Medidas da condutividade térmica efetiva de modelos de polpas de frutas no estado congelado

Resende, Jaime Vilela de; Silveira, Vivaldo

doi:10.1590/s0101-20612002000200013

Cited by 8 publications

(5 citation statements)

References 9 publications

(28 reference statements)

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“…According to Resende and Silveira Júnior [22] in a frozen state, a system is composed of two phases: the ice phase (dispersed phase) and the phase containing liquid water and solutes (continuous phase). In this case, it is assumed that ice is also dispersed in the second phase, consisting of unfrozen water and solutes.…”

Section: Maxwell-eucken Modelmentioning

confidence: 99%

Thermal Conductivity of Mango Pulp (Mangifera indicaL.) Cultivar “Ubari” in Freezing Temperatures

Giarola

Pereira

Resende

2015

International Journal of Food Properties

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The aim of this study was the experimental determination of the thermal conductivity of mango pulp of cultivar "ubari" and its subsequent comparison with values estimated using different structural models, such as the serial, parallel, and Maxwell-Eucken models. A linear heating probe calibrated with a solution of 10% sucrose was utilized. Heat was applied to the probe, generating an increase in temperature, which was recorded through a signal conditioning system. The thermal conductivity of the pulp was calculated from the slope obtained by linear regression of the natural logarithm of the temperature versus time. The data was within the range of 0.465 to 1.595 W/m.K for temperatures of 0 and -30°C, respectively, showing an increase of conductivity with freezing. The initial freezing temperature and ice fraction were determined at every temperature of analysis for further use in the models. The best fit to the experimental data were obtained using the Maxwell-Eucken model, yielding minimum and maximum errors of 10.8 and 17.8%, respectively. An exponential model was fitted, which allowed for the direct calculation of the thermal conductivity of mango pulp using only the knowledge of the recorded temperature during thermal processing.

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Section: Maxwell-eucken Modelmentioning

confidence: 99%

Thermal Conductivity of Mango Pulp (Mangifera indicaL.) Cultivar “Ubari” in Freezing Temperatures

Giarola

Pereira

Resende

2015

International Journal of Food Properties

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“…2001; Resende and Silveira Júnior 2002). In the freezing of foodstuffs, the air velocity profile and velocity–time variation around the product determine the process efficiency and homogeneity (Resende and Silveira Júnior 2002). The measurement of time–average velocities at different positions in a tunnel is very important, but it is time‐consuming and not always precise.…”

Section: Introductionmentioning

confidence: 99%

“…In industrial cooling and freezing tunnels, convective heat transfer from the product to the cold air takes place with air flowing at velocities of 3 to 8 m/s and temperatures ranging from -35 to -45C. Inside the tunnels, uniform spatial distribution of air velocities is very desirable to achieve uniform product cooling and freezing (Pardi et al 2001;Resende and Silveira Júnior 2002). In the freezing of foodstuffs, the air velocity profile and velocity-time variation around the product determine the process efficiency and homogeneity (Resende and Silveira Júnior 2002).…”

Section: Introductionmentioning

confidence: 99%

“…Inside the tunnels, uniform spatial distribution of air velocities is very desirable to achieve uniform product cooling and freezing (Pardi et al. 2001; Resende and Silveira Júnior 2002). In the freezing of foodstuffs, the air velocity profile and velocity–time variation around the product determine the process efficiency and homogeneity (Resende and Silveira Júnior 2002).…”

Section: Introductionmentioning

confidence: 99%

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Determination of Heat Transfer Coefficient in Cooling‐freezing Tunnels Using Experimental Time–temperature Data

