Journals / Atmospheric Pollution Research / 2020 / Cilt: 11 - Sayı: 9

Multi-objective optimization of axial-flow-type gas-particle cyclone separator using response surface methodology and computational fluid dynamics

Pages
1488–1499
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The optimized design is obtained based on a trade-off between static pressure drop and separation efficiency.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Pressure drop and separation efficiency are two critical performance parameters in the design of gas-particle separators.
📊
Key Findings & Data:Second, a screening experiment is set up to select the significant factors out of nine factors of interest.
💡
Conclusion & Impact:The optimized design is obtained based on a trade-off between static pressure drop and separation efficiency.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Özet

Pressure drop and separation efficiency are two critical performance parameters in the design of gas-particle separators. In this study, multi-objective optimization of an axial-flow-type gas-particle cyclone separator is conducted using the response surface methodology (RSM) and computational fluid dynamics (CFD) to minimize the pressure drop and maximize the separation efficiency. First, the accuracy of numerical simulation of airflow and particles predicted by the Reynolds stress model and discrete phase model is verified by experiments. Second, a screening experiment is set up to select the significant factors out of nine factors of interest. Four of the factors are studied using a central composite design in the RSM, and second-order response surface modeling is performed for two responses. A structural optimized design is obtained by the desirability function approach. Finally, the differences between the original and optimized designs are explained. Compared with the original design, the optimized design increases the removal efficiency for 8- µm particles by 100% and the static pressure drop by 69.32%. Based on the analysis of the flow field and the particle trajectory, the cause of performance change is explained. The optimized design is obtained based on a trade-off between static pressure drop and separation efficiency.

🔖

Cite This Article

YU, L. J., WANG, T., ZHANG, L., CHANG, J., SONG, Z., MA, C. (2020). Multi-objective optimization of axial-flow-type gas-particle cyclone separator using response surface methodology and computational fluid dynamics. Atmospheric Pollution Research, Cilt: 11 - Sayı: 9, 1488–1499. https://ajindex.com/articles/144914

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (15)

  1. Balestrin, E., Decker, R.K., Noriler, D., et al., 2017. An alternative for the collection of small particles in cyclones: experimental analysis and CFD modeling. Separ. Purif. Technol. 184, 54–65.
  2. Box, G.E.P., Hunter, J.S., 1957. Multi-factor experimental designs for exploring response surfaces. Ann. Math. Stat. 28 (1), 195–241.
  3. Chen, M., Wu, G., Zhang, D., et al., 1981. The Basic Theory and Application of Dust Removal Technology. China Architecture & Building Press.
  4. Chen, J., Shi, M., 2003. Analysis on cyclone collection efficiencies at high temperatures. China Particuol. 1 (1), 20–26.
  5. Chu, K., Wang, B., Xu, D., et al., 2011. CFD–DEM simulation of the gas–solid flow in a cyclone separator. Chem. Eng. Sci. 66 (5), 834–847.
  6. Chu, K., Chen, J., Yu, A., 2016. Applicability of a coarse-grained CFD–DEM model on dense medium cyclone. Miner. Eng. 90, 43–54.
  7. Cowpe, J.S., Astin, J.S., Pilkington, R.D., et al., 2007. Application of Response Surface Methodology to laser-induced breakdown spectroscopy: influences of hardware configuration. Spectrochim. Acta, Part B 62 (12), 1335–1342.
  8. Deng, X., Ling, Z., Zhou, B., 1999. Experimental investigation on flow filed and performance of a new axial cyclone dust collector. J. Eng. Thermophys. 20 (5), 589–592.
  9. Elsayed, K., Lacor, C., 2011. Numerical modeling of the flow field and performance in cyclones of different cone-tip diameters. Comput. Fluids 51 (1), 48–59.
  10. 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 (2), 84–99.
  11. Elsayed, K., Lacor, C., 2013a. 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.
  12. Elsayed, K., Lacor, C., 2013b. The effect of cyclone vortex finder dimensions on the flow pattern and performance using LES. Comput. Fluids 71 (2), 224–239.
  13. Filippone, A., Bojdo, N., 2010. Turboshaft engine air particle separation. Prog. Aero. Sci. 46 (5), 224–245.
  14. Griffiths, W.D., Boysan, F., 1996. Computational fluid dynamics (CFD) and empirical modelling of the performance of a number of cyclone samplers. J. Aerosol Sci. 27 (2), 281–304.
  15. Gong, G., Yang, Z., Zhu, S., 2012. Numerical investigation of the effect of helix angle and leaf margin on the flow pattern and the performance of the axial flow cyclone separator. Appl. Math. Model. 36 (8), 3916–3930. Gimbun,