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Vahedi Torshizi, Mohammad, Rokhbin, Arash, Ziaratban, Armin. (1404). Energy and exergy analysis of apple drying using fluidized bed method: A comparison of GMDH neural networks and ANN-GA models. , 1(1), 1-10. doi: 10.22069/bere.2024.22896.1000
Mohammad Vahedi Torshizi; Arash Rokhbin; Armin Ziaratban. "Energy and exergy analysis of apple drying using fluidized bed method: A comparison of GMDH neural networks and ANN-GA models". , 1, 1, 1404, 1-10. doi: 10.22069/bere.2024.22896.1000
Vahedi Torshizi, Mohammad, Rokhbin, Arash, Ziaratban, Armin. (1404). 'Energy and exergy analysis of apple drying using fluidized bed method: A comparison of GMDH neural networks and ANN-GA models', , 1(1), pp. 1-10. doi: 10.22069/bere.2024.22896.1000
Vahedi Torshizi, Mohammad, Rokhbin, Arash, Ziaratban, Armin. Energy and exergy analysis of apple drying using fluidized bed method: A comparison of GMDH neural networks and ANN-GA models. , 1404; 1(1): 1-10. doi: 10.22069/bere.2024.22896.1000

Energy and exergy analysis of apple drying using fluidized bed method: A comparison of GMDH neural networks and ANN-GA models

Biosystems Engineering and Renewable Energies
دوره 1، شماره 1، فروردین 2025، صفحه 1-10    اصل مقاله (1.09 M)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22069/bere.2024.22896.1000
نویسندگان
Mohammad Vahedi Torshizi* 1؛ Arash Rokhbin2؛ Armin Ziaratban2
1Department of Biosystems Engineering, Tarbiat Modares University, Tehran, Iran
2Department of Biosystem Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
چکیده
This study, which includes practical implications in the food processing industry, estimated the energy and exergy of apple drying using group method of data handling (GMDH) neural networks and a hybrid artificial neural network-genetic algorithm (ANN-GA) model. Osmotic and natural samples were tested in the form of apple cubes with a height of 5 mm and lateral dimensions of 6 × 6 mm, 8 × 8 mm, and 10 × 10 mm, dried by fluidized bed method at three air velocities of 4, 6, and 8 m s-1, and temperatures of 40, 45, and 50 °C. The results showed that energy consumption, energy use ratio, exergy efficiency, and exergy loss were elevated by increasing the temperature and air velocity and reducing the sample sizes in natural and osmotic samples. It was also observed that the GMDH neural network exerted the linear correlation coefficients (R) of 0.95, 0.92, 0.91, and 0.91, for the prediction of energy consumption, energy use ratio, exergy loss, and exergy efficiency, respectively, outperforming the ANN-GA model. The weakest performance of the GMDH network was associated with the exergy efficiency. The ANN-GA exhibited the best prediction performance for energy consumption and the lowest exergy loss.

تازه های تحقیق
  •  The study compares the performance of two advanced machine learning models—Group Method of Data Handling (GMDH) and hybrid artificial neural network-genetic algorithm (ANN-GA)—in predicting energy consumption and exergy efficiency during apple drying.
  • Results show that increased temperature, air velocity, and reduced sample size significantly improve energy and exergy efficiency, providing valuable insights for optimizing industrial food drying processes.
  • The GMDH model demonstrated superior accuracy in predicting energy-related parameters compared to the ANN-GA model, suggesting its potential for improving energy management in drying systems.
کلیدواژه‌ها
Apple؛ Artificial neural networks؛ Drying؛ Genetic algorithm
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