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Multiple approaches for large-scale CO2 capture by adsorption with 13X zeolite in multi-stage fluidized beds assessment

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Abstract

Adsorption is a promising technology for reducing CO2 emissions from combustion gases. However, some issues must be considered when designing large-scale units with low environmental impact and cost. In this contribution, some of these issues are addressed. Firstly, sugarcane bagasse is selected as a meaningful model system. 13XBF Kostrolith zeolite is used as the adsorbent, and adsorption equilibrium data are determined using a magnetic suspension balance from Rubotherm. 13XBF showed a high CO2 capacity and CO2/N2 selectivity under post-combustion scenario conditions. Secondly, the fluidized bed process was optimized using a 3-stage adsorber and a 3-stage desorber, with heat exchangers that can be used in each stage. The constrained variable in the adsorption column is the solid-to-gas mass feed ratios. It was found that substantial CO2 recovery with 98% purity can be achieved and that the heat load in the desorption column accounts for the highest energy-optimized cost in the process. Thirdly, a detailed CFD model for the fluid dynamics and heat transfer of fluidization for one stage is presented. Bubbles were defined using a hyperbolic filter as predicted by the model, which was consistent with the semi-empirical model and emphasized the significance of bubble size in gas–solid heat transfer, which is essential to the effectiveness of adsorption/desorption. The bubble portion and its effects on the temperature are estimated. Finally, a semi-empirical approach is applied to understanding the controlling factors in a fluidized bed adsorption stage. Results show that the mass transfer from the bubbles to the emulsion is likely the most important resistance to the fluidized adsorber.

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Abbreviations

i :

Component i

j :

Column stage j

F :

Molar flow (mol/s)

y i :

Molar fraction of component i

m s :

Mass flow (kg/s)

q i :

Concentration of component i in the solid phase (mol/kg)

h g :

Gas phase enthalpy (J/mol)

h s :

Solid phase enthalpy (J/kg)

QTSA :

Heat transfer with the heat exchanger (W)

C pi :

Molar heat capacity of component I (J/(mol K))

C ps :

Mass heat capacity of solid (J/(kg K))

Λ(qj,i):

Adsorption enthalpy (kJ/kg)

Q k :

Heat load in each heat exchanger during cycle activity (kJ/s)

ck :

Energy cost ($/kJ)

mex :

Excess mass adsorbed (kg)

Vs :

Solid volume of adsorbent (m3)

Vsc :

Volume of the balance suspended components (m3)

g :

Gas density (kgm-3)

s :

Particle density (kgm-3)

sol :

Solid density (kgm-3)

ε p :

Particle porosity (-)

T :

Temperature (K)

P :

Pressure (bar)

Δmads :

Mass difference at fixed temperature between the mass at each pressure and the mass after regeneration (kg)

q m :

Amount of gas adsorbed per mass of adsorbent corresponding to the complete coverage (mol kg-1)

b i :

Affinity constant (bar-1)

1/n :

Surface heterogeneity (-)

Q :

Energy of adsorption (kJ mol-1)

R :

Gas constantkJ (mol-1K-1)

b oi :

Value of bi at reference temperature T0 (bar-1)

X i :

Fitting parameter (K)

n 0 :

Fitting parameter (-)

K :

Fitting parameter (K)

S i.j :

Gas i/gas j selectivity (mol i/mol j)

y ij :

Molar composition of gas i or j (-)

ΔH ads :

Isosteric heat of adsorption (kJmol-1)

μ g :

Viscosity (Pa.s)

k s :

Solid Thermal conductivity (W/m.K)

k g :

Gas Thermal conductivity (W/m.K)

d s :

Particle diameter (m)

φ f :

Hyperbolic filter (-)

θ p :

Hyperbolic adjustment parameter 1 (-)

β :

Hyperbolic adjustment parameter 2 (-)

φ :

Input variable (-)

φ avg :

Averaged variable value (-)

ε s,g :

Solid/Gas portion (1)

u s,g :

Solid/gas velocity (m/s)

p s,g :

Solid/gas pressure (Pa)

τ s,g :

Solid/gas stress tensor (-)

D :

Drag coefficient (-)

g :

Gravity (m/s²)

T s,g :

Solid/gas temperature (K)

Q g,s/s,g :

Heat exchange coefficient (-)

References

  1. Davoodi, S., Al-Shargabi, M., Wood, D.A., Rukavishnikov, V.S., Minaev, K.M.: Review of technological progress in carbon dioxide capture, storage, and utilization. Gas Sci. Eng. (2023). https://doi.org/10.1016/j.jgsce.2023.205070

    Article  Google Scholar 

  2. Davoodi, S., Al-Shargabi, M., Wood, D.A., Rukavishnikov, V.S., Minaev, K.M.: Review of technological progress in carbon dioxide capture storage and utilization. Gas Sci. Eng. (2023). https://doi.org/10.1016/j.jgsce.2023.205070

    Article  Google Scholar 

  3. Gheidan, A.A.S., Wahid, M.A., Munir, F.A., Opia, A.C.: Feasibility study of bio-fuel as a sustainable product of biomass: an overview of its fundamentals application and environmental impact. J. Adv. Res. Fluid Mech. Therm. Sci. 88(2), 106–122 (2021). https://doi.org/10.37934/arfmts.88.2.106122

    Article  Google Scholar 

  4. Karampinis, E., Kourkoumpas, D.S., Grammelis, P., Kakaras, E.: New power production options for biomass and cogeneration. WIREs Energy Environ. 4(6), 471–485 (2015). https://doi.org/10.1002/wene.163

    Article  CAS  Google Scholar 

  5. Yong, Z., Mata, V., Rodrigues, A.: Adsorption of carbon dioxide at high temperature—a review. Sep. Purif. Technol. 26(2–3), 195–205 (2002)

    Article  CAS  Google Scholar 

  6. WBA. World Bioenergy Association. (2020). Global Bioenergy Statistics 2020. http://Www.Worldbioenergy.Org/Uploads/201210%20wba%20gbs%202020.Pdf

  7. Conab—Companhia nacional de abastcimento. acompanhamentoda safra brasileira: cana-deaçúcar safra 2021/22 [Internet]. Available At: https://Www.Conab.Gov.Br/Info-Agro/Safras/Cana/Boletim-Da-Safra-De-Cana-De-Acucar/Item/Download/39835_2a34b37eec6ef5d4dac107978bb0103d

  8. Nachiluk, K. Alta Na Produção E Exportações De Açúcar Marcam A Safra 2020/21 De Cana. Análises E Indicadores Do Agronegócio Available At: http://Www.Iea.Sp.Gov.Br/Out/Tertexto.Php?Codtexto=15925#:~:Text=O Brasil É O Maior, De Litros De Etanol1.

  9. Ardila, Y.C., Figueroa, J.E.I., Lunelli, B.H., Filho, R.M., Maciel, M.R.W.: Syngas production from sugar cane bagasse in a circulating fluidized bed gasifier using aspen plustm: modelling and simulation. Comput. Aided Chem. Eng. (2012). https://doi.org/10.1016/B978-0-444-59520-1.50077-4

    Article  Google Scholar 

  10. Fu, L., Ren, Z., Si, W., Ma, Q., Huang, W., Liao, K., Huang, Z., Wang, Y., Li, J., Xu, P.: Research progress on CO2 capture and utilization technology. J. CO2 Util. (2022). https://doi.org/10.1016/j.jcou.2022.102260

    Article  Google Scholar 

  11. Nicodème, T., Berchem, T., Jacquet, N., Richel, A.: Thermochemical conversion of sugar industry by-products to biofuels. Renew. Sustain. Energy Rev. 88, 151–159 (2018). https://doi.org/10.1016/j.rser.2018.02.037

    Article  CAS  Google Scholar 

  12. Samanta, A., Zhao, A., Shimizu, G.K.H., Sarkar, P., Gupta, R.: Post-combustion CO2 capture using solid sorbents: a review. Ind. Eng. Chem. Res. 51(4), 1438–1463 (2012). https://doi.org/10.1021/ie200686q

