Prediction of the Local Particle Velocity in the Spout Region of Cylindrical Spouted Beds Using an Artificial Neural Network

Document Type : Research Article

Authors

Department of Chemical Engineering, Ilam University, Ilam, 69315-516, Iran.

Abstract

The local particle velocity is an important hydrodynamic parameter in the design and optimization of gas-solid systems, such as the spouted beds. Predicting the particle velocity in the spout region is often complex, and developing a simple and reliable method for estimating this parameter is both valuable and highly beneficial for engineers and system designers. In the present study, the intelligent predictive approach of a multi-layer perceptron neural network (MLP-NN) is used for the first time to investigate the feasibility of measuring the local particle velocity within the spout region of spouted beds. Accordingly, 224 measured data points are collected, covering a broad range of factors such as bed diameter, inlet diameter, static bed height, cone angle, minimum spouting velocity, and inlet gas velocity for Geldart D particles with varying densities and diameters. The data are then used for training, validating, and testing the intelligence-based model. The MLP-NN-based model shows a strong predictive capability for estimating particle velocity in the spouted bed, with an Average Absolute Relative Error (AARE) of 13.32%, an R2 value of 0.9842, and a Root Mean Squared Error (RMSE) of 0.033 for the test dataset. In addition, for all data, the model achieves an AARE of 6.30%, an RMSE of 0.0098, and an R2 value of 0.9942. Furthermore, in the present study, a sensitivity analysis is conducted on the data to determine the degree of influence of the input parameters on the target function.

