Mathematical modeling on vacuum drying of Zizyphus jujuba Miller slices

Lee, Jun Ho; Zuo, Liang

doi:10.1007/s13197-011-0312-5

Cited by 37 publications

(39 citation statements)

References 44 publications

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“…Drying rate decreased with decrease in moisture content and temperature. Similar results have been reported by Lee and Zuo (2013) for vacuum drying of Z. jujube miller.…”

Section: Resultssupporting

confidence: 90%

“…Effective moisture diffusivity (m 2 s -1 ) 50 6.274 × 10 -10 60 1.45 × 10 -9 70 1.9 × 10 -9 range10 -11 -10 -9 m 2 s -1 for vacuum drying of food materials (Domyaz and Ismail, 2011;Lee and Zuo, 2013). The moisture diffusivity values increased steadily with increase in drying temperature.…”

Section: Temperature (C)mentioning

confidence: 99%

“…Mathematical modeling of thin layer drying is important for performance improvement of drying systems (Cihan et al, 2007). Drying kinetics models are essential for equipment design, process optimization and product quality improvement (Lee and Zuo, 2013). In recent years, thin layer drying behaviour of chillies has been studied by several researchers (Charmongkolpradit et al, 2010;Banout et al, 2011;Fadhel et al, 2014) which focussed on either sun, solar or hot air drying methods for drying of red pepper.…”

Section: Introductionmentioning

confidence: 99%

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Modelling of vacuum drying of cherry pepper

Boris

Shrilekha

Sujata

2018

AJDFR

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Drying is a complex phenomenon and study of drying kinetics and modelling is very important for describing the moisture movement with respect to time and predicting the dryer performance. Considering this fact, vacuum drying characteristics of cherry pepper was studied at different drying temperatures (50, 60 and 70oC). Cherry pepper were dried from initial moisture content of about 400% (dry basis) to 13 – 14.5% (dry basis) at different temperatures at 630 mm Hg vacuum. Time required to dry cherry peppers at 50, 60 and 70p C plate temperatures were 19 h, 9.75 h and 8 h respectively. Moisture reduction of cherry pepper at various temperatures was modelled using thin-layer models viz. Lewis, Page, Modified Page and Henderson and Pabis model. Based on highest value of coefficient of determination, lowest values of reduced chi square and root mean square error, Modified Page model was found to be the best fit. Moisture diffusivity increased from 6.27 × 10-10 to 1.9× 10-9m2s-1 as plate temperature increased from 50 – 70oC. Activation energy was estimated to be 50.98 kJkgmol-1.

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“…Drying rate decreased with decrease in moisture content and temperature. Similar results have been reported by Lee and Zuo (2013) for vacuum drying of Z. jujube miller.…”

Section: Resultssupporting

confidence: 90%

Section: Temperature (C)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modelling of vacuum drying of cherry pepper

Boris

Shrilekha

Sujata

2018

AJDFR

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“…This implies that a little amount of energy will be required to exhaust water from the internal regions of turnip. The obtained amount of the activation energy for turnip slices were in a appropriate range and in agreement with the previous works (Tunde‐Akintunde and Ogunlakin, 2010; Ioannou et al ., ; Chen et al ., ; Rodriguez et al ., 2013; Lee and Zuo, ).…”

Section: Resultsmentioning

confidence: 99%

Modeling Thin-Layer Drying of Turnip Slices Under Semi-Industrial Continuous Band Dryer

Kaveh

Chayjan

2016

Journal of Food Processing and Preservation

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This study aims at investigating the thin‐layer drying behavior of turnip slices in a multistage semi‐industrial continuous band dryer. Turnip slices with the thickness of 4 mm were used for the drying experiments. The experiments were conducted at three air temperatures of 45, 60, and 75C, three air velocities of 1, 1.5, and 2 m/s, and three belt linear speeds of 2.5, 6.5, and 10.5 mm/s with three replications for each treatment. To estimate the drying kinetic of turnip slices, six mathematical models were used to fit the experimental data of thin layer drying. Consequently, the Midilli et al. model was selected as the best mathematical model to describe the drying kinetics of the turnip slices. The effective moisture diffusivity varied from 8.37 × 10−10 to 4.82 × 10−9 m2/s. The energy of activation varied from 12.80 to 26.31 kJ/mol using Arrhenius type equation. After well training of the ANN models, proved that the ANN model was relatively better than the empirical models. The best neural network for the prediction of moisture ratio (MR) and drying rate was feed forward back‐propagation with 4‐10‐10‐2 structure, training algorithm of Bayesian regulation and threshold functions of tansig‐purelin‐logsig. The best R2 value for predication of MR and drying rate were 0.9990 and 0.9619, respectively. Practical Applications Turnip is one of the oldest cultivated vegetables that have been used for human expenditures since prehistoric times. Drying process has been used for decades in food processing industries for efficient long‐term preservation of final products. It has the potential to deliver safe food products through enzyme inactivation and microbe destruction. Advantages of semi‐industrial continuous band drying are including short processing time, high mass transfer coefficients, low energy consumption, and high quality. To model experimental drying data, many correlations that are available in literature may be used. However, artificial neural network (ANN) methodology has become increasingly popular recently because of its capability of giving more general and precise results as also presented in this study.

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“…The regression analysis was performed using the MATLAB Program. The correlation coefficient (R 2 ), reduced squared-chi (χ 2 ) and the root mean square error (RMSE) were used as criteria for adequacy of the fit (Chandra Mohan & Talukdar 2010;Lee & Zuo 2013;Leite et al 2018). The higher R-values and the lower χ 2 and RMSE values indicate the better goodness of fit.…”

Section: Drying Modelmentioning

confidence: 99%

Experimental Study of Drying Characteristics and Mathematical Modeling for Air Drying of Germinated Brown Rice

Yousaf¹,

Wang²,

Chen³

2019

JSM

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In view of existing problems in the drying process of germinated brown rice (GBR), the self-made hot air drying test system was utilized. The drying medium temperature and wind speed were selected as the drying parameters, and different constraints were set for the test. The effects of the drying medium temperature and wind speed on the drying rate and unit energy consumption were examined, and the drying mathematical models of GBR were established. The results perceived that as the temperature rose, and the wind speed increased, the drying rate increased accordingly. When the temperature was above 95°C, wind speed exceeded 3.6 m/s; the drying rate would not change deliberately. When the temperature of the drying medium rose, the change rate during the drying preheating stage and the deceleration stage increased sharply, whereas the drying rate in the constant-speed drying stage increased, and the drying time was greatly shortened. Unit energy consumption decreased with the increase of temperature and increased with increasing wind speed. Furthermore, when the drying temperature was ranged between 50°C and 80°C, the unit energy consumption changed meaningfully; when the medium temperature was between 80°C and 110°C, the unit heat consumption turned slowly. Wang and Singh's model could best simulate the drying process of GBR within the experimental settings. And then comparing the RMSE and under the various dry conditions, the data of Wang and Singh model were between 1.6% -2.8% and 2.5×10-4 -5×10-4. The R 2 values of the model were higher than 0.98. ABSTRAKDisebabkan masalah sedia ada dalam proses pengeringan percambahan beras perang (GBR), sistem ujian pengeringan udara panas buatan sendiri telah digunakan. Pengeringan suhu sederhana dan kelajuan angin dipilih sebagai parameter pengeringan dan kekangan berbeza ditetapkan untuk ujian. Kesan daripada pengeringan suhu sederhana dan kelajuan angin pada kadar pengeringan dan penggunaan tenaga unit disemak dan pengeringan model matematik GBR telah ditubuhkan. Keputusan menunjukkan apabila suhu meningkat dan kelajuan angin bertambah, kadar pengeringan meningkat dengan sewajarnya. Apabila suhu melebihi 95°C, kelajuan angin melebihi 3.6 m/s; kadar pengeringan tidak akan berubah secara terancang. Apabila suhu pengeringan itu meningkat, kadar perubahan pada peringkat pra-pemanasan pengeringan dan peringkat nyahpecutan meningkat secara mendadak, sedangkan kadar pengeringan pada peringkat pengeringan laju meningkat dan masa pengeringan telah banyak dipendekkan. Penggunaan tenaga unit menurun dengan peningkatan suhu dan meningkat dengan kelajuan angin. Tambahan pula, apabila suhu pengeringan berada dalam lingkungan 50°C hingga 80°C, penggunaan tenaga unit juga turut berubah; apabila suhu sederhana adalah antara 80°C dan 110°C, penggunaan haba unit bertukar menjadi perlahan. Model Wang dan Singh adalah stimulasi terbaik bagi proses pengeringan GBR dalam tetapan uji kaji. Apabila perbandingan RMSE dijalankan pada pelbagai keadaan pengeringan, data bagi model Wang dan Singh adal...

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Mathematical modeling on vacuum drying of Zizyphus jujuba Miller slices

Cited by 37 publications

References 44 publications

Modelling of vacuum drying of cherry pepper

Modelling of vacuum drying of cherry pepper

Modeling Thin-Layer Drying of Turnip Slices Under Semi-Industrial Continuous Band Dryer

Experimental Study of Drying Characteristics and Mathematical Modeling for Air Drying of Germinated Brown Rice

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