    Article  CAS  Google Scholar 

  13. Webley, P.A.: Adsorption technology for CO2 separation and capture: a perspective. Adsorption 20(2–3), 225–231 (2014)

    Article  CAS  Google Scholar 

  14. Yang, R.T.: Gas separation by adsorption processes, p. 352. World Scientific, Singapore (1997)

    Book  Google Scholar 

  15. Dziejarski, B., Serafin, J., Andersson, K., Krzyżyńska, R.: CO2 capture materials: a review of current trends and future challenges. Mater. Today Sustain. (2023). https://doi.org/10.1016/j.mtsust.2023.100483

    Article  Google Scholar 

  16. Bahamon, D., Vega, L.F.: Systematic evaluation of materials for post-combustion CO2 capture in a temperature swing adsorption process. Chem. Eng. J. 284, 438–447 (2016). https://doi.org/10.1016/j.cej.2015.08.098

    Article  CAS  Google Scholar 

  17. Harlick, P.J.E., Tezel, F.H.: An experimental adsorbent screening study for CO2 removal from N2. Micropor. Mesopor. Mater. 76(1–3), 71–79 (2004). https://doi.org/10.1016/j.micromeso.2004.07.035

    Article  CAS  Google Scholar 

  18. Hedayati, A., Delica, B.A., Perez-Gil, S., Prieto-Fernandez, S.: Evaluation of high-performance adsorbents for separation of CO2 from industrial effluent gases. Greenh. Gases Sci. Technol. 13(2), 216–226 (2023). https://doi.org/10.1002/ghg.2197

    Article  CAS  Google Scholar 

  19. Morales-Ospino, R., Santiago, R.G., Siqueira, R.M., et al.: Assessment of CO2 desorption from 13X zeolite for a prospective TSA process. Adsorption 26, 813–824 (2020). https://doi.org/10.1007/s10450-019-00192-5

    Article  CAS  Google Scholar 

  20. Tun, H., Chen, C.C.: Isosteric heat of adsorption from thermodynamic Langmuir isotherm. Adsorption 27, 979–989 (2021). https://doi.org/10.1007/s10450-020-00296-3

    Article  CAS  Google Scholar 

  21. Wilkins, N.S., Rajendran, A.: Measurement of competitive CO2 and N2 adsorption on zeolite 13X for post-combustion CO2 capture. Adsorption 25, 115–133 (2019). https://doi.org/10.1007/s10450-018-00004-2

    Article  CAS  Google Scholar 

  22. Tlili, N., Grevillot, G., Valli, E.C.: Carbon dioxide capture and recovery by means of tsa and/or vsa. Int. J. Greenh. Gas Control 3(5), 519–527 (2009). https://doi.org/10.1016/j.ijggc.2009.04.005

    Article  CAS  Google Scholar 

  23. Schony, G., Dietrich, F., Fuchs, J., Preoll, T., Hofbauer, H.: A multi-stage fluidized bed system for continuous CO2 capture by means of temperature swing adsorption e first results from bench scale experiments. Powder Technol. 316, 519–527 (2017). https://doi.org/10.1016/j.powtec.2016.11.066

    Article  CAS  Google Scholar 

  24. Proll, T., Schony, G., Sprachmann, G., Hofbauer, H.: Introduction and evaluation of a double loop staged fluidized bed system for post-combustion CO2 capture using solid sorbents in a continuous temperature swing adsorption process. Chem. Eng. Sci. 141, 166–174 (2016). https://doi.org/10.1016/j.ces.2015.11.005

    Article  CAS  Google Scholar 

  25. Raganati, F., Chirone, R., Ammendola, P.: CO2 capture by temperature swing adsorption: working capacity as affected by temperature and CO2 partial pressure. Ind. Eng. Chem. Res. 59, 3593–3605 (2020). https://doi.org/10.1021/acs.iecr.9b04901

    Article  CAS  Google Scholar 

  26. Hosseini, S.S., Denayer, J.F.M.: Biogas upgrading by adsorption processes: mathematical modeling, simulation and optimization approach—a review. J. Environ. Chem. Eng. (2022). https://doi.org/10.1016/j.jece.2022.107483

    Article  Google Scholar 

  27. Plaza, M.G., Rubiera, F., Pevida, C.: Evaluating the feasibility of a TSA process based on steam stripping in combination with structured carbon adsorbents to capture CO2 from a coal power plant. Energy Fuels 31(9), 9760–9775 (2017). https://doi.org/10.1021/acs.energyfuels.7b01508

    Article  CAS  Google Scholar 

  28. Joss, L., Gazzani, M., Mazzotti, M.: Rational design of temperature swing adsorption cycles for post-combustion CO2 capture. Chem. Eng. Sci. 158, 381–394 (2017). https://doi.org/10.1016/j.ces.2016.10.013

    Article  CAS  Google Scholar 

  29. Merel, J., Clausse, M., Meunier, F.: Experimental investigation on CO2 post−combustion capture by indirect thermal swing adsorption using 13X and 5A zeolites. Ind. Eng. Chem. Res. (2007). https://doi.org/10.1021/ie071012x

    Article  Google Scholar 

  30. Pirngruber, G.D., Guillou, F., Gomez, A., Clausse, M.: A theoretical analysis of the energy consumption of post-combustion CO2 capture processes by temperature swing adsorption using solid sorbents. Int. J. Greenh. Gas Control 14, 74–83 (2013). https://doi.org/10.1016/j.ijggc.2013.01.010

    Article  CAS  Google Scholar 

  31. Qiao, Z., Wang, Z., Zhang, C., Yuan, S., Zhu, Y., Wang, J.: PVAm–PIP/PS composite membrane with high performance for CO2/N2 separation. AIChE J. 59(4), 215–228 (2012). https://doi.org/10.1002/aic.13781

    Article  CAS  Google Scholar 

  32. Kannan, C. S., Rao, S. S., & Varma, Y. B. G. (1994). A study of stable range of operation in multistage fluidised beds. Powder Technol. 78(3), 203–211. https://doi.org/10.1016/0032-5910(93)02785-9

    Article  CAS  Google Scholar 

  33. Krutka, H., Sjostrom, S., Starns, T., Dillon, M., Silverman, R.: Post-combustion CO2 capture using solid sorbents: 1 MW e pilot evaluation. Energy Procedia 37, 73–88 (2013). https://doi.org/10.1016/j.egypro.2013.05.087

    Article  CAS  Google Scholar 

  34. Pielichowska, K., Pielichowski, K.: Phase change materials for thermal energy storage. Prog. Mater. Sci. 65, 67–123 (2014). https://doi.org/10.1016/j.pmatsci.2014.03.005

    Article  CAS  Google Scholar 

  35. Das, D., Samal, D.P., Meikap, B.C.: Removal of CO2 in a multistage fluidized bed reactor by diethanol amine impregnated activated carbon. J. Environ. Sci. Health Part A 51(9), 769–775 (2016)

    Article  CAS  Google Scholar 

  36. Dietrich, F., Schöny, G., Fuchs, J., Hofbauer, H.: Experimental study of the adsorber performance in a multi-stage fluidized bed system for continuous CO2 capture by means of temperature swing adsorption. Fuel Process (2018). https://doi.org/10.1016/j.fuproc.2018.01.013

    Article  Google Scholar 

  37. Duarte, G.S., Parenti, P., Cataldo, S., Annoni, M.P.G., Mahmoodan, M., Aliakbarzadeh, H., et al. Carbon dioxide removal from industrial gases using an indirectly heated and cooled temperature swing adsorption process [Internet]. Vol. 6, Jurnal Sains dan Seni ITS. Universität Duisburg-Essen genehmigte Dissertation zum Erwerb des akademischen Grades; 2017. Available at: https://www.uni-due.de/imperia/md/content/verfahrenstechnik/dissertation_duarte.pdf

