Artificial neural network simulation of cyclone pressure drop: selection of the best activation function in Iraq

• Autorzy: Demir S. , Karadeniz A. , Demir N.M.
• Języki publikacji: EN

Abstrakty

EN

A total of 162 cyclones with distinct geometries were used to obtain experimental pressure drops at six different inlet velocities between 10 and 24 m/s. Pressure drops were measured between 84 and 2,045 Pa. Pressure drop coefficients were calculated by the well-known formulation of a cyclone pressure drop. The values ranged between 1.09 and 9.07, with an average of value of 3.76. A backpropagation neural network algorithm was implemented in Visual Basic for Applications with nine built-in activation of linear, rectified linear, sigmoid, hyperbolic tangent, arctangent, Gaussian, Elliot, sinusoid, and sinc functions to test their ability to satisfactorily explain the complex relationship between cyclone geometry and the pressure drop coefficient. The neural network was run 25 times for each activation function with randomly selected 70% of data set as the ratios of inlet height, cylinder height, cone height, vortex finder diameter, and vortex finder length-to-body diameter being the independent variables, and the pressure drop coefficient being the dependent variable. Neural network results showed that sigmoid was the one activation function that explains the complex relationship between cyclone geometry and pressure drop coefficient with an average mean square error (MSE) of 0.00085. The coefficients of determination between measured and predicted values of pressure drop coefficient were over 0.99. Also, the percent residuals from sigmoid activation function concentrated around the mean value of zero, with very small standard deviation.

• Wydawca: -
• Czasopismo: Polish Journal of Environmental Studies
• Rocznik: 2016
• Tom: 25
• Numer: 5
• Opis fizyczny: p.1891-1899,fig.,ref.

Twórcy

autor

Demir S.

• Environmental Engineering Department, Faculty of Civil Engineering, Yıldız Technical University, 34220, Esenler, Istanbul, Turkey

autor

Karadeniz A.

• Environmental Engineering Department, Faculty of Civil Engineering, Yıldız Technical University, 34220, Esenler, Istanbul, Turkey

autor

Demir N.M.

• Environmental Engineering Department, Faculty of Civil Engineering, Yıldız Technical University, 34220, Esenler, Istanbul, Turkey

Bibliografia

• 1. Cooper C.D., Alley F.C. Air Pollution Control: A Design Approach, 4th ed., Waveland Press, Inc., Illionis, USA, 135, 2011.
• 2. Elsayed K., Lacor C. The effect of vortex finder diameter on cyclone separator performance and flow field. Proceedings of the 5th European Conference on Computational Fluid Dynamics (ECCOMAS CFD 2010), Portugal, 1, 2010.
• 3. Elsayed K., Lacor C. Numerical modeling of the flow field and performance in cyclones of different cone-tip diameters. Comput. Fluids 51, 48, 2011.
• 4. Herrick T.C., Davis W.T. Cyclones and Inertial Separators, in: W.T. Davis (Ed.), Air Pollution Engineering Manual, Air and Waste Management Association, John Wiley & Sons, USA, 66, 2000.
• 5. Stairmand C.J. Pressure drop in cyclone separators. Eng. Lond. 16B, 409, 1949.
• 6. Casal J., Martine z-Benet J.M. A better way to calculate cyclone pressure drop. Chem. Eng. N.Y. 99, 100, 1983.
• 7. Chen J., Shi M. A universal model to calculate cyclone pressure drop. Powder Technol. 171, 184, 2007.
• 8. Demir S. A practical model for estimating pressure drop in cyclone separators: An experimental study. Powder Technol. 268, 329, 2014.
• 9. Kaya F., Karag öz I. Investigation into the suitability of turbulence models in swirling flows. Uludağ University Journal of Engineering and Archiecture Faculty 12, 85, 2007 [In Turkish].
• 10. Raoufi A., Shams M., Far zaneh M., Ebrahimi R. Numerical simulation and optimization of fluid flow in cyclone vortex finder. Chem. Eng. Process. 47, 128, 2008.
• 11. Elsayed K., Lacor C. Optimization of cyclone separator geometry for minimum pressure drop using mathematical models and CFD simulations. Chem. Eng. Sci. 65, 6048, 2010.
• 12. Elsayed K., Lacor C. The effect of cyclone inlet dimensions on the flow pattern and performance. Appl. Math. Model. 35, 1952, 2011.
• 13. Elsayed K., Lacor C. Modeling, analysis and optimization of aircyclones using artifical neural network, response surface methodology and CFD simulation Approaches. Powder Technol. 212, 115, 2011.
• 14. Elsayed K., Lacor C. The effect of the dust outlet geometry on the performance and hydrodynamics of gas cyclones. Comput. Fluids 68, 134, 2012.
• 15. Brar L.S., Sharma R.P., Elsayed K. The effect of the cyclone length on the performance of stairmand highefficiency cyclone. Powder Technol. 286, 668, 2015.
• 16. Brar L.S., Sharma R.P. Effect of varying diameter on the performance of industrial scale gas cyclone dust separators. Materials Today: Proceedings 2, 3230, 2015.
• 17. Zhao B., Su Y. Artificial neural network-based modeling of pressure drop coefficient for cyclone separators. Chem. Eng. Res. Des. 88 (5-6), 606, 2010.
• 18. Elsayed K., Lacor C. Modeling and pareto optimization of gas cyclone separator performance using RBF type artificial neural networks and genetic algorithms. Powder Technol. 217, 84, 2012.
• 19. Karadeni z A. Effect of modifications on stairmand high efficiency type cyclone geometry on particle collection efficiency and pressure drop. MSc Thesis, Graduate School of Natural and Applied Sciences, Yıldız Technical University, Istanbul, 2015 [In Turkish].
• 20. MUCHELKNAUTZ E. The calculations for cyclone separators for gases. Chem. Ing. Tech. 44 (1-2), 63, 1972 [In German].
• 21. Hoffmann A.C., van Santen A., Allen R.W.K. Effects of geometry and solid loading on the performance of gas cyclones. Powder Technol. 70, 1326, 1992.
• 22. Sibi P., Jones A.A., Siddarth P. Analysis of different activation functions using back propagation neural networks. Journal of Theoretical and Applied Information Technology 47 (3), 1264, 2013.

Identyfikatory

DOI

10.15244/pjoes/62907