An Efficient Multiple Shooting Based Reduced SQP Strategy for Large-Scale Dynamic Process Optimization - Part II: Software Aspects and Applications

Daniel B. Leineweber, Andreas Schäfer, Hans Georg Bock, Johannes P. Schlöder

Abstract. As model based optimization techniques play a more and more important role in the chemical process industries, there is a great demand for ever more efficient and reliable process optimization software. In the first part of this paper, the theoretical aspects of a tailored multiple shooting based solution strategy for dynamic process optimization have been presented (Leineweber et al., 2002). The current second part describes software aspects of the specific implementation MUSCOD-II and provides numerical results for several application examples. MUSCOD-II has been coupled with the dynamic process modeling software gPROMS via the standard ESO interface of CAPE-OPEN. Thereby, an advanced dynamic optimization platform for integrated batch processes has been created, where each process stage is separately modeled in gPROMS, and the multistage dynamic optimization problem is assembled and solved with MUSCOD-II. The code has also been parallelized based on the portable MPI standard.

It is shown that the use of directional sensitivities becomes very important for larger problems with many algebraic variables, leading to drastically reduced computing times compared to strategies with complete constraint linearization. In addition, gPROMS ESO models are compared with classical Fortran models in terms of computational performance, and it is found that only a moderate loss of performance occurs if so-called in-process ESOs are employed. Finally, it is demonstrated that a significant speed-up can be obtained through parallel function and gradient evaluations.

Keywords. Dynamic Optimization Software, CAPE-OPEN Standard, Equation Set Object (ESO), Multiple Shooting, Structured RSQP Methods, Directional Sensitivities, Integrated Batch Processes, Multistage Optimal Control Problems, Parallel Computing.


Correspondence should be addressed to daniel.leineweber.dl@bayertechnology.com