Modeling of Drying Process of Bittim Nuts (pistacia terebinthus) in a Fixed Bed Dryer System by Using Extreme Learning Machine

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dc.authoridSAHIN, Omer/0000-0003-4575-3762
dc.authoridBalbay, Asim/0000-0003-1052-5903
dc.authoridCOTELI, Resul/0000-0002-7365-4318
dc.contributor.authorBalbay, Asim
dc.contributor.authorAvci, Engin
dc.contributor.authorSahin, Omer
dc.contributor.authorCoteli, Resul
dc.date.accessioned2024-12-24T19:30:05Z
dc.date.available2024-12-24T19:30:05Z
dc.date.issued2012
dc.departmentSiirt Üniversitesi
dc.description.abstractArtificial neural networks (ANNs) have been widely used in modeling of various systems. Training of ANNs is commonly performed by backpropagation based on a gradient-based learning rule. However, it is well-known that such learning rule has several shortcomings such as slow convergence and training failures. This paper proposes a modeling technique based on Extreme Learning Machine (ELM) eliminating disadvantages of backpropagation based on a gradient-based learning rule for the drying of bittim (pistacia terebinthus). The samples for ELM based model are obtained by experimental studies. In experimental studies, the sample mass loss rate as a function time was investigated in different air velocities (0.5 and 1 m/s) and air temperatures (40, 60 and 80 C) in a designed dryer system. The obtained samples from experiments are used for training and testing of ELM. Further, some parameters of ELM such as type of activation function and the number of hidden neurons are set to obtain the best possible modelling results. The obtained prediction results show that ELM algorithm with tangent sigmoid activation function and 20 hidden neurons is appeared to be most optimal topology since maximum R2 and minimum rms (0.0500) and cov (0.2256) values are obtained. Thus, it is concluded that ELM can be used as an effective modelling tool in the drying of bittim (pistacia terebinthus) in fixed bed dryer system.
dc.identifier.doi10.1515/1556-3758.2737
dc.identifier.issn2194-5764
dc.identifier.issn1556-3758
dc.identifier.issue4
dc.identifier.scopus2-s2.0-84870479366
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1515/1556-3758.2737
dc.identifier.urihttps://hdl.handle.net/20.500.12604/7389
dc.identifier.volume8
dc.identifier.wosWOS:000310368900010
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWalter De Gruyter Gmbh
dc.relation.ispartofInternational Journal of Food Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectdrying
dc.subjectextreme learning machine
dc.subjectheat transfer
dc.subjectpistacia terebinthus
dc.titleModeling of Drying Process of Bittim Nuts (pistacia terebinthus) in a Fixed Bed Dryer System by Using Extreme Learning Machine
dc.typeArticle

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