Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/105307
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Type: Journal article
Title: An artificial intelligence solution for heat flux estimation using temperature history; a two-input/two-output problem
Author: Mirsepahi, A.
Chen, L.
O Neill, B.
Citation: Chemical Engineering Communications, 2017; 204(3):289-294
Publisher: Taylor & Francis
Issue Date: 2017
ISSN: 0098-6445
1563-5201
Statement of
Responsibility: 
Ali Mirsepahi, Lei Chen & Brian O’Neill
Abstract: In order to check the applicability of Artificial Intelligent (AI) techniques to act as reliable inverse models to solve the multi-input/ multi-output heat flux estimation classes of inverse heat transfer problems (IHTPs), in a newly reconstructed experimental setup, a two-input/two-two output (TITO) heat flux estimation problem was defined in which the radiation acts as the main mode of thermal energy. A simple three-layer perceptron Artificial Neural Network (ANN) was designed, trained, and employed to estimate the input powers (represent emitted heats-heat fluxes from two halogen lamps) to irradiative batch drying process. To this end, different input power functions (signals) were input to the furnace/dryer’s halogen lamps, and the resultant temperature histories were measured and recorded for two different points of the dryer/furnace. After determining the required parameters, the recorded data were prepared and arranged to be used for inverse modelling purposes. Next, an ANN was designed and trained to play the role of the inverse heat transfer model. The results showed that ANNs are applicable to solve heat flux estimation classes of IHTPs.
Keywords: Artificial neural networks; Intelligent techniques; Inverse radiation; Irradiative furnace; MIMO problems; TITO problems
Rights: Copyright © Taylor & Francis Group, LLC
DOI: 10.1080/00986445.2016.1253008
Appears in Collections:Aurora harvest 3
Mechanical Engineering publications

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