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

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dc.contributor.author Praneetpolkrang P.
dc.contributor.author Sathapornprasath K.
dc.date.accessioned 2022-03-10T13:17:16Z
dc.date.available 2022-03-10T13:17:16Z
dc.date.issued 2021
dc.identifier.issn 25396161
dc.identifier.other 2-s2.0-85104363025
dc.identifier.uri https://ir.swu.ac.th/jspui/handle/123456789/17492
dc.identifier.uri https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104363025&doi=10.14456%2feasr.2021.20&partnerID=40&md5=d769112baab0a7b4a78baf13418f273d
dc.description.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.
dc.language en
dc.title Thin-layer drying model of jackfruit using artificial neural network in a far infrared dryer
dc.type Article
dc.rights.holder Scopus
dc.identifier.bibliograpycitation Engineering and Applied Science Research. Vol 48, No.2 (2021), p.181-189
dc.identifier.doi 10.14456/easr.2021.20


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