  38. Treybal, R.E. Mass—transfer operations book description. 240 (1980)

  39. Dhoke, C., Zaabout, A., Cloete, S., Amini, S.: Review on reactor configurations for adsorption-based CO2 capture. Ind. Eng. Chem. Res. 60(10), 3779–3798 (2021). https://doi.org/10.1021/acs.iecr.0c04547

    Article  CAS  Google Scholar 

  40. Esmaeili Rad, F., Abbasian, J., Arastoopour, H.: Numerical simulation of CO2 adsorption in a fluidized bed using solid-supported amine sorbent. Can. J. Chem. Eng. 99(7), 1595–1606 (2021). https://doi.org/10.1002/cjce.24000

    Article  CAS  Google Scholar 

  41. Loha, C., Chattopadhyay, H., & Chatterjee, P. K. (2012). Assessment of drag models in simulating bubbling fluidized bed hydrodynamics. Chem. Eng. Sci. 75, 400–407. https://doi.org/10.1016/j.ces.2012.03.044

    Article  CAS  Google Scholar 

  42. Zhou, Y., Han, Y., Lu, Y., Bai, H., Hu, X., Zhang, X., Xie, F., Luo, X., Wang, J., & Yang, Y. (2020). Numerical simulations and comparative analysis of two-and three-dimensional circulating fluidized bed reactors for CO2 capture. Chin. J. Chem. Eng. 28(12), 2955–2967. https://doi.org/10.1016/j.cjche.2020.07.003

    Article  CAS  Google Scholar 

  43. Ngo, S.I., Lim, Y.I., Lee, D., Seo, M.W.: Flow behavior and heat transfer in bubbling fluidized-bed with immersed heat exchange tubes for CO2 methanation. Powder Technol. 380, 462–474 (2021). https://doi.org/10.1016/j.powtec.2020.11.027

    Article  CAS  Google Scholar 

  44. Sakaunnapaporn, C., Chaiwang, P., Piumsomboon, P., Chalermsinsuwan, B.: Effect of operating parameters on carbon dioxide depressurized regeneration in circulating fluidized bed downer using computational fluid dynamics. J. Therm. Sci. 30(3), 1057–1067 (2021). https://doi.org/10.1007/s11630-021-1437-0

    Article  CAS  Google Scholar 

  45. Zhou, Y., Han, Y., Lu, Y., Bai, H., Hu, X., Zhang, X., Xie, F., Luo, X., Wang, J., Yang, Y.: Numerical simulations and comparative analysis of two- and three-dimensional circulating fluidized bed reactors for CO2 capture. Chin. J. Chem. Eng. 28(12), 2955–2967 (2020). https://doi.org/10.1016/j.cjche.2020.07.003

    Article  CAS  Google Scholar 

  46. Sornvichai, A., Piemjaiswang, R., Piumsomboon, P., Chalermsinsuwan, B.: Computational fluid dynamic model of nonisothermal circulating fluidized bed riser for CO2 capture. Energy Rep. 6, 1512–1518 (2020). https://doi.org/10.1016/j.egyr.2020.10.062

    Article  Google Scholar 

  47. Fuss, S., Jones, C.D., Kraxner, F., Peters, G.P., Smith, P., Tavoni, M., et al.: Research priorities for negative emissions. Environ. Res. Lett. (2016). https://doi.org/10.1088/1748-9326/11/11/115007

    Article  Google Scholar 

  48. Pollex, A., Ortwein, A., Kaltschmitt, M.: Thermo-chemical conversion of solid biofuels: conversion technologies and their classification. Biomass Convers. Biorefinery 2(1), 21–39 (2012). https://doi.org/10.1007/s13399-011-0025-z

    Article  CAS  Google Scholar 

  49. Parenti, P., Cataldo, S., Annoni, M.P.G., Mahmoodan, M., Aliakbarzadeh, H., Gholamipour, R., et al.: Carbon dioxide removal from industrial gases using an indirectly heated and cooled temperature swing adsorption process. Int. J. Fatigue 6(1), 51–66 (2017)

    Google Scholar 

  50. Toth, J.: State equations of the solid–gas interface layers. Acta Chim. Acad. Sci. Hung. 69, 311–328 (1971)

    CAS  Google Scholar 

  51. Do, D.D.: Adsorption analysis: equilibria and kinetics. Imperial College Press And Distributed By World Scientific Publishing Co (1998)

    Book  Google Scholar 

  52. Rouquerol, F., et al.: Adsorption by powders and porous solids principles, methodology and applications, 2nd edn., p. 1. Academic Press, Oxford (2014)

    Book  Google Scholar 

  53. Kasivisvanathan, H., Ubando, A.T., Ng, D.K.S., Tan, R.R.: Robust optimization for process synthesis and design of multifunctional energy systems with uncertainties. Ind. Eng. Chem. Res. 53(8), 3196–3209 (2014)

    Article  CAS  Google Scholar 

  54. Yoro, K.O., Sekoai, P.T., Isafiade, A.J., Daramola, M.O.: A review on heat and mass integration techniques for energy and material minimization during CO2 capture. Int. J. Energy Environ. Eng. 10(3), 367–387 (2019)

    Article  CAS  Google Scholar 

  55. Seider, W.D., Lewin, D.R., Seader, J.D., Widagdo, S., Gani, R., Ng, K.M. Design of chemical devices, functional products, and formulated products. Prod. Process. Des. Princ. (2017) [citado 15 de fevereiro de 2022];155–9. Available at: https://www.wiley.com/en-us/Product+and+Process+Design+Principles%3A+Synthesis%2C+Analysis+and+Evaluation%2C+4th+Edition-p-9781119282631

  56. Spigarelli, B.P., Kawatra, S.K.: Opportunities, and challenges in carbon dioxide capture. J. CO2 Util. 1, 69–87 (2013)

    Article  CAS  Google Scholar 

  57. Toomey, R.D., Johnstone, H.F.: Gaseous fluidization of solid particles. Chem. Eng. Prog. 48, 220–226 (1952)

    CAS  Google Scholar 

  58. Davarpanah, M., Hashisho, Z., Crompton, D., Anderson, J.E.: Mathematical correlations in two-phase modeling of fluidized bed adsorbers. J. Hazard. Mater. (2022). https://doi.org/10.1016/j.jhazmat.2021.127218

    Article  PubMed  Google Scholar 

  59. Davidson, J.F., Harrison, D.: Fluidized particles. Cambridge University Press, New York (1963)

    Google Scholar 

  60. Kunii, D., Levenspiel, O.: Fluidization engineering. Wiley, New York (1969)

    Google Scholar 

  61. Davarpanah, M., Hashisho, Z., Phillips, J.H., Crompton, D., Anderson, J.E., Nichols, M.: Modeling VOC adsorption in a multistage countercurrent fluidized bed adsorber. Chem. Eng. J. 394, 124963 (2020). https://doi.org/10.1016/j.cej.2020.124963

    Article  CAS  Google Scholar 

  62. Hatzantonis, H., Yiannoulakis, H., Yiagopoulos, A., Kiparissides, C.: Recent developments in modeling gas-phase catalyzed olefin polymerization fluidized-bed reactors: the effect of bubble size variation on the reactor’s performance. Chem. Eng. Sci. 55(16), 3237–3259 (2000). https://doi.org/10.1016/S0009-2509(99)00565-5

    Article  CAS  Google Scholar 

  63. Alobaid, F., Almohammed, N., Massoudi Farid, M., May, J., Rößger, P., Richter, A., et al.: Progress in CFD simulations of fluidized beds for chemical and energy process engineering. Prog. Energy Combust. Sci. (2021). https://doi.org/10.1016/j.pecs.2021.100930

    Article  Google Scholar 

  64. Bocksell, T.L., Loth, E.: Stochastic modeling of particle diffusion in a turbulent boundary layer. Int. J. Multiph. Flow 32(10–11), 1234–1253 (2006). https://doi.org/10.1016/j.ijmultiphaseflow.2006.05.013

    Article  CAS  Google Scholar 

  65. Thommes, M., Kaneko, K., Neimark, A.V., Olivier, J.P., Rodriguez-Reinoso, F., Rouquerol, J., Sing, K.S.W.: Physisorption of gases, with special reference to the evaluation of surface area and pore size distribution (IUPAC Technical Report). Pure Appl. Chem. 87(9–10), 1051–1069 (2015). https://doi.org/10.1515/pac-2014-1117

    Article  CAS  Google Scholar 

  66. Montanari, T., Busca, G.: On the mechanism of adsorption and separation of CO2 on LTA zeolites: an IR investigation. Vib. Spectrosc. 46(1), 45–51 (2008). https://doi.org/10.1016/j.vibspec.2007.09.001

    Article  CAS  Google Scholar 

  67. Morales-Ospino, R., Santos, V.N., Lima, A.R.A., Torres, A.E.B., Vilarrasa-García, E., Bastos-Neto, M., et al.: Parametric analysis of a moving bed temperature swing adsorption (MBTSA) process for postcombustion CO2 capture. Ind. Eng. Chem. Res. 60(29), 10736–10752 (2021). https://doi.org/10.1021/acs.iecr.0c05067

    Article  CAS  Google Scholar 

  68. Mondino, G., Grande, C.A., Blom, R., Nord, L.O.: Moving bed temperature swing adsorption for CO2 capture from a natural gas combined cycle power plant. Int. J. Greenh. Gas Control 85, 58–70 (2019). https://doi.org/10.1016/j.ijggc.2019.03.021

    Article  CAS  Google Scholar 

  69. Beleli, Y.S., Paiva, J.L., Seckler, M.M., Roux, G.A.C.L.: Optimization of a continuous multi-stage fluidized bed system for CO2 capture utilizing temperature swing adsorption. In: 33rd European symposium on computer aided process engineering, pp. 3316–3321. Elsevier Inc., Amsterdam (2023)

    Google Scholar 

  70. Hu, S., & Liu, X. (2023). 3D CFD-PBM simulation of gas–solid bubbling beds of Geldart A particles with sub-grid drag correction. Chem. Eng. Sci. 275. https://doi.org/10.1016/j.ces.2023.118660

    Article  CAS  Google Scholar 

  71. Drud, A. S. (1994). CONOPT—A Large-Scale GRG Code. ORSA J Comput, 6(2), 207–216. https://doi.org/10.1287/ijoc.6.2.207

  72. Morgado, J. F., Cloete, S., Morud, J., Gurker, T., Quinta-Ferreira, R. M., & Amini, S. (2017). 1D modelling of membrane-assisted chemical looping reforming. Energy Procedia, 136, 277–282. https://doi.org/10.1016/j.egypro.2017.10.242

  73. Jiang, N., Shen, Y., Liu, B., Zhang, D., Tang, Z., Li, G., & Fu, B. (2020). CO2 capture from dry flue gas by means of VPSA, TSA and TVSA. J. CO2 Util. 35, 153–168. https://doi.org/10.1016/j.jcou.2019.09.012

  74. Almohammed, N., Alobaid, F., Breuer, M., & Epple, B. (2014). A comparative study on the influence of the gas flow rate on the hydrodynamics of a gas-solid spouted fluidized bed using Euler-Euler and Euler-Lagrange/DEM models. Powder Technol. 264, 343–364. https://doi.org/10.1016/j.powtec.2014.05.024

  75. Lazarov, B.S., Sigmund, O.: Filters in topology optimization based on Helmholtz-type differential equations. Int. J. Numer. Meth. Eng. 86(6), 765–781 (2011). https://doi.org/10.1002/nme.3072

    Article  Google Scholar 

  76. Yang, N., Wang, W., Ge, W., & Li, J. (2003). CFD simulation of concurrent-up gas-solid flow in circulating fluidized beds with structure-dependent drag coefficient. Chem. Eng. J 96(1–3), 71–80. https://doi.org/10.1016/j.cej.2003.08.006

  77. Cui, H., Mostoufi, N., Chaouki, J.: Characterization of dynamic gas–solid distribution in fluidized beds. Chem. Eng. J. 79(2), 133–143 (2000)

    Article  CAS  Google Scholar 

  78. Broadhurst, T.E., Becker, H.A.: Onset of fluidization and slugging in beds of uniform particles. AIChE J. 21(2), 238–247 (1975)

    Article  CAS  Google Scholar 

  79. Kunii, D., Levenspiel, O.: Fluidization engineering. Butterworth-Heinemann (1991)

    Google Scholar 

  80. Yates, J.G., Ruiz-Martinez, R.S., Cheesman, D.J.: Prediction of bubble size in a fluidized bed containing horizontal tubes. Chem. Eng. Sci. 45(4), 1105–1111 (1990)

    Article  CAS  Google Scholar 

  81. Darton, R.C. et al. Bubble growth due to coalescence in fluidised beds. (1977)

  82. Bird, R.B., Stewart, W.E., Lightfoot, E.N.: Transport phenomena. Wiley, New York (1960)

    Google Scholar 

  83. Sung Won, K., et al.: Heat transfer and bubble characteristics in a fluidized bed with immersed horizontal tube bundle. Int. J. Heat Mass Transf. 46, 399–409 (2003)

    Article  Google Scholar 

  84. Li, J., Mason, D.J.: A computational investigation of transient heat transfer in pneumatic transport of granular particles. Powder Technol. 112(3), 273–282 (2000)

    Article  CAS  Google Scholar 

  85. Dantas, T.L.P., et al.: Carbon dioxide–nitrogen separation through adsorption on activated carbon in a fixed bed. Chem. Eng. J. 169(1–3), 11–19 (2011)

    Article  CAS  Google Scholar 

  86. Scala, F.: Mass transfer around freely moving active particles in the dense phase of a gas fluidized bed of inert particles. Chem. Eng. Sci. 62(16), 4159–4176 (2007)

    Article  CAS  Google Scholar 

  87. Dantas, T.L.P. et al. Separação de dióxido de carbono por adsorção a partir de misturas sintéticas do tipo gás de exaustão. (2009)

  88. Onyestyák, G., Shen, D., Rees, L.V.: Frequency-response study of micro-and macro-pore diffusion in manufactured zeolite pellets. J. Chem. Soc. Faraday Trans. 91, 1399–1405 (1995)

    Article  Google Scholar 

  89. Ma, Y., et al.: Synthesis and characterization of 13X zeolite from low-grade natural kaolin. Adv. Powder Technol. 25(2), 495–499 (2014)

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge support of the RCGI—Research Centre for Gas Innovation, hosted by the University of São Paulo (USP) and sponsored by FAPESP—São Paulo Research Foundation (2014/50279-4 and 2020/15230-5) and Shell Brasil, and the strategic importance of the support given by ANP (Brazil’s National Oil, Natural Gas and Biofuels Agency) through the R&D levy regulation. The first author thanks the financial support of FAPESP under Grant 2023/10333-9. E.C.N Silva thanks the financial support of FAPESP under Grant 2022/14475-0 and CNPq (National Council for Scientific and Technological Development) under Grant 302658/2018-1. M.M. Seckler thanks the financial support of FAPESP under Grant 2017/19087-0 and CNPq (National Council for Scientific and Technological Development) under Grant 426482/2016-6.

Funding

The authors gratefully acknowledge support of the RCGI—Research Centre for Gas Innovation, hosted by the University of São Paulo (USP) and sponsored by FAPESP—São Paulo Research Foundation (2014/50279-4 and 2020/15230-5) and Shell Brasil, and the strategic importance of the support given by ANP (Brazil’s National Oil, Natural Gas and Biofuels Agency) through the R&D levy regulation. The first author thanks the financial support of FAPESP under Grant 2023/10333-9. E.C.N Silva thanks the financial support of FAPESP under Grant 2022/14475-0 and CNPq (National Council for Scientific and Technological Development) under Grant 302658/2018-1. M.M. Seckler thanks the financial support of FAPESP under Grant 2017/19087-0 and CNPq (National Council for Scientific and Technological Development) under Grant 426482/2016-6.

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed to the conception and design of the study. DSP implemented the Euler–Euler mode, run simulations, discussed the results, wrote and revised the manuscript; EVG developed the experimental process, performed experiments discussed the results wrote and revised the manuscript; ES implemented the B–E model, run simulations, discussed the results, wrote and revised the manuscript; YSB implemented the process optimization, run simulations, discussed the results, wrote and revised the manuscript; FSM elaborated literature review, discussed the results, wrote and revised the manuscript; JLP participated in the conception of all models and simulations, supervised all models implementation, discussed the results, wrote and revised the manuscript; GACLR worked in the conception of the process optimization, supervised the process optimization implementation, discussed the results, wrote and revised the manuscript; MBN supervised the experiments conception, and material characterization, discussed the results, wrote and revised the manuscript; DCSA supervised the experiments conception, and material characterization, discussed the results, wrote and revised the manuscript; ECNS supervised the Euler–Euler model conception, discussed the results, wrote and revised the manuscript; MMS supervised all the models, discussed the results, wrote and revised the manuscript. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to D. S. Prado.

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Appendices

Appendix 1

1.1 CFD model

The two-fluid model utilizes governing equations that contain conservative equations and constitutive correlations for solid stress and interfacial drag coefficient. These relations are written as:

  • Mass conservative equations for phases \((k=g,\mathrm{s})\) [43]:

    $$\frac{\partial }{\partial t}\left({\varepsilon }_{k}{\rho }_{k}\right)+\nabla \cdot \left({\varepsilon }_{k}{\rho }_{k}{{\varvec{u}}}_{{\varvec{k}}}\right)=0$$
    (1)

    where \({\varepsilon }_{k}\) is the portion of gas or solid, \({\rho }_{k}\) is the density and \({{\varvec{u}}}_{{\varvec{k}}}\) is the velocity.

  • Momentum conservative equations for phases \((k=g,s\)) [43]:

    $$\frac{\partial }{\partial t}\left( {\varepsilon_{k} \rho_{k} {\varvec{u}}_{{\varvec{k}}} } \right) + \nabla \cdot \left( {\varepsilon_{k} \rho_{k} {\varvec{u}}_{{\varvec{k}}} {\varvec{u}}_{{\varvec{k}}} } \right) = - \varepsilon_{k} \nabla p_{k} + \nabla \cdot \overline{\overline{\tau }}_{k} - D\left( {{\varvec{u}}_{{\varvec{k}}} - {\varvec{u}}_{{\varvec{k}}} } \right) + \varepsilon_{k} \rho_{k} {\varvec{g}}$$
    (2)

    where \({p}_{k}\) is the pressure of gas or solid, \(\overline{\overline{\tau }}_{k}\) is the gas or phase stress tensor, \({\varvec{g}}\) is the gravity vector and \(D\) is the drag coefficient.

  • Energy conservative equations for phases \((k=g,s, j=s,g)\) [43]:

    $$\frac{\partial }{\partial t}\left( {\varepsilon_{k} \rho_{k} C_{pk} T_{k} } \right) + \rho_{k} \nabla \cdot \left( {\varepsilon_{k} {\varvec{u}}_{{\varvec{k}}} C_{pk} T_{k} } \right) = \nabla \cdot \left( {\varepsilon_{k} k_{k} \nabla T_{k} + \overline{\overline{\tau }}_{k} \cdot {\varvec{u}}_{{\varvec{k}}} } \right) + Q_{kj} - \varepsilon_{k} \frac{{\partial p_{k} }}{\partial t}$$
    (3)

    where \({C}_{pk}\) is the specific heat, \({T}_{k}\) is the temperature or phase stress tensor, \({{\varvec{k}}}_{{\varvec{k}}}\) is the thermal conductivity and \({Q}_{kj}\) is the heat exchange between both phases.

  • Gas phase stress:

    $$\overline{\overline{\tau }}_{{\text{g}}} = \varepsilon_{{\text{g}}} \mu_{{\text{g}}} \left\{ {\left[ {\nabla {\varvec{u}}_{{\varvec{g}}} + \left( {\nabla {\varvec{u}}_{{\varvec{g}}} } \right)^{{\text{T}}} } \right] - \frac{2}{3}\left( {\nabla \cdot {\varvec{u}}_{{\varvec{g}}} } \right)\overline{\overline{I}} } \right\}$$
    (4)
  • Solid phase stress:

    $$\overline{\overline{\tau }}_{{\text{p}}} = p_{{\text{p}}} \overline{\overline{I}} + \varepsilon_{{\text{p}}} \mu_{{\text{p}}} \left\{ {\left[ {\nabla {\varvec{u}}_{{\varvec{s}}} + \left( {\nabla {\varvec{u}}_{{\varvec{s}}} } \right)^{{\text{T}}} } \right] - \frac{2}{3}\left( {\nabla \cdot {\varvec{u}}_{{\varvec{s}}} } \right)\overline{\overline{I}} } \right\}$$
    (5)
  • Solid pressure [76]:

    $$\nabla {p}_{s}={10}^{-8.686{\varepsilon }_{\mathrm{g}}+6.385}\nabla {\varepsilon }_{\mathrm{p}}$$
    (6)
  • Solid viscosity [[76]:

    $${\mu }_{\mathrm{p}}=0.5{\varepsilon }_{\mathrm{p}}$$
    (7)
  • Drag coefficient [43]:

    If \(\left({\varepsilon }_{\mathrm{g}}<0.80\right)\):

    $$\begin{array}{cc}& D=150\frac{{\left(1-{\varepsilon }_{\mathrm{g}}\right)}^{2}{\mu }_{\mathrm{g}}}{{\varepsilon }_{\mathrm{g}}{d}_{\mathrm{p}}^{2}}+1.75\frac{\left(1-{\varepsilon }_{\mathrm{g}}\right){\rho }_{\mathrm{g}}\left|{{\varvec{u}}}_{{\varvec{g}}}-{{\varvec{u}}}_{{\varvec{s}}}\right|}{{d}_{\mathrm{p}}}\end{array}$$
    (8)

    If \(\left({\varepsilon }_{\mathrm{g}}\ge 0.80\right)\):

    $$\begin{array}{c}D=\frac{3}{4}\frac{\left(1-{\varepsilon }_{\mathrm{g}}\right){\varepsilon }_{\mathrm{g}}}{{d}_{\mathrm{p}}}{\rho }_{\mathrm{g}}\left|{{\varvec{u}}}_{{\varvec{g}}}-{{\varvec{u}}}_{{\varvec{s}}}\right|{C}_{\mathrm{D}0}{\varepsilon }_{\mathrm{g}}^{-2.7}\end{array}$$
    (9)

    where \({d}_{p}\) is the particle diameter and \({C}_{D}\) is given by [43]:

    $${C}_{D}=\frac{24}{{\varepsilon }_{\mathrm{g}}R{e}_{s}}\left[1+0.15{\left({\varepsilon }_{\mathrm{g}}R{e}_{S}\right)}^{0.687}\right]$$
    (10)

    where \({\mathrm{Re}}_{\mathrm{s}}\) is given by:

    $$R{e}_{S}=\frac{{\varepsilon }_{\mathrm{g}}{\rho }_{\mathrm{g}}{d}_{\mathrm{s}}\left|{{\varvec{u}}}_{{\varvec{s}}}-{{\varvec{u}}}_{{\varvec{g}}}\right|}{{\mu }_{\mathrm{g}}}$$
    (11)

Appendix 2

2.1 B–E model

The mass balance for component i in the gas phase of the bubble region at stage j is given by:

$$\frac{d{F}_{i,j}^{(b)}}{dV}={f}_{b,j}{{K}_{be}}_{i,j}\left({y}_{i,j}^{\left(e\right)}-{y}_{i,j}^{\left(b\right)}\right)$$
(1)
$$\sum_{i=1}^{m}{y}_{i,j}^{(b)}=1$$
(2)

