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An Evolutionary Approach to the Estimation of Reactivity Ratios
Author(s) -
Monett Dagmar,
Méndez José A.,
Abraham Gustavo A.,
Gallardo Alberto,
San Román Julio
Publication year - 2002
Publication title -
macromolecular theory and simulations
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.37
H-Index - 56
eISSN - 1521-3919
pISSN - 1022-1344
DOI - 10.1002/1521-3919(20020601)11:5<525::aid-mats525>3.0.co;2-k
Subject(s) - reactivity (psychology) , copolymer , monomer , inverse , polymer chemistry , genetic algorithm , linearization , chemistry , methacrylate , computer science , materials science , mathematics , nonlinear system , mathematical optimization , organic chemistry , polymer , physics , medicine , alternative medicine , geometry , pathology , quantum mechanics
Here we apply evolutionary techniques to the calculation of copolymerization reactivity ratios from an inverse problem perspective. To estimate monomer reactivity ratios, we take into account the main aspects of both inverse problems and evolutionary computation techniques. Copolymers of methyl methacrylate (MMA) and α ‐tocopheryl methacrylate (MVE) were prepared by free radical copolymerization in dioxane solution using 2,2′‐azoisobutyronitrile as the initiator. The reactivity ratios were calculated according to the general copolymerization equation using the Fineman–Röss and Kelen–Tüdos linearization methods, as well as the Tidwell–Mortimer nonlinear least‐squares treatment. Reactivity ratios were compared with four different simulations of an evolutionary approach that implements a genetic algorithm. The reactivity ratios obtained with these four simulations were similar, the values being r MMA = 0.92 and r MVE = 1.00. Results obtained with the application of evolutionary techniques demonstrate high‐quality solutions and show the convenient use by estimating monomer reactivity ratios in MMA‐co‐MVE (copolymer of MMA and MVE), a chain addition copolymerization system with potential biomedical applications. The numerous advantages of genetic parameters, performance, and major features of genetic algorithms, are also discussed.