The objective of this work is to propose new methods for assisting and improving the parametrization of models of chemical processes using the measurement obtained during their operation. The Simulated Moving Bed (SMB) process has been chosen as a case study. The first contribution of this work is an optimization-based method tailored for the estimation of the states and the parameters of the individual columns of the SMB plant. The estimation task is difficult, as typically the measurement information available from the process is scarce. The proposed estimation scheme considers explicitly in its formulation the switchings of the sensors at the product ports. The second contribution of this thesis is a new approach for the determination of operation conditions to drive chemical processes to deliver measurements which, if used for parameter estimation, lead to estimate parameters with minimum variance. The operation conditions are determined by designing optimal dynamic experiments online, i.e. during the operation of the process, taking into account the most relevant constraints imposed on the process. It is shown that the solution of optimal dynamic experiment design (ODED) problems is very challenging for systems described by models of large dimensions and with many parameters. An approach for the reduction of the complexity of the online ODED is proposed. The approach consists of decomposing the original experimental design problem into a series of smaller problems which extend a preexisting experiment. Computational strategies for the application of the scheme online are also proposed.