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Mathematical modeling of thin layer drying of carrot slices by forced convection

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Abstract

The aim of this study is to evaluate the effect of drying air temperature, velocity and slice thickness on thin layer drying model of carrot slices. A laboratory dryer was used for thin layer carrot drying process and moisture ratio values (MR) for any drying time were compared using 14 different models, i.e., Newton, Page, Modified Page I and II, Henderson and Pabis, Logarithmic, Two term, Two term exponential, Wang and Singh, Thompson, Diffision approximation, Verma et al., Modified Henderson and Pabis and Midilli et al. models. Root mean square error (RMSE), reduced Chi square (χ2) and modeling efficiency (EF) were used as statistical parameters to determine the most suitable model among them. The effects of drying parameters on the best suited drying model coefficients were than determined with linear, logarithmic, power exponential and Arhenius type equations. According to the results, moisture ratio values could be predicted by the Midilli et al. model with high accuracy. It is also possible to predict the moisture content of the product with a generalized model showing the effect of the drying air temperature, velocity and slice thickness with the range of 55–75 °C, 2.0–3.0 m s−1 and 3–9 mm, respectively.

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Authors and Affiliations

  1. Faculty of Agriculture, Department of Agricultural Machinery & Technologies Engineering, Selcuk University, Konya, Turkey

    Mehmet Hakan Sonmete & Hakan Okyay Mengeş

  2. Faculty of Agriculture, Department of Agricultural Machinery & Technologies Engineering, Akdeniz University, Antalya, Turkey

    Can Ertekin

  3. Faculty of Agriculture, Department of Food Engineering, Selcuk University, Konya, Turkey

    Mehmet Musa Özcan

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  1. Mehmet Hakan Sonmete
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  2. Hakan Okyay Mengeş
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  3. Can Ertekin
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  4. Mehmet Musa Özcan
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Correspondence to Mehmet Musa Özcan.

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Sonmete, M.H., Mengeş, H.O., Ertekin, C. et al. Mathematical modeling of thin layer drying of carrot slices by forced convection. Food Measure 11, 629–638 (2017). https://doi.org/10.1007/s11694-016-9432-y

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  • DOI: https://doi.org/10.1007/s11694-016-9432-y

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