The Application of Monte Carlo Combined Methods For Modeling of Polymerization Kinetics

Abstract

The advantages and disadvantages of the two major categories of numerical methods, deterministic and stochastic approaches, in polymer reaction engineering are discussed. Combinations of methods are suggested in order to take advantage of both techniques. A hybrid deterministic/stochastic approach and a combined stochastic/stochastic method are developed to represent two polymerization systems of interest. The distribution of functional groups in polymer chains produced in radical copolymerization by starved-feed semibatch operation is simulated using three different methodologies. A deterministic model is formulated to separately track the homopolymer chains that are produced without the desired functionality, a Monte Carlo (MC) model is written to represent the system, and a hybrid deterministic/MC approach is taken using new capabilities within the software package PREDICI. Two Monte Carlo algorithms (dynamic and static) are combined in order to model and simulate the branch distribution and topology of polymer chains synthesized in hyper-branched polymerization of polyethylene with Pd-diimine catalysts. A sensitivity analysis is conducted in order to investigate the impact of kinetic and stochastic parameters on the branch distribution as well as average chain length. Simulated results show excellent agreement with experimental observations.