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Neural network‐based hybrid models developed for free radical polymerization of styrene
Author(s) -
Ghiba Luciana,
Drăgoi Eleiculina,
Curteanu Silvia
Publication year - 2021
Publication title -
polymer engineering and science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.503
H-Index - 111
eISSN - 1548-2634
pISSN - 0032-3888
DOI - 10.1002/pen.25611
Subject(s) - artificial neural network , polymerization , radical polymerization , materials science , phenomenology (philosophy) , differential evolution , copolymer , biological system , styrene , process (computing) , computer science , artificial intelligence , polymer , composite material , philosophy , epistemology , biology , operating system
In the present work, the free radical polymerization of styrene is modeled by considering the phenomenology of the process (a simplified model, which does not include the diffusional effects, gel, and glass effects) in combination with an empirical model represented by an artificial neural network. Differential evolution (DE) algorithm, belonging to the class of evolutionary algorithms, is applied for developing the neural models in optimal forms. For improving the results—predicted conversion and molecular weights as function of time, temperature, and initiator concentration—different combinations between phenomenological model and neural network are tested; also, individual and stacked neural networks have been developed for the polymerization process. This methodology based on hybrid models, including neural networks aggregated in stacks, provides accurate results.