Prediction kinetic, energy and exergy of quince under hot air dryer using ANNs and ANFIS.

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ID: 100252
2020
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Abstract
This study aimed to predict the drying kinetics, energy utilization ( ), energy utilization ratio (), exergy loss, and exergy efficiency of quince slice in a hot air (HA) dryer using artificial neural networks and ANFIS. The experiments were performed at air temperatures of 50, 60, and 70°C and air velocities of 0.6, 1.2, and 1.8 m/s. The thermal parameters were determined using thermodynamic relations. Increasing air temperature and air velocity increased the effective moisture diffusivity ( ), , , exergy efficiency, and exergy loss. The value of the was varied from 4.19 × 10 to 1.18 × 10 m/s. The highest value , and exergy loss and exergy efficiency were calculated 0.0694 kJ/s, 0.882, 0.044 kJ/s, and 0.879, respectively. Midilli et al. model, ANNs, and ANFIS model, with a determination coefficient () of .9992, .9993, and .9997, provided the best performance for predicting the moisture ratio of quince fruit. Also, the ANFIS model, in comparison with the artificial neural networks model, was better able to predict , , exergy efficiency, and exergy loss, with of .9989, .9988, .9986, and .9978, respectively.
Reference Key
abbaspourgilandeh2020predictionfood Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Abbaspour-Gilandeh, Yousef;Jahanbakhshi, Ahmad;Kaveh, Mohammad;
Journal Food science & nutrition
Year 2020
DOI
10.1002/fsn3.1347
URL
Keywords
artificial neural networks drying adaptive neuro‐fuzzy inference system quince thermodynamic parameters

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