Hot air drying of whole fruit Chinese jujube (Zizyphus jujubaMiller): thin-layer mathematical modelling

Microwave drying is a promising and effective way to drying and upgrading lignite. The influence of temperature (100–140 °C) and microwave power levels (500–800 W) on thin-layer drying characteristics of Zhaotong lignite under microwave irradiation were investigated. Fourteen thin-layer drying models were used to analyze the microwave drying process while six thin-layer drying models were used to analyze the hot-air drying process. The microwave drying processes at all temperature (100–140 °C) or low microwave power levels (500–700 W) exhibited four periods: a warm-up period, a short constant period, the first and second falling rate period, while one falling rate period was found during hot-air drying. The effective diffusion coefficient of lignite were calculated and it increases with increasing temperature and microwave power levels. During microwave drying, the two-term exponential model is the most suitable model for all applied conditions, while the Modified Page model is the most suitable model to describe the hot-air drying experiments. The apparent activation energy were determined from Arrhenius equation and the values for the first and second falling rate period are 3.349 and 20.808 kJ·mol−1 at different temperatures, while they are 13.455 and 19.580 W·g−1 at different microwave power levels. This implies the apparent activation energy is higher during the second falling rate period, which suggest that the dewatering of absorbed water is more difficult than capillary water. The value of apparent activation energy in hot-air drying is between the first and second falling rate period of microwave drying. Results indicate that microwave drying is more suitable to dewatering free water and capillary water of lignite.

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Application of Computational Intelligence in Describing the Drying Kinetics of Persimmon Fruit (Diospyros kaki) During Vacuum and Hot Air Drying Process

Processes ◽

10.3390/pr8050544 ◽

2020 ◽

Vol 8 (5) ◽

pp. 544 ◽

Cited By ~ 2

Author(s):

Alfadhl Yahya Khaled ◽

Abraham Kabutey ◽

Kemal Çağatay Selvi ◽

Čestmír Mizera ◽

Petr Hrabe ◽

...

Keyword(s):

Thin Layer ◽

Computational Intelligence ◽

Drying Kinetics ◽

Support Vector ◽

Hot Air Drying ◽

Air Drying ◽

Persimmon Fruit ◽

Hot Air ◽

Computational Intelligence Methods ◽

Kinetics Of

This study examines the potential of applying computational intelligence modelling to describe the drying kinetics of persimmon fruit slices during vacuum drying (VD) and hot-air-drying (HAD) under different drying temperatures of 50 °C, 60 °C and 70 °C and samples thicknesses of 5 mm and 8 mm. Kinetic models were developed using selected thin layer models and computational intelligence methods including multi-layer feed-forward artificial neural network (ANN), support vector machine (SVM) and k-nearest neighbors (kNN). The statistical indicators of the coefficient of determination (R2) and root mean square error (RMSE) were used to evaluate the suitability of the models. The effective moisture diffusivity and activation energy varied between 1.417 × 10−9 m2/s and 1.925 × 10−8 m2/s and 34.1560 kJ/mol to 64.2895 kJ/mol, respectively. The thin-layer models illustrated that page and logarithmic model can adequately describe the drying kinetics of persimmon sliced samples with R2 values (>0.9900) and lowest RMSE (<0.0200). The ANN, SVM and kNN models showed R2 and RMSE values of 0.9994, 1.0000, 0.9327, 0.0124, 0.0004 and 0.1271, respectively. The validation results indicated good agreement between the predicted values obtained from the computational intelligence methods and the experimental moisture ratio data. Based on the study results, computational intelligence methods can reliably be used to describe the drying kinetics of persimmon fruit.

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