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CFD modeling and simulation of expanded polystyrene pyrolysis in a fluidized bed reactor

  • Vahid Khebri EMAIL logo
Published/Copyright: January 1, 2026
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Abstract

This study presents a multiphase CFD model, predicated upon the Eulerian–Eulerian framework integrated with the granular kinetic theory, to study expanded polystyrene (EPS) pyrolysis in a fluidized-bed reactor under stable fluidization conditions. The proposed model was validated against experimental data from the literature. Subsequently, the fluidization characteristics and overall gas-particle flow patterns; the temperature profiles and heat-transfer rates; as well as the product distributions and pyrolysis reaction progression within a fluidized-bed reactor were studied in detail, both qualitatively and quantitatively. To deepen the analysis, a comprehensive parametric study was conducted, analyzing the influence of operating temperature, inlet superficial gas velocity, bed particle size, and initial bed height on the yields and compositions of pyrolysis products. The simulation results indicate that operating temperature has the greatest influence on product distribution. For the main product classes, as operating temperature increases, the yield of condensable volatiles diminishes, whereas the yield of non-condensable volatiles rises. Regarding specific products, the yield of styrene – often the target product in EPS pyrolysis – exhibits a non-monotonic trend, initially rising with temperature, reaching a maximum of 61.64 wt% at 600 °C, and then decreasing at higher temperatures. In contrast, inlet superficial gas velocity, bed particle size, and initial bed height are found to have a limited impact on product yields.


Corresponding author: Vahid Khebri, Department of Applied Chemistry Faculty of Chemistry, University of Tabriz, 29 Bahman Street, P. O. Box: 51666-16471, Tabriz, Iran, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: The author has accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The author states no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

Nomenclature

Roman letters

A j

apparent pre-exponential factor of the j-th reaction, 1/s

C d

drag coefficient, dimensionless

c p,q

specific heat capacity of phase q, J/(kg·K)

D eff,i,q

effective mass-diffusion coefficient of species i in phase q, m2/s

D i,q

molecular mass diffusivity of species i in phase q, m2/s

d p

particle diameter in the particle phase, m

E a j

apparent activation energy of the j-th reaction, kJ/mol

e pp

restitution coefficient for particle-particle collisions, dimensionless

F r

constant in Eq. (T1-5)

g

acceleration because of gravitational force, m/s2

g 0,pp

radial distribution function for the particle phase, dimensionless

H q

specific sensible enthalpy of phase q, J/kg

h gp

gas-particle volumetric heat-transfer coefficient, W/(m3·K)

I

unit tensor

I 2D

second invariant of the deviatoric stress tensor

k j

rate constant for the j-th reaction, 1/s

k Θ p

diffusion coefficient for particle-phase fluctuation kinetic energy, kg/(m·s)

M i,g

molar mass of species i in the gas phase, kg/kmol

Mwpl,U

molar mass per structural unit of the polymer, kg/mol

Nu p

Nusselt number for the particle phase, dimensionless

P

static pressure shared by all phases, Pa

P op

operating pressure, Pa

P p

solids pressure of the particle phase, Pa

P f,p

solids frictional pressure of the particle phase, Pa

Pr g

Prandtl number for the gas phase, dimensionless

PrT,g

turbulent Prandtl number for the gas phase, dimensionless

R

gas constant, kJ/(mol·K)

Re p

relative Reynolds number for the particle phase, dimensionless

R i,q

net generation rate of species i in phase q due to homogeneous reactions, kg/(m3·s)

S i,q

net generation rate of species i in phase q due to heterogeneous reactions, kg/(m3·s)

S q

mass source term in phase q due to interphase mass exchange, kg/(m3·s)

S q V g

momentum source term in phase q due to interphase mass exchange, kg/(m2·s2)

