Mathematical modeling on hot air drying of thin layer apple pomace

Wang, Zhengfu; Sun, Junhong; Liao, Xiaojun; Chen, Fang; Zhao, Guanghua; Wu, Jihong; Hu, Xiaosong

doi:10.1016/j.foodres.2006.07.017

Cited by 300 publications

(219 citation statements)

References 31 publications

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“…It indicated that increasing the drying temperature decreases the drying time (Akpinar 2006;Fang et al 2009). The decrease in drying time with an increase in the drying air temperature have been reported for many foodstuffs, such as eggplant (Ertekin and Yaldiz 2004;Wu et al 2007), apple pomace (Wang et al 2007), pumpkin slices (Doymaz 2007), and Asian white radish (Lee and Kim 2009). The drying rates versus average moisture content curve of Z. jujuba samples are illustrated in Fig.…”

Section: Resultsmentioning

confidence: 84%

Mathematical modeling on vacuum drying of Zizyphus jujuba Miller slices

Lee

Zuo

2011

J Food Sci Technol

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The thin-layer vacuum drying behavior of Zizyphus jujuba Miller slices was experimentally investigated at the temperature of 50, 60, and 70°C and the mathematical models were used to fit the thin-layer vacuum drying of Z. jujuba slices. The increase in drying air temperature resulted in a decrease in drying time. The drying rate was found to increase with temperature, thereby reducing the total drying time. It was found that Z. jujuba slices with thickness of 4 mm would be dried up to 0.08 kg water/kg dry matter in the range of 180-600 min in the vacuum dryer at the studied temperature range from 70 to 50°C. The Midilli et al. model was selected as the most appropriate model to describe the thin-layer drying of Z. jujuba slices. The diffusivity coefficient increased linearly over the temperature range from 1.47×10 −10 to 3.27×10 −10 m 2 /s, as obtained using Fick's second law. The temperature dependence of the effective diffusivity coefficient followed an Arrhenius-type relationship. The activation energy for the moisture diffusion was determined to be 36.76 kJ/mol.

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Section: Resultsmentioning

confidence: 84%

Mathematical modeling on vacuum drying of Zizyphus jujuba Miller slices

Lee

Zuo

2011

J Food Sci Technol

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“…Where X o is the initial moisture content, X t is the moisture content at time t and X e is the equilibrium moisture content (Shittu and Raji 2011;Ertekin and Yaldiz 2004;Wang et al 2007). Equation 1 can be further simplified to MR = X t /X o as the values of X e is relatively small compared to X o and X t for long drying time (Diamante and Munro 1993;Esturk 2012;Wang et al 2007).…”

Section: Mathematical Modelingmentioning

confidence: 99%

“…Equation 1 can be further simplified to MR = X t /X o as the values of X e is relatively small compared to X o and X t for long drying time (Diamante and Munro 1993;Esturk 2012;Wang et al 2007).…”

Section: Mathematical Modelingmentioning

confidence: 99%

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Hot air drying characteristics of mango ginger: Prediction of drying kinetics by mathematical modeling and artificial neural network

Murthy

Manohar

2013

J Food Sci Technol

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Mango ginger (Curcuma amada) was dried in a through-flow dryer system at different temperatures (40-70°C) and air velocities (0.84 -2.25 m/s) to determine the effect of drying on drying rate and effective diffusivity. As the temperature and air velocity increased, drying time significantly decreased. Among the ten different thin layer drying models considered to determine the kinetic drying parameters, semi empirical Midilli et al., model gave the best fit for all drying conditions. Effective moisture diffusivity varied from 3.7 × 10 −10 m 2 /s to 12.5 × 10 −10 m 2 /s over the temperature and air velocity range of study. Effective moisture diffusivity regressed well with Arrhenius model and activation energy of the model was found to be 32.6 kJ/mol. Artificial neural network modeling was also employed to predict the drying behaviour and found suitable to describe the drying kinetics with very high correlation coefficient of 0.998.

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“…The data on the variation in the mass of the pellets samples employed the concept of moisture ratio (Equation 1), as the dependent variable represents the relation between the gradient of moisture content of the sample in real time, comparing initial moisture content and equilibrium moisture content (VEGA-GÁLVEZ et al, 2009;WANG et al, 2007; LEMUS et al, Table 3. Codified matrix of the experimental design (3 2 ).…”

Section: Modeling Of Drying Kineticsmentioning

confidence: 99%