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In relation to this article, we declare that there is no conflict of interest.
Publication history
Received January 16, 2023
Revised June 3, 2023
Accepted July 10, 2023
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Study of the influence of operational parameters on biomass conversion in a pyrolysis reactor via CFD

1Process Engineering, UFCG, Aprigio Veloso, 882, Campina Grande, 58429900, PB, Brazil 2Mechanical Engineering, UFCG, Aprigio Veloso, 882, Campina Grande, 58429900, PB, Brazil
alysson.dantas@eq.ufcg.edu.br
Korean Journal of Chemical Engineering, December 2023, 40(12), 2787-2799(13), 10.1007/s11814-023-1528-6
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Abstract

Pyrolysis has been one of the technologies used to convert biomass into biofuels. Therefore, mathematical models that can represent its phenomena are of fundamental importance in understanding the reaction progression and optimizing the process. In this sense, this study compared the results obtained from the lumped-capacitance thermal model proposed in this work with the thermal discretization model that considers thermal conductivity as a function of temperature. Then, the effect of operational parameters such as temperature, gas velocity, and biomass particle diameter, was compared on the reaction conversion rate. To describe the behavior and interaction between the phases, we utilized an Eulerian-Lagrangian CFD modeling approach, solving the continuity, momentum, energy, species, and turbulence equations using OpenFOAM. A factorial design of the type 2k was used to manipulate the model’s input parameters, with biomass conversion as the response variable. The numerical results of biomass conversion from the lumped-capacitance model showed good agreement with the data reported in the literature for the discretized model. However, we observed a difference of 9.13% in the particle mass behavior and 7.63% in the particle residence time. The design of experiments (DoE) enabled us to determine the impact of individual parameters and their interactions on the pyrolysis conversion rate with temperature identified as the most sensitive parameter. Therefore, despite the observed errors when comparing the two models, the lumped-capacitance model accurately represented the reaction yields and proved to be suitable for simulations involving a large number of particles, facilitating optimization studies.

References

1. J. Clissold, S. Jalalifar, F. Salehi, R. Abbassi and M. Ghodrat, Fuel, 273, 117791 (2020).
2. S. Hameed, A. Sharma, V. Pareek, H. Wu and Y. Yu, Biomass Bioenerg., 123, 104 (2019).
3. S. Vikram, P. Rosha and S. Kumar, Energy Fuels, 35(9), 7406 (2021).
4. S. Sobek and S. Werle, Renew. Energy, 143, 1939 (2019).
5. A. Sharma, V. Pareek and D. Zhang Renew. Sust. Energ. Rev., 50, 1081 (2015).
6. S. Yang, Z. Wan, S. Wang and H. Wang, J. Environ. Chem. Eng.,9(2), 105047 (2021).
7. C. R. Duarte, I. P. Junior and D. A. dos Santos, in Horizonte Científico (2015).
8. B. Hooshdaran, M. Haghshenasfard, S. H. Hosseini, M. N. Esfahany, G. Lopez and M. Olazar, J. Anal. Appl. Pyrol., 154, 105011 (2021).
9. K. Papadikis, H. Gerhauser, A. Bridgwater and S. Gu, Biomass Bioenerg., 33(1), 97 (2009).
10. K. Papadikis, S. Gu and A. Bridgwater, Chem. Eng. J., 149(1), 417 (2009).
11. K. Papadikis, S. Gu, A. Bridgwater and H. Gerhauser, Fuel Process. Technol., 90(4), 504 (2009).
12. K. Papadikis, S. Gu and A. Bridgwater, Fuel Process. Technol., 91(7), 749 (2010).
13. K. Papadikis, S. Gu and A. Bridgwater, Fuel Process. Technol., 91(1), 68 (2010).
14. C. Hu, K. Luo, S. Wang, L. Sun and J. Fan, Ind. Eng. Chem. Res., 58(3), 1404 (2019).
15. H. C. Park and H. S. Choi, Renew. Energy, 143, 1268 (2019).
16. J. Smagorinsky, Monthly Weather Rev., 91(3), 99 (1963).
17. S. Ergun and A. A. Orning, Ind. Eng. Chem., 41(6), 1179 (1949).
18. S. Yang, Z. Wan, S. Wang and H. Wang, J. Environ. Chem. Eng., 9(2), 105047 (2021).
19. M. Bashir, X. Yu, M. Hassan and Y. Makkawi, ACS Sustainable Chem. Eng., 5(5), 3795 (2017).
20. J. E. Lee, H. C. Park and H. S. Choi, ACS Sustainable Chem. Eng., 5(3), 2196 (2017).
21. S. Maduskar, G. G. Facas, C. Papageorgiou, C. L. Williams and P. J. Dauenhauer, ACS Sustainable Chem. Eng., 6(1), 1387 (2018).
22. J. Clissold, S. Jalalifar, F. Salehi, R. Abbassi and M. Ghodrat, Fuel, 273, 117791 (2020).
23. S. Yang, R. Dong, Y. Du, S. Wang and H. Wang, Energy, 214, 118839 (2021).
24. W.-C. R. Chan, M. Kelbon and B. B. Krieger, Fuel, 64(11), 1505 (1985).
25. T. Li, P. Gopalakrishnan, R. Garg and M. Shahnam, Particuology, 10(5), 532 (2012).
26. H. K. Versteeg and W. Malalasekera, An introduction to computational fluid dynamics, Harlow: Pearson Prentice Hall (2007). 27. C. Hu, K. Luo, S. Wang, L. Sun and J. Fan, Ind, Eng. Chem. Res.,
58(3), 1404 (2019).
28. Z. Peng, E. Doroodchi, C. Luo and B. Moghtaderi, AIChE J., 60(6), 2000 (2014).
29. D. Clarke, A. Sederman, L. Gladden and D. Holland, Ind. Eng. Chem. Res., 57(8), 3002 (2018).
30. C. Boyce, D. Holland, J. Dennis and S. Scott, Ind. Eng. Chem. Res., 54(43), 10684 (2015).
31. Q. Xiong, S.-C. Kong and A. Passalacqua, Chem. Eng. Sci., 99, 305 (2013).
32. H.R. Norouzi, R. Zarghami, R. SotudehGharebagh and N. Mostoufi

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