Efficient Modeling of the Bivariate Molecular Weight Distribution ? Copolymer Composition Distribution in SAN Copolymerization Using Parallel Computing

PINTOS, E.; FORTUNATTI, C.; BRANDOLIN, A.; SARMORIA, C.; ASTEASUAIN, M.

Boston

Congreso; AIChE Annual Meeting 2021; 2021

AIChE

The implementation using parallel computing of a model based on the probability generating function technique proved to be a very valuable tool for modeling multivariate distributions in polymer systems. A mathematical model of the styrene-acrylonitrile (SAN) copolymerization that predicts the bivariatemolecular weight distribution (MWD)-copolymer composition distribution (CCD) of the copolymer, as well as the overall MWD and CCD was programmed in Julia. Julia is an open-source language specifically designed for scientific computing that has native support for parallel computing. The parallelized implementation of the code in Julia allowed solving a huge system of equations to compute the bivariate distributions in relatively short times. A valuable insight on the polymer microstructure under different operating conditions was achieved.