Measurement of thermal conductivity of dairy products

Tavman, İsmail; Tavman, Şebnem

doi:10.1016/s0260-8774(99)00079-5

Cited by 51 publications

(45 citation statements)

References 9 publications

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“…Thermal conductivity values of samples increase linearly with moisture content in this study, in agreement with typical results reported in the literature for different food materials (Andrieu, Gonnet, & Laurent, 1989;Bilanski & Fisher, 1976;Chuma, Uchida, & Shemsanga, 1981;Kustermann, Scherer, & Kutzbach, 1981;Sharma & Thompson, 1973;Tavman & Tavman, 1988;Tavman & Tavman, 1999;Zuritz, Sastry, McCoy, Murakami, & Blaisdell, 1989).…”

Section: Resultssupporting

confidence: 91%

Thermal conductivity measurements of a traditional fermented dough in the frozen state

Kumcuoğlu¹,

Tavman²,

Nesvadba³

et al. 2007

Journal of Food Engineering

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Section: Resultssupporting

confidence: 91%

Thermal conductivity measurements of a traditional fermented dough in the frozen state

Kumcuoğlu¹,

Tavman²,

Nesvadba³

et al. 2007

Journal of Food Engineering

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“…Techniques of temperature measurement in this method were standardized in 1978 by the DIN 51046 Standard-Part 2 and e.g. in [12,13,14]. The solution of equation (1) is temperature rise T(t).…”

Section: Thermal Probe Methodsmentioning

confidence: 99%

An Idea of a Measurement System for Determining Thermal Parameters of Heat Insulation Materials

Chudzik¹,

Minkina²

2011

Metrology and Measurement Systems

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The article presents the prototype of a measurement system with a hot probe, designed for testing thermal parameters of heat insulation materials. The idea is to determine parameters of thermal insulation materials using a hot probe with an auxiliary thermometer and a trained artificial neural network. The network is trained on data extracted from a nonstationary two-dimensional model of heat conduction inside a sample of material with the hot probe and the auxiliary thermometer. The significant heat capacity of the probe handle is taken into account in the model. The finite element method (FEM) is applied to solve the system of partial differential equations describing the model. An artificial neural network (ANN) is used to estimate coefficients of the inverse heat conduction problem for a solid. The network determines values of the effective thermal conductivity and effective thermal diffusivity on the basis of temperature responses of the hot probe and the auxiliary thermometer. All calculations, like FEM, training and testing processes, were conducted in the MATLAB environment. Experimental results are also presented. The proposed measurement system for parameter testing is suitable for temporary measurements in a building site or factory.

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“…They found that the water content was the factor that had the greatest influence on the properties, while the fat content had less influence. Tavman and Tavman (1999) evaluated the thermal conductivity of eleven types of cheese, four types of yogurt and a sample of butter using the hot wire method and evaluated the influence of fat, protein and water on the thermal conductivity of these products. The measured values were linearly dependent on the water content and inversely dependent on the fat and protein contents of the various dairy products.…”

Section: Source: Elaboration Of the Authorsmentioning

confidence: 99%

Thermal conductivity of milk with different levels of moisture and fat: experimental measures and prediction models

et al. 2013

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Thermal conductivity is one of the most influential properties affecting the pasteurization of milk and is highly dependent on the milk composition and the temperature used in the process. The purpose of this work was to model the thermal conductivity of milk with a moisture content from 88.3 to 91.2% and fat content from 0 to 3.5% from 20 to 80°C. For milk with a lower moisture percentage, the experimental conductivity ranged from 0.560 to 0.637 W m -1 K -1 , and for those samples with a higher moisture percentage, conductivity ranged from 0.584 to 0.652 W m -1 K -1 . Three theoretical predictive models, series, parallel and Maxwell-Eucken, which use food composition data, were tested, and polynomial functions were fit to model the behavior of this property as a function of moisture and fat content. The results showed that thermal conductivity values are linearly dependent on the moisture content and inversely dependent on the fat content. Among the models, the parallel model was the best fit to the experimental data with a maximum error of 2.7%. Key words: Thermal conductivity, structural models, hot wire probe, moisture, fat, milk ResumoA condutividade térmica é uma das propriedades mais influentes durante a pasteurização, sendo altamente dependente da composição do leite e da temperatura utilizada no processo. Este trabalho objetivou modelar a condutividade em uma faixa de temperatura de 20 a 80°C para leites com teores de umidade de 88,3 a 91,2% e teores de gordura entre 0 e 3,5%. Para o leite com menor porcentagem de umidade, a condutividade variou de 0,560 a 0,637 W m -1 K -1 e para aquele com maior teor de umidade a condutividade esteve entre 0,584 a 0,652 W m -1 K -1 . Três modelos teóricos de predição, série, paralelo e de Maxwell-Eucken, os quais utilizam dados da composição do alimento foram testados e funções polinomiais foram ajustadas para modelar o comportamento desta propriedade em função dos teores de umidade e gordura do leite. Os resultados mostraram que os valores da condutividade térmica são linearmente dependentes do teor de umidade e inversamente dependentes dos teores de gordura. Entre os modelos testados, o modelo Paralelo foi o que melhor se ajustou aos dados experimentais com erro máximo de 2,7%.

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Measurement of thermal conductivity of dairy products

Cited by 51 publications

References 9 publications

Thermal conductivity measurements of a traditional fermented dough in the frozen state

Thermal conductivity measurements of a traditional fermented dough in the frozen state

An Idea of a Measurement System for Determining Thermal Parameters of Heat Insulation Materials

Thermal conductivity of milk with different levels of moisture and fat: experimental measures and prediction models

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