Simulation and Optimization of Batch Crystallization Processes

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Chemical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Ma, David Lei

Contributors dc:contributor

• Braatz, Richard D.

• Engineering, Chemical

Language dc:language

eng

Identifier

(MiAaPQ)AAI3044166

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/82337