Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-9969
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Main Title: Uncertainty Analysis for Data-Driven Chance-Constrained Optimization
Author(s): Häußling Löwgren, Bartolomeus
Weigert, Joris
Esche, Erik
Repke, Jens-Uwe
Type: Article
Language Code: en
Abstract: In this contribution our developed framework for data-driven chance-constrained optimization is extended with an uncertainty analysis module. The module quantifies uncertainty in output variables of rigorous simulations. It chooses the most accurate parametric continuous probability distribution model, minimizing deviation between model and data. A constraint is added to favour less complex models with a minimal required quality regarding the fit. The bases of the module are over 100 probability distribution models provided in the Scipy package in Python, a rigorous case-study is conducted selecting the four most relevant models for the application at hand. The applicability and precision of the uncertainty analyser module is investigated for an impact factor calculation in life cycle impact assessment to quantify the uncertainty in the results. Furthermore, the extended framework is verified with data from a first principle process model of a chloralkali plant, demonstrating the increased precision of the uncertainty description of the output variables, resulting in 25% increase in accuracy in the chance-constraint calculation.
URI: https://depositonce.tu-berlin.de/handle/11303/11081
http://dx.doi.org/10.14279/depositonce-9969
Issue Date: 20-Mar-2020
Date Available: 4-May-2020
DDC Class: 006 Spezielle Computerverfahren
Subject(s): uncertainty analysis
optimization under uncertainty
chance-constrained optimization
skewed distribution
Sponsor/Funder: BMWi, 0350013A, ChemEFlex - Umsetzbarkeitsanalyse zur Lastflexibilisierung elektrochemischer Verfahren in der Industrie; Teilvorhaben: Modellierung der Chlor-Alkali-Elektrolyse sowie anderer Prozesse und deren Bewertung hinsichtlich Wirtschaftlichkeit und möglicher Hemmnisse
DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berlin
License: https://creativecommons.org/licenses/by/4.0/
Journal Title: Sustainability
Publisher: MDPI
Publisher Place: Basel
Volume: 12
Issue: 6
Article Number: 2450
Publisher DOI: 10.3390/su12062450
EISSN: 2071-1050
Appears in Collections:FG Dynamik und Betrieb technischer Anlagen » Publications

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