Methodology for predicting oily mixture properties in the mathematical modeling of molecular distillation

Authors

DOI:

https://doi.org/10.3989/gya.1051162

Keywords:

Modeling, Molecular distillation, Oily mixtures, Properties

Abstract


A methodology for predicting the thermodynamic and transport properties of a multi-component oily mixture, in which the different mixture components are grouped into a small number of pseudo components is shown. This prediction of properties is used in the mathematical modeling of molecular distillation, which consists of a system of differential equations in partial derivatives, according to the principles of the Transport Phenomena and is solved by an implicit finite difference method using a computer code. The mathematical model was validated with experimental data, specifically the molecular distillation of a deodorizer distillate (DD) of sunflower oil. The results obtained were satisfactory, with errors less than 10% with respect to the experimental data in a temperature range in which it is possible to apply the proposed method.

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References

Babeanu N, Nita S, Popa O, Ioan Marin D. 2016. Squalene recovery from amaranth oil by short path distillation. J. Biotechnol. 231, 10. https://doi.org/10.1016/j.jbiotec.2016.05.200

Bose A, Palmer H. 1984. Influence of heat and mass transfer resistances on the separation efficiency in molecular distillations. Ind. Eng. Chem. Fundamen. 23, 459-465. https://doi.org/10.1021/i100016a014

Cvengros J, Lutisan J, Micov M. 2000. Feed temperature influence on the efficiency of a molecular evaporator. Chem. Eng. J. 78, 61-67. https://doi.org/10.1016/S1385-8947(99)00159-X

García Solaesa A, Sanza M T, Falkeborgb M, Beltrána S, Guob Z. 2016. Production and concentration of monoacylglycerols rich in omega-3 polyunsaturated fatty acids by enzymatic glycerolysis and molecular distillation. Food Chem. 190, 960–967. https://doi.org/10.1016/j.foodchem.2015.06.061 PMid:26213062

Langmuir I. 1913. The vapor pressure of metallic tungsten. Phys. Rev. 2, 329-342. https://doi.org/10.1103/PhysRev.2.329

Martinello M, Leone I, Pramparo M. 2008. Simulation of deacidification process by molecular distillation of deodorizer distillate. Latin. Am. Appl. Res. 38, 299-304.

Micov M, Lutisan J, Cvengros J. 1997. Balance equations for molecular distillation. Sep. Sci. Technol. 32, 3051-3066. https://doi.org/10.1080/01496399708000795

Naz S, Sherazi ST, Talpur FN, Kara H, Uddin S, Khaskheli AR. 2014. Chemical characterization of canola and sunflower oil deodorizer distillates. Pol. J. Food Nutr. Sci. 64, 115–120. https://doi.org/10.2478/pjfns-2013-0008

Perry RH, Chilton CH, Kirkpatrik SD. 1963. Perry's Chemical Engineers' Handbook. McGraw-Hill. New York.

Perry RH, Green DW. 2008. Perry's Chemical Engineers' Handbook. McGraw-Hill. New York.

Perry ES, Hecker JC. 1951. Distillation Under High Vacuum. in Weissberger (Ed.) Techniques of Organic Chemistry, vol. 4, Interscience Publishers, New York, pp. 495–602.

Rossi P, Gayol M F, Renaudo C, Pramparo MC, Nepote V, Grosso NR. 2014. The use of artificial neural network modeling to represent the process of concentration by molecular distillation of omega-3 from squid oil. Grasas Aceites 65, e052. https://doi.org/10.3989/gya.0231141

Yu J, Yuan X, Zeng A. 2015. A novel purification process for dodecanedioic acid by molecular distillation. Chin. J. Chem. Eng. 23, 499-504. https://doi.org/10.1016/j.cjche.2014.10.019

Published

2017-06-30

How to Cite

1.
Gayol MF, Pramparo MC, Miró Erdmann SM. Methodology for predicting oily mixture properties in the mathematical modeling of molecular distillation. grasasaceites [Internet]. 2017Jun.30 [cited 2022Nov.29];68(2):e193. Available from: https://grasasyaceites.revistas.csic.es/index.php/grasasyaceites/article/view/1660

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Research