Using the following initial condition:

$${F}_{i,j}^{\left(b\right)}{|}_{\left(V=0\right)}={\delta }_{b,j}{F}_{j-1}{y}_{i,j-1}$$
(3)

The mass balance for component i in the gas phase of the emulsion region at stage j is given by:

$$\frac{{\delta }_{e,j}{F}_{j-1}{y}_{i,j-1}-{F}_{j}^{\left(e\right)}{y}_{i,j}^{\left(e\right)}}{{V}_{j}}={\int }_{0}^{{V}_{j}}{f}_{b,j}{{K}_{be}}_{i,j}\left({y}_{i,j}^{\left(e\right)}-{y}_{i,j}^{\left(b\right)}\right)dV+{f}_{e,j}\left(1-{\varepsilon }_{e,j}\right){\rho }_{p}{{k}_{LDF}}_{i,j}\left({q}_{i,j}^{*}-{q}_{i,j}\right)$$
(4)
$$\sum_{i=1}^{m}{y}_{i,j}^{(e)}=1$$
(5)

The mass balance for component i adsorbed on the solid phase at stage j is given by:

$$\frac{{\dot{m}}_{s}\left({q}_{i,j+1}-{q}_{i,j}\right)}{{V}_{j}}=-{f}_{e,j}\left(1-{\varepsilon }_{e,j}\right){\rho }_{p}{{k}_{LDF}}_{i,j}\left({q}_{i,j}^{*}-{q}_{i,j}\right)$$
(6)

The molar flow rate and composition of the outlet gas stream of stage j is determined using the following Equations, valid for 1 ≤ j ≤ N (j = 0 represents the system feed and N is the column number of stages):

$${F}_{j}={F}_{j}^{\left(e\right)}+{F}_{j}^{\left(b\right)}{|}_{\left(V={V}_{j}\right)}$$
(7)
$${y}_{i,j}=\frac{{y}_{i,j}^{\left(e\right)}{F}_{j}^{\left(e\right)}+\left({{y}_{i,j}^{\left(b\right)}F}_{j}^{\left(b\right)}\right){|}_{\left(V={V}_{j}\right)}}{{F}_{j}^{\left(e\right)}+{F}_{j}^{\left(b\right)}{|}_{\left(V={V}_{j}\right)}}$$
(8)

The energy balance for the gas phase of the bubble region at stage j is given by Equation:

$${f}_{b,j}\frac{d}{dV}\left({F}_{j}^{(b)}{H}_{g}^{(b)}\right)={f}_{b,j}{H}_{be,j}\left({T}_{g,j}^{\left(e\right)}-{T}_{g,j}^{\left(b\right)}\right)+-\sum_{i=1}^{m}{{J}_{be}^{{\prime}{\prime}{\prime}}}_{i,j}\left({\varphi }_{{be}_{i,j}}{H}_{i,j}^{\left(e\right)}+(1-{\varphi }_{{be}_{i,j}}){H}_{i,j}^{\left(b\right)}\right)-{\alpha }_{w,j}{h}_{wb,j}\left({T}_{g,j}^{\left(b\right)}-{T}_{w,j}\right)$$
(9)

Using the following initial condition:

$$\left({F}_{j}^{(b)}{H}_{g,j}^{\left(b\right)}\right){|}_{\left(V=0\right)}={\delta }_{b,j}{F}_{j-1}{H}_{g,j-1}$$
(10)

where φbe takes on values of 0 or 1 as a function of the difference in the mole fractions of i in the bubble gas and the emulsion, according to the relationship shown below:

$${\varphi }_{{be}_{i}}=\left\{\begin{array}{c}1 if {y}_{i}^{(e)}\ge {y}_{i}^{(b)}\\ 0 if {y}_{i}^{(e)}<{y}_{i}^{(b)}\end{array}\right.$$
(11)

The energy balance for the gas phase of the emulsion region at stage j is given by:

$$\frac{{\delta }_{e,j}{F}_{j-1}{H}_{g,j-1}-{F}_{j}^{\left(e\right)}{H}_{g,j}^{\left(e\right)}}{{V}_{j}}={\int }_{0}^{{V}_{j}}{f}_{b,j}{H}_{be,j}\left({T}_{g,j}^{\left(e\right)}-{T}_{g,j}^{\left(b\right)}\right)dV+-\sum_{i=1}^{m}{\int }_{0}^{{V}_{j}}{{J}_{be}^{{\prime}{\prime}{\prime}}}_{i,j}\left({\varphi }_{{be}_{i,j}}{H}_{i,j}^{\left(e\right)}+(1-{\varphi }_{{be}_{i,j}}){H}_{i,j}^{\left(b\right)}\right)dV+\sum_{i=1}^{m}{\dot{q}}_{i,j}^{{\prime}{\prime}{\prime}}\left({\varphi }_{{es}_{i,j}}{H}_{i,j}^{\left(e\right)}+(1-{\varphi }_{{es}_{i,j}}){H}_{i,j}^{\left(s\right)}\right)+-{f}_{e,j}\left(1-{\varepsilon }_{e,j}\right)\frac{6{h}_{p,j}}{{d}_{p}}\left({T}_{s,j}-{T}_{g,j}^{\left(e\right)}\right)+{\alpha }_{w,j}{h}_{we,j}{\left({T}_{g,j}^{\left(e\right)}-{T}_{w,j}\right)}_{lm}$$
(12)

where φes takes on values of 0 or 1 as a function of the difference in the equilibrium adsorbed amount and the actual adsorbed amount of i in the solids, according to the relationship shown below:

$${\varphi }_{{es}_{i,j}}=\left\{\begin{array}{c}1 if {q}_{i,j}^{*}\ge {q}_{i,j}\\ 0 if {q}_{i,j}^{*}<{q}_{i,j}\end{array}\right.$$
(13)

The molar flux of component i between the gas phase in the bubbles and the gas phase in the emulsion per unit bed volume at stage j, the molar flux of component i between the gas phase in the emulsion and the solids per unit bed volume at stage j, and the heat exchanger area per unit bed volume are obtained by Equations:

$${{J}_{be}^{{\prime}{\prime}{\prime}}}_{i,j}=-{f}_{b,j}{{K}_{be}}_{i,j}\left({y}_{i,j}^{\left(e\right)}-{y}_{i,j}^{\left(b\right)}\right)$$
(14)
$${\dot{q}}_{i,j}^{\mathrm{^{\prime}}\mathrm{^{\prime}}\mathrm{^{\prime}}}={f}_{e,j}\left(1-{\varepsilon }_{e,j}\right){\rho }_{p}{{k}_{LDF}}_{i,j}\left({q}_{i,j}^{*}-{q}_{i,j}\right)$$
(15)
$${\alpha }_{w,j}=\frac{{A}_{wext,j}}{{V}_{j}}$$
(16)

The energy balance for the solid phase at stage j is given by Equation:

$$\frac{{\dot{m}}_{s}{C}_{p,s}\left({T}_{s,j+1}-{T}_{s,j}\right)}{{V}_{j}}={f}_{e,j}\left(1-{\varepsilon }_{e,j}\right)\frac{6{h}_{p,j}}{{d}_{p}}\left({T}_{s,j}-{T}_{g,j}^{\left(e\right)}\right)+-\sum_{i=1}^{m}{\dot{q}}_{i,j}^{\mathrm{^{\prime}}\mathrm{^{\prime}}\mathrm{^{\prime}}}\left({\varphi }_{{es}_{i,j}}{H}_{i,j}^{\left(e\right)}+(1-{\varphi }_{{es}_{i,j}}){H}_{i,j}^{\left(s\right)}+(-\Delta {H}_{{ads}_{i,j}})\right)$$
(17)

The change in the enthalpy of adsorption for component i is described by the Equation:

$$\Delta {H}_{{ads}_{i,j}}={\overline{\lambda }}_{{ads}_{i,j}}+{H}_{i,j}^{\left(s\right)}-({\varphi }_{{es}_{i,j}}{H}_{i,j}^{\left(e\right)}+(1-{\varphi }_{{es}_{i,j}}){H}_{i,j}^{\left(s\right)})$$
(18)

