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The formulation of optimal mixtures with generalized disjunctive programming: A solvent design case study
Author(s) -
Jonuzaj Suela,
Akula Paul T.,
Kleniati PolyxeniMargarita,
Adjiman Claire S.
Publication year - 2016
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.15122
Subject(s) - set (abstract data type) , solvent , computer science , binary number , optimal design , solubility , mathematical optimization , mathematics , chemistry , organic chemistry , arithmetic , programming language , machine learning
Systematic approaches for the design of mixtures, based on a computer‐aided mixture/blend design (CAM b D) framework, have the potential to deliver better products and processes. In most existing methodologies the number of mixture ingredients is fixed (usually a binary mixture) and the identity of at least one compound is chosen from a given set of candidate molecules. A novel CAM b D methodology is presented for formulating the general mixture design problem where the number, identity and composition of mixture constituents are optimized simultaneously. To this end, generalized disjunctive programming is integrated into the CAM b D framework to formulate the discrete choices. This generic methodology is applied to a case study to find an optimal solvent mixture that maximizes the solubility of ibuprofen. The best performance in this case study is obtained with a solvent mixture, showing the benefit of using mixtures instead of pure solvents to attain enhanced behavior. © 2016 The Authors AIChE Journal published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers AIChE J , 62: 1616–1633, 2016