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dc.contributor.author
Corsano, Gabriela  
dc.contributor.author
Montagna, Jorge Marcelo  
dc.contributor.author
Iribarren, Oscar Alberto  
dc.contributor.author
Aguirre, Pio Antonio  
dc.date.available
2021-08-06T12:29:53Z  
dc.date.issued
2006-09  
dc.identifier.citation
Corsano, Gabriela; Montagna, Jorge Marcelo; Iribarren, Oscar Alberto; Aguirre, Pio Antonio; Design and operation issues using NLP superstructure modelling; Elsevier Science Inc.; Applied Mathematical Modelling; 30; 9; 9-2006; 974-992  
dc.identifier.issn
0307-904X  
dc.identifier.uri
http://hdl.handle.net/11336/137944  
dc.description.abstract
Till present, models that determined batch plants configurations in the chemical process industry resorted to models with binary variables to represent the different admissible options. This approach allowed representing the problem in a simple way while considering a significant number of alternatives. Nevertheless, the non-convexity that arises when dealing with detailed models for representing the involved units operation prevents its correct resolution or has a low performance. This work presents a representation of the problem through a superstructure that takes explicitly into account all the alternatives without resorting to binary variables. By using extremely simple modeling, it is possible to manage an appropriate number of options for this type of problems by means of a non-linear programming (NLP) model. Moreover, it is possible to consider duplication in series of production stages, which is an alternative that has not been used till now. This approach is posed for the case of a fermentors network. The solution is reached with very low requirements as regards employed computer time and without the aforementioned difficulties.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Elsevier Science Inc.  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
DESIGN AND OPERATION OF BATCH PLANTS  
dc.subject
NLP PROBLEMS  
dc.subject
OPTIMIZATION  
dc.subject
SUPERSTRUCTURE MODELING  
dc.subject.classification
Ingeniería de Procesos Químicos  
dc.subject.classification
Ingeniería Química  
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS  
dc.title
Design and operation issues using NLP superstructure modelling  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2020-08-05T16:44:46Z  
dc.identifier.eissn
1872-8480  
dc.journal.volume
30  
dc.journal.number
9  
dc.journal.pagination
974-992  
dc.journal.pais
Estados Unidos  
dc.description.fil
Fil: Corsano, Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina  
dc.description.fil
Fil: Montagna, Jorge Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina  
dc.description.fil
Fil: Iribarren, Oscar Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina  
dc.description.fil
Fil: Aguirre, Pio Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina  
dc.journal.title
Applied Mathematical Modelling  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.apm.2005.07.003  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0307904X05001253