Study on infrared drying kinetics of quince slices and modelling of drying process using genetic algorithm-artificial neural networks (GA-ANNs)

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

YUSEFI ALIREZA; Dilmaghanian Sonia; Ziaforoughi Amin; Moezzi Meysam

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF INNOVATIVE FOOD TECHNOLOGIES (JIFT) Year:2019 | Volume:6 | Issue:2 Page(s): 175-186

Download Full-Text

Persian Verion

View:

648

Download:

Cites:

Information Journal Paper

Title

Study on infrared drying kinetics of quince slices and modelling of drying process using genetic algorithm-artificial neural networks (GA-ANNs)

Author(s)

YUSEFI ALIREZA | Dilmaghanian Sonia | Ziaforoughi Amin | Moezzi Meysam | Issue Writer Certificate

Keywords

GA-ANNsQ1

ModelingQ2

Quince fruitQ1

InfraredQ1

Diffusivity coefficientQ1

Activation energyQ1

Abstract

In this research Infrared radiation was used for drying of quince slices. For this reason, the influence of drying temperature of 50, 60, 70 and 80 ° C acquired by 51, 73, 98 and 12 W Infrared lamp was investigated. Drying results showed that the drying rate increased with increasing temperature. The drying decreased up to 60% when temperature was increased from 50 to 80 ° C. Affected by the lamp power from 51 to 125 W, the moisture content diminished from 453% (d. b. ) to 16% (d. b. ). Modeling of drying process using genetic algorithm-artificial neural networks (GA-ANNs) with 3 inputs (drying time, drying temperature and the slice enter temperature) and one output (the amount of moisture ratio (MR)) was done. The Modeling results demonstrated a network with 7 neurons in hidden layer and tangent hyperbolic transport function could precisely predict the moisture content of slices during drying (R2 = 0. 9997 and RMSE = 0. 0044). This precision for optimized GA-ANNs was even higher than that of Midilli model-the best empirical model-(R2 = 0. 9987-0. 9994 and RMSE = 0. 0068-0. 0098) at all the temperatures tested. The results obtained from the sensitivity analysis by the optimized neural networks revealed that the center temperature of slices was the most pronounced factor (0. 0044) to control the MR. Increase in temperature resulted in an increase in the effective Diffusivity coefficient, so that this coefficient reached to 26. 1×10-9 m2/s at 80 ° C from the initial value of 10. 8×10-9 m2/s at 50 ° C. The Activation energy (Ea) calculated for the quince slices were 28. 68 kJ/mol.

Cites

No record.

References

No record.

Cite

APA: Copy

YUSEFI, ALIREZA, Dilmaghanian, Sonia, Ziaforoughi, Amin, & Moezzi, Meysam. (2019). Study on infrared drying kinetics of quince slices and modelling of drying process using genetic algorithm-artificial neural networks (GA-ANNs). JOURNAL OF INNOVATIVE FOOD TECHNOLOGIES (JIFT), 6(2 ), 175-186. SID. https://sid.ir/paper/258714/en

Vancouver: Copy

YUSEFI ALIREZA, Dilmaghanian Sonia, Ziaforoughi Amin, Moezzi Meysam. Study on infrared drying kinetics of quince slices and modelling of drying process using genetic algorithm-artificial neural networks (GA-ANNs). JOURNAL OF INNOVATIVE FOOD TECHNOLOGIES (JIFT)[Internet]. 2019;6(2 ):175-186. Available from: https://sid.ir/paper/258714/en

IEEE: Copy

ALIREZA YUSEFI, Sonia Dilmaghanian, Amin Ziaforoughi, and Meysam Moezzi, “Study on infrared drying kinetics of quince slices and modelling of drying process using genetic algorithm-artificial neural networks (GA-ANNs),” JOURNAL OF INNOVATIVE FOOD TECHNOLOGIES (JIFT), vol. 6, no. 2 , pp. 175–186, 2019, [Online]. Available: https://sid.ir/paper/258714/en

Related Journal Papers