Rigorous dynamic modeling and simulation of wine distillations

dc.creatorOsorio, D
dc.creatorPerez Correa, R
dc.creatorBelancic, A
dc.creatorAgosin, E
dc.date.accessioned2024-01-10T14:21:35Z
dc.date.accessioned2024-05-02T17:44:13Z
dc.date.available2024-01-10T14:21:35Z
dc.date.available2024-05-02T17:44:13Z
dc.date.created2024-01-10T14:21:35Z
dc.date.issued2004
dc.identifier10.1016/j.foodcont.2003.08.003
dc.identifier1873-7129
dc.identifier0956-7135
dc.identifierhttps://doi.org/10.1016/j.foodcont.2003.08.003
dc.identifierhttps://repositorio.uc.cl/handle/11534/79715
dc.identifierWOS:000222742000003
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9268882
dc.description.abstractA novel simulation strategy for dynamic distillation of complex mixtures, such as wine, is proposed and evaluated in terms of computing efficiency and accuracy. The model developed describes wine distillation as a multicomponent reactive batch distillation process. The simulation approach transforms the system of differential algebraic equations (DAE) into a set of ordinary differential equations, by pre-solving the algebraic equations and replacing them with artificial neural networks.
dc.description.abstractThis new simulation strategy for wine distillation is 40% faster than the rigorous solution of the DAE system, compared at the same level of accuracy. The model can be applied to the distillation of other spirits or complex Mixtures, as well as in other separation processes in which the recovery of aromas is essential. (C) 2003 Elsevier Ltd. All rights reserved.
dc.languageen
dc.publisherELSEVIER SCI LTD
dc.rightsacceso restringido
dc.subjectreactive-batch distillation
dc.subjectterpenes
dc.subjectPisco
dc.subjectREACTIVE DISTILLATION
dc.subjectNEURAL-NETWORKS
dc.titleRigorous dynamic modeling and simulation of wine distillations
dc.typeartículo


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