date: 2019-12-04T10:08:36Z
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pdf:docinfo:title: Surrogate Modeling for Liquid?Liquid Equilibria Using a Parameterization of the Binodal Curve
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subject: Computational effort and convergence problems can pose serious challenges when employing advanced thermodynamic models in process simulation and optimization. Data-based surrogate modeling helps to overcome these problems at the cost of additional modeling effort. The present work extends the range of methods for efficient data-based surrogate modeling of liquid?liquid equilibria. A new model formulation is presented that enables smaller surrogates with box-constrained input domains and reduced input dimensions. Sample data are generated efficiently by using numerical continuation. The new methods are demonstrated for the surrogate modeling and optimization of a process for the hydroformylation of 1-decene in a thermomorphic multiphase system. 
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dc:title: Surrogate Modeling for Liquid?Liquid Equilibria Using a Parameterization of the Binodal Curve
modified: 2019-12-04T10:08:36Z
cp:subject: Computational effort and convergence problems can pose serious challenges when employing advanced thermodynamic models in process simulation and optimization. Data-based surrogate modeling helps to overcome these problems at the cost of additional modeling effort. The present work extends the range of methods for efficient data-based surrogate modeling of liquid?liquid equilibria. A new model formulation is presented that enables smaller surrogates with box-constrained input domains and reduced input dimensions. Sample data are generated efficiently by using numerical continuation. The new methods are demonstrated for the surrogate modeling and optimization of a process for the hydroformylation of 1-decene in a thermomorphic multiphase system. 
pdf:docinfo:subject: Computational effort and convergence problems can pose serious challenges when employing advanced thermodynamic models in process simulation and optimization. Data-based surrogate modeling helps to overcome these problems at the cost of additional modeling effort. The present work extends the range of methods for efficient data-based surrogate modeling of liquid?liquid equilibria. A new model formulation is presented that enables smaller surrogates with box-constrained input domains and reduced input dimensions. Sample data are generated efficiently by using numerical continuation. The new methods are demonstrated for the surrogate modeling and optimization of a process for the hydroformylation of 1-decene in a thermomorphic multiphase system. 
pdf:docinfo:creator: Christian Kunde, Tobias Keßler, Steffen Linke, Kevin McBride, Kai Sundmacher, and Achim Kienle
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dc:description: Computational effort and convergence problems can pose serious challenges when employing advanced thermodynamic models in process simulation and optimization. Data-based surrogate modeling helps to overcome these problems at the cost of additional modeling effort. The present work extends the range of methods for efficient data-based surrogate modeling of liquid?liquid equilibria. A new model formulation is presented that enables smaller surrogates with box-constrained input domains and reduced input dimensions. Sample data are generated efficiently by using numerical continuation. The new methods are demonstrated for the surrogate modeling and optimization of a process for the hydroformylation of 1-decene in a thermomorphic multiphase system. 
Keywords: surrogate modeling; liquid?liquid equilibrium; parameterization; numerical continuation; optimization; multistage extraction
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dc:creator: Christian Kunde
description: Computational effort and convergence problems can pose serious challenges when employing advanced thermodynamic models in process simulation and optimization. Data-based surrogate modeling helps to overcome these problems at the cost of additional modeling effort. The present work extends the range of methods for efficient data-based surrogate modeling of liquid?liquid equilibria. A new model formulation is presented that enables smaller surrogates with box-constrained input domains and reduced input dimensions. Sample data are generated efficiently by using numerical continuation. The new methods are demonstrated for the surrogate modeling and optimization of a process for the hydroformylation of 1-decene in a thermomorphic multiphase system. 
dcterms:created: 2019-10-16T08:12:40Z
Last-Modified: 2019-12-04T10:08:36Z
dcterms:modified: 2019-12-04T10:08:36Z
title: Surrogate Modeling for Liquid?Liquid Equilibria Using a Parameterization of the Binodal Curve
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pdf:docinfo:keywords: surrