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Thermodynamic and intelligent modeling of a fluidized bed dryer for eggplant drying: A combined energy–exergy–artificial neural network Study | ||
| Biosystems Engineering and Renewable Energies | ||
| دوره 1، شماره 2، آذر 2025، صفحه 141-149 اصل مقاله (1.4 M) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22069/bere.2025.24182.1034 | ||
| نویسندگان | ||
| Mohammad Vahedi Torshizi* 1؛ Hajar Aghili2 | ||
| 1Biosystems Engineering Department, Tarbiat Modares University, Tehran, Iran | ||
| 2Department of Biosystems Engineering, Isfahan University of Technology, Isfahan, Iran | ||
| چکیده | ||
| The influences of the air temperature (40, 50, and 60 °C), air velocity (3, 5, and 7 m/s), sample size (0.5, 1.0 and 1.3 cm) on thermodynamic performance of a fluidized-bed dryer were studied in the drying of eggplant cubes (Solanum melongena L.). New samples of the eggplant were sliced into cubes and dried in the controlled laboratory conditions. The first and the second law of thermodynamics were used in calculating energy utilization, energy utilization ratio, exergy loss, and exergy efficiency. Findings showed that the utilization of energy and exergy loss rose with the higher drying temperatures and air velocities and reduced with the increased sample size. The results showed the highest exergy efficiency (0.72) at 60 °C, 5 m/s and a sample size of 1.3 cm, while the lowest efficiency (0.017) at 40 °C, 3 m/s and 0.5 cm sample size. The highest energy utilization (3.64 kJ/s) was achieved at 60 °C, 7 m/s, and 0.5 cm, while the lowest (1.08 kJ/s) was observed at 40 °C, 3 m/s, and 1.3 cm. An artificial neural network (ANN) model was created to forecast the energy and exergy variables and the trained model showed good consistency with the experimental data (R2 > 0.99), which supports its effectiveness in prediction of thermodynamics. Finally, higher air temperature and velocity increased the drying performance but decreased the exergy efficiency, especially with smaller samples. ANN modelling provides a significant instrument of maximizing energy and exergy behavior in fluidized-bed dryers utilized in the food industry. | ||
| کلیدواژهها | ||
| Artificial neural network؛ Eggplants؛ Energy؛ Exergy loss؛ Fluidized-bed dryer | ||
| مراجع | ||
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Aghbashlo, M., Kianmehr, M. H., & Arabhosseini, A. (2008). Energy and exergy analyses of thin-layer drying of potato slices in a semi-industrial continuous band dryer. Drying Technology, 26(12), 1501–1508. Akbulut, A., & Durmuş, A. 2010. Energy and exergy analyses of thin layer drying of mulberry in a forced solar dryer. Energy, 35(4), 1754–1763. Akpinar, E. (2004). Energy and exergy analyses of drying of red pepper slices in convective type dryer. International Communications in Heat and Mass Transfer, 31(8), 1165–1176. Akpinar, E. K. (2007). Thermodynamic analysis of strawberry drying process in a cyclone type dryer. Journal of Scientific & Industrial Research, 66(2), 152–161. Aviara, N. A., Onuoha, L. N., Falola, O. E., & Igbeka, J. C. (2014). Energy and exergy analyses of native cassava starch drying in a tray dryer. Energy, 73, 809–817. Azadbakht, M., Aghili, H., Ziaratban, A., & Torshizi, M. V. (2017). Application of artificial neural network method to exergy and energy analyses of fluidized bed dryer for potato cubes. Energy, 120, 947–958. Azadbakht, M., Mahmoodi, M. J., Ghazagh Jahed, R., & Vahedi Torshizi, M. (2022). Mathematical modeling of the biochemical properties of carrots by microwave drying with different pretreatments using response surface methodology. Food Engineering Research, 21(1), 35–56. Azadbakht, M., Torshizi, M. V., Aghili, H., & Ziaratban, A. (2017). Thermodynamic analysis of drying potato cubes in a fluidized bed dryer. Carpathian Journal of Food Science & Technology, 9(4). Azadbakht, M., Torshizi, M. V., Noshad, F., & Rokhbin, A. (2018). Application of artificial neural network method for prediction of osmotic pretreatment based on the energy and exergy analyses in microwave drying of orange slices. Energy, 165, 836–845. Azadbakht, M., Vahedi Torshizi, M., & Jafari, H. (2020). Investigation of drying process of different Iranian rice cultivars by Ohmic pre-treatment in microwave dryer and modeling by response surface methodology and artificial neural network. Innovative Food Technologies, 7(4), 497–515. Azadbakht, M., Vahedi Torshizi, M., Noshad, F., & Rokhbin, A. (2020). Energy and exergy analyses in microwave drying of orange slices. Iranian Food Science and Technology Research Journal, 16(3), 1–13. Azadbakht, M., Vehedi Torshizi, M., Aghili, H., & Ziaratban, A. (2018). Application of artificial neural network (ANN) in drying kinetics analysis