Realistic Assessment of Parameter Uncertainty in Dynamic Parameter Estimation

Assessment of the uncertainty in parameter estimation is essential for confidence in subsequent use of the dynamic model with the associated parameters. The assessment of the parameter uncertainty in highly nonlinear kinetic models is often a very difficult task. In this paper a new method for parameter uncertainty assessment is presented and its use is demonstrated for a cellulose hydrolysis kinetic model. The new method involves generation of pseudo-experimental data using a known set of "reference" parameter values. Stepwise regression is used in an attempt to generate alternative sets of parameter values that yield results with precision similar to the reference set. The difference between the individual parameter values in the separate sets represents the uncertainty of these values. High uncertainty level indicates that no physical meaning can be attributed to the predicted parameter values. Application of the proposed method is therefore recommended prior to applying the individual parameter values in other models.