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.
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 levelsthe syntheses of hydrogen peroxide,
adiponitrile, and 1,2-dichloroethane via chloralkali electrolysisto
illustrate the individual steps of the proposed methodology.
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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