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2013 | 27 | 1 |
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Drying kinetics of dill leaves in a convective dryer

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Thin layer drying characteristics of dill leaves under fixed, semi-fluidized, and fluidized bed conditions were studied at air temperatures of 30, 40, 50, and 60°C. In order to find a suitable drying curve, 12 thin layer-drying models were fitted to the experimental data of the moisture ratio. Among the applied mathematical models, the Midilli et al. model was the best for drying behavior prediction in thin layer drying of dill leaves. To obtain the optimum network for drying of dill leaves, various numbers of multilayer feed-forward neural networks were made and tested with different numbers of hidden layers and neurons. The best neural network feed-forward back-propagation topology for the prediction of drying of dill leaves (moisture ratio and drying rate) was the 3-45-2 structure with the training algorithm trainlm and threshold functions logsig and purelin. The coefficient of determination for this topology for training, validation, and testing patterns was 0.9998, 0.9981, and 0.9990, respectively. Effective moisture diffusivity of dill leaves during the drying process in different bed types was found to be in the range from 7.10 10-12 to 1.62 10-10 m2 s-1. Also, the values of activation energy were determined to be between 75.435 and 80.118 kJ mol-1.
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  • Department of Engineering, Shahre Rey Branch, Islamic Azad University, Tehran, Iran
  • Department of Mechanical Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran
  • Department of Agricultural Machinery Engineering, Bu-Ali Sina University, Hamedan, Iran
  • Department of Mechanical Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran
  • Department of Agricultural Machinery, Agricultural Faculty, Tarbiat Modares University,Tehran, Iran
  • Alborzi M., 2003. Introduction with Neural Networks. Sharif University of Technology Press, Tehran, Iran.
  • Arumuganathan T., Manikantan M.R., Rai R.D., Anandakumar S., and Khare V., 2009. Mathematical modeling of drying kinetics of milky mushroom in a fluidized bed dryer. Int. Agrophys., 23, 1-7.
  • Bakal S.B., Sharma G.P., Sonawane S.P., and Verma R.C., 2011. Kinetics of potato drying using fluidized bed dryer. J. Food Sci. Technol., DOI 10.1007/s13197-011-0328-x.
  • Chayjan R.A., Salari K., Abedi Q., and Sabziparvar A.A., 2011. Modeling moisture diffusivity, activation energy and specific energy consumption of squash seeds in a semi fluidized and fluidized bed drying. J. Food Sci. Technol., DOI 10.1007/s13197-011-0399-8.
  • Dandamrongrak R., Young G., and Mason R., 2002. Evaluation of various pre-treatments for the dehydration of banana and selection of suitable drying models. J. Food Eng., 95, 139-146.
  • Diamente L.M. and Munro P.A., 1991. Mathematical modeling of hot air drying of sweet potato slices. Int. J. Food Sci. Technol., 26, 99-109.
  • Doymaz I., 2004. Convective air drying characteristics of thin layer carrots. J. Food Eng., 61, 359-364.
  • Doymaz I., 2007. Influence of pretreatment solution on the drying of sour cherry. J. Food Eng., 78, 591-596.
  • Doymaz I. and Ismail O., 2011. Drying characteristics of sweet cherry. Food Bioprod. Proces., 89, 31-38.
  • Ertekin C. and Yaldiz O., 2004. Drying of eggplant and selection of a suitable thin layer drying model. J. Food Eng., 63, 349-359.
  • Giner S.A. and Calvelo A., 1987. Modeling of wheat drying on fluidized beds. J. Food Sci., 52, 1358-1363.
  • Izadifar M. and Mowla D., 2003. Simulation of a cross-flow continuous fluidized bed dryer for paddy rice. J. Food Eng., 58, 325-329.
  • Kunii D. and Levenspiel O., 1991. Fluidisation Engineering. Butterworth-Heinemann, London, UK.
  • Midilli A., Kucuk H., and Yapar Z., 2002. A new model for single-layer drying. Drying Technol., 20(7), 1503-1513.
  • Motevali A., Minaei S., Khoshtaghaza M.H., and Amirnejat H., 2011a. Comparison of energy consumption and specific energy requirements of different methods for drying mushroom slices. Energy, 36, 6433-6441.
  • Motevali A., Najafi G.H., Abbasi S., Minaei S., and Ghaderi A., 2011b. Microwave-vacuum drying of sour cherry: comparison of mathematical models and artificial neural networks. J. Food Sci. Technol., DOI 10.1007/s13197-011-0393-1.
  • Motevali A., Minaei S., Khoshtaghaza M.H., Kazemi M., and Nikbakht A.M., 2010. Drying of pomegranate arils: comparison of predictions from mathematical models and neural networks. Int. J. Food Eng., 6(3).
  • Pathare P.B. and Sharma G.P., 2006. Effective moisture diffusivity of onion slices undergoing infrared convective drying. Biosyst. Eng., 93, 285-291.
  • Wang C.Y. and Singh R.P., 1978. Use of variable equilibrium moisture content in modeling rice drying. Trans. ASAE, 11, 668-672.
  • Wang Z., Sun J., Liao X., Chen F., Zhao G., Wu J., and Hu X., 2007. Mathematical modeling on hot air drying of thin layer apple pomace. Food Res. Int., 40, 39-46.
  • Zomorodian A. and Moradi M., 2010. Mathematical modeling of forced convection thin layer solar drying for Cuminum cyminum. J. Agri Sci. Tech., 12, 401-408.
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