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

Ali Mirsephai, Morteza Mohammadzaheri, Lei Chen, Brian O'Neill

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

In 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.

Original languageEnglish
Pages (from-to)40-45
Number of pages6
JournalInternational Communications in Heat and Mass Transfer
Volume39
Issue number1
DOIs
Publication statusPublished - Jan 2012

Fingerprint

drying apparatus
artificial intelligence
drying
Artificial intelligence
Drying
heat transfer
Heat transfer
heat
Neural networks
Halogens
Thermal energy
Electric lamps
emissivity
thermal energy
halogens
luminaires
furnaces
Furnaces
estimating
Thermodynamic properties

Keywords

  • Artificial neural networks
  • Intelligent techniques
  • Inverse heat transfer problems
  • Inverse radiation
  • Irradiative dryers

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Chemical Engineering(all)
  • Condensed Matter Physics

Cite this

An artificial intelligence approach to inverse heat transfer modeling of an irradiative dryer. / Mirsephai, Ali; Mohammadzaheri, Morteza; Chen, Lei; O'Neill, Brian.

In: International Communications in Heat and Mass Transfer, Vol. 39, No. 1, 01.2012, p. 40-45.

Research output: Contribution to journalArticle

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