Next Article in Journal
Systematic and Model-Assisted Evaluation of Solvent Based- or Pressurized Hot Water Extraction for the Extraction of Artemisinin from Artemisia annua L.
Next Article in Special Issue
Special Issue: Combined Scheduling and Control
Previous Article in Journal / Special Issue
Combined Noncyclic Scheduling and Advanced Control for Continuous Chemical Processes
Article Menu
Issue 4 (December) cover image

Export Article

Open AccessFeature PaperArticle
Processes 2017, 5(4), 85; https://doi.org/10.3390/pr5040085

Efficient Control Discretization Based on Turnpike Theory for Dynamic Optimization

Process Systems Engineering Laboratory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
*
Author to whom correspondence should be addressed.
Current address: Aspen Technology, 20 Crosby Dr, Bedford, MA 01730, USA.
Received: 12 November 2017 / Revised: 8 December 2017 / Accepted: 11 December 2017 / Published: 18 December 2017
(This article belongs to the Special Issue Combined Scheduling and Control)
  |  
PDF [2062 KB, uploaded 20 December 2017]
  |  

Abstract

Dynamic optimization offers a great potential for maximizing performance of continuous processes from startup to shutdown by obtaining optimal trajectories for the control variables. However, numerical procedures for dynamic optimization can become prohibitively costly upon a sufficiently fine discretization of control trajectories, especially for large-scale dynamic process models. On the other hand, a coarse discretization of control trajectories is often incapable of representing the optimal solution, thereby leading to reduced performance. In this paper, a new control discretization approach for dynamic optimization of continuous processes is proposed. It builds upon turnpike theory in optimal control and exploits the solution structure for constructing the optimal trajectories and adaptively deciding the locations of the control discretization points. As a result, the proposed approach can potentially yield the same, or even improved, optimal solution with a coarser discretization than a conventional uniform discretization approach. It is shown via case studies that using the proposed approach can reduce the cost of dynamic optimization significantly, mainly due to introducing fewer optimization variables and cheaper sensitivity calculations during integration. View Full-Text
Keywords: dynamic optimization; turnpike theory; control parametrization; adaptive discretization; optimal control dynamic optimization; turnpike theory; control parametrization; adaptive discretization; optimal control
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed
Printed Edition Available!
A printed edition of this Special Issue is available here.

Share & Cite This Article

MDPI and ACS Style

Sahlodin, A.M.; Barton, P.I. Efficient Control Discretization Based on Turnpike Theory for Dynamic Optimization. Processes 2017, 5, 85.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Processes EISSN 2227-9717 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top