Dynamic optimization with path constraints

Dynamic optimization problems, also called constrained optimal control problems, are of interest in many areas of engineering. However, numerical solution of such problems is difficult and thus the application of dynamic optimization in process en.eering has been limited. The dynamic optimization problems of interest in process engineering typically consist of large systems of differential and algebraic equations (DAEs), and often contain path equality or inequality constraints on the state variables. The objective of this thesis was to improve the efficiency with which large-scale dynamic optimization problems may be solved and to develop improved methods for including path constraints. The most efficient method for numerical solution of large dynamic optimization problems is the control parameterization method. The cost of solving the dynamic optimization problem is typically dominated by the cost of solving the sensitivity system. The efficiency with which the sensitivity system can be solved is significantly improved with the staggered corrector sensitivity algorithm which was developed and implemented during the course of this thesis.