Drying kinetics of grape seeds

Roberts, J.S.; Kidd, David R.; Qiu, Shuang

doi:10.1016/j.jfoodeng.2008.05.030

Cited by 282 publications

(201 citation statements)

References 25 publications

Supporting

Mentioning

147

Contrasting

Unclassified

Order By: Relevance

“…In these curves, an increase of drying rate, given by the curve slope, with increase in temperature was observed, being consistent with the reported in literature for this product (Roberts;Kidd;Padilla-Zakour, 2008;Kaleemullah;Kailappan, 2006). On the other hand, orange seeds did not exhibit a constant rate period of drying, observing only the falling rate period, behavior was also observed by Cantu-Lozano et al (2013) in study with grapefruit seed.…”

Section: Experimental Drying Kineticssupporting

confidence: 90%

“…Similar values were found in products such as grapefruit seeds (Cantú-Lozano et al, 2013) and grape seeds (Roberts;Kidd;Padilla-Zakour, 2008). Also, the D eff increase when the temperature increases for each drying velocity level.…”

Section: Mathematical Modelling Of Drying Kineticssupporting

confidence: 80%

“…), x e is the equilibrium moisture content (kg/kg, d.b. ), k is the drying rate constant (1/s) and β 1 and β 2 are the constants (dimensionless) (Kaleemullah;Kailappan, 2006;Roberts;Kidd;PadillaZakour, 2008 …”

Section: Experimental Drying Kineticsmentioning

confidence: 99%

“…(all drying velocities) was 8640 s (2.4 h), 7200 s (2.0 h), 5760 s (1.6 h) and 5760 s (1.6 h) at the drying temperatures of 40, 50, 60 and 70 °C, respectively. The time of drying for orange seeds was faster compared to other seeds such as grape seeds (4.6 h) (Roberts;Kidd;Padilla-Zakour, 2008), cuphea seeds (3.7 h) (Cermak et al, 2005) and amaranth seeds (3.1 h) (Abalone et al, 2006). Curves of moisture content versus drying time and the drying rate versus moisture content of orange seeds for the different drying air temperatures and velocities are shown in Figure 2.…”

Section: Experimental Drying Kineticsmentioning

confidence: 99%

See 3 more Smart Citations

Mathematical Modeling of Orange Seed Drying Kinetics

et al. 2015

View full text Add to dashboard Cite

Drying of orange seeds representing waste products from juice processing was studied in the temperatures of 40, 50, 60 and 70 °C and drying velocities of 0.6, 1.0 and 1.4 m/s. Experimental drying kinetics of orange seeds were obtained using a convective air forced dryer. Three thin-layer models: Page model, Lewis model, and the Henderson-Pabis model and the diffusive model were used to predict the drying curves. The Henderson-Pabis and the diffusive models show the best fitting performance and statistical evaluations. Moreover, the temperature dependence on the effective diffusivity followed an Arrhenius relationship, and the activation energies ranging from 16.174 to 16.842 kJ/mol. Index terms: Dehydration; diffusive model; Henderson-Pabis model; moisture content. RESUMOA secagem de sementes de laranja que representam produtos residuais do processamento de suco foi estudada nas temperaturas de 40, 50, 60 e 70 °C e velocidades de secagem de 0,6, 1,0 e 1,4 m/s. As cinéticas de secagem experimental de sementes de laranja foram obtidas utilizando um secador de ar forçado por convecção. Três modelos de camada fina: modelo de Page, modelo Lewis, e o modelo de Henderson-Pabis e o modelo difusivo foram utilizados para prever as curvas de secagem. Os modelos de HendersonPabis e difusivo mostraram o melhor desempenho adequado e avaliações estatísticas. Além disso, a dependência da temperatura na difusividade efetiva seguida a relação de Arrhenius, e as energias de ativação variam entre 16,174-16,842 kJ / mol. Termos de indexação:Desidratação; modelo difuso; modelo Henderson-Pabis; teor de umidade.

show abstract

Section: Experimental Drying Kineticssupporting

confidence: 90%

Section: Mathematical Modelling Of Drying Kineticssupporting

confidence: 80%

Section: Experimental Drying Kineticsmentioning

confidence: 99%

Section: Experimental Drying Kineticsmentioning

confidence: 99%

See 2 more Smart Citations

Mathematical Modeling of Orange Seed Drying Kinetics

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Relative percentage deviation compares the absolute differences between the predicted moisture content with the experimental moisture content throughout the drying process. The relative percent errors below 10 % indicate very good fit (Roberts et al 2008;Ozdemir and Devres 1999). RMSE and CS compare the differences between the model predicted values of moisture ratios to the experimental moisture ratios.…”

Section: Statistical Analysis and Evaluation Of Drying Modelsmentioning

confidence: 99%

Hot air drying characteristics of mango ginger: Prediction of drying kinetics by mathematical modeling and artificial neural network

Murthy

Manohar

2013

J Food Sci Technol

View full text Add to dashboard Cite

Mango ginger (Curcuma amada) was dried in a through-flow dryer system at different temperatures (40-70°C) and air velocities (0.84 -2.25 m/s) to determine the effect of drying on drying rate and effective diffusivity. As the temperature and air velocity increased, drying time significantly decreased. Among the ten different thin layer drying models considered to determine the kinetic drying parameters, semi empirical Midilli et al., model gave the best fit for all drying conditions. Effective moisture diffusivity varied from 3.7 × 10 −10 m 2 /s to 12.5 × 10 −10 m 2 /s over the temperature and air velocity range of study. Effective moisture diffusivity regressed well with Arrhenius model and activation energy of the model was found to be 32.6 kJ/mol. Artificial neural network modeling was also employed to predict the drying behaviour and found suitable to describe the drying kinetics with very high correlation coefficient of 0.998.

show abstract

Spatial reaction engineering approach as an alternative for nonequilibrium multiphase mass‐transfer model for drying of food and biological materials

Putranto

Chen

2012

AIChE Journal

View full text Add to dashboard Cite

in Wiley Online Library (wileyonlinelibrary.com).Drying is a complex process which involves simultaneous heat and mass transfer. Complicated structure and heterogeneity of food and biological materials add to the complexity of drying. Drying models are important for improving dryer design and for evaluating dryer performance. The lumped reaction engineering approach (L-REA) has been shown to be an accurate and robust alternative for cost-effective simulations of challenging drying systems. However, more insightful physics has to be shown spatially. In this study, the REA is coupled with the standard mechanistic drying models to yield the spatial-REA (S-REA) as nonequilibrium multiphase mass-transfer model. The S-REA consists of a system of equations of conservation with the REA representing the local evaporation and wetting rate. Results of the modeling using the S-REA match well with the experimental data reported previously. This is the first comprehensive REA approach to model the profiles of water vapor concentration during drying of food and biological materials. This study indicates that the S-REA can be an accurate nonequilibrium multiphase mass-transfer model with appropriate account of the local evaporation rate. The overall REA concept is expected to contribute substantially for better and cost-effective representation of transport phenomena of drying process.

show abstract

Drying kinetics of grape seeds

Cited by 282 publications

References 25 publications

Mathematical Modeling of Orange Seed Drying Kinetics

Mathematical Modeling of Orange Seed Drying Kinetics

Hot air drying characteristics of mango ginger: Prediction of drying kinetics by mathematical modeling and artificial neural network

Spatial reaction engineering approach as an alternative for nonequilibrium multiphase mass‐transfer model for drying of food and biological materials

Contact Info

Product

Resources

About