Low Order-Value Multiple Fitting for supercritical fluid extraction models

Autor(es)

Carvalho, Esdras P. ; Pisnitchenko, Feodor ; Mezzomo, Natalia ^[1] ; Ferreira, Sandra R. S. ^[1] ; Martinez, J. M. ^{[2, 3]} ; Martinez, Julian

Afiliação do(s) autor(es)

^[1] Univ Fed Santa Catarina, Chem & Food Engn Dept, BR-88040900 Florianopolis, SC - Brazil

^[2] Univ Estadual Campinas, Dept Food Engn, Campinas, SP - Brazil

^[3] Univ Estadual Campinas, Dept Appl Math, Campinas, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Computers & Chemical Engineering; v. 40, p. 148-156, MAY 11 2012.
• Citações Web of Science: 4

Resumo

Low Order-Value Optimization (LOVO) is a useful tool for nonlinear estimation problems in the presence of observations with different levels of relevance. In this paper LOVO is associated with a Multiple Fitting strategy for the estimation of parameters in supercritical fluid extraction models. Experimental data of supercritical CO2 extraction of peach almond oil are considered. Multiple fitting makes it possible to impose constraints on the estimation procedure that improve the physical meaning of the parameters. A novel combination of minimization methods is used to solve problems in the LOVO setting. Numerical results are reported. (C) 2012 Elsevier Ltd. All rights reserved. (AU)

• Processo FAPESP: 06/53768-0 - Métodos computacionais de otimização
• Beneficiário: José Mário Martinez Perez
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 05/57684-2 - Lagrangeano aumentado e problemas de equilíbrio
• Beneficiário: María Laura Schuverdt
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado