Dynamic optimization of distributed parameter systems using second-order directional derivatives

Abstract

The dynamic optimization of combined lumped and distributed process systems, governed by nonlinear ordinary and partial differential equations (ODEs and PDEs), is considered in this work. The application of a recently developed method based on the combination of the control vector parametrization approach with the calculation of exact gradients and projected Hessians (Hp's), is presented as an alternative for the efficient computation of the control policies needed to optimize a specific performance criterion. The exact first- and second-order information is calculated by means of the solution of an extended initial value problem (IVP) whose particular time-varying Jacobian structure is exploited by a sparse implicit solver to increase efficiency. Finally, the applicability of this method is shown through the solution of a number of case studies demonstrating that a significant speed-up can be obtained through the use of exact second-order information.