ScT,g

turbulent Schmidt number for the gas phase, dimensionless

T q

absolute temperature of phase q, K

T 0,q

reference temperature of phase q, K

T GT

glass-transition temperature of the polymer, K

t

time, s

V p

particle fluctuation velocity of the particle phase, m/s

V q

velocity vector of the particle phase, m/s

V G

molar volume of glassy amorphous polymer, m3/mol

V w

molar Van der Waals volume of the polymer, m3/mol

Y i,q

mass fraction of species i in phase q, dimensionless

Greek letters

α eff,q

effective thermal diffusivity of phase q, m2/s

α q

molecular thermal diffusivity of phase q, m2/s

β gp

gas-particle momentum-transfer coefficient, kg/(m3·s)

γ Θ p

collisional dissipation of particle-phase fluctuation kinetic energy, kg/(m·s3)

Δ H r q

enthalpy source term in phase q due to chemical reactions, W/m3

ϵ q

volume fraction of phase q, dimensionless

ϵ p,max

volume fraction of the particle phase at packing limit, dimensionless

Θ p

granular temperature of the particle phase, m2/s2

κ g

turbulence kinetic energy of the gas phase, m2/s2

λ q

thermal conductivity of phase q, W/(m·K)

μ g

dynamic viscosity of the gas phase, Pa·s

μ p

shear viscosity of the particle phase, Pa·s

ν eff,g

effective kinematic viscosity of the gas phase, m2/s

ν g

kinematic viscosity of the gas phase, m2/s

ν T,g

turbulent kinematic viscosity of the gas phase, m2/s

ν B,p

bulk viscosity of the particle phase, Pa·s

ρ q

density of phase q, kg/m3

ρ i,p

true density of species i in the particle phase, kg/m3

ρ pl

density of molten plastic, kg/m3

τ q

stress tensor of gas q, Pa

ϕ f

angle of internal friction, deg

ϕ gp

transfer of fluctuation kinetic energy from the particle phase to the gas phase, kg/(m·s3)

Sub/superscripts

eff

effective

f

frictional

g

gas phase

i

species index

j

reactions index

m, n

constants in Eq. (T1-5)

op

operating

p

particle phase

pl

plastic

q

phase index

T

turbulent

Abbreviations

AAE

average absolute error

BC

boundary conditions

BFBRs

bubbling fluidized-bed reactors

CFD

computational fluid dynamics

CVs

condensable volatiles

EE

Eulerian–Eulerian

EL

Eulerian–Lagrangian

EPS

expanded polystyrene

FBRs

fluidized-bed reactors

FKE

fluctuation kinetic energy

HDPE

high-density polyethylene

IC

initial conditions

KTGF

kinetic theory for granular flows

LDPE

low-density polyethylene

MMT

million metric tonnes

MSW

municipal solid-waste

NCVs

non-condensable volatiles

PE

polyethylene

PET

polyethylene terephthalate

PS

polystyrene

TCTs

thermochemical conversion technologies

TFM

two-fluid model

WP

waste plastic

References

1. OECD. Global plastics outlook: economic drivers, environmental impacts and policy options. Paris: OECD Publishing; 2022.Search in Google Scholar

2. Anuar Sharuddin, SD, Abnisa, F, Wan Daud, WMA, Aroua, MK. A review on pyrolysis of plastic wastes. Energy Convers Manag 2016;115:308–26. https://doi.org/10.1016/j.enconman.2016.02.037.Search in Google Scholar

3. Qureshi, MS, Oasmaa, A, Pihkola, H, Deviatkin, I, Tenhunen, A, Mannila, J, et al.. Pyrolysis of plastic waste: opportunities and challenges. J Anal Appl Pyrolysis 2020;152:104804. https://doi.org/10.1016/j.jaap.2020.104804.Search in Google Scholar

4. Iannello, S, Sebastiani, A, Errigo, M, Materazzi, M. The behaviour of plastic particles during pyrolysis in bubbling fluidized bed reactors: incipient agglomeration and axial segregation. Powder Technol 2024;441:119846. https://doi.org/10.1016/j.powtec.2024.119846.Search in Google Scholar

5. Chari, S, Sebastiani, A, Paulillo, A, Materazzi, M. The environmental performance of mixed plastic waste gasification with carbon capture and storage to produce hydrogen in the U.K. ACS Sustainable Chem Eng 2023;11:3248–59. https://doi.org/10.1021/acssuschemeng.2c05978.Search in Google Scholar

