CONICET | Buscador de Institutos y Recursos Humanos

Polymers usually present distributed molecular properties (i.e. molecular weight distribution (MWD), copolymer composition distribution (CCD), short (SCBD) and long (LCBD) chain branching distribution, etc.). In many cases, a proper characterization of a polymer sample will require simultaneous information on more than one property distribution. For instance, MW-CCD in the case of copolymers, MW-SCBD and/or LCBD for branched polymers. This requirement must be taken into account in the development of ad-vanced mathematical models of polymer systems.Different methods for the calculation of single dis-tributions have been reported in the literature. The MWD has been one of the more extensively studied, al-though modeling of other distributions, such as the CCD, has also been published. However, the joint pre-diction of multiple distributions is an area of limited development. Reported methods include the numerical fractionation technique, employed for predicting the bivariate molecular weight-long chain branching distribution. Other approaches in-clude the combination of border density functions with the h-p-Galerkin method, and the Markov chains method , for the prediction of the bivariate MW-L(or S)CBD. Recently, 2-D sectional grid methods and Monte Carlo methods have been employed for computing MW-CCD .In this work, we present a new approach for the pre-diction of bivariate distributions of polymer properties, based on the transformation of population mass bal-ances using probability generating functions (pgfs). In previous works by the authors, the pgf technique was developed as a comprehensive numerical tool for the prediction of the MWD in free radical polymer processes. This approach employed univariate pgfs, which allowed modeling a distribution with a single independent variable. Here, we present an extension of this technique to deal with 2-D pgfs, in order to model bivariate distributions.