Optimization Strategies for Complex Process Models

Abstract

The optimization of models can be described by differential or algebraic equations (DAEs). This approach allows the direct enforcement of profile constraints for state and control variables. Also, the successive quadratic programming (SQP) algorithms can be tailored to the DAE system to allow for moving finite elements and the accurate determination of state and optimal control profiles. Parameter optimization is frequently encountered in process design and analysis. This chapter provides an overview of simultaneous optimization strategies for process engineering. Over the past decade, the recognition of the effectiveness of sophisticated nonlinear programming algorithms, such as SQP, has led to the formulation of larger and more difficult optimization problems. The key to this advance lies in flexible formulations of the optimization problems. Inefficient convergence algorithms that are incorporated within a calculation procedure can now be replaced with a simultaneous Newton-type algorithm. Finally, simultaneous solution and optimization strategies have been extended and demonstrated on large optimization problems.