Optimal experiment design techniques integrated with time-series segmentation

Abstract

Process models play important role in computer aided process engineering. Although the structure of these models is a priori known, model parameters should be estimated based on experiments. The accuracy of the estimated parameters largely depends on the information content of the experimental data presented to the parameter identification algorithm. Optimal Experiment Design (OED) can maximize the confidence on the model parameters. Considering that OED is an iterative process, it may happen that the designed experiment contains segments which are not or less useful for parameter identification. Using the tools of the OED there is the opportunity to qualify the segments of the time-series of different data sets. After the segmentation, it will be possible to choose the most appropriate segments for identification of each parameter, i.e. to determine the parameters as accurate as possible.