SRCosmos:

Order of appearence	Full citation	SRCosmos Link
1	Aversa M, et al., 2007. An analysis of the transport phenomena occurring during food drying process. J. Food Eng., 78(3), 922-932.
2	Comsol, 2007. Comsol multiphysics user's manual. Stockholm: Comsol AB.
3	Davidson VJ, et al., 2000. Effects of drying air temperature and humidity on stress cracks and breakage of maize kernels. J. Agric. Eng. Res., 77(3), 303-308.
4	Eaton JW, 2002. Gnu octave manual. UK: Network Theory Limited.
5	Green DW, Perry RH, 2008. Perry's chemical engineer's handbook. 8th ed. McGraw-Hill Professional.
6	Gustafson RJ, et al., 1979. Temperature and stress analysis of corn kernel - finite element analysis. Trans. ASAE, 22(4), 955-960.
7	Hatamipour MS, Mowla D, 2003. Correlations for shrinkage, density and diffusivity for drying of maize and green peas in a fluidized bed with energy carrier. J. Food Eng., 59(2-3), 221-227.
8	Hatamipour MS, Mowla D, 2006. Drying behaviour of maize and green peas immersed in fluidized bed of inert energy carrier particles. Food Bioproducts Process., 84(C3), 220-226.
9	Jayas DS, et al., 1991. Review of thin-layer drying and wetting equations. Drying Technol., 9(3), 551-588.
10	Kunii D, Levenspiel O, 1991. Fluidization engineering. 2nd ed. Newton, MA, USA: Butterworth-Heinemann Ltd.
11	Lupano CE, Anon MC, 1987. Denaturation of wheat endosperm proteins during drying. Cereal Chem., 64(6), 437-442.
12	Malumba P, et al., 2008. Influence of drying temperature on the solubility, the purity of isolates and the electrophoretic patterns of corn proteins. Food Chem., 111(3), 564-572.
13	Malumba P, et al., 2009a. Influence of drying temperature on the wet-milling performance and the proteins solubility indexes of corn kernels. J. Food Eng., 95(3), 393-399.
14	Malumba P, et al., 2009b. Influence of drying temperature on functional properties of wet-milled starch granules. Carbohydr. Polym., 75(2), 299-306.
15	Matlab, 2008. Matlab R2008b. Natick, MA, USA: The MathWorks.
16	Mourad M, et al., 1995a. The drying of corn in a floatation fluidized bed. An experimental study of the drying rate. Chem. Eng. J., 59(3), 221-228.
17	Mourad M, et al., 1995b. Drying of corn in a fluidized floatation bed. 2. Modeling of the drying kinetics. Chem. Eng. J., 60(1-3), 39-47.
18	Mourad M, et al., 1996a. A new correlation for the estimation of moisture diffusivity in corn kernels from drying kinetics. Drying Technol., 14(3-4), 873-894.
19	Mourad M, et al., 1996b. Experimental validation of a mathematical model for the batch drying of corn kernels - comments. Drying Technol., 14(3-4), 927-935.
20	Mujumdar AS, 2006. Handbook of industrial drying. 3rd ed. Taylor & Francis Ltd.
21	Nemenyi M, et al., 2000. Investigation of simultaneous heat and mass transfer within the maize kernels during drying. Comput. Electron. Agric., 26(2), 123-135.
22	Prachayawarakorn S, et al., 2004. Methodology for enhancing drying rate and improving maize quality in a fluidised-bed dryer. J. Stored Products Res., 40(4), 379-393.
23	Samapundo S, et al., 2007. Sorption isotherms and isosteric heats of sorption of whole yellow dent corn. J. Food Eng., 79(1), 168-175.

Keywords	corn, drying, finite element method, heat transfer, mass transfer, modelization
Abstract	Comparison of two drying models applied to corn drying in fluidized bed. In this article, two predictive models of the temperature and water content of corn grains during their drying in a fluidized bed are compared. The first model is a simplified one, where the physical phenomena implied in the process are not described. It can be solved using freewares available on Internet. The second is a more complex model, based on the basic physical laws governing the phenomena of heat and mass transfer within the product. It requires the use of commercial finite element software to solve it. The two models are parameterized with four dryings where the temperature remains constant during the process, then validated on dryings with variable temperature and an intermittent drying. The two models are able to describe with an acceptable precision the evolutions of water content during continuous dryings, and to predict the evolutions of water content during dryings with variable temperature and the intermittent drying. The dynamic model is however not able to describe the evolution of the grains temperature during dryings at variable temperature with a precision lower than one degree Celsius. If this precision is sufficient, the use of the dynamic model will reduce considerably the costs in time and license of software for the modeling of the corn drying in a fluidized bed.
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Name	Affiliation	Home page	e-mail	Total pubs
Bera F	Univ. Liege - Gembloux Agro-Bio Tech. Unite de Technologie des Industries agro-alimentaires. Passage des Deportes, 2. B-5030 Gembloux (Belgique)			4
Deroanne C	Gembloux Agricultural University – FUSAGx. Department of Food Technology. Passage des Deportes, 2. B-5030 Gembloux (Belgium)			8
Janas S	Univ. Liege - Gembloux Agro-Bio Tech. Unite de Technologie des Industries agro-alimentaires. Passage des Deportes, 2. B-5030 Gembloux (Belgique)		s.janas@ulg.ac.be	2
Malumba P	Universite de Kinshasa. Faculte des Sciences agronomiques. BP 1471 Kinshasa 1. Republique Democratique du Congo.		paul_malu@yahoo.fr	2