Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/106965
Type: Artigo de evento
Title: Development Of Data Reconciliation For Dynamic Nonlinear System: Application The Polymerization Reactor
Author: Barbosa Jr. V.P.
Wolf M.R.M.
Fo R.M.
Abstract: This work studies the problem of dynamic data reconciliation through a nonlinear dynamic data reconciliation (NLDDR) code based on the dynamic optimization problem with nonlinear constraints associated to a certain calculation horizon. The algorithm is tested on a continuous stirred tank reactor (CSTR) polymerization reactor. A simultaneous strategy of solution and optimization is used to solve the optimization problem. For such a purpose, the optimization problem is turned into a nonlinear programming problem (NLP) through transformation of the ordinary differential equations of the model into a system of algebraic residual equations, which are introduced as a constraint on NLP. The transformation is made using the orthogonal collocation method on finite elements. Successive quadratic programming (SQP) is the technique used to solve the NLP problem, allowing insertion of equalities and inequalities algebraic constraints calculated on-line. To reach a good performance, same methods demand a higher number of samples, which increases the optimization problem dimension and turns the on-line numeric solution to nonviable. An appropriate technique manner to solve or attenuate such a problem is the implementation of calculation horizon along the operation time. Results emphasize the effects of such horizon in the methodology employed. (C) 2000 Elsevier Science Ltd.This work studies the problem of dynamic data reconciliation through a nonlinear dynamic data reconciliation (NLDDR) code based on the dynamic optimization problem with nonlinear constraints associated to a certain calculation horizon. The algorithm is tested on a continuous stirred tank reactor (CSTR) polymerization reactor. A simultaneous strategy of solution and optimization is used to solve the optimization problem. For such a purpose, the optimization problem is turned into a nonlinear programming problem (NLP) through transformation of the ordinary differential equations of the model into a system of algebraic residual equations, which are introduced as a constraint on NLP. The transformation is made using the orthogonal collocation method on finite elements. Successive quadratic programming (SQP) is the technique used to solve the NLP problem, allowing insertion of equalities and inequalities algebraic constraints calculated on-line. The reach a good performance, same methods demand a higher number of samples, which increases the optimization problem dimension and turns the on-line numeric solution to nonviable. An appropriate technique manner to solve or attenuate such a problem is the implementation of calculation horizon along the operation time. Results emphasize the effects of such horizon in the methodology employed.
Editor: Elsevier Science Ltd, Exeter, United Kingdom
Rights: fechado
Identifier DOI: 10.1016/S0098-1354(00)00516-0
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-0343192351&partnerID=40&md5=c2bc6a18889ec6522caf63ff53f1df4b
Date Issue: 2000
Appears in Collections:Unicamp - Artigos e Outros Documentos

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