6. Soni, VK, Singh, G, Vijayan, BK, Chopra, A, Kapur, GS, Ramakumar, SSV. Thermochemical recycling of waste plastics by pyrolysis: a review. Energy Fuel 2021;35:12763–808. https://doi.org/10.1021/acs.energyfuels.1c01292.Search in Google Scholar

7. Dai, L, Zhou, N, Lv, Y, Cheng, Y, Wang, Y, Liu, Y, et al.. Pyrolysis technology for plastic waste recycling: a state-of-the-art review. Prog Energy Combust Sci 2022;93:101021. https://doi.org/10.1016/j.pecs.2022.101021.Search in Google Scholar

8. Kim, J, Yun, GN, Song, IH, Song, KH, Baek, SJ, Kim, J, et al.. Process modeling and assessment of waste polystyrene pyrolysis: comparing catalytic and thermal methods. Chem Eng J 2025;505:159261. https://doi.org/10.1016/j.cej.2025.159261.Search in Google Scholar

9. Park, JJ, Park, K, Kim, JS, Maken, S, Song, H, Shin, H, et al.. Characterization of styrene recovery from the pyrolysis of waste expandable polystyrene. Energy Fuel 2003;17:1576–82. https://doi.org/10.1021/ef030102l.Search in Google Scholar

10. Pérez-Bravo, G, Contreras-Larios, JL, Rodríguez, JF, Zeifert-Soares, B, Angeles-Beltrán, D, López-Medina, R, et al.. Catalytic pyrolysis process to produce styrene from waste expanded polystyrene using a semi-batch rotary reactor. Sustainability 2022;14:14914. https://doi.org/10.3390/su142214914.Search in Google Scholar

11. Verma, A, Sharma, S, Pramanik, H. Pyrolysis of waste expanded polystyrene and reduction of styrene via in-situ multiphase pyrolysis of product oil for the production of fuel range hydrocarbons. Waste Manag 2021;120:330–9. https://doi.org/10.1016/j.wasman.2020.11.035.Search in Google Scholar PubMed

12. Lee, CG, Cho, YJ, Song, PS, Kang, Y, Kim, JS, Choi, MJ. Effects of temperature distribution on the catalytic pyrolysis of polystyrene waste in a swirling fluidized-bed reactor. Catal Today 2003;79–80:453–64. https://doi.org/10.1016/s0920-5861(03)00060-9.Search in Google Scholar

13. Chauhan, RS, Gopinath, S, Razdan, P, Delattre, C, Nirmala, GS, Natarajan, R. Thermal decomposition of expanded polystyrene in a pebble bed reactor to get higher liquid fraction yield at low temperatures. Waste Manag 2008;28:2140–5. https://doi.org/10.1016/j.wasman.2007.10.001.Search in Google Scholar PubMed

14. Lee, CG, Kim, JS, Song, PS, Cho, YJ, Kang, Y, Choi, MJ. Effects of catalyst on the pyrolysis of polystyrene wastes in a fluidized bed catalytic reactor. Korean Chem Eng Res 2002;40:445–9.Search in Google Scholar

15. Artetxe, M, Lopez, G, Amutio, M, Barbarias, I, Arregi, A, Aguado, R, et al.. Styrene recovery from polystyrene by flash pyrolysis in a conical spouted bed reactor. Waste Manag 2015;45:126–33. https://doi.org/10.1016/j.wasman.2015.05.034.Search in Google Scholar PubMed

16. Li, H, Aguirre-Villegas, HA, Allen, RD, Bai, X, Benson, CH, Beckham, GT, et al.. Expanding plastics recycling technologies: chemical aspects, technology status and challenges. Green Chem 2022;24:8899–9002. https://doi.org/10.1039/d2gc02588d.Search in Google Scholar

17. Kaminsky, W. Chemical recycling of plastics by fluidized bed pyrolysis. Fuel Commun 2021;8:100023. https://doi.org/10.1016/j.jfueco.2021.100023.Search in Google Scholar

18. Cocco, RA, Chew, JW. Fluidized bed scale-up for sustainability challenges. 1. tomorrow’s tools. Ind Eng Chem Res 2024;63:2519–33. https://doi.org/10.1021/acs.iecr.3c04146.Search in Google Scholar

