Determination of Pressure Drop in Positive Dilute Phase Pneumatic Teff Grain Conveyor Using Experimental CFD-DPM Simulation and ANN Modeling Approach

Authors

  • Lemi Demissie Boset School of Mechanical, Chemical and Material Engineering, Adama Science and Technology University, Adama, Oromia, 0000, Ethiopia
  • Zewdu Abdi Debele Faculty of Agriculture, University of Eswatini, Luyengo, Eswatini, M205, Eswatini
  • Amana Wako Koroso School of Mechanical, Chemical and Material Engineering, Adama Science and Technology University, Adama, Oromia, 0000, Ethiopia

DOI:

https://doi.org/10.13052/ijfp1439-9776.2631

Keywords:

Pressure drop, pneumatic conveyor, CFD-DPM simulation, ANN model, pressure drop coefficient, Teff grain

Abstract

Pressure drop in pneumatic conveying systems significantly influences both system performance and energy efficiency. This study combines laboratory experiments with computational fluid dynamics (CFD) simulations to create a predictive model for the pressure drop coefficient using artificial neural networks (ANN), focusing on key components such as feeders, horizontal and vertical pipes, bends, and cyclone separators. Laboratory experiments yield crucial empirical data that validate the CFD simulations, which provide in-depth analyses. The findings reveal a strong correlation between the calculated pressure drop coefficient (K) and the square of the inlet air velocity, expressed as K α v2. The regression coefficients (R2) for various components are as follows: feeder (R2 = 0.982), horizontal pipe (R2 = 0.989), vertical pipe (R2 = 0.991), bends (R2 = 0.972), and cyclone separator (R2 = 0.942). These results indicate that the model can lead to more efficient designs that accurately reflect real-world conditions, thereby enhancing the overall performance of pneumatic grain conveying systems.

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Author Biographies

Lemi Demissie Boset, School of Mechanical, Chemical and Material Engineering, Adama Science and Technology University, Adama, Oromia, 0000, Ethiopia

Lemi Demissie Boset, PhD Candidate, is currently pursuing his PhD in Mechanical Engineering at Adama Science and Technology University and serves as a lecturer at Dilla University. He earned his BSc degree in Mechanical Engineering from Wollo University in 2017 and his MSc in Mechanical Design from Addis Ababa University in 2018. His dissertation focuses on Investigation of Teff Grain Characteristics in Positive Dilute Phase Pneumatic Conveyors Using Experimental CFD-DPM Simulation and ANN Modeling Approach. His research interests include agricultural machinery design optimization, Discrete Element Modeling (DEM), Computational Fluid Dynamics (CFD), and machine learning.

Zewdu Abdi Debele, Faculty of Agriculture, University of Eswatini, Luyengo, Eswatini, M205, Eswatini

Zewdu Abdi Debele, PhD, is currently serving as a faculty member in the Faculty of Agriculture at the University of Eswatini. His academic journey includes positions at Addis Ababa Institute of Technology and Addis Ababa University in Ethiopia, where he was engaged in teaching and research from 2014 to 2016. He earned his PhD from Technische Universität Dresden in Germany, where he also gained valuable international research experience. His work focuses on the moisture-dependent physical properties of seeds, with notable publications in journals such as Engineering in Agriculture, Environment and Food and Biosystems Engineering.

Amana Wako Koroso, School of Mechanical, Chemical and Material Engineering, Adama Science and Technology University, Adama, Oromia, 0000, Ethiopia

Amana Wako Koroso, PhD, is a faculty member at Adama Science and Technology University, specializing in Agricultural Machinery Engineering. He earned both his BSc and MSc degrees from Adama Science and Technology University and completed his PhD at Seoul National University, South Korea, in 2016. His doctoral dissertation was titled “Farm Mechanization of Small Farms in Ethiopia: A Case of Cereal Crops in Hetosa District.” His research interests include agricultural mechanization and biosystems engineering.

References

G. Abebaw, 2020. The Study of Some Particle Size Distribution of Teff [Eragrostis teff (Zucc.) Trotter] Grain Cultivars and Its Flour, J. Food Nutr. Sci., 8, pp. 108–201.

B. Minten, E. Engida, and S. Tamru, 2016. How big are post-harvest losses in Ethiopia? Evidence from teff, Int. J. Mech. Eng. Appl., 7, p. 89–101.

