Bayesian parameter estimation in batch polymerisation

A. Bayesian estimation framework is proposed for the tracking of time-varying parameters. The Bayesian approach is a statistical procedure that allows the systematic incorporation of prior knowledge about the model and model parameters, the appropriate weighting of experimental data, and the use of probabilistic models for the modelling of sources of experimental error. The interplay between these elements determines the best model parameter estimates. The proposed approach is evaluated by application to a dynamic simulation of a solution methyl methacrylate (MMA) batch polymerisation reactor. The Bayesian parameter adaptive filter is shown to be particularily successful in tracking time-varying model parameters.