Determination and Modeling of Thermophysical and Transport Properties of Tropical Pulps

Varela, Magdiely Stefanes de Santana

doi:10.11648/j.ejb.20170505.11

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…Eugenia uniflora Berg ("pitanga") pulps (Varela, Pitombeira, Silva, & Pereira, 2017). The decrease in density values as the temperature increased may be related to the agitation level of the molecules.…”

Section: Mathematical Modelingmentioning

confidence: 99%

Thermophysical properties of carbohydrate solutions: Correlation between thermal and transport properties

Martins

Guimarães

Polachini

et al. 2020

J Food Process Engineering

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Sugar solutions such as sucrose, glucose and fructose are often used in osmotic dehydration processes. Thus, the experimental determination of their thermophysical properties is a must for designing purposes. Density, specific heat and thermal conductivity were determined in a wide range of solute concentration (10–60 °Brix) and temperature (273.15–358.15 K). Empirical equations were considered to fit the experimental data and to evaluate the data behavior. An assessment based on the Prandtl number was considered to correlate thermal and rheological data. Higher density and lower specific heat and thermal conductivity values were observed when temperature decreased and solute concentration increased. Prandtl number behaved similarly as density, indicating that momentum transfer is favored in comparison with heat transfer in more concentrated solutions at lower temperatures. A versatile polynomial model concerning the significant variables could predict the experimental values for all solutions with good accuracy (R2 > .9863 and MRE < 1.20%). Reported data and equations showed to be essential for engineering aspects and transport phenomena analysis. Practical Applications Sucrose, glucose and fructose solutions are commonly used in osmotic dehydration processes to promote solute incorporation, increasing shelf‐life and quality of food products. The availability of thermophysical properties in a wide range of solute concentration and temperature covers the most of conventional and nonconventional conditions for designing osmotic dehydration and many other food processes. Among the unit operations, it could be found pumping, mixing/stirring, pasteurization, sterilization, evaporation and so on. These data are useful to understand how properties change when the conditions are altered during the processes. They also provide important information about momentum and heat transfer through the fluid. By analyzing dimensionless parameters, as Prandtl number, one phenomenon or other can be favored under specific conditions. The presence of accurate mathematical models results in greater agility and reliability in the execution of processes, especially for those operating in wide ranges of temperature and solute concentration.

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Section: Mathematical Modelingmentioning

confidence: 99%

Thermophysical properties of carbohydrate solutions: Correlation between thermal and transport properties

Martins

Guimarães

Polachini

et al. 2020

J Food Process Engineering

View full text Add to dashboard Cite

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Modeling thermal properties of exotic fruits pulps: an artificial neural networks approach

Santos

Moraes

Lopes

et al. 2020

RSD

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Thermophysical properties are important in design, simulation, optimization, and control of food processing. Its prediction is very important but theoretical basis is difficult and empirical models were commonly used. In this work, the modeling of neural networks was applied as an alternative to predict density, thermal conductivity and thermal diffusivity from the temperature and moisture content of jackfruit, genipap and umbu. Data sets from literature were used, combined and individually, to obtain four networks. Supervised multilayer perceptron networks were developed, using the back-propagation algorithm. Several configurations of artificial neural networks (ANNs) were evaluated with one or two hidden layers and a maximum of 21 and 12 neurons in each one, respectively. Data sets were divided to learning (60%) and verification (40%) steps. Best ANNs were chosen based on correlation coefficient and root mean square errors (RMSE), and compared with polynomial models using average absolute deviations (AADs). From total disposable data set, the best ANN developed presents one hidden layer with 15 neurons and shows the same predictive ability of ANNs created from individual fruits data sets, presenting close RMSE and correlation coefficient. The ANNs developed presents AADs near to polynomial models and appers as alternative to conventional modeling. Results indicate that the ANN created from total data set can replace nine polynomial models to predict the thermophysical properties of jackfruit, genipap and umbu pulps.

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Determination and Modeling of Thermophysical and Transport Properties of Tropical Pulps

Cited by 2 publications

References 18 publications

Thermophysical properties of carbohydrate solutions: Correlation between thermal and transport properties

Thermophysical properties of carbohydrate solutions: Correlation between thermal and transport properties

Modeling thermal properties of exotic fruits pulps: an artificial neural networks approach

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