Simulation and Optimization of Batch Crystallization Processes

162 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2002.Crystallization from solution has been widely used in the industry because of its ability to provide high purity separation. The increased competition has motivated great interest toward quickly modeling and simulating the crystallization processes, as well as the development of optimal control strategies for these processes. Here an iterative procedure is developed for the robust optimal identification and control of batch and semibatch processes. The procedure uses a small number of batch experiments to identify the kinetic parameters and to quantify the parametric uncertainty for multidimensional crystallization processes. Analysis tools are developed which can estimate the effects of uncertainties in the model or in the implementation to the final optimal control policy and performance. Based on these analysis tools a robust optimum control algorithm is proposed which takes into account both model parameter and control implementation uncertainties, and enables a linking between the objective of model identification and the objective of optimal control. In addition, a novel finite difference algorithm is developed here. The algorithm simulates the dynamics of a multidimensional crystallization process, providing short computation times and high accuracy.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD