Abstract
For the long-term dynamic simulation of a fluidized bed reactor (FBR), a two-way coupled computational fluid dynamics (CFD)-direct quadrature method of moments (DQMOM) approach is proposed. In this approach, CFD is first used only for hydrodynamic information without simulating any other chemical reactions or physical phenomena. Subsequently, the derived information is applied to the DQMOM calculation in MATLAB. From the calculation, a particle size distribution is obtained and subsequently adopted in a new CFD model to reflect the flow change caused by a change in the particle size distribution. Through several iterative calculations, long-term dynamic simulations are performed. To evaluate the efficacy of the proposed approach, the results from the suggested approach are compared for 60 s with those of the CFD-quadrature method of moments (QMOM) approach, which calculates hydrodynamics and physical phenomena simultaneously in CFD. The proposed approach successfully simulated the FBR for 6 h. The results confirmed that the proposed method can simulate complex flow patterns, which cannot be obtained in conventional CFD models. Another advantage of the approach is that it can be applied to various industrial multiphase reactors without any tuning parameters.
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Abbreviations
- ds :
-
particle size [m]
- d32 :
-
sauter mean diameter [mm]
- g:
-
gravitational acceleration [m/s2]
- Kgs :
-
gas/solid drag coefficient
- L:
-
particle size as internal coordinate [m]
- m:
-
moments
- n:
-
bubble number density
- P:
-
pressure [N/m2]
- sp :
-
phase p among particles
- t:
-
time [s]
- u :
-
velocity vector [m/s]
- w:
-
quadrature weight
- α :
-
phase volume fraction
- δ :
-
dirac delta function
- μ :
-
molecular dynamic viscosity [Pa s]
- λ s :
-
solid bulk viscosity [Pa s]
- ρ :
-
density [kg/m3]
- θ s :
-
granular temperature [m2/s2]
- \(\mathrm{k}_{\theta_{s}}\) :
-
granular energy
- \(\gamma_{\theta_{s}}\) :
-
collisional dissipation of energy
- ϕ gs :
-
energy exchange between gas and solid
- ess :
-
restitution coefficient
- g0,ss :
-
radial distribution function
- τ :
-
stress-strain tensor [Pa]
- CFD:
-
computational fluid dynamics
- CFD-hydrodynamics model:
-
simulate only hydrodynamics without reactions or the PBM
- CFD-QMOM:
-
simulate flows and PBM together in FLUENT’s own implementation code
- FBR:
-
fluidized bed reactor
- PBM:
-
population balance model
- QMOM:
-
quadrature method of moments
- DQMOM:
-
direct quadrature method of moments
- NDF:
-
number density function
- g:
-
gas
- s:
-
solid
- i:
-
specified number of moments
References
C. Kiparissides, J. Process Control, 16, 205 (2006).
W. C. Yan, Z. H. Luo, Y. H. Lu and X. D. Chen, AIChE J., 58, 1717 (2012).
Y. Che, Z. Tian, Z. Liu, R. Zhang, Y. Gao, E. Zou, S. Wang and B. Liu, Powder Technol., 286, 107 (2015).
A. D. Randolph and M. A. Larson, Theory of particulate processes: Analysis and techniques of continuous crystallization, Academic Press Inc., New York (1971).
J. R. Grace and F. Taghipour, Powder Technol., 139, 99 (2004).
J.-P. Torré, D. F. Fletcher, T. Lasuye and C. Xuereb, Chem. Eng. Sci., 62, 6246 (2007).
J. Jung and I. K. Gamwo, Powder Technol., 183, 401 (2008).
J. A. Mendoza and S. Hwang, Korean J. Chem. Eng., 35, 11 (2018).
F. Kerdouss, A. Bannari and P. Proulx, Chem. Eng. Sci., 61, 3313 (2006).
S. Park, J. Na, M. Kim, J. An, C. Lee and C. Han, Korean Chem. Eng. Res., 54, 612 (2016).
N. Jurtz, M. Kraume and G. D. Wehinger, Rev. Chem. Eng., 35, 139 (2019).
M. Kim, J. Na, S. Park, J.-H. Park and C. Han, Chem. Eng. Sci., 177, 301 (2018).
S. Zimmermann and F. Taghipour, Ind. Eng. Chem. Res., 44, 9818 (2005).
S. Park, J. Na, M. Kim and J. M. Lee, Comput. Chem. Eng., 119, 25 (2018).
R. Fan, D. L. Marchisio and R. O. Fox, Powder Technol., 139, 7 (2004).
S.-S. Liu and W.-D. Xiao, Chem. Eng. Sci., 111, 112 (2014).
Y. Yao, Y.-J. He, Z.-H. Luo and L. Shi, Adv. Powder Technol., 25, 1474 (2014).
Y. Yao, J.-W. Su and Z.-H. Luo, Powder Technol., 272, 142 (2015).
V. Alopaeus, M. Laakkonen and J. Aittamaa, Chem. Eng. Sci., 61, 6732 (2006).
D. L. Marchisio, A. A. Barresi and M. Garbero, AIChE J., 48, 2039 (2002).
J. Akroyd, A. J. Smith, L. R. McGlashan and M. Kraft, Chem. Eng. Sci., 65, 1915 (2010).
Z. Li, J. Kessel, G. Grünewald and M. Kind, Drying Technol., 31, 1888 (2013).
A. Delafosse, M.-L. Collignon, S. Calvo, F. Delvigne, M. Crine, P. Thonart and D. Toye, Chem. Eng. Sci., 106, 76 (2014).
M. Gresch, R. Brügger, A. Meyer and W. Gujer, Environ. Sci. Technol., 43, 2381 (2009).
A. Nørregaard, C. Bach, U. Krühne, U. Borgbjerg and K. V. Gernaey, Chem. Eng. J., 356, 161 (2019).
Y. Shah, B. G. Kelkar, S. Godbole and W. D. Deckwer, AIChE J., 28, 353 (1982).
S. Yang, S. Kiang, P. Farzan and M. Ierapetritou, Processes, 7, 9 (2019).
M. Kim, S. Park, D. Lee, S. Lim, M. Park and J. M. Lee, Chem. Eng. J., 395, 125034 (2020).
F. Bezzo, S. Macchietto and C. Pantelides, AIChE J., 49, 2133 (2003).
W. Zhao, A. Buffo, V. Alopaeus, B. Han and M. Louhi-Kultanen, AIChE J., 63, 378 (2017).
H. Hatzantonis, A. Goulas and C. Kiparissides, Chem. Eng. Sci., 53, 3251 (1998).
D. L. Marchisio and R. O. Fox, J. Aerosol Sci., 36, 43 (2005).
D. L. Marchisio, R. D. Vigil and R. O. Fox, J. Colloid Interface Sci., 258, 322 (2003).
D. L. Marchisio, R. D. Vigil and R. O. Fox, Chem. Eng. Sci., 58, 3337 (2003).
R. G. Gordon, J. Math. Phys., 9, 655 (1968).
L. Metzger and M. Kind, Chem. Eng. Sci., 169, 284 (2017).
A. Shamiri, M. A. Hussain, F. S. Mjalli and N. Mostoufi, Chem. Eng. J., 161, 240 (2010).
R. Fan, Ph.D. thesis, Iowa State University, Ames, IA (2006).
T. J. Niemi, M.S. thesis, Aalto University (2012).
C. Chen, Q. Dai and H. Qi, Chem. Eng. Sci., 141, 8 (2016).
H. Qi, F. Li, B. Xi and C. You, Chem. Eng. Sci., 62, 1670 (2007).
Q. Zhou and J. Wang, Chem. Eng. Sci., 122, 637 (2015).
F. Vejahati, N. Mahinpey, N. Ellis and M. B. Nikoo, Can. J. Chem. Eng., 87, 19 (2009).
E. Esmaili and N. Mahinpey, Adv. Eng. Software, 42, 375 (2011).
E. Ghadirian and H. Arastoopour, Powder Technol., 288, 35 (2016).
Acknowledgements
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (MSIT) of the Korean government (No. 2020R1A2C100550311).
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Kim, M., Lee, K., Bak, Y. et al. A two-way coupled CFD-DQMOM approach for long-term dynamic simulation of a fluidized bed reactor. Korean J. Chem. Eng. 38, 342–353 (2021). https://doi.org/10.1007/s11814-020-0701-4
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DOI: https://doi.org/10.1007/s11814-020-0701-4