T. Gonite, 2019. Design and Prototyping of Teff () Row Planter and Fertilizer Applier, Int. J. Mech. Eng. Appl., 6, p. 91–1015.

T. K. Amentae, E. G. Tura, G. Gebresenbet, and D. Ljungberg, 2016. Exploring value chain and post-harvest losses of Teff in Bacho and Dawo districts of central Ethiopia, J. Stored Prod. Postharvest Res., 7, pp. 11–28.

A. Awgichew, 2019. Aerodynamic Properties of Tef for Separation from Chaff, Civ. Environ. Res., 11, pp. 39–43.

S. Tadić, M. Krstić, M. Božić, S. Dabić-Miletić, and S. Zeèević, 2024. Ranking of Technologies for Intralogistic Bulk Material Handling Processes Using Fuzzy Step-Wise Weight Assessment Ratio Analysis and Axial-Distance-Based Aggregated Measurement Methods, Appl. Sci., 14, pp. 143–167.

L. Stone, D. Hastie, and S. Zigan, 2019. Using a coupled CFD – DPM approach to predict particle settling in a horizontal air stream, Adv. Powder Technol., 30, pp. 869–878.

O. J. I. Kramer et al., 2020. Improvement of voidage prediction in liquid-solid fluidized beds by inclusion of the Froude number in effective drag relations, Int. J. Multiph. Flow, 127, pp. 103–261.

B. Zhao and Y. Su, 2009. Artificial neural network-based modeling of pressure drop coefficient for cyclone separators, Chem. Eng. Res. Des., 88, pp. 606–613.

S. Kuang, M. Zhou, and A. Yu, 2019. CFD-DEM modelling and simulation of pneumatic conveying: A review Powder Technol., 68, pp. 2–23.

S. K. Senapati and S. K. Dash, 2021. Computation of pressure drop for dilute gas–solid suspension across thin and thick orifices, Particuology, 55, pp. 209–221.

S. Kuang, K. Li, and A. Yu, 2020. CFD-DEM Simulation of Large-Scale Dilute-Phase Pneumatic Conveying System, Ind. Eng. Chem. Res., 59, pp. 4150–4160.

S. F. Moujaes and S. Deshmukh, Three-Dimensional CFD Predications and Experimental Comparison of Pressure Drop of Some Common Pipe Fittings in Turbulent Flow, 83, pp. 29–68.

I. Khazaee, 2017. Numerical investigation of the effect of number and shape of inlet of cyclone and particle size on particle separation, Heat Mass Transf. und Stoffuebertragung, 53, pp. 2009–2016.

F. Kaya and I. Karagoz, 2019. Experimental and numerical investigation of pressure drop coefficient and static pressure difference in a tangential inlet cyclone separator, Chem. Pap., 66, pp. 1019–1025.

X. Wu, J. Liu, X. Xu, and Y. Xiao, 2023. Modeling and experimental validation on pressure drop in a reverse-flow cyclone separator at high inlet solid loading, J. Therm. Sci., 20, pp. 343–348.

S. Demir, 2014. A practical model for estimating pressure drop in cyclone separators: An experimental study, Powder Technol., 268, pp. 329–338.

M. E. Orakoglu Firat and O. Atila, 2022. Investigation of the thermal conductivity of soil subjected to freeze–thaw cycles using the artificial neural network model, J. Therm. Anal. Calorim., 147, pp. 8077–8093.

M. Demetgul, I. N. Tansel, and S. Taskin, 2009. Fault diagnosis of pneumatic systems with artificial neural network algorithms, Expert Syst. Appl., 36, pp. 10512–10519.

A. Haghighi, M. S. Shadloo, A. Maleki, and M. Y. Abdollahzadeh Jamalabadi, 2022. Using committee neural network for prediction of pressure drop in two-phase microchannels, Appl. Sci., 10, pp. 15–28.

B. G. Asefa, F. Tsige, M. Mehdi, T. Kore, and A. Lakew, 2023. Rapid classification of tef [Eragrostis tef (Zucc.) Trotter] grain varieties using digital images in combination with multivariate technique, Smart Agric. Technol., vol. 3, pp. 56–83.

B. Zhao, 2009. Modeling pressure drop coefficient for cyclone separators: A support vector machine approach, Chem. Eng. Sci., 64, pp. 4131–4136.

