(259d) Methodology Of Kinetic Modeling Of Single-Site Olefin Polymerization

Novstrup, K. A., School of Chemical Engineering, Purdue University
Medvedev, G., Purdue University
Travia, N. E., Purdue University
Stanciu, C., Purdue University
Manz, T. A., Purdue University
Delgass, W. N., Purdue University
Abu-Omar, M. M., Purdue University

Single-site catalysts are revolutionizing polyolefin production because they enable more precise control of the polymer molecular architecture, which in turn controls the physical properties of the polymer. The enormous structural variety possible in single-site catalysts gives rise to the need for a quantitative method to design a catalyst system to achieve a desired molecular architecture of the polymer. Development of a quantitative model requires fundamental understanding of single-site polymerization catalysts, including detailed knowledge of the governing kinetic mechanisms and values for all of the rate constants. This task is difficult because for each of the individual steps in the polymerization (i.e. activation, initiation, propagation, chain transfer and termination) there are many different mechanistic possibilities, resulting in potentially thousands of different polymerization models. A variety of experimental techniques presently exists (e.g. 1H-NMR, 13C-NMR, UV-VIS, and GPC) for monitoring the polymerization and analyzing the resulting polymer. The key challenge is to rapidly and robustly use the available experimental information to determine which candidate polymerization models can describe the experimental data and then design new experiments to discriminate between candidate models.

We will show that batch polymerization data is ideal for modeling single-site polymerization. Since a single experiment samples the rate of polymerization over a range of monomer concentrations, fewer experiments are required for model building and discrimination, although model building is more complex. Kinetic rate constants are primarily determined by fitting (i) monomer versus time profiles and (ii) the time evolution of the molecular weight distribution data for batch polymerizations with (iii) constraints imposed by other measurements like activated catalyst as determined by UV-VIS spectrophotometry. This integrated analysis approach is unique to our group. Special computational tools have been developed for the population balance models of the polymerization that can include up to 100,000 ODEs, where a new kinetic model can be formulated, computer code automatically generated and the model parameters optimized with a set of experimental data ? all within a few hours. An example of this method of kinetic analysis for the polymerization of 1-hexene with [rac-(C2H4(1-Ind)2)ZrMe][MeB(C6F5)3] will be discussed. During the course of this analysis it was discovered that a new mechanism beyond those considered in the literature for this catalyst is required to fit the molecular weight distribution. This example clearly indicates how quantitative modeling in conjunction with batch polymerization data helped discover a new mechanism that would otherwise not have been considered.