Next Article in Journal / Special Issue
Methods and Tools for Robust Optimal Control of Batch Chromatographic Separation Processes
Previous Article in Journal
Special Issue on “Modeling and Analysis of Signal Transduction Networks” in the Journal Processes
Previous Article in Special Issue
Dynamic Optimization in JModelica.org
Article

Multi-Period Dynamic Optimization for Large-Scale Differential-Algebraic Process Models under Uncertainty

Department of Chemical Engineering, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4L7, Canada
*
Author to whom correspondence should be addressed.
Academic Editor: Carl D. Laird
Processes 2015, 3(3), 541-567; https://doi.org/10.3390/pr3030541
Received: 29 May 2015 / Revised: 6 July 2015 / Accepted: 6 July 2015 / Published: 14 July 2015
(This article belongs to the Special Issue Algorithms and Applications in Dynamic Optimization)
A technique for optimizing large-scale differential-algebraic process models under uncertainty using a parallel embedded model approach is developed in this article. A combined multi-period multiple-shooting discretization scheme is proposed, which creates a significant number of independent numerical integration tasks for each shooting interval over all scenario/period realizations. Each independent integration task is able to be solved in parallel as part of the function evaluations within a gradient-based non-linear programming solver. The focus of this paper is on demonstrating potential computation performance improvement when the embedded differential-algebraic equation model solution of the multi-period discretization is implemented in parallel. We assess our parallel dynamic optimization approach on two case studies; the first is a benchmark literature problem, while the second is a large-scale air separation problem that considers a robust set-point transition under parametric uncertainty. Results indicate that focusing on the speed-up of the embedded model evaluation can significantly decrease the overall computation time; however, as the multi-period formulation grows with increased realizations, the computational burden quickly shifts to the internal computation performed within the non-linear programming algorithm. This highlights the need for further decomposition, structure exploitation and parallelization within the non-linear programming algorithm and is the subject for further investigation. View Full-Text
Keywords: multi-period dynamic optimization; differential-algebraic equations; applied non-linear programming; multiple-shooting; parallel computing multi-period dynamic optimization; differential-algebraic equations; applied non-linear programming; multiple-shooting; parallel computing
Show Figures

Figure 1

MDPI and ACS Style

Washington, I.D.; Swartz, C.L.E. Multi-Period Dynamic Optimization for Large-Scale Differential-Algebraic Process Models under Uncertainty. Processes 2015, 3, 541-567. https://doi.org/10.3390/pr3030541

AMA Style

Washington ID, Swartz CLE. Multi-Period Dynamic Optimization for Large-Scale Differential-Algebraic Process Models under Uncertainty. Processes. 2015; 3(3):541-567. https://doi.org/10.3390/pr3030541

Chicago/Turabian Style

Washington, Ian D., and Christopher L.E. Swartz. 2015. "Multi-Period Dynamic Optimization for Large-Scale Differential-Algebraic Process Models under Uncertainty" Processes 3, no. 3: 541-567. https://doi.org/10.3390/pr3030541

Find Other Styles

Article Access Map by Country/Region

1
Only visits after 24 November 2015 are recorded.
Back to TopTop