Increasing the Reliability of Parameter Estimates by Iterative Model-based Design of Experiments Using a Flowsheet-Simulator
The reliability of flowsheet simulation results depends heavily on the knowledge of model parameters, among which are, for example, kinetic parameters of chemical reactions or substance property data describing thermodynamic behavior. These parameters are usually estimated by nonlinear regression with respect to data from a laboratory setup, mini-plant or operating data from a production process. Applying model-based design of experiments (DoE), operating conditions can be identified which maximize the information content of the resulting data for the regression problem. In this contribution, we present gradient-plots in order to make DoE-plans more transparent to the engineer. In a second step, we report on the implementation of an iterative DoE-scheme in a flowsheet-simulator. Thus, the interplay between estimating model parameters, planning experiments, adding resulting experimental data and re-adjusting the parameters is supported.