The average isosteric heat of adsorption of the inlet and outlet solid adsorbed concentrations of component i at stage j is obtained from Equation:

$${\overline{\lambda }}_{{ads}_{i,j}}=\frac{{\int }_{{q}_{i,j+1}}^{{q}_{i,j}}{\lambda }_{ads,i}dq}{{q}_{i,j}-{q}_{i,j+1}}$$
(19)

The specific enthalpy of the j-stage outlet gas stream formed by mixing the emulsion and bubble outlet gas streams is calculated using Equation:

$${H}_{g,j}=\frac{{F}_{j}^{(e)}\sum_{i=1}^{m}{y}_{i,j}^{(e)}{H}_{i,j}^{(e)}+\left({F}_{j}^{(b)}\sum_{i=1}^{m}{y}_{i,j}^{(b)}{H}_{i,j}^{(b)}\right){|}_{\left(V={V}_{j}\right)}}{{F}_{j}^{(e)}+{F}_{j}^{(b)}{|}_{\left(V={V}_{j}\right)}}$$
(20)

The volume fraction of emulsion and bubbles in a Geldart B particle bed at stage j is obtained from the following correlation [77], assuming stages of equivalent cross-sectional area.

$${f}_{e,j}=\mathrm{0,466}+\mathrm{0,534} exp\left(-\frac{({u}_{0,j-1}-{u}_{mf,j})}{\mathrm{0,413}}\right)$$
(21)
$${f}_{e,j}+{f}_{b,j}=1$$
(22)

The minimum fluidization porosity in the j-stage bed can be estimated using Broadhurst and Becker’s [78] correlation for minimum bubbling porosity, which can be approximated to the minimum fluidization porosity in Geldart B particle beds:

$${\varepsilon }_{mf}\cong {\varepsilon }_{mb}=\mathrm{0,586}\cdot {\phi }_{s}^{-\mathrm{0,72}}{\left[\frac{{\mu }_{g}^{ 2}}{{\rho }_{g}g\left({\rho }_{p}-{\rho }_{g}\right){\left({d}_{p}/{\phi }_{s}\right)}^{3}}\right]}^{\mathrm{0,029}}{\cdot \left(\frac{{\rho }_{g}}{{\rho }_{p}}\right)}^{\mathrm{0,021}}$$
(23)

The pressure drop in fixed bed is calculated from Ergun’s Equation and that in fluidized bed is approximately equal to the apparent weight of the bed [79], according to:

$$\frac{\Delta {P}_{m}}{{L}_{m}}=150\frac{{(1-{\varepsilon }_{m})}^{2}}{{{\varepsilon }_{m}}^{3}}\frac{{\mu }_{g}{u}_{0}}{{({\phi }_{s}{d}_{p})}^{2}}+\mathrm{1,75}\frac{(1-{\varepsilon }_{m})}{{{\varepsilon }_{m}}^{3}}\frac{{\rho }_{g}{u}_{0}^{ 2}}{{\phi }_{s}{d}_{p}}$$
(24)
$$\frac{\Delta {P}_{f}}{{L}_{f}}=(1-{\varepsilon }_{f})\left[({\rho }_{p}-{\rho }_{g})g\right]$$
(25)

Equalizing the pressure drop terms from the two Equations above for the imminent fluidization condition (u0 = umf) gives:

$$150\frac{{(1-{\varepsilon }_{mf})}^{2}}{{\varepsilon }_{mf}^{ 3}}\frac{{\mu }_{g}{u}_{mf}}{{({\phi }_{s}{d}_{p})}^{2}}+\mathrm{1,75}\frac{(1-{\varepsilon }_{mf})}{{\varepsilon }_{mf}^{ 3}}\frac{{\rho }_{g}{u}_{mf}^{ 2}}{{\phi }_{s}{d}_{p}}=(1-{\varepsilon }_{mf})\left[({\rho }_{p}-{\rho }_{g})g\right]$$
(26)

The pressure in stage j is given by the following equation:

$${P}_{j}={P}_{j-1}-\Delta {P}_{f,j}$$
(27)

The gas flow velocity in the bubble region of stage j is obtained from the following relation [79]:

$${u}_{0,j}={f}_{b,j}{u}_{b,j}+{f}_{e,j}{u}_{e,j}$$
(28)

The average fluidized bed porosity and the fluidized bed height relative to the initial fixed bed state are calculated by Equations [79]:

$${\varepsilon }_{f,j}={f}_{b,j}{\varepsilon }_{b,j}+{f}_{e,j}{\varepsilon }_{e,j}$$
(29)
$$\frac{{L}_{f,j}}{{L}_{m,j}}=\frac{\left(1-{\varepsilon }_{m,j}\right)}{(1-{\varepsilon }_{f,j})}$$
(30)

The superficial velocity of the outlet gas stream determines the volumetric outlet gas flow rate of stage j, and the volumetric outlet gas flow rate is related to the molar flow rate of the outlet gas of stage j using the ideal gas mixture model, according to Equations:

$${\dot{v}}_{g,j}={u}_{0,j}{A}_{t,bed}$$
(31)
$${P}_{j}{\dot{v}}_{j}={F}_{j}{R}_{g}{T}_{g,j}$$
(32)

The fractions of the j-stage gas feed flow rate that are directed to the emulsion and bubbles are calculated from Equations:

$${\delta }_{e,j}=\frac{{f}_{e,j}{u}_{e,j}}{{u}_{0,j}}$$
(33)
$${\delta }_{b,j}=\frac{{f}_{b,j}{u}_{b,j}}{{u}_{0,j}}$$
(34)

According to Yates, Ruiz-Martinez, and Cheesman [80], the equation of Darton et al. [81] for the volumetric equivalent diameter of spherical bubbles in fluidized beds in the absence of immersed tubes can be modified to determine the equivalent bubble diameter in a fluidized bed that contains rows of immersed horizontal tubes. The bubble growth in the fluidized bed is given by the following Equation:

$${d}_{b}(z)=\mathrm{0,54}\cdot {\left[\frac{{u}_{0}-{u}_{mf}}{{a}_{tw}}\right]}^{\mathrm{0,4}}\cdot {\left(z+4\sqrt{{A}_{cd}}\right)}^{\mathrm{0,8}}\cdot {g}^{-\mathrm{0,2}}$$
(35)

The breakdown of bubbles when passing through a row of horizontal tubes is represented by the following correlation [80]:

$$\frac{{t}_{w}}{{d}_{b,spl}}=\mathrm{0,4}{\left(\frac{{d}_{w,ext}}{{d}_{b,imp}}\right)}^{2}+\left[\mathrm{1,38}-\mathrm{0,65}\left(\frac{{d}_{w,ext}}{{d}_{b,imp}}\right)\right]\left(\frac{{t}_{w}}{{d}_{b,imp}}\right)$$
(36)

The fraction of the cross-sectional area of the bed free for bubble flow in the region of the tube bank is given by Equation:

$${a}_{tw}=1-\frac{{A}_{pr,w}}{{A}_{t,bed}}$$
(37)

where A(pr,w) is the projected area of a row of tubes on the horizontal plane of the bed which, for cylindrical horizontal tubes, can be obtained by Equation:

$${A}_{pr,w}={n}_{t}{L}_{w}{d}_{w,ext}$$
(38)

Thus, it is possible to calculate the average equivalent bubble diameter along the bed by the following Equation:

$${d}_{b,m}=\frac{{\int }_{0}^{{L}_{f}}{d}_{b}(z)dz}{{L}_{f}}$$
(39)