19. Chew, JW, Cocco, RA. Fluidized bed scale-up for sustainability challenges. 2. new pathway. Ind Eng Chem Res 2024;63:8025–43. https://doi.org/10.1021/acs.iecr.4c00421.Search in Google Scholar

20. Zhong, W, Yu, A, Zhou, G, Xie, J, Zhang, H. CFD simulation of dense particulate reaction system: approaches, recent advances and applications. Chem Eng Sci 2016;140:16–43. https://doi.org/10.1016/j.ces.2015.09.035.Search in Google Scholar

21. van der Hoef, MA, van Sint Annaland, M, Deen, NG, Kuipers, JAM. Numerical simulation of dense gas-solid fluidized beds: a multiscale modeling strategy. Annu Rev Fluid Mech 2008;40:47–70. https://doi.org/10.1146/annurev.fluid.40.111406.102130.Search in Google Scholar

22. Pannala, S, Syamlal, M, O’Brien, TJ. Computational gas-solids flows and reacting systems: theory, methods and practice, 1st ed. Hershey, PA: IGI Global; 2010.10.4018/978-1-61520-651-3Search in Google Scholar

23. Tsuji, Y, Kawaguchi, T, Tanaka, T. Discrete particle simulation of two-dimensional fluidized bed. Powder Technol 1993;77:79–87. https://doi.org/10.1016/0032-5910(93)85010-7.Search in Google Scholar

24. Hoomans, BPB, Kuipers, JAM, Briels, WJ, van Swaaij, WPM. Discrete particle simulation of bubble and slug formation in a two-dimensional gas-fluidised bed: a hard-sphere approach. Chem Eng Sci 1996;51:99–118. https://doi.org/10.1016/0009-2509(95)00271-5.Search in Google Scholar

25. Almohammed, N, Alobaid, F, Breuer, M, Epple, B. 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 2014;264:343–64. https://doi.org/10.1016/j.powtec.2014.05.024.Search in Google Scholar

26. Chiesa, M, Mathiesen, V, Melheim, JA, Halvorsen, B. Numerical simulation of particulate flow by the Eulerian–Lagrangian and the Eulerian–Eulerian approach with application to a fluidized bed. Comput Chem Eng 2005;29:291–304. https://doi.org/10.1016/j.compchemeng.2004.09.002.Search in Google Scholar

27. Lichtenegger, T, Pirker, S. CFD-DEM modeling of strongly polydisperse particulate systems. Powder Technol 2018;325:698–711. https://doi.org/10.1016/j.powtec.2017.11.058.Search in Google Scholar

28. Gidaspow, D. Multiphase flow and fluidization: continuum and kinetic theory descriptions, 1st ed. San Diego, CA: Academic Press Inc.; 1994.10.1016/B978-0-08-051226-6.50005-4Search in Google Scholar

29. Enwald, H, Peirano, E, Almstedt, AE. Eulerian two-phase flow theory applied to fluidization. Int J Multiphas Flow 1996;22:21–66. https://doi.org/10.1016/s0301-9322(96)90004-x.Search in Google Scholar

30. Kuipers, JAM, van Swaaij, WPM. Computational fluid dynamics applied to chemical reaction engineering. London: Academic Press Inc.; 1998:227–328 pp.10.1016/S0065-2377(08)60094-0Search in Google Scholar

31. Lu, H, Gidaspow, D, Wang, S. Computational fluid dynamics and the theory of fluidization: applications of the kinetic theory of granular flow, 1st ed. Singapore: Springer; 2021.10.1007/978-981-16-1558-0_1Search in Google Scholar

32. Abdelmotalib, HM, Youssef, MAM, Hassan, AA, Youn, SB, Im, IT. Heat transfer process in gas–solid fluidized bed combustors: a review. Int J Heat Mass Tran 2015;89:567–75. https://doi.org/10.1016/j.ijheatmasstransfer.2015.05.085.Search in Google Scholar

