Local parameter identifiability of large-scale nonlinear models based on the output sensitivity covariance matrix

The use of first-principle models is motivated by the potential of detailed information available as well as their versatility. Therefore, it is important to keep these models up to date so the models represent accurate enough the processes at hand. However, most of these models are nonlinear with a large number of states and parameters but with a relatively low number of measured outputs. This lack of measurements hinders the possibility to estimate all of the parameters present in the model. In this work, parameter identifiability of large-scale nonlinear models is explored using the empirical output controllability covariance matrix approach. This empirical covariance matrix is used to extract the output sensitivity matrix of the model to assess parameter identifiability. The advantages of the proposed methods are discussed while different sensitivity indexes are evaluated to draw sound conclusions on the parameter ranking results. A large-scale reactive batch distillation process simulation is used as a demonstrator.