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Advisor(s)
Abstract(s)
A comparative approach was carried out between artificial
neural networks (ANNs) and response surface methodology
(RSM) to optimize the drying parameters during infrared–con-
vective drying of white mulberry. The drying experiments were
performed at different air temperatures (40°C, 55°C, and 70°C),
air velocities (0.4, 1, and 1.6 m/s), and three levels of infrared
radiation power (500, 1000, and 1500 W). RSM focuses on the
maximization of effective moisture diffusivity (D eff ) and minimi-
zation of specific energy consumption (SEC) in the drying pro-
cess. The optimized conditions were encountered for the air
temperature of 70°C, the air velocity of 0.4 m/s, and the infrared
power level of 1464.57 W. The optimum values of D eff and SEC
were 1.77 × 10
−9
m
2
/s and 166.554 MJ/kg, respectively, with the
desirability of 0.9670. Based on the statistical indices, the results
showed that the feed and cascade-forward back-Propagation
neural systems with application of Levenberg-Marquardt train-
ing algorithm and topologies of 3–20-20-1 and 3–10-10-1 were
the best neural models to predict D eff and SEC, respectively. This
finding suggests that the ANN as an intelligent method with
better performance compared to the RSM can be used to pre-
dict the drying parameters of the infrared-convective drying of
white mulberry fruit.
Description
Keywords
White mulberry effective moisture diffusivity specific energy consumption response surface methodology artificial neural network