Presentation of three dimensional geometric characteristics of food porous systems in the form of statistical functions

• Main Authors: V. G. Zhukov, N. D. Lukin, V. M. Chesnokov
• Format: Article
• Language: Russian
• Published: Voronezh state university of engineering technologies 2019-11-01
• Series: Vestnik Voronežskogo Gosudarstvennogo Universiteta Inženernyh Tehnologij
• Subjects: porous materials, pores, geometric dimensionless parameters, discontinuity, transparency, porosity, random variable, log-normal distribution, statistical formulas of parameters
• Online Access: https://www.vestnik-vsuet.ru/vguit/article/view/2196

The article discusses the method of representing the three dimensionless geometric characteristics of porous materials in the form of statistical functions. The technique allows to obtain formulas for histograms of porous materials. The study relates to the analytical development of a method for determining the dimensionless parameters of food porous media. As an example, we consider a porous material similar in geometrical characteristics to a typical food product with a homogeneous and isotropic porous medium similar to starch, finely divided food additives, and flour. The study is based on the statistical lognormal distribution of random variables and the analytical relationship between the three dimensionless integral parameters of porous systems. The formulas of three dimensionless geometric parameters of a porous medium are obtained analytically: discontinuity, transparency, and porosity. They take into account the statistics of random pore size distribution. The formulas include an experimental integral parameter of porosity, defined by standard techniques. It corrects the results of the automated determination of the pore size distribution. The formulas allow calculating the influence of individual size groups of pores or of their entire size ensemble, which is important in calculating heat and mass transfer processes in porous food, chemical and other technologies. The considered technique allows to apply it in similar studies for statistical tasks of various types.