Dynamic optimization of constrained semi-batch processes using Pontryagin's minimum principle-An effective quasi-Newton approach

- PMP is generalized for solving constrained semi-batch dynamic optimization problems with nonlinear mixed and pure state constraints.
- Indirect adjoining is used in order to deal with path constraints.
- Although the optimal input profiles differ significantly, the differences in the optimal cost values are negligible.
- The proposed PMP-based method converges faster than the direct simultaneous method for increasing grid sizes.

This work considers the numerical optimization of constrained batch and semi-batch processes, for which direct as well as indirect methods exist. Direct methods are often the methods of choice, but they exhibit certain limitations related to the compromise between feasibility and computational burden. Indirect methods, such as Pontryagin's Minimum Principle (PMP), reformulate the optimization problem. The main solution technique is the shooting method, which however often leads to convergence problems and instabilities caused by the integration of the co-state equations forward in time.This study presents an alternative indirect solution technique. Instead of integrating the states and co-states simultaneously forward in time, the proposed algorithm parameterizes the inputs, and integrates the state equations forward in time and the co-state equations backward in time, thereby leading to a gradient-based optimization approach. Constraints are handled by indirect adjoining to the Hamiltonian function, which allows meeting the active constraints explicitly at every iteration step. The performance of the solution strategy is compared to direct methods through three different case studies. The results show that the proposed PMP-based quasi-Newton strategy is effective in dealing with complicated constraints and is quite competitive computationally.