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

Document Type : Original Article

Authors

1 Department of Biosystems Engineering, Tarbiat Modares University, Tehran, Iran

2 Department of Biosystem Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

Abstract

This research, which has practical implications for the food engineering and processing industry, estimated the energy and exergy of apple drying using a Group Method of Data Handling (GMDH) neural network and a hybrid artificial neural network-genetic algorithm (ANN-GA). Osmotic and natural samples were tested in the form of apple cubes with a height of 5 mm and sides of 10 mm ×10 mm, 8 mm × 8 mm, and 6 mm × 6 mm dried by fluidized bed method at three air velocities of 4, 6, and 8 m/s and temperatures of 40, 45, and 50℃. 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, which were directly relevant to the industry. It was also observed that the GMDH neural network for energy consumption, energy use ratio, exergy loss, and exergy efficiency have linear correlation coefficients (R) of 0.95097, 0.92202, 0.91464, and 0.91258, respectively, suggesting that it outperforms the ANN-GA. The weakest performance of the GMDH network was associated with the exergy efficiency. The ANN-GA had the best energy consumption and lowest exergy loss performance.

Keywords

Research Highlights:

  • Study compares GMDH and ANN-GA models in predicting energy use and exergy in apple drying
  • Higher heat, faster air, and smaller samples improve drying energy and exergy efficiency.
  • GMDH predicted energy better than ANN-GA, aiding drying system energy management
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Biosystems Engineering and Renewable Energies
Volume 1, Issue 1
January 2025
Pages 1-10
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