MOSAIC – Enabling Large‐Scale Equation‐Based Flow Sheet Optimization

Esche, Erik; Hoffmann, Christian; Illner, Markus; Müller, David; Fillinger, Sandra; Tolksdorf, Gregor; Bonart, Henning; Wozny, Günter; Repke, Jens‐Uwe

doi:10.1002/cite.201600114

Cited by 22 publications

(6 citation statements)

References 19 publications

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“…As its main difference compared to the dynamic one are the absence of time derivatives and its lack of controller equations, the model is not presented here, but it can be deduced from the dynamic model. As part of this work, both steady-state and dynamic model have been implemented in MOSAICmodeling, a web-based modeling, simulation, and optimization environment [34,35]. Using its code generator for various programming languages and modeling environ-ments, the steady-state system is exported to AMPL [36] and solved with IPOPT [37], while the dynamic system is exported to the gPROMS model builder [38].…”

Section: Methodsmentioning

confidence: 99%

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

Hoffmann

Weigert

Esche

et al. 2020

Chemical Engineering Science

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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.

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Section: Methodsmentioning

confidence: 99%

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

Hoffmann

Weigert

Esche

et al. 2020

Chemical Engineering Science

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“…On the other hand, EO methods require more expertise to solve large models, because a general-purpose Newton-based solver is typically used and complex initializations [17,18] must be supplied. For instance, Aspen Plus has been extended to deal with EO features in Aspen-EO where initialization is handled through partial solves within the SM mode.…”

Section: Current Practice and Enabling Toolsmentioning

confidence: 99%

The IDAES process modeling framework and model library—Flexibility for process simulation and optimization

Lee

Ghouse

Eslick

et al. 2021

J Adv Manuf & Process

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Energy systems and manufacturing processes of the 21st century are becoming increasingly dynamic and interconnected, which require new capabilities to effectively model and optimize their design and operations. Such next generation computational tools must leverage state-of-the-art techniques in optimization and be able to rapidly incorporate new advances. To address these requirements, we have developed the Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform, which builds on the strengths of both process simulators (model libraries) and algebraic modeling languages (advanced solvers). This paper specifically presents the IDAES Core Modeling Framework (IDAES-CMF), along with a case study demonstrating the application of the framework to solve process optimization problems. Capabilities provided by this framework include a flexible, modifiable, open-source platform for optimization of process flowsheets utilizing state-of-the-art solvers and solution techniques, fully open and extensible libraries of dynamic unit operations models and thermophysical property models, and integrated support for superstructure-based conceptual design and optimization under uncertainty.

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“…The model is implemented in MOSAICmodeling, a web-based modeling, simulation, and optimization environment (Merchan et al, 2015;Esche et al, 2017). A modular setup for the model components is chosen, which is illustrated in Figure 5.…”

Section: Model Structure and Implementationmentioning

confidence: 99%

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

Hoffmann

Weigert

Esche

et al. 2020

Computers & Chemical Engineering

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MOSAIC – Enabling Large‐Scale Equation‐Based Flow Sheet Optimization

Cited by 22 publications

References 19 publications

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

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

The IDAES process modeling framework and model library—Flexibility for process simulation and optimization

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

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