100967
  26. 26) Z.Qiao et al., "Performance analysis and optimization design of an axial-flow vane separator for supercritical CO2 (sCO2)-water mixtures from geothermal reservoirs," International Journal of Energy Research, vol. 43, no. 6, pp. 2327-2342, 2019/05/01 2019 doi:10.1002/er.4452
  27. 27) K.Elsayed and C. Lacor, "Modeling, analysis and optimization of aircyclones using artificial neural network, response surface methodology and CFD simulation approaches," Powder Technology, vol. 212, no. 1, pp. 115-133, 2011/09/15/ 2011 doi:10.1016/j.powtec.2011.05.002
  28. 28) H.Safikhani, "Modeling and multi-objective Pareto optimization of new cyclone separators using CFD, ANNs and NSGA II algorithm," Advanced Powder Technology, vol. 27, no. 5, pp. 2277-2284, 2016/09/01/ 2016 doi:10.1016/j.apt.2016.08.017
  29. 29) L.S. Brar and K. Elsayed, "Analysis and optimization of multi-inlet gas cyclones using large eddy simulation and artificial neural network," Powder Technology, vol. 311, pp. 465-483, 2017/04/15/ 2017 doi:10.1016/j.powtec.2017.02.004
  30. 30) B.Zhao and Y. Su, "Artificial neural network-based modeling of pressure drop coefficient for cyclone separators," Chemical Engineering Research and Design, vol. 88, no. 5, pp. 606-613, 2010/05/01/ 2010 doi:10.1016/j.cherd.2009.11.010
  31. 31) L.S. Brar and K. Elsayed, "Analysis and optimization of cyclone separators with eccentric vortex finders using large eddy simulation and artificial neural network," Separation and Purification Technology, vol. 207, pp. 269-283, 2018/12/22/ 2018 doi:10.1016/j.seppur.2018.06.013
  32. 32) K.Elsayed and C. Lacor, "CFD modeling and multi-objective optimization of cyclone geometry using desirability function, artificial neural networks and genetic algorithms," Applied Mathematical Modelling, vol. 37, no. 8, pp. 5680-5704, 2013/04/15/ 2013 doi:10.1016/j.apm.2012.11.010
  33. 33) S.Mirjalili, P. Jangir, and S. Saremi, "Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems," Applied Intelligence, vol. 46, no. 1, pp. 79-95, 2017/01/01 2017 doi:10.1007/s10489-016-0825-8
  34. 34) G.Yu et al., "Experimental and numerical studies on a new double-stage tandem nesting cyclone," Chemical Engineering Science, vol. 236, p. 116537, 2021/06/08/ 2021 doi:10.1016/j.ces.2021.116537
  35. 35) S.Ganegama Bogodage and A. Y. T. Leung, "Improvements of the cyclone separator performance by down-comer tubes," Journal of Hazardous Materials, vol. 311, pp. 100-114, 2016/07/05/ 2016 doi:10.1016/j.jhazmat.2016.02.072
  36. 36) A.Atmanlı, B. Yüksel, E. İleri, and A. Deniz Karaoglan, "Response surface methodology based optimization of diesel–n-butanol –cotton oil ternary blend ratios to improve engine performance and exhaust emission characteristics," Energy Conversion and Management, vol. 90, pp. 383-394, 2015/01/15/ 2015 doi:10.1016/j.enconman.2014.11.029
  37. 37) P.J. W. Mark J. Anderson, DOE Simplified - Practical Tools for Effective Experimentation, Third Edition, 3rd Edition ed. New York: Productivity Press., 2015
  38. 38) P.Dixit, R. Tiwari, A. K. Mukherjee, and P. K. Banerjee, "Application of response surface methodology for modeling and optimization of spiral separator for processing of iron ore slime," Powder Technology, vol. 275, pp. 105-112, 2015/05/01/ 2015 doi:10.1016/j.powtec.2015.01.068
  39. 39) L.Singh Brar, "Application of response surface methodology to optimize the performance of cyclone separator using mathematical models and CFD simulations," Materials Today: Proceedings, vol. 5, no. 9, Part 3, pp. 20426-20436, 2018/01/01/ 2018 doi:10.1016/j.matpr.2018.06.418
  40. 40) A.K. Gupta, "Predictive modelling of turning operations using response surface methodology, artificial neural networks and support vector regression," International Journal of Production Research, vol. 48, no. 3, pp. 763-778, 2010/02/01 2010 doi:10.1080/00207540802452132
  41. 41) S.Neamat and M. Hassan, "A Review on Using ANOVA and RSM Modelling in The Glass Powder Replacement of The Concrete Ingredients," Journal of Applied Science and Technology Trends, vol. 2, no. 02, pp. 93 - 98, 08/04 2021 doi:10.38094/jastt202103
  42. 42) F.Burden and D. Winkler, "Bayesian Regularization of Neural Networks," in Artificial Neural Networks: Methods and Applications, D. J. Livingstone Ed. Totowa, NJ: Humana Press, 2009, pp. 23-42
  43. 43) K.Elsayed and C. Lacor, "Modeling and Pareto optimization of gas cyclone separator performance using RBF type artificial neural networks and genetic algorithms," Powder Technology, vol. 217, pp. 84-99, 2012/02/01/ 2012 doi:10.1016/j.powtec.2011.10.015
  44. 44) S.Chakraborty, "TOPSIS and Modified TOPSIS: A comparative analysis," Decision Analytics Journal, vol. 2, p. 100021, 2022/03/01/ 2022 doi:10.1016/j.dajour.2021.100021
Other Papers in This Issue