Santos

Carciofi

Dannenhauer

et al. 2007

J Food Process Engineering

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In industrial freezing tunnels, knowledge of the spatial distribution of convective heat transfer (h) is crucial in predicting process times. The objective of this study was to develop an appropriate and simple methodology to determine the spatial distribution of h values inside tunnels. A dedicated device, consisting of a rectangular metal tank filled with an alcoholic solution, with a temperature sensor inserted in its interior, was used as a model system. The transient temperatures of the system (tank solution) were used to calculate h values at different positions inside the tunnels. The main result of this paper is the experimental method itself, which is a reproducible and reliable procedure for determining heat transfer coefficients inside industrial tunnels. The method is sensitive enough to determine spatial heterogeneities and is versatile with respect to the system geometry, which makes it a useful and simple tool to carry out diagnostic tests in industrial plants. PRACTICAL APPLICATIONS In cooling/freezing tunnels, it is important to have convective heat transfer conditions that are spatially homogeneous, leading to homogeneous cooling/freezing processes. This is not obvious in industrial tunnels because of the arrangement of packages and fan positions. Tunnel operational conditions and layout lead to a specific airflow pattern, creating zones of high and low air flux that leads to zones of high and low convective heat transfer (h) values. This is the case for industrial tunnels used to freeze pack poultry cuts. The method proposed in this work is a useful tool to detect h value heterogeneities and to determine the average h value of preexisting tunnels. Such diagnoses can support decisions regarding layout changes or changes in the amount and positions of the fans in a tunnel. Computational fluid dynamics can also be used, but this provides more realistic data if actual h values are used.

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“…Experimental determination of the thermal properties is difficult and is connected with the different fruit composition and applied temperature. Estimation of these properties using mathematical equations of heat-mass transfer is widely applied on different computation levels (conjugate, semiconjugate, or non-conjugate; Barbin, Davila, & Silveira Júnior, 2010;Resende & Silveira Jr., 2002;USDA, 2008).…”

mentioning

confidence: 99%

Determination of thermal properties (and their uncertainties) during the cooling of apples (Malus communis)

Santos

Silva

Gomes

et al. 2022

J Food Process Engineering

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The present study goal was to determine the thermal properties of apples during their cooling. Fuji apple was cooled in a domestic refrigerator by natural convection. Thermal properties were determined assuming the geometry of an equivalent sphere. An optimizer software (LS Optimizer) was used for the inverse problem. A new heat conduction software was developed to solve the direct problem, using the spherical geometry of the diffusion equation with the boundary condition of the third kind. Thus, thermal properties of apples and their uncertainties were calculated during cooling. The developed software for the direct problem was used to simulate the cooling kinetics for any previously specified point within the sphere. The fitting of the simulated curve to the experimental points showed coefficient of determination (R2) greater than 0.99 and low values of the chi‐square function (χ2). Therefore, it can be concluded that the spherical geometry and the boundary condition of the third kind were adequate to describe the process of heat removal for apple. Determined values of thermal diffusivity and convective heat transfer coefficient were α = (1.38 ± 0.07) × 10−7 m2 s−1 and h = (1.360 ± 0.018) × 10−6 m s−1, respectively, with confidence intervals of 95.4%. Another confirmation of the developed model was the fact that the thermal diffusivity was similar to the estimate by Riedel correlation (α = 1.38 × 10−7 m2 s−1). The position of the thermocouple in the equivalent sphere calculated by numerical solution was r = 6.6 mm. Practical Applications One of the main contributions of this paper is the calculation of thermal parameters along cooling with their uncertainties. This will result in very accurate simulation of the pasteurization and freezing processes. Furthermore, the values obtained in the study can be used to predict new cooling curves for the same product, but with different dimensions.

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Medidas da condutividade térmica efetiva de modelos de polpas de frutas no estado congelado

Cited by 8 publications

References 9 publications

Thermal Conductivity of Mango Pulp (Mangifera indicaL.) Cultivar “Ubari” in Freezing Temperatures

Thermal Conductivity of Mango Pulp (Mangifera indicaL.) Cultivar “Ubari” in Freezing Temperatures

Determination of Heat Transfer Coefficient in Cooling‐freezing Tunnels Using Experimental Time–temperature Data

Determination of thermal properties (and their uncertainties) during the cooling of apples (Malus communis)

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