Model Discrimination between RAFT Polymerization Models Using Sequential Bayesian Methodology

Abstract

Abstract The use of sequential Bayesian methodology for model discrimination purposes in reversible addition‐fragmentation transfer (RAFT) polymerization is analyzed and discussed from a mathematical model discrimination point of view. The RAFT models are detailed nonlinear mechanistic models from the literature, where the debate is still ongoing about their validity. A sensitivity analysis is performed first on the simulated models in order to identify the most informative process (measured) outputs from the candidate models with respect to model discrimination. Next, sequential Bayesian Monte Carlo model discrimination (SBMCMD) methodology is applied to discriminate between the two rival models. The effectiveness of the SBMCMD procedure in discriminating between the two proposed models (both describing basic RAFT polymerization kinetic trends successfully) is explored further. Most informative experiments are designed and suggested based on the design of experiments step of the SBMCMD methodology. The methodology is capable of selecting the “real” model.