S. Demir, 2014. A practical model for estimating pressure drop in cyclone separators: An experimental study, Powder Technol., 268, pp. 329–338.

B. Zhao and Y. Su, 2009. Chemical Engineering Research and Design Artificial neural network-based modeling of pressure drop coefficient for cyclone separators, Chem. Eng. Res. Des, 88, pp. 606–613.

N. Behera, V. K. Agarwal, M. Jones, and K. C. Williams, 2013. Modeling and analysis of solids friction factor for fluidized dense phase pneumatic conveying of powders, Part. Sci. Technol., 31, pp. 136–146.

K. Sharma, S. S. Mallick, A. Mittal, and P. Wypych, 2023. Modelling solids friction for fluidized dense-phase pneumatic conveying, Part. Sci. Technol., 38, pp. 391–403,.

W. B. Faulkner, B. W. Shaw, an W. B. Faulkner, 2022. Efficiency And Pressure Drop Of Cyclones Across A Range Of Inlet Velocities, Powder Technology, 22, pp. 342–352.

L. Cao, Q. Zhang, and R. Meng, 2022. CFD–DPM Simulation Study of the Effect of Powder Layer Thickness on the SLM Spatter Behavior, Metals (Basel)., 12, pp. 11–28.

F. Huang et al., 2021. Role of CFD based in silico modelling in establishing an in vitro-in vivo correlation of aerosol deposition in the respiratory tract.

C. Wang, W. Li, B. Li, Z. Jia, S. Jiao, and H. Ma, 2023. Study on the Influence of Different Factors on Pneumatic Conveying in Horizontal Pipe, Appl. Sci., 13, pp. 9–23.

P. Dutta, S. K. Saha, N. Nandi, and N. Pal, 2016. Numerical study on flow separation in 90∘

pipe bend under high Reynolds number by k-ε

modelling, Eng. Sci. Technol. an Int. J, 19, pp. 904–910.

J. Singh, H. S. Gill, and H. Vasudev, 2023. Computational fluid dynamics analysis on role of particulate shape and size in erosion of pipe bends, Int. J. Interact. Des. Manuf., 17, pp. 2631–2646.

H. Y. Chiu, H. S. Chao, and Y. M. Chen, 2022. Application of Artificial Intelligence in Lung Cancer. Powder Technology, 511, pp. 204–257.

J. S. Shijo and N. Behera, 2021. Performance prediction of pneumatic conveying of powders using artificial neural network method, Powder Technology, 388, pp. 149–157.

S. Demir, A. Karadeniz, and N. Demir, 2016. Artificial neural network simulation of cyclone pressure drop: Selection of the best activation function in Iraq, Polish J. Environ. Study, 25, pp. 1891–1899.

K. Elsayed and C. Lacor, 2012. Modeling and Pareto optimization of gas cyclone separator performance using RBF type artificial neural networks and genetic algorithms, Powder Technolology, 217, pp. 84–99.

W. Abebe and F. Ronda, 2 015. Flowability, moisture sorption and thermal properties of tef [Eragrostis tef (Zucc.) Trotter] grain flours, J. Cereal Sci. 63, pp. 14–20.

C. Ratnayake, B. K. Datta, and M. C. Melaaen, 2007. A unified scaling-up technique for pneumatic conveying systems, Part. Sci. Technol., 25, pp. 289–302.

B. K. Datta and C. Ratnayaka, 2003. A simple technique for scaling up pneumatic conveying systems, Appl. Sci., 13, pp. 109–230.

E. Fedorova, E. Pupysheva, and V. Morgunov, 2022. Modelling of Red-Mud Particle-Solid Distribution in the Feeder Cup of a Thickener Using the Combined CFD-DPM Approach, Symmetry (Basel)., 14, pp. 141–209.

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Published

2025-12-03

How to Cite

Boset, L. D. ., Debele, Z. A. ., & Koroso, A. W. . (2025). Determination of Pressure Drop in Positive Dilute Phase Pneumatic Teff Grain Conveyor Using Experimental CFD-DPM Simulation and ANN Modeling Approach. International Journal of Fluid Power, 26(03), 343–380. https://doi.org/10.13052/ijfp1439-9776.2631

Issue

2025: Vol 26 Iss 3

Section

Original Article