TY - JOUR
T1 - An artificial intelligence approach to inverse heat transfer modeling of an irradiative dryer
AU - Mirsephai, Ali
AU - Mohammadzaheri, Morteza
AU - Chen, Lei
AU - O'Neill, Brian
PY - 2012/1
Y1 - 2012/1
N2 - 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.
AB - 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.
KW - Artificial neural networks
KW - Intelligent techniques
KW - Inverse heat transfer problems
KW - Inverse radiation
KW - Irradiative dryers
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U2 - 10.1016/j.icheatmasstransfer.2011.09.015
DO - 10.1016/j.icheatmasstransfer.2011.09.015
M3 - Article
AN - SCOPUS:84855342257
SN - 0735-1933
VL - 39
SP - 40
EP - 45
JO - International Communications in Heat and Mass Transfer
JF - International Communications in Heat and Mass Transfer
IS - 1
ER -