An Investigation of Drying Process of Shelled Pistachios in a Newly Designed Fixed Bed Dryer System by Using Artificial Neural Network

Basit Öğe Kaydı
dc.authoridSAHIN, Omer/0000-0003-4575-3762
dc.authoridBalbay, Asim/0000-0003-1052-5903
dc.contributor.authorBalbay, Asim
dc.contributor.authorSahin, Omer
dc.contributor.authorKarabatak, Murat
dc.date.accessioned2024-12-24T19:28:11Z
dc.date.available2024-12-24T19:28:11Z
dc.date.issued2011
dc.departmentSiirt Üniversitesi
dc.description.abstractIn this paper, the drying of Siirt pistachios (SSPs) in a newly designed fixed bed dryer system and the prediction of drying characteristics using artificial neural network (ANN) are presented. Drying characteristics of SSPs with initial moisture content (MC) of 42.3% dry basis (db) were studied at different air temperatures (60, 80, and 100 degrees C) and air velocities (0.065, 0.1, and 0.13 m/s) in a newly designed fixed bed dryer system. Obtained results of experiments were used for ANN modeling and compared with experimental data. Falling rate period was observed during all the drying experiments; constant rate period was not observed. Furthermore, in the presented study, the application of ANN for predicting the drying time (DT) for a good quality product (output parameter for ANN modeling) was investigated. In order to train the ANN, experimental measurements were used as training data and test data. The back propagation learning algorithm with two different variants, so-called Levenberg-Marguardt (LM) and scaled conjugate gradient (SCG), and tangent sigmoid transfer function were used in the network so that the best approach can be determined. The most suitable algorithm and neuron number in the hidden layer are found out as LM with 15 neurons. For this number level, after the training, it is found that Root-mean squared (RMS) value is 0.3692, and absolute fraction of variance (R-2) value is 99.99%. It is concluded that ANNs can be used for prediction of drying SSPs as an accurate method in similar systems.
dc.identifier.doi10.1080/07373937.2011.600843
dc.identifier.endpage1696
dc.identifier.issn0737-3937
dc.identifier.issn1532-2300
dc.identifier.issue14
dc.identifier.scopus2-s2.0-84860389241
dc.identifier.scopusqualityQ1
dc.identifier.startpage1685
dc.identifier.urihttps://doi.org/10.1080/07373937.2011.600843
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6937
dc.identifier.volume29
dc.identifier.wosWOS:000294805700010
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTaylor & Francis Inc
dc.relation.ispartofDrying Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectArtificial neural network
dc.subjectDrying
dc.subjectHeat transfer
dc.subjectPistachio
dc.titleAn Investigation of Drying Process of Shelled Pistachios in a Newly Designed Fixed Bed Dryer System by Using Artificial Neural Network
dc.typeArticle

Dosyalar

Koleksiyon

WOS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu