Estimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling

Abstract

Two different drying methods were applied for dehydration of apple, i.e., convective drying (CD) and microwave drying (MD). The process of convective drying through divergent temperatures; 50, 60 and 70 °C at 1.0 m/s air velocity and three different levels of microwave power (90, 180, and 360 W) were studied. In the analysis of the performance of our approach on moisture ratio (MR) of apple slices, artificial neural networks (ANNs) was used to provide with a background for further discussion and evaluation. In order to evaluate the models mentioned in the literature, the Midilli et al. model was proper for dehydrating of apple slices in both MD and CD. The MD drying technology enhanced the drying rate when compared with CD drying significantly. Effective diffusivity (Deff) of moisture in CD drying (1.95 × 10−7–4.09 × 10−7 m2/s) was found to be lower than that observed in MD (2.94 × 10−7–8.21 × 10−7 m2/s). The activation energy (Ea) values of CD drying and MD drying were 122.28–125 kJ/mol and 14.01–15.03 W/g respectively. The MD had the lowest specific energy consumption (SEC) as compared to CD drying methods. According to ANN results, the best R2 values for prediction of MR in CD and MD were 0.9993 and 0.9991, respectively.

Exergy and Energy Analyses of Microwave Dryer for Cantaloupe Slice and Prediction of Thermodynamic Parameters Using ANN and ANFIS Algorithms

“…As observable in Table 2, a rise in the Mw power enhanced the value of D eff which can be assigned to the increment of the heat energy which declined the viscosity of the water present in the samples and hence increased its activity and accelerated the evaporation [29]. Sharabiani et al [39] and Raj and Dash [45] also reported similar results in drying apple and dragon fruit slices using an MW dryer. Moreover, the statistical analysis indicated that at a given MW power, the sample thickness significantly affected the effective diffusion coefficient (p < 0.05); however, the increase in the thickness from 2 to 6 mm elevated the diffusion coefficient, which can be attributed to the surface hardness of the samples as surface hardening occurs more rapidly in thinner samples while the evaporation rate of thinner samples is far higher [46].…”

“…Each neuron acts as a processor in the network and receives and processes neural signals (input) from other neurons or their surroundings. Similar to the human brain, an artificial neural network can learn everything on its own [39]. Neurons are trained by applying a training algorithm to the network.…”

“…The increase in the energy consumption by changing the power from 180 to 540 W was significant at all thicknesses (p < 0.05); the results indicated that a rise in the MW power and a decrement in the sample thicknesses can decrement the energy consumption. The reason could be the enhanced destruction of the product thickness and accelerated moisture release [39,41]. Moreover, with increasing MW output power, the thermal gradient inside the sample increased, followed by an increment in evaporation rate and a decrease in drying time.…”

Application of Artificial Neural Networks, Support Vector, Adaptive Neuro-Fuzzy Inference Systems for the Moisture Ratio of Parboiled Hulls

“…Three indicators (R-square, mean squared error (MSE), and mean absolute error (MAE)) were applied for the evaluation of the ANN, ANFIS, and SVR. These indices are shown in Equations ( 17)- (19), respectively [52].…”

Analysis of Material-Characterization Properties of Post-Production Waste—The Case of Apple Pomace

“…The apple is one of the oldest fruits known to mankind. It is one of the most important horticultural products in the world and is also one of the most sold fruits in the world [ 1 , 2 ]. Apples are the fruit most often grown in the European Union, and Poland ranks in the leading position [ 3 ].…”