Online Parameter Identification of Facet Growth Kinetics in Crystal Morphology Population Balance Models

Particle shape plays an important role in many industrial applications since it can have significant impact on both, processability of particles as well as the properties of the final product. For this reason modeling of the corresponding production process is crucial for developing efficient process optimization and control strategies. The shape evolution of crystals on the process scale can be described conveniently within the framework of morphological population balance modeling. In order of being a reliable tool for the prediction of the crystal shape distribution during the production process as well as for the design of suitable control and optimal production strategies, the models require the estimation of several parameters characterizing the growth rates of the different crystal facets. This is particularly challenging due to the infinite dimensional state space of the models. In this contribution online parameter estimation for the growth rates of L-glutamic acid cooling crystallization is presented. Using a Lyapunov-based approach the parameter adaption laws are computed directly from the infinite dimensional problem formulation. It will be shown that a reasonably fast convergence of the parameter estimates can be achieved even in the presence of measurement noise using appropriate filters