Hybrid deterministic and Monte Carlo modeling of controlled degradation of polypropylene

Controlled degradation of polypropylene (CPP) is a post-polymerization process used to produce many PP grades from high-molecular-weight PP. During CPP, PP of predominantly isotactic composition is fed to an extruder along with initiator, which induces scission. During CPP, the isotactic content of PP decreases due to radical-migration side reactions, which influence the properties of the PP. Two previous models predict tacticity changes during CPP: a computationally expensive kinetic Monte Carlo (kMC) model and a deterministic model that ignores certain reactions. The current study develops a new hybrid model that incorporates all important radical migration reactions, without the computational expense of kMC methods. The model is then used to estimate kinetic parameters. The proposed model uses off-line stochastic simulations to determine instantaneous rates of changed pentad formation, based on local species concentrations at different locations along the extruder. This instantaneous pentad information is stored in 3D arrays, which are subsequently used for deterministic model calculations. The deterministic portion of the model consists of dynamic material balances, which predict local radical concentrations and accumulate the changed pentads of different types. Model predictions agree with a previous kMC model and require only a fraction of the computing time. Chain-transfer-to-polymer and two types of radical-migration rate coefficients are estimated using ¹³C NMR data from industrial CPP experiments. The proposed model provides a good fit to the experimental data. This model will be useful to companies who use reactive extrusion to modify PP and wish to better understand and control PP tacticity.