Premium
Application of Genetic Algorithm in Simultaneous Deconvolution: Case Studies of Ethylene/1‐Butene Copolymers with Direct and Inverse MW/CC Relationships
Author(s) -
Khayanying Kett,
Anantawaraskul Siripon
Publication year - 2020
Publication title -
macromolecular symposia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.257
H-Index - 76
eISSN - 1521-3900
pISSN - 1022-1360
DOI - 10.1002/masy.201900024
Subject(s) - deconvolution , comonomer , inverse , algorithm , genetic algorithm , copolymer , biological system , ethylene , nonlinear system , polymer , mathematical optimization , materials science , computer science , mathematics , chemistry , catalysis , physics , organic chemistry , composite material , geometry , quantum mechanics , biology
Abstract For multiple‐site‐type catalytic system, simultaneous deconvolution of molecular weight distribution and chemical composition distribution can be used to determine the number of site types and chain microstructures of polymers produced on each site type. In the conventional deconvolution with generalized reduced gradient (GRG) nonlinear algorithm, appropriate initial guesses are often required. In the case that the relationship between molecular weight and comonomer content (MW/CC) is not known, inadequate initial guesses can lead to inaccurate parameter estimation. In this work, genetic algorithm is considered as an alternative optimization tool during simultaneous deconvolution with the advantage that it can reach the global solution without relying on the adequate initial guesses. The proposed approach is validated with simulated data of model ethylene/1‐butene copolymers with inverse and direct MW/CC relationships and benchmarked with the conventional GRG technique.