Application of artificial neural network method for prediction of osmotic pretreatment based on the energy and exergy analyses in microwave drying of orange slices

Mohsen Azadbakht, Mohammad Vahedi Torshizi, Fatemeh Noshad and Arash Rokhbin

Energy, 2018, vol. 165, issue PB, 836-845

Abstract: In the present study, artificial neural network (ANN) method was applied for predicting osmotic pretreatment based on the energy and exergy analyses in microwave drying of orange slices. For this purpose, the oranges were cut into slices with a thickness of 4 mm and treated with salt (NaCl) and distilled water solution (7% by weight) for 30, 60, and 90 min as osmosis pre-treatment. Then, they were dried in three replicates using a microwave dryer and at three powers of 90, 360, and 900 W. The statistical analysis results showed that the osmosis time is significant for the energy efficiency and exergy efficiency and specific exergy loss at 1% level. The highest energy and exergy efficiency was observed at 900 W and in the osmosis time of 90 min. The highest energy and exergy efficiency was observed at 42.1% and 31.08%, respectively. The maximum exergy loss was seen at 360 W and osmosis time of 60 min. The osmosis time did not affect the specific energy loss. The microwave power was statistically significant for all the parameters (energy and exergy) such that with increasing the microwave power, the energy and exergy efficiency increased, while the specific exergy and energy loss decreased. Overall, with increasing osmosis time and microwave power, the energy and exergy levels of the microwave dryer increased. The maximum coefficient of determination (R2) in a network containing 6 neurons in the hidden layer was 0.999 for energy efficiency, R2 = 0.871 for specific energy loss, R2 = 0.999 for specific exergy loss, and R2 = 0.972 for exergy efficiency. This amount was seen in a network containing 4 neurons in the hidden layer.

Keywords: Microwave dryer; Energy; Exergy; Osmosis; Orange; Pre-treatment (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:165:y:2018:i:pb:p:836-845

DOI: 10.1016/j.energy.2018.10.017

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