Optimal experiment design for dynamic processes

Process models include a variety of parameters that need to be estimated from experimental data. To obtain an accurate estimate of the parameters, it is imperative that the experiments provide sufficient informative data. To ensure the information content with reduced experimental efforts, model-based design of experiments can be used. In this chapter, optimal experiment designs for model discrimination as well as parameter estimation are discussed. The optimal experiment design is formulated into an optimization problem that can be solved using a variety of computational approaches. Application of optimal designs for model discrimination and parameter estimation has been demonstrated with in vivo experiments for microbial growth. Furthermore, advanced topics such as robust optimal experiment design to include the effect of parametric uncertainty, and multicriteria optimal experiment design to simplify criteria choice are also discussed.