(600y) Kinetic Modeling of Propylene Polymerization with Me2Si[Ind]2ZrCl2/MAO Catalyst System

Abstract

Polyolefins are the largest group of thermoplastics, often referred to as commodity thermoplastics, these are polymers of simple olefins such as ethylene, propylene, butenes, isoprene, and pentenes, and their copolymers. Two most important and common polyolefins are polyethylene and polypropylene and they are very popular due to their low cost and wide range of applications. Polypropylene is produced by polymerizing propylene with suitable catalysts and has demonstrated certain advantages in improved strength, stiffness and higher temperature capability over polyethylene. World commodity polymers consumption is estimated to reach 214 million tonnes by 2015, with polypropylene accounting for the largest share. ^[1]

Metallocene catalyzed propylene polymerization has recently attracted research interest since these catalysts allow the production of tailored macromolecules with properties those can be accurately designed. A broad spectrum of properties and applications of the polypropylene can be attained with metallocenes due to their single types of sites. Kinetic studies of catalytic polymerization provide substantial insight into the mechanism of the reactions and scale-up or commercialization of a polymerization process tremendously depends on the understanding of the kinetic behavior of the system under various operating conditions.

Metallocene catalyst system refers to the combination of bis(cyclopentadienyl)metal complexes of Group 4 (IVB) [especially zirconium, also titanium and to a lesser extent hafnium], or cyclopentadienyl-substituted derivatives thereof, and a cocatalyst, typically methylalumoxane (MAO). Titanocene and zirconocene dichlorides were the first metallocenes studied. ^{[2, 3]}

In this work, the mechanistic aspects of metallocene catalyst systems have been canvassed in detail and used in building up mathematical model for solution phase polymerization of propylene with Me₂Si[Ind]₂ZrCl₂/MAO catalyst system and kinetic parameters are obtained. Data for the model validation are taken from Marques et al. ^[4]

A novel natural logarithmic differential evolution (NLDE) approach of optimization, a remediated version of differential evolution algorithm ^[5]is proposed and used to solve parameter estimation problem in this work.

Model equations developed in this study include a set of coupled, nonlinear and stiff ordinary differential equations for the dynamic polymerization. These ordinary differential equations (ODEs) are solved with MATLAB™ 7.0.1 software. ^[6]

Developed model is able to capture essential polymer properties. Number- and weight average molecular weights, polydispersity index (PDI); fraction of vinyl terminated chains, butenyl-terminated chains, isobutyl-terminated chains and vinylidene-terminated chains in propylene polymerization are determined with the applied kinetic model.

Study on the effects of various parameters like monomer concentration, polymerization temperature, catalyst concentrations, and cocatalyst to catalyst molar ratio etc. upon rate of polymerization, molecular weights and poly dispersity index and stereoregularity is carried out.

References:

1. ICIS, Homepage of ICIS. the URL of which is http://www.icis.com/Articles/2012/03/26/9544287/global-polymers-to-face-... as on 27^th Dec, 2012.
2. Natta, G., P. Pino, G. Mazzanti, U. Giannini, E. Mantica, and E. Peraldo, "The nature of some soluble catalysts for low pressure ethylene polymerization", J. Polym. Sci. 26, 120-123 (1957a).
3. Natta, G., P. Pino, G. Mazzanti and U. Giannini, "Crystallizable organometallic complex containing titanium and aluminum", J. Am. Chem. Soc. 79, 2975-2976 (1957b).
4. Marques, M. F. V., M. Poloponsky and É. G. Chaves, "Comparative study of propylene polymerization with different metallocene catalysts using a statistic experimental planning model", Polímeros: Ciência e Tecnologia 12 (1), 48-59, (2002).
5. Price K.V., Storn R. (1997). Differential evolution: A simple evolution strategy for fast optimization. Dr. Dobb’s Journal, 22, 18–24.
6. MATLAB version 7.0.1, computer software, The MathWorks, Inc., Natick, Massachusetts 2004.