This work proposes new methods to enhance the parametrization of chemical process models using operational measurements, focusing on the Simulated Moving Bed (SMB) process, a continuous chromatographic separation method. The SMB's performance hinges on factors affecting adsorption behavior, particularly the stationary phase characteristics and operational conditions like temperature. While conservative operating points can meet product quality requirements, they compromise economic performance. Optimal conditions can be achieved through model-based strategies, contingent upon accurate model representation of the chromatographic system. The study addresses the variability in solid phase properties across SMB columns, which can change over time due to aging or damage. The first contribution is an optimization-based method for estimating states and parameters of individual columns, despite the challenge posed by limited measurement information. This method effectively tracks key adsorption isotherm parameters and is computationally efficient. The second contribution involves determining operational conditions that yield measurements for parameter estimation with minimal variance. This is achieved through optimal dynamic experiment design (ODED) conducted online during operation, considering relevant process constraints. The complexity of ODED for high-dimensional systems is tackled by decomposing the problem into smaller, manag
