An artificial intelligence approach to inverse heat transfer modeling of an irradiative dryer

dc.contributor.authorMirsepahi, A.
dc.contributor.authorMohammadzaheri, M.
dc.contributor.authorChen, L.
dc.contributor.authorO'Neill, B.
dc.date.issued2012
dc.descriptionCorrected by: Erratum to "An artificial intelligence approach to inverse heat transfer modeling of an irradiative dryer", in Vol. 39, no. 6, p.885. The first author's name was misspelt.
dc.description.abstractIn this work, a new solution approach was developed for heat estimation class of inverse heat transfer problems where radiation provides the dominant mode thermal energy transport. An Artificial Neural Network (ANN) was designed, trained and employed to estimate the heat emitted to irradiative batch drying process. In a simple laboratory drying furnace, various input signals (different input power functions) were input to the dryer's halogen lamp and the resulting temperature history were measured and recorded for a point on the bottom surface of the dryer. After estimating the order, the sampling time and the dead-time of the system, the recorded data were arranged for inverse modelling purposes. Next, an artificial neural network (ANN) was designed and trained to play the role of the inverse heat transfer model. The results showed that ANNs are applicable to solve inverse heat estimation problems of irradiative batch drying process. An important advantage of this method in comparison with classical inverse heat transfer modelling approaches, detailed knowledge of the geometrical and thermal properties of the system (such as wall conductivity, emissivity, etc.) is not necessary. Such properties are difficult to measure and may undergo significant changes during the temperature transient. © 2011 Elsevier Ltd.
dc.description.statementofresponsibilityAli Mirsephai, Morteza Mohammadzaheri, Lei Chen and Brian O'Neill
dc.identifier.citationInternational Communications in Heat and Mass Transfer, 2012; 39(1):40-45
dc.identifier.doi10.1016/j.icheatmasstransfer.2011.09.015
dc.identifier.issn0735-1933
dc.identifier.orcidChen, L. [0000-0002-2269-2912]
dc.identifier.urihttp://hdl.handle.net/2440/71292
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.rights© 2011 Elsevier Ltd. All rights reserved.
dc.source.urihttps://doi.org/10.1016/j.icheatmasstransfer.2011.09.015
dc.titleAn artificial intelligence approach to inverse heat transfer modeling of an irradiative dryer
dc.typeJournal article
pubs.publication-statusPublished

Files