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- W2964757098 abstract "Including uncertainty in design problems can cause highly complex mixed integer nonlinear programming problems (MINLP). A possible approach of solving such problems is the application of chance-constrained optimization. However, the computational effort for the probability calculation in this method is still a major challenge. In this contribution, a framework for generating data-driven input-output and uncertainty models based on either first principle models or measurement data is presented. These models can be applied in chance-constrained optimization to significantly decrease the required calculation time. The framework is evaluated by applying it to plant data from a real chloralkali process. Furthermore, the created data-driven models are tested in a framework for chance-constrained optimization." @default.
- W2964757098 created "2019-08-13" @default.
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- W2964757098 date "2019-01-01" @default.
- W2964757098 modified "2023-10-03" @default.
- W2964757098 title "Generation of Data-Driven Models for Chance-Constrained Optimization" @default.
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- W2964757098 doi "https://doi.org/10.1016/b978-0-12-818597-1.50049-7" @default.
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