Keywords

References

[1] Ranade, V. V., 2002. Computational flow modeling for chemical reactor engineering, Published by Academic Press, California, USA.
[2] Olazar, M., San José, M., Zabala, G., Bilbao, J., 1994. New reactor in jet spouted bed regime for catalytic polymerizations, Chemical Engineering Science, 49, 4579-4588.
[3] Lopes, N.E.C., Moris, V.A.S., Taranto, O.P., 2009. Analysis of spouted bed pressure fluctuations during particle coating, Chemical Engineering and Processing: Process Intensification, 48, 1129–1134. https://doi.org/10.1016/j.cep.2009.03.003
[4] Jono, K., Ichikawa, H., Miyamoto, M., Fukumori, Y., 2000. A review of particulate design for pharmaceutical powders and their production by spouted bed coating, Powder Technology, 113, 269–277.
[5] Alvarez, J., Hooshdaran, B., Cortazar, M., Amutio, M., Lopez, G., Freire, F.B., Haghshenasfard, M., Hosseini, S.H., Olazar, M., 2018. Valorization of citrus wastes by fast pyrolysis in a conical spouted bed reactor, Fuel 224, 111–120. https://doi.org/10.1016/j.fuel.2018.03.028
[6] Hosseini, S.H., Ahmadi, G., Olazar, M., 2014. CFD study of particle velocity profiles inside a draft tube in a cylindrical spouted bed with conical base, Journal of the Taiwan Institute of Chemical Engineers, 45, 2140–2149. https://doi.org/10.1016/J.JTICE.2014.05.027
[7] Hosseini, S.H., Zivdar, M., Rahimi, R., 2009. CFD simulation of gas – solid flow in a spouted bed with a non-porous draft tube, Chemical Engineering and Processing: Process Intensification, 48, 1539–1548. https://doi.org/10.1016/j.cep.2009.09.004
[8] Hossein, S.H., Fattahi, M., Ahmadi, G., 2016. CFD Study of hydrodynamic and heat transfer in a 2D spouted bed: Assessment of radial distribution function, Journal of the Taiwan Institute of Chemical Engineers, 58, 107–116. https://doi.org/10.1016/j.jtice.2015.06.027
[9] Mahmoodi, B., Hossein, S.H., Olazar, M., Altzibar, H., 2017. CFD-DEM simulation of a conical spouted bed with open-sided draft tube containing fine particles, Journal of the Taiwan Institute of Chemical Engineers, 81, 275–287. https://doi.org/10.1016/j.jtice.2017.09.051
[10] Hosseini, S.H., Rezaei, M.J., Bag-Mohammadi, M., Karami, M., Moradkhani, M.A., Panahi, M., Olazar, M., 2019. Estimation of the minimum spouting velocity in shallow spouted beds by intelligent approaches: Study of fine and coarse particles, Powder Technology 354, 456–465. https://doi.org/10.1016/j.powtec.2019.06.025
[11] Moradkhani, M.A., Hosseini, S.H., Olazar, M., Altzibar, H., Valizadeh, M., 2021. Estimation of the minimum spouting velocity and pressure drop in open-sided draft tube spouted beds using genetic programming, Powder Technology, 387, 363–372. https://doi.org/10.1016/j.powtec.2021.04.049
[12] Saldarriaga, J.F., Atxutegi, A., Aguado, R., Altzibar, H., Bilbao, J., Olazar, M., 2017. Correlations for calculating peak and spouting pressure drops in conical spouted beds of biomass, Journal of the Taiwan Institute of Chemical Engineers, 80, 678–685. https://doi.org/10.1016/j.jtice.2017.09.001
[13] Estiati, I., Atxutegi, A., Altzibar, H., Freire, F.B., Aguado, R., Olazar, M., 2021. Multiple-Output Artificial Neural Network to Estimate Solid Cycle Times in Conical Spouted Beds, Chemical Engineering & Technology, 44, 542–550. https://doi.org/10.1002/ceat.202000491
[14] Estiati, I., Tellabide, M., Saldarriaga, J.F., Altzibar, H., Freire, F.B., Freire, J.T., Olazar, M., 2019. Comparison of artificial neural networks with empirical correlations for estimating the average cycle time in conical spouted beds, Particuology, 42, 48–57. https://doi.org/10.1016/j.partic.2018.03.010
[15] Makibar, J., Fernandez-akarregi, A.R., Alava, I., Cueva, F., Lopez, G., Olazar, M., 2011. Investigations on heat transfer and hydrodynamics under pyrolysis conditions of a pilot-plant draft tube conical spouted bed reactor, Chemical Engineering and Processing: Process Intensification, 50, 790–798. https://doi.org/10.1016/j.cep.2011.05.013
[16] Sukunza, X., Aguado, R., Pablos, A., Tellabide, M., Estiati, I., Olazar, M., 2022. Mass transfer in conical spouted beds equipped with internal devices, Powder Technology, 410, 117850. https://doi.org/10.1016/j.powtec.2022.117850
[17] Altzibar, H., Lopez, G., Bilbao, J., Olazar, M., 2013. Minimum spouting velocity of conical spouted beds equipped with draft tubes of different configuration, Industrial and Engineering Chemistry Research, 52, 2995–3006. https://doi.org/10.1021/ie302407f
[18] Moradkhani, M.A., Hosseini, S.H., Karami, M., Olazar, M., Saldarriaga, J.F., 2023. Applying conventional and intelligent approaches to model the minimum spouting velocity of vegetable biomasses in conical spouted beds, Powder Technology, 418, 118300. https://doi.org/10.1016/j.powtec.2023.118300
[19] Saldarriaga, J.F., Cruz, Y., Estiati, I., Tellabide, M., Olazar, M., 2022. Assessment of pressure drop in conical spouted beds of biomass by artificial neural networks and comparison with empirical correlations, Particuology, 70, 1–9. https://doi.org/10.1016/j.partic.2021.12.004
[20] Karimi, M., Vaferi, B., Hosseini, S.H., Olazar, M., Rashidi, S., 2021. Smart computing approach for design and scale-up of conical spouted beds with open-sided draft tubes, Particuology, 55, 179–190. https://doi.org/10.1016/j.partic.2020.09.003
[21] Du, K.L., Swamy, M.N.S., 2006. Neural Networks in a Soft computing Framework, Published by Springer, London.
[22] Olazar, M., San José, M.J., Izquierdo, M.A., De Salazar, A.O., Bilbao, J., 2001. Effect of operating conditions on solid velocity in the spout, annulus and fountain of spouted beds, Chemical Engineering Science, 56, 3585–3594.
[23] Aradhya, S., Taofeeq, H., Al-dahhan, M., 2017. International Journal of Multiphase Flow Evaluation of the dimensionless groups based scale-up of gas – solid spouted beds, 94, 209–218. https://doi.org/10.1016/j.ijmultiphaseflow.2017.04.006
[24] Aradhya, S., Taofeeq, H., Al-dahhan, M., 2016. Chemical Engineering and Processing: Process Intensification, A new mechanistic scale-up methodology for gas-solid spouted beds, Chemical Engineering and Processing: Process Intensification, 110, 146–159. https://doi.org/10.1016/j.cep.2016.10.005
[25] Pianarosa, D.L., Freitas, L.A.P., Lim, C.J., Grace, J.R., Dogan, M., 2000. Voidage and Particle Velocity Profiles in a Spout-Fluid Bed, The Canadian Journal of Chemical Engineering, 78, 132–142.
Chemical Process Design
Volume 5, Issue 2
December 2026
Pages 18-26

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