Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-11374
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Main Title: Systematic parameter selection for optimization under uncertainty
Author(s): Müller, David
Esche, Erik
Lopez C., Diana C.
Wozny, Günter
Type: Book Part
URI: https://depositonce.tu-berlin.de/handle/11303/12554
http://dx.doi.org/10.14279/depositonce-11374
License: http://rightsstatements.org/vocab/InC/1.0/
Abstract: Considering uncertainty is crucial for the decision making process in chemical engineering. However, when working in optimization under uncertainty a systematic selection of relevant uncertain parameters is required. In this contribution, an algorithm is presented in which uncertain parameters are selected based on their linear- independence to one another, their sensitivity towards state variables, and their sensitivity towards a user-defined process objective function. This workflow is applied in a case study. To analyze the information loss due to the reduction of uncertain parameters, Monte Carlo simulations are performed.
Subject(s): uncertainty
parameter estimation
optimization
Monte Carlo simulation
Issue Date: 14-Jul-2014
Date Available: 4-Feb-2021
Language Code: en
DDC Class: 660 Chemische Verfahrenstechnik
Sponsor/Funder: DFG, 53182490, EXC 314: Unifying Concepts in Catalysis
DFG, 56091768, TRR 63: Integrierte chemische Prozesse in flüssigen Mehrphasensystemen
Book Title: Proceedings of the 8th International Conference on Foundations of Computer-Aided Process Design
Editor: Eden, Mario R.
Siirola, John D.
Towler, Gavin P.
Publisher: Elsevier
Publisher DOI: 10.1016/B978-0-444-63433-7.50104-8
Page Start: 717
Page End: 722
Series: Computer Aided Chemical Engineering
Series Number: 34
ISBN: 978-0-444-63433-7
ISSN: 1570-7946
TU Affiliation(s): Fak. 3 Prozesswissenschaften » Inst. Prozess- und Verfahrenstechnik » FG Dynamik und Betrieb technischer Anlagen
Appears in Collections:Technische Universität Berlin » Publications

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