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Uncertainty Analysis for Data-Driven Chance-Constrained Optimization

Author

Listed:
  • Bartolomeus Häussling Löwgren

    (Process Dynamics and Operations Group, Technische Universität Berlin, Sekr. KWT 9, Str. Des 17. Juni 135, D-10623 Berlin, Germany)

  • Joris Weigert

    (Process Dynamics and Operations Group, Technische Universität Berlin, Sekr. KWT 9, Str. Des 17. Juni 135, D-10623 Berlin, Germany)

  • Erik Esche

    (Process Dynamics and Operations Group, Technische Universität Berlin, Sekr. KWT 9, Str. Des 17. Juni 135, D-10623 Berlin, Germany)

  • Jens-Uwe Repke

    (Process Dynamics and Operations Group, Technische Universität Berlin, Sekr. KWT 9, Str. Des 17. Juni 135, D-10623 Berlin, Germany)

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.

Suggested Citation

  • Bartolomeus Häussling Löwgren & Joris Weigert & Erik Esche & Jens-Uwe Repke, 2020. "Uncertainty Analysis for Data-Driven Chance-Constrained Optimization," Sustainability, MDPI, vol. 12(6), pages 1-17, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:6:p:2450-:d:334938
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    References listed on IDEAS

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    1. McDonald, James B. & Xu, Yexiao J., 1995. "A generalization of the beta distribution with applications," Journal of Econometrics, Elsevier, vol. 69(2), pages 427-428, October.
    2. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    3. Robert Fourer & David M. Gay & Brian W. Kernighan, 1990. "A Modeling Language for Mathematical Programming," Management Science, INFORMS, vol. 36(5), pages 519-554, May.
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    2. Sandi Baressi Šegota & Nikola Anđelić & Mario Šercer & Hrvoje Meštrić, 2022. "Dynamics Modeling of Industrial Robotic Manipulators: A Machine Learning Approach Based on Synthetic Data," Mathematics, MDPI, vol. 10(7), pages 1-17, April.

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