Model predictive temperature control in long ducts by means of a neural network approximation tool

Aggelogiannaki, E; Sarimveis, H; Koubogiannis, D

dc.contributor.author	Aggelogiannaki, E	en
dc.contributor.author	Sarimveis, H	en
dc.contributor.author	Koubogiannis, D	en
dc.date.accessioned	2014-03-01T01:26:41Z
dc.date.available	2014-03-01T01:26:41Z
dc.date.issued	2007	en
dc.identifier.issn	1359-4311	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18168
dc.subject	Hyperbolic distributed parameter systems	en
dc.subject	Long ducts	en
dc.subject	Model predictive control	en
dc.subject	Radial basis function neural networks	en
dc.subject	Temperature control	en
dc.subject.classification	Thermodynamics	en
dc.subject.classification	Energy & Fuels	en
dc.subject.classification	Engineering, Mechanical	en
dc.subject.classification	Mechanics	en
dc.subject.other	Approximation theory	en
dc.subject.other	Computational methods	en
dc.subject.other	Model predictive control	en
dc.subject.other	Nonlinear systems	en
dc.subject.other	Radial basis function networks	en
dc.subject.other	Temperature control	en
dc.subject.other	Conventional modeling	en
dc.subject.other	Hyperbolic distributed parameter systems	en
dc.subject.other	Long ducts	en
dc.subject.other	Neural network approximation tools	en
dc.subject.other	Ducts	en
dc.title	Model predictive temperature control in long ducts by means of a neural network approximation tool	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.applthermaleng.2007.03.005	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.applthermaleng.2007.03.005	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	In this paper, a nonlinear model predictive control (MPC) configuration for hyperbolic distributed thermal systems is presented and applied in the flow-based temperature control in a long duct. At first, a radial basis function neural network is developed to estimate the temperature distribution along the duct with respect to flow velocity, assuming constant ambient temperature. The nonlinear model is then incorporated in the context of an MPC procedure. The use of the neural network model avoids the spatial discretization and decreases significantly the computational effort required to solve the optimization problem that is formulated in real time, compared to conventional modeling approaches. The proposed MPC scheme is able to overcome delay effects and accelerates the outlet temperature response. Reduced tuning effort is another advantage of the proposed control scheme. (c) 2007 Elsevier Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Applied Thermal Engineering	en
dc.identifier.doi	10.1016/j.applthermaleng.2007.03.005	en
dc.identifier.isi	ISI:000247865200003	en
dc.identifier.volume	27	en
dc.identifier.issue	14-15	en
dc.identifier.spage	2363	en
dc.identifier.epage	2369	en