Uncertainty Analysis for Data-Driven Chance-Constrained Optimization

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.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 HemmnisseDFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Development of a State Estimation Environment for the Optimal Control of a Mini-plant for the Hydroformylation in Microemulsions

A state estimation framework for a surfactant containing multiphase process for the hydroformylation of longchained alkenes is presented. Firstly, available state estimation methods, such as the extended Kalman filter, the unscented Kalman filter and the particle filter are compared regarding their usability in processes with high model and measurement uncertainty. Subsequently, an MHE-based state estimation algorithm is introduced. This includes an approach, which handles the occurring multi-rate measurements by dividing the state estimation into two separate steps. Finally, the implementation is discussed regarding necessary requirements and the state estimation framework is applied within long-term real process operation in a mini-plant.DFG, 56091768, TRR 63: Integrierte chemische Prozesse in flüssigen Mehrphasensysteme

Towards demand-side management of the chlor-alkali electrolysis: Dynamic, pressure-driven modeling and model validation of the 1,2-dichloroethane synthesis

A promising application of demand-side management is the chlor-alkali electrolysis. However, storing the produced chlorine for flexibility should be avoided whenever possible. If PVC is produced from chlorine, storing the intermediate 1,2-dichloroethane resulting from direct chlorination of ethene is a better alternative as it is less toxic than chlorine and can be easily stored. Currently, no dynamic process models to study the process behavior or to develop optimal trajectories for the 1,2-dichloroethane production under different demand response scenarios are available. Hence, we formulate and solve a dynamic, pressure-driven model of the synthesis of 1,2-dichloroethane and validate it with real process data in this contribution. As part of this dynamic model, differentiable formulations for weeping and the flow over a weir of a distillation tray are presented, which are also valid whenever certain trays run dry.BMWi, 0350013A, Verbundvorhaben: 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 Hemmniss

A pressure-driven, dynamic model for distillation columns with smooth reformulations for flexible operation

Dynamic models for plants including the startup or shutdown phase are still scarce as the (dis-)appearence of phases or streams is challenging to implement. We present an approach to model a distillation column, in which these operation modes are also considered without exchanging equations. For this purpose, the well-known modeling equations for distillation columns are reformulated robustly to allow for the disappearance of the vapor phase without discontinuities. The reformulation does not depend on solving an optimization problem and could easily be applied to other column types or different unit operations. The proposed model is solved in two case studies with 10 and 40 trays, respectively. In these case studies, the influence of single phenomena on the obtained dynamic profiles is investigated, e.g., weeping, which are often neglected. The proposed modeling approach yields a dynamic model that can be solved without reinitialization for a realistically large number of trays.BMBF, 0350013A, Verbundvorhaben: 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 Hemmniss

Adaptive sampling of dynamic systems for generation of fast and accurate surrogate models

For economic nonlinear model predictive control and dynamic real-time optimization fast and accurate models are necessary. Consequently, the use of dynamic surrogate models to mimic complex rigorous models is increasingly coming into focus. For dynamic systems, the focus so far had been on identifying a system's behavior surrounding a steady-state operation point. In this contribution, we propose a novel methodology to adaptively sample rigorous dynamic process models to generate a dataset for building dynamic surrogate models. The goal of the developed algorithm is to cover an as large as possible area of the feasible region of the original model. To demonstrate the performance of the presented framework it is applied on a dynamic model of a chlor-alkali electrolysis

Towards demand-side management of the chlor-alkali electrolysis: dynamic modeling and model validation

The chlor-alkali electrolysis promises advantageous application of demand-side management and several research groups have proposed dynamic models to obtain optimal trajectories for such applications. However, no dynamic model of those proposed so far has yet been validated with real, industrial plant data. This is highly important to determine trajectories, which are indeed feasible. This contribution addresses this issue by proposing a dynamic model of the chlor-alkali electrolysis that contains those variables relevant for dynamic operation, especially temperature and composition of the electrolytes. This model is validated with real plant data from an electrolysis located in Marl, Germany, for two scenarios: (1) several small load changes of around 4 MW and (2) a large load change of more than 50 MW.BMBF, 0350013A, Verbundvorhaben: 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 Hemmniss

Assessing the realizable flexibility potential of electrochemical processes

Demand response is a viable concept to deal with and benefit from fluctuating electricity prices and is of growing interest to the electrochemical industry. To assess the flexibility potential of such processes, a generic, interdisciplinary methodology is required. We propose such a methodology, in which the electrochemical fundamentals and the theoretical potential are determined first by analyzing strengths, weaknesses, opportunities, and threats. Afterward, experiments are conducted to determine selectivity and yield under varying loads and to assess the additional long-term costs associated with flexible operation. An industrial-scale electrochemical process is assessed regarding its technical, economic, and practical potential. The required steps include a flow sheet analysis, the formulation and solution of a simplified model for operation scheduling under various business options, and a dynamic optimization based on rigorous, dynamic process models. We apply the methodology to three electrochemical processes of different technology readiness levels—the syntheses of hydrogen peroxide, adiponitrile, and 1,2-dichloroethane via chloralkali electrolysis—to illustrate the individual steps of the proposed methodology.BMBF, 0350013A, Verbundvorhaben: 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 Hemmniss