33. Norouzi, HR, Mostoufi, N, Mansourpour, Z, Sotudeh-Gharebagh, R, Chaouki, J. Characterization of solids mixing patterns in bubbling fluidized beds. Chem Eng Res Des 2011;89:817–26. https://doi.org/10.1016/j.cherd.2010.10.014.Search in Google Scholar

34. Arena, U, Mastellone, ML. Defluidization phenomena during the pyrolysis of two plastic wastes. Chem Eng Sci 2000;55:2849–60. https://doi.org/10.1016/s0009-2509(99)00533-3.Search in Google Scholar

35. Arena, U, Mastellone, ML. The phenomenology of bed defluidization during the pyrolysis of a food-packaging plastic waste. Powder Technol 2001;120:127–33. https://doi.org/10.1016/s0032-5910(01)00359-x.Search in Google Scholar

36. Mastellone, ML, Arena, U. Fluidized‐bed pyrolysis of polyolefins wastes: predictive defluidization model. AIChE J 2002;48:1439–47. https://doi.org/10.1002/aic.690480708.Search in Google Scholar

37. Aguado, R, Prieto, R, José, MJS, Alvarez, S, Olazar, M, Bilbao, J. Defluidization modelling of pyrolysis of plastics in a conical spouted bed reactor. Chem Eng Process Process Intensif 2005;44:231–5.10.1016/j.cep.2004.02.016Search in Google Scholar

38. Han, SW, Lee, JJ, Tokmurzin, D, Lee, SH, Nam, JY, Park, SJ, et al.. Gasification characteristics of waste plastics (SRF) in a bubbling fluidized bed: effects of temperature and equivalence ratio. Energy 2022;238:121944. https://doi.org/10.1016/j.energy.2021.121944.Search in Google Scholar

39. Tokmurzin, D, Nguyen, HK, Nam, JY, Park, SJ, Yoon, SJ, Mun, TY, et al.. Three-dimensional CFD simulation of co-gasification of biomass and plastic wastes (SRF) in a bubbling fluidized bed with detailed kinetic chemical model. Energy Environ 2025. https://doi.org/10.1177/0958305x241310193.Search in Google Scholar

40. ANSYS I. ANSYS fluent theory guide. Canonsburg, PA: ANSYS, Inc.; 2013.Search in Google Scholar

41. Chapman, S, Cowling, TG. The mathematical theory of non-uniform gases: an account of the kinetic theory of viscosity, thermal conduction and diffusion in gases. Cambridge: Cambridge University Press; 1970.Search in Google Scholar

42. Cruz, ME, Verissimo, GL, Brandão, FL, Leiroz, AJK. A computational study of the influence of drag models and heat transfer correlations on the simulations of reactive polydisperse flows in bubbling fluidized beds. Fluid 2023;8:290. https://doi.org/10.3390/fluids8110290.Search in Google Scholar

43. Loha, C, Chattopadhyay, H, Chatterjee, PK. Assessment of drag models in simulating bubbling fluidized bed hydrodynamics. Chem Eng Sci 2012;75:400–7. https://doi.org/10.1016/j.ces.2012.03.044.Search in Google Scholar

44. Natarajan, VVR, Hunt, ML. Kinetic theory analysis of heat transfer in granular flows. Int J Heat Mass Tran 1998;41:1929–44. https://doi.org/10.1016/s0017-9310(97)00315-3.Search in Google Scholar

45. Aguado, R. Kinetics of polystyrene pyrolysis in a conical spouted bed reactor. Chem Eng J 2003;92:91–9. https://doi.org/10.1016/s1385-8947(02)00119-5.Search in Google Scholar

46. Niksiar, A, Sohrabi, M. Mathematical modeling of waste plastic pyrolysis in conical spouted beds: heat, mass, and momentum transport. J Anal Appl Pyrolysis 2014;110:66–78. https://doi.org/10.1016/j.jaap.2014.08.005.Search in Google Scholar

47. Liu, Y, Qian, J, Wang, J. Pyrolysis of polystyrene waste in a fluidized-bed reactor to obtain styrene monomer and gasoline fraction. Fuel Process Technol 2000;63:45–55. https://doi.org/10.1016/s0378-3820(99)00066-1.Search in Google Scholar

