Publication:
Thin-layer drying model of jackfruit using artificial neural network in a far infrared dryer

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Issued Date

2021

Resource Type

Article

Language

eng

File Type

application/pdf

ISSN

25396161

DOI

10.14456/easr.2021.20

Other identifier(s)

2-s2.0-85104363025

Rights Holder(s)

มหาวิทยาลัยศรีนครินทรวิโรฒ

Bibliographic Citation

Engineering and Applied Science Research. Vol 48, No.2 (2021), p.181-189

Suggested Citation

Praneetpolkrang P., Sathapornprasath K. Thin-layer drying model of jackfruit using artificial neural network in a far infrared dryer. Engineering and Applied Science Research. Vol 48, No.2 (2021), p.181-189. doi:10.14456/easr.2021.20 Retrieved from: https://hdl.handle.net/20.500.14740/8062

Title

Thin-layer drying model of jackfruit using artificial neural network in a far infrared dryer

Author(s)

Praneetpolkrang P.
 Sathapornprasath K.

Abstract

The purpose of this article was to find the optimal model to illustrate the drying behaviors of jackfruit in a far-infrared (FIR) dryer and to examine the drying characteristics. The drying conditions were operated at drying temperatures of 60, 70 and 80 °C. In the empirical models, the Newton, Page, Modified Page, Midilli et al., Two term exponential, Henderson and Pabis, Logarithmic, and Wang and Singh model, were investigated to find the most suitable model. An artificial neural network model was also studied, with drying temperature and time selected as input variables, and MR values selected as output parameters. The dependability of the model was assessed using the R2, X2, RMSE and r statistical criteria. The results showed that for the empirical model, the Page model offered excellent results, while the optimal ANN structure was identified as 2-12-1 with Tan-sigmoid transfer functions. © 2021, Paulus Editora. All rights reserved.

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URI

https://hdl.handle.net/20.500.14740/8062

Collections

Scopus: Year 1983-2021

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