Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
11015697 | Energy | 2018 | 28 Pages |
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 4â¯mm and treated with salt (NaCl) and distilled water solution (7% by weight) for 30, 60, and 90â¯min as osmosis pre-treatment. Then, they were dried in three replicates using a microwave dryer and at three powers of 90, 360, and 900â¯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 900â¯W and in the osmosis time of 90â¯min. The highest energy and exergy efficiency was observed at 42.1% and 31.08%, respectively. The maximum exergy loss was seen at 360â¯W and osmosis time of 60â¯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, R2â¯=â¯0.871 for specific energy loss, R2â¯=â¯0.999 for specific exergy loss, and R2â¯=â¯0.972 for exergy efficiency. This amount was seen in a network containing 4 neurons in the hidden layer.
Related Topics
Physical Sciences and Engineering
Energy
Energy (General)
Authors
Mohsen Azadbakht, Mohammad Vahedi Torshizi, Fatemeh Noshad, Arash Rokhbin,