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Vahedi Torshizi, Mohammad, Aghili, Hajar. (1404). Thermodynamic and intelligent modeling of a fluidized bed dryer for eggplant drying: A combined energy–exergy–artificial neural network Study. , 1(2), 141-149. doi: 10.22069/bere.2025.24182.1034
Mohammad Vahedi Torshizi; Hajar Aghili. "Thermodynamic and intelligent modeling of a fluidized bed dryer for eggplant drying: A combined energy–exergy–artificial neural network Study". , 1, 2, 1404, 141-149. doi: 10.22069/bere.2025.24182.1034
Vahedi Torshizi, Mohammad, Aghili, Hajar. (1404). 'Thermodynamic and intelligent modeling of a fluidized bed dryer for eggplant drying: A combined energy–exergy–artificial neural network Study', , 1(2), pp. 141-149. doi: 10.22069/bere.2025.24182.1034
Vahedi Torshizi, Mohammad, Aghili, Hajar. Thermodynamic and intelligent modeling of a fluidized bed dryer for eggplant drying: A combined energy–exergy–artificial neural network Study. , 1404; 1(2): 141-149. doi: 10.22069/bere.2025.24182.1034

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.

تازه های تحقیق
  • Two clutch types—diaphragm spring and pinned—were experimentally and numerically analyzed.
  • Finite Element Method (ANSYS) was used to evaluate stress and deformation.
  • Thermal analysis revealed lower heat concentration and improved energy efficiency.
  • Results confirmed the diaphragm spring clutch as a more durable and efficient design for heavy-duty vehicles and agricultural tractors.

 

کلیدواژه‌ها
Artificial neural network؛ Eggplants؛ Energy؛ Exergy loss؛ Fluidized-bed dryer
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