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Evaluation of input parameterization for batch process optimization
Author(s) -
Welz C.,
Srinivasan B.,
Marchetti A.,
Bonvin D.,
Ricker N. L.
Publication year - 2006
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.10905
Subject(s) - mathematical optimization , process (computing) , context (archaeology) , batch distillation , computer science , distillation , fractionating column , function (biology) , mathematics , fractional distillation , paleontology , chemistry , organic chemistry , evolutionary biology , biology , operating system
For the optimization of dynamic systems, it is customary to use measurements to combat the effect of uncertainty. In this context, an approach that consists of tracking the necessary conditions of optimality is gaining in popularity. The approach relies strongly on the ability to formulate an appropriate solution model , that is, an approximate parameterization of the optimal inputs with a precise link to the necessary conditions of optimality. Hence, the need to be able to assess the capability of a solution model to optimize an uncertain process. A loss function is introduced that can be used to verify the conjecture that the solution model derived from a simplified process model can be applied to a more rigorous process model with negligible loss in performance. This conjecture is tested in simulation via the dynamic optimization of a batch distillation column. © 2006 American Institute of Chemical Engineers AIChE J, 2006