48. Van Krevelen, DW, Te Nijenhuis, K. Properties of polymers, 4th ed. Amsterdam: Elsevier; 2009.Search in Google Scholar

49. Rößger, P, Richter, A. Numerical modeling of a batch fluidized-bed gasifier: interaction of chemical reaction, particle morphology development and hydrodynamics. Powder Technol 2021;384:148–59. https://doi.org/10.1016/j.powtec.2021.01.072.Search in Google Scholar

50. Cardoso, J, Silva, V, Eusébio, D, Brito, P, Boloy, RM, Tarelho, L, et al.. Comparative 2D and 3D analysis on the hydrodynamics behaviour during biomass gasification in a pilot-scale fluidized bed reactor. Renew Energy 2019;131:713–29.10.1016/j.renene.2018.07.080Search in Google Scholar

51. Xie, N, Battaglia, F, Pannala, S. Effects of using two- versus three-dimensional computational modeling of fluidized beds. Powder Technol 2008;182:1–13. https://doi.org/10.1016/j.powtec.2007.07.005.Search in Google Scholar

52. Armstrong, LM, Luo, KH, Gu, S. Two-dimensional and three-dimensional computational studies of hydrodynamics in the transition from bubbling to circulating fluidised bed. Chem Eng J 2010;160:239–48. https://doi.org/10.1016/j.cej.2010.02.032.Search in Google Scholar

53. Puhan, P, Mukherjee, AK, Atta, A. Analyzing the influence of key model parameters in two-fluid CFD modeling of an inclination-augmented liquid–solid fluidized bed. Ind Eng Chem Res 2023;62:18759–79. https://doi.org/10.1021/acs.iecr.3c02743.Search in Google Scholar

54. Johnson, PC, Jackson, R. Frictional-collisional constitutive relations for granular materials, with application to plane shearing. J Fluid Mech 1987;176:67. https://doi.org/10.1017/s0022112087000570.Search in Google Scholar

55. Anantharaman, A, Cocco, RA, Chew, JW. Evaluation of correlations for minimum fluidization velocity (U) in gas-solid fluidization. Powder Technol 2018;323:454–85. https://doi.org/10.1016/j.powtec.2017.10.016.Search in Google Scholar

56. Chang, J, Wang, G, Gao, J, Zhang, K, Chen, H, Yang, Y. CFD modeling of particle–particle heat transfer in dense gas-solid fluidized beds of binary mixture. Powder Technol 2012;217:50–60. https://doi.org/10.1016/j.powtec.2011.10.008.Search in Google Scholar

57. Cui, R, Sun, Z, Deng, Z, Zhu, J. A numerical study of fluidization characteristics in transition from aggregative to particulate flow. Powder Technol 2024;432:119108. https://doi.org/10.1016/j.powtec.2023.119108.Search in Google Scholar

58. Askarishahi, M, Salehi, MS, Godini, HR, Wozny, G. CFD study on solids flow pattern and solids mixing characteristics in bubbling fluidized bed: effect of fluidization velocity and bed aspect ratio. Powder Technol 2015;274:379–92. https://doi.org/10.1016/j.powtec.2015.01.025.Search in Google Scholar

59. Chang, J, Yang, S, Zhang, K. A particle-to-particle heat transfer model for dense gas–solid fluidized bed of binary mixture. Chem Eng Res Des 2011;89:894–903. https://doi.org/10.1016/j.cherd.2010.08.004.Search in Google Scholar

60. Kunii, D, Levenspiel, O, Brenner, H. Fluidization engineering, 2nd ed. Boston: Butterworth-Heinemann; 1991.Search in Google Scholar

61. Lungu, M, Sun, J, Wang, J, Zhu, Z, Yang, Y. Computational fluid dynamics simulations of interphase heat transfer in a bubbling fluidized bed. Kor J Chem Eng 2014;31:1148–61. https://doi.org/10.1007/s11814-014-0022-6.Search in Google Scholar


Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/cppm-2025-0269).


Received: 2025-10-25
Accepted: 2025-12-12
Published Online: 2026-01-01

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