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Article

Uncertainty Analysis for Data-Driven Chance-Constrained Optimization

Process Dynamics and Operations Group, Technische Universität Berlin, Sekr. KWT 9, Str. Des 17. Juni 135, D-10623 Berlin, Germany
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Sustainability 2020, 12(6), 2450; https://doi.org/10.3390/su12062450
Received: 31 January 2020 / Revised: 6 March 2020 / Accepted: 11 March 2020 / Published: 20 March 2020
(This article belongs to the Special Issue Process Integration and Optimisation for Sustainable Systems)
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. View Full-Text
Keywords: uncertainty analysis; optimization under uncertainty; chance-constrained optimization; skewed distribution uncertainty analysis; optimization under uncertainty; chance-constrained optimization; skewed distribution
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MDPI and ACS Style

Häussling Löwgren, B.; Weigert, J.; Esche, E.; Repke, J.-U. Uncertainty Analysis for Data-Driven Chance-Constrained Optimization. Sustainability 2020, 12, 2450. https://doi.org/10.3390/su12062450

AMA Style

Häussling Löwgren B, Weigert J, Esche E, Repke J-U. Uncertainty Analysis for Data-Driven Chance-Constrained Optimization. Sustainability. 2020; 12(6):2450. https://doi.org/10.3390/su12062450

Chicago/Turabian Style

Häussling Löwgren, Bartolomeus, Joris Weigert, Erik Esche, and Jens-Uwe Repke. 2020. "Uncertainty Analysis for Data-Driven Chance-Constrained Optimization" Sustainability 12, no. 6: 2450. https://doi.org/10.3390/su12062450

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