for potato cubes. Carpathian Journal of Food Science and Technology, 10(2), 96–106. Azadbakht, M., Ziaratban, A., Torshizi, M. V., & Aghili, H. (2017). Energy and exergy analyses during eggplant drying in a fluidized bed dryer. Agricultural Engineering International: CIGR Journal, 19(3), 177–182. Bennamoun, L., & Belhamri, A. (2003). Design and simulation of a solar dryer for agriculture products. Journal of Food Engineering, 59(2–3), 259–266. Ceylan, I. (2009). Energy analysis of PID controlled heat pump dryer. Engineering, 1(3), 188. Colak, N., & Hepbasli, A. (2007). Performance analysis of drying of green olive in a tray dryer. Journal of Food Engineering, 80(4), 1188–1193. Corzo, O., Bracho, N., Vásquez, A., & Pereira, A. (2008). Energy and exergy analyses of thin layer drying of coroba slices. Journal of Food Engineering, 86(2), 151–161. Erbay, Z., & Icier, F. (2011). Energy and exergy analyses of drying olive leaves (Olea europaea L.) in a tray drier. Journal of Food Process Engineering, 34(6), 2105–2123. İçier, F., Çolak, N., Erbay, Z., Hancıoğlu, E., & Hepbaslı, A. (2008). A comparative study on exergetic efficiencies of two different drying processes. Tarım Makinaları Bilimi Dergisi, 4(3), 279–284. Inaba, H., Husain, S., Horibe, A., & Haruki, N. (2007). Heat and mass transfer analysis of fluidized bed grain drying. Memoirs of the Faculty of Engineering, Okayama University, 41(1), 52–62. Kaminski, W., & Tomczak, E. (1999). An integrated neural model for drying and thermal degradation of selected products. Drying Technology, 17(7–8), 1291–1301. Kariman, M., Tabarsa, F., Zamani, S., Kashi, P. A., & Torshizi, M. V. (2019). Classification of the energy and exergy of microwave dryers in drying kiwi using artificial neural Networks. Carpathian Journal of Food Science & Technology, 11(2). Kashaninejad, M., Dehghani, A. A., & Kashiri, M. (2009). Modeling of wheat soaking using two artificial neural networks (MLP and RBF). Journal of Food Engineering, 91(4), 602–607. Martynenko, A., & Zheng, W. (2016). Electrohydrodynamic drying of apple slices: Energy and quality aspects. Journal of Food Engineering, 168, 215–222. Midilli, A., & Kucuk, H. (2003). Energy and exergy analyses of solar drying process of pistachio. Energy, 28(6), 539–556. Minaei, S., Chenarbon, H. A., Motevalia, A., & Hosseini, A. A. (2014). Energy consumption, thermal utilization efficiency and hypericin content in drying leaves of St John’s Wort (Hypericum perforatum). Journal of Energy in Southern Africa, 25(3), 21–27. Nazghelichi, T., Aghbashlo, M., Kianmehr, M. H., & Omid, M. (2011). Prediction of energy and exergy of carrot cubes in a fluidized bed dryer by artificial neural networks. Drying Technology, 29(3), 295–307. Nazghelichi, T., Kianmehr, M. H., & Aghbashlo, M. (2010). Thermodynamic analysis of fluidized bed drying of carrot cubes. Energy, 35(12), 4679–4684. Ramezani, A., Azadbakht, M., Roghaei.arabkhazaeli, Zamani, S., & Torshizi, M. V. (2020). Pre-Treatment (Ohmic and Oven) Effect on Thermodynamic Parameters of Kiwi Drying in Microwave Dryer. In Carpathian Journal of Food Science and Technology, 12, 1–21. Ranjbaran, M., & Zare, D. (2013). Simulation of energetic-and exergetic performance of microwave-assisted fluidized bed drying of soybeans. Energy, 59, 484–493. Saidur, R., BoroumandJazi, G., Mekhlif, S., & Jameel, M. (2012). Exergy analysis of solar energy applications. Renewable and Sustainable Energy Reviews, 16(1), 350–356. Sarker, M. S. H., Ibrahim, M. N., Abdul Aziz, N., & Punan, M. S. (2015). Energy and exergy analysis of industrial fluidized bed drying of paddy. Energy, 84, 131–138. Sihachakr, D., Chaput, M. H., Serraf, I., & Ducreux, G. (1993). Regeneration of plants from protoplasts of eggplant (Solanum melongena L.). In Plant Protoplasts and Genetic Engineering IV (pp. 108–122). Springer. Vahedi Torshizi, M., Azadbakht, M., & Kashaninejad, M. (2024). Analyzing greenhouse gas emissions in the ohmic heating drying process of sour orange juice. Agricultural Engineering International: CIGR Journal, 26(3). Vahedi Torshizi, M., Azadbakht, M., & Kashaninejad, M. (2020). Application of response surface method to energy and exergy analyses of the ohmic heating dryer for sour orange juice. Fuel, 278, 118261. Vahedi Torshizi, M., Rokhbin, A., & Ziaratban, A. (2025). Energy and exergy analysis of apple drying using fluidized bed method: A comparison of GMDH neural network and ANN-GA models. Biosystems Engineering and Renewable Energies, 1(1), 1–10. Vahedi Torshizi, M., Rokhbin, A., & Mahmoodi, M. J. (2025). The effect of different pre-treatments on the rate of changes in the thermal properties of rosemary leaves. Biosystems Engineering and Renewable Energies, 1(1), 29-36. | ||
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