The gas-phase mass exchange coefficients of component i in the bubble–cloud (bc) and cloud-emulsion (ce) paths can be calculated using the following equations [79]:

$${K}_{bc,i}=\mathrm{4,5}\left(\frac{{u}_{mf}}{{d}_{b,m}}\right)+\mathrm{5,85}\frac{{\left({D}_{i,m}\right)}^{1/2}{g}^{1/4}}{{\left({d}_{b,m}\right)}^{5/4}}$$
(40)
$${K}_{ce,i}=\mathrm{6,77}{\left[\frac{{D}_{i,m}{\varepsilon }_{mf}{u}_{br}}{{\left({d}_{b,m}\right)}^{3}}\right]}^{1/2}$$
(41)

The natural rise velocity of a gas bubble in a fluidized bed can be estimated by the following correlation:

$${u}_{br}=\mathrm{0,711}{(g{d}_{b,eq})}^{1/2}$$
(42)

The overall mass transfer coefficient between the bubble and the is obtained by Equation:

$${K}_{be,i}={{C}_{g,be}\left(\frac{1}{{K}_{bc,i}}+\frac{1}{{K}_{ce,i}}\right)}^{-1}$$
(43)

Using an analogy between heat and mass transfer, the heat transfer coefficients from bubble to cloud and cloud to emulsion, considering emulsion packet properties for the cloud-emulsion heat transfer coefficient, can be calculated according to the equations:

$${H}_{bc}=\mathrm{4,5}\left(\frac{{u}_{mf}{\rho }_{g}{C}_{p,g}}{{d}_{b,m}}\right)+\mathrm{5,85}\frac{{\left({k}_{g}{\rho }_{g}{C}_{p,g}\right)}^{1/2}{g}^{1/4}}{{\left({d}_{b,m}\right)}^{5/4}}$$
(44)
$${H}_{ce}=\mathrm{6,77}{\left[\frac{{k}_{ce}{\rho }_{ce}{C}_{p,ce}{\varepsilon }_{mf}{u}_{br}}{{\left({d}_{b,m}\right)}^{3}}\right]}^{1/2}$$
(45)
$${H}_{be}={\left(\frac{1}{{H}_{bc}}+\frac{1}{{H}_{ce}}\right)}^{-1}$$
(46)

The dynamic viscosity and thermal conductivity of the gas mixture are determined by Wilke’s Mixing Rule [82], according to the Equations below.

$${\phi }_{i,j}=\frac{{\left[1+{\left(\frac{{\mu }_{i}}{{\mu }_{j}}\right)}^{1/2}{\left(\frac{{M}_{w,j}}{{M}_{w,i}}\right)}^{1/4}\right]}^{2}}{\sqrt{8}{\left[1+\left(\frac{{M}_{w,i}}{{M}_{w,j}}\right)\right]}^{1/2}}$$
(47)
$${\mu }_{g}=\sum_{i=1}^{m}\frac{{\mu }_{i}}{1+\frac{1}{{y}_{i}}\sum_{j=1,j\ne i}^{m}{y}_{j}{\phi }_{i,j}}$$
(48)
$${k}_{g}=\sum_{i=1}^{m}\frac{{k}_{i}}{1+\frac{1}{{y}_{i}}\sum_{j=1,j\ne i}^{m}{y}_{j}{\phi }_{i,j}}$$
(49)

The properties of the cloud-emulsion film packets are obtained by an analogy with the particle packet model from Kim et al. [83] using the Equations:

$${\rho }_{ce}=\left(1-{\varepsilon }_{ce}\right){\rho }_{p}+{\varepsilon }_{ce}{\rho }_{g}^{(ec)}$$
(50)
$${C}_{p,ec}=\left(1-{\varepsilon }_{ec}\right){C}_{p,s}+{\varepsilon }_{e}{C}_{p,g}^{(ec)}$$
(51)
$${k}_{ec}={\varepsilon }_{ec}{k}_{g}^{(ec)}+(1-{\varepsilon }_{ec}){k}_{s}\left(\frac{1}{{\varphi }_{e}({k}_{s}/{k}_{g}^{(ec)})+2/3}\right)$$
(52)

where εce represents the porosity of the cloud-emulsion film and is approximated as equal to the emulsion porosity and φe is the equivalent thickness of the gas film on the contact point between particles. For the system in this work, a value of 0.13 for φe was adopted [79].

The heat transfer coefficient between the gas in the emulsion and the particles in fluidized beds can be estimated by means of the following correlations for the particle Nusselt Number over different ranges of the particle Reynolds Number (Rep) [84].

$${Nu}_{p}=2+\mathrm{0,6}{\varepsilon }_{e}^{ \mathrm{3,5}}{Re}_{p}^{ 1/2}{Pr}^{1/3} , {Re}_{p}<200$$
(53)
$${Nu}_{p}=2+\mathrm{0,5}{\varepsilon }_{e}^{\mathrm{ 3,5}}{Re}_{p}^\frac{1}{2}{Pr}^\frac{1}{3}+\mathrm{0,02}{\varepsilon }_{e}^{\mathrm{ 3,5}}{Re}_{p}^{\mathrm{ 0,8}}{Pr}^\frac{1}{3}, 200<{Re}_{p}<1500$$
(54)
$${Nu}_{p}=2+\left(\mathrm{4,5}{\cdot 10}^{-5}\right){\varepsilon }_{e}^{ \mathrm{3,5}}{Re}_{p}^{ \mathrm{1,8}}, {Re}_{p}>1500$$
(55)

where the dimensionless numbers Rep and Nup are given by the following expressions:

$${Re}_{p}=\frac{{\rho }_{g}{u}_{e}{d}_{p}}{{\mu }_{g}}$$
(56)
$${Nu}_{p}=\frac{{h}_{p}{d}_{p}}{{k}_{g}}$$
(57)

The mass transfer of component i between the gas in the emulsion and the solids through the adsorptive process is described by a Linear Driving Force model using the global kinetic coefficient for adsorption of i (\({\text{k}}_{{\text{LDF,i}}}\)), whose value is calculated using the mass transport resistances in the outer film, of the diffusion in the macropores and the diffusion in the micropores of the particle, according to the expression [85]:

$$\frac{1}{{k}_{LDF,i}}=\frac{{r}_{p}{q}_{i,0}}{3{k}_{f,i}{C}_{i,0}}+\frac{{r}_{p}^{ 2}{q}_{i,0}}{15{\varepsilon }_{p}{D}_{p,i}{C}_{i,0}}+\frac{{r}_{c}^{ 2}}{15{D}_{c,i}}$$
(58)

The coefficient kf,i was calculated from the correlation of the Sherwood Number of particle in fluidized beds given by Scala [86], according to Equations:

$${Sh}_{p,i}=\mathrm{2,0}{\varepsilon }_{mf}+\mathrm{0,70}{\left({Re}_{p,mf}/{\varepsilon }_{mf}\right)}^{1/2}{{Sc}_{i}}^{1/3}$$
(59)
$${Sh}_{p,i}=\frac{{k}_{f,i}{d}_{p}}{{D}_{m,i}}$$
(60)

where Rep,mf is the particle Reynolds Number at the minimum bed fluidization conditions and Sci represents the Schmidt Number, given by Equation:

$${Sc}_{i}=\frac{{\mu }_{g}}{{\rho }_{g}{D}_{m,i}}$$
(61)

The diffusivity of i in the particle macropores was calculated using the Bosanquet Equation [82]:

$$\frac{1}{{D}_{p,i}}={\tau }_{p}\left(\frac{1}{{D}_{m,i}}+\frac{1}{{D}_{k,i}}\right)$$
(62)

The Knudsen diffusivity of i is given by Equation (Tables 8 and 9):

$${D}_{k,i}=9700{r}_{mp}\sqrt{\frac{T}{{M}_{w,i}}}$$
(63)
Table 8 Properties of the 13X zeolite adsorbent particles applied to the model
Table 9 Bed geomety parameters

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Prado, D.S., Vilarrasa-García, E., Sampronha, E. et al. Multiple approaches for large-scale CO2 capture by adsorption with 13X zeolite in multi-stage fluidized beds assessment. Adsorption (2023). https://doi.org/10.1007/s10450-023-00422-x

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