Statistical tools for optimal dynamic model building

A general, systematic procedure is presented to support the development and statistical verification of dynamic process models. Within this procedure, methods are presented to address several key aspects, such as structural i dentifiability and distinguishability testing, as well as optimal design of dynamic experiments for both model discrimination and improving parameter precision. A novel optimisation-based approach is introduced for testing of model structural identifiability and distinguishability, involving semi-in finite programming and max-min problems. The design of dynamic experiments is cast as an optimal control problem within a framework that enables the c alculation of optimal sampling points, experiment duration, fixed and varia ble external control profiles, and initial conditions of a dynamic experime nt subject to general constraints on inputs and outputs. Within this framew ork, methods are presented to provide experiment design robustness, account ing for parameter uncertainty. The procedure is demonstrated through a prac tical biotechnology example. (C) 2000 Elsevier Science Ltd. All rights rese rved.