Generation of Data-Driven Models for Chance-Constrained Optimization

Weigert, Joris; Esche, Erik; Hoffmann, Christian; Repke, Jens‐Uwe

doi:10.1016/b978-0-12-818597-1.50049-7

Cited by 4 publications

(8 citation statements)

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“…Combining rigorous non-linear process models with environmental models containing highly uncertain parameters for real-time optimization, requires: (1) A stable and precise method for optimization under uncertainty [19], (2) a framework with a computational time allowing for real-time application [16] and (3) an accurate uncertainty modelling framework, quantifying the distribution for a wide variety of probability distribution shapes.…”

Section: Methodsmentioning

confidence: 99%

“…This is achieved by exchanging the rigorous models with data-driven ones. Additionally, using a data-driven uncertainty model, which maps the uncertainty of the outputs over the input space, reduces the computationally effort for the probability calculation significantly [16].…”

Section: Data-driven Chance-constrained Optimization Frameworkmentioning

confidence: 99%

“…The DDPUM generation can be separated into three steps beginning with the sampling of a rigorous model and ending with the training of data-driven input-output and uncertainty models. The workflow is shown schematically in Figure 1: Simplified workflow from rigorous model to chance-constrained optimization, adapted from [16]. The upstream data-driven process and uncertainty model generation is highlighted by the dashed box.…”

Section: Data-driven Chance-constrained Optimization Frameworkmentioning

confidence: 99%

“…Stochastic programming with recourse, robust optimization and chance-constrained optimization [2]. In this study we focus on chance-constrained optimization, in line with previous works at our department [16,17].…”

Section: Introductionmentioning

confidence: 99%

“…The uncertainty in the data is generated by sampling the rigorous models with parameters subjected to uncertainty, for which a probability distribution might be known. However, modelling of uncertainty in the model outputs in the current framework is limited up to now to normal distributions [16].…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty Analysis for Data-Driven Chance-Constrained Optimization

et al. 2020

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Data-driven Chance-constrained Optimization Frameworkmentioning

confidence: 99%

Section: Data-driven Chance-constrained Optimization Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Uncertainty Analysis for Data-Driven Chance-Constrained Optimization

et al. 2020

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Dynamic Process Operation Under Demand Response – A Review of Methods and Tools

Esche

Repke

2020

Chemie Ingenieur Technik

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Participating in electricity markets through demand response causes new requirements for optimizing process control of chemical plants. The last ten years have brought great advances in the formulation and solution of economic nonlinear model predictive control and state estimation to support operation of processes under dynamic constraints. However, gaps remain regarding the availabilities of suitable plant models capable of describing processes active in demand response as well as of robust schemes for state estimation and economic nonlinear model predictive control in commercial tools.

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