University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Performance Analysis of Shooting Algorithms in Chance-Constrained Optimization

Werk, S, Barz, T, Arellano-Garcia, H and Wozny, G (2012) Performance Analysis of Shooting Algorithms in Chance-Constrained Optimization In: 11th International Symposium on Process Systems Engineering (PSE2012), 2012-07-15 - 2012-07-19, National University of Singapore, Singapore.

Text (licence)
Available under License : See the attached licence file.

Download (33kB) | Preview


An important aspect for model-based design and development as well as for process monitoring and control is the consideration of uncertain process parameters. One approach for the explicit consideration of such uncertainties is the formulation of Chance-Constrained optimization problems. Within the last years, several different methods for the efficient solution of these problems have been presented. In this work, chance constraints are evaluated following the idea of the variable mapping approach. Because the efficiency of the original approach deteriorates with an increasing number of uncertain parameters, the probability integration has been extended recently to the exploitation of sparse grids. In this work, additional techniques for improving the efficiency of the variable mapping approach are presented. Firstly, the solution of a subproblem, the so called shooting task is analyzed in detail and enhanced through an idea called here result recycling. Secondly, possible extensions are presented which make use of second order derivative information. The new methods are verified by application to an industrially validated process model of a vacuum distillation column for the separation of multicomponent fatty acids.

Item Type: Conference or Workshop Item (Conference Paper)
Subjects : Chemical Engineering
Divisions : Faculty of Engineering and Physical Sciences > Chemical and Process Engineering
Authors :
Werk, S
Barz, T
Arellano-Garcia, H
Wozny, G
Date : 1 August 2012
DOI : 10.1016/B978-0-444-59506-5.50133-4
Copyright Disclaimer : © 2012 Elsevier B.V. All rights reserved.
Contributors :
ContributionNameEmailORCID, IA, R
Uncontrolled Keywords : Chance Constrained Optimization, Uncertainty, Stochastic optimization, Higher order derivatives, Result recycling
Depositing User : Symplectic Elements
Date Deposited : 01 Sep 2016 09:46
Last Modified : 31 Oct 2017 18:39

Actions (login required)

View Item View Item


Downloads per month over past year

Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800