A neural network approach for modeling and control of continuous digesters

Alexandridis, A; Sarimveis, H; Bafas, G; Retsina, T

dc.contributor.author	Alexandridis, A	en
dc.contributor.author	Sarimveis, H	en
dc.contributor.author	Bafas, G	en
dc.contributor.author	Retsina, T	en
dc.date.accessioned	2014-03-01T02:49:12Z
dc.date.available	2014-03-01T02:49:12Z
dc.date.issued	2002	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/34398
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-0142156242&partnerID=40&md5=3fd6d043b649a8f7177bf6ab38f76a44	en
dc.subject.other	Identification (control systems)	en
dc.subject.other	Predictive control systems	en
dc.subject.other	Radial basis function networks	en
dc.subject.other	Model predictive control (MPC) scheme	en
dc.subject.other	Pulp digesters	en
dc.subject.other	Neural Networks	en
dc.subject.other	Pulps	en
dc.title	A neural network approach for modeling and control of continuous digesters	en
heal.type	conferenceItem	en
heal.publicationDate	2002	en
heal.abstract	In this paper we present a methodology for developing dynamical models for continuous digesters using input-output data. The methodology uses the Radial Basis Function (RBF) neural network architecture, which is continuously increasing its popularity in solving system identification problems, due its simple network structure and the short training times it employs. The produced dynamic RBF network model can be utilized to predict the future behavior of the process, analyze the dynamics of the digester and control the process through a Model Predictive Control (MPC) scheme.	en
heal.journalName	TAPPI Fall technical Conference and Trade Fair	en
dc.identifier.spage	355	en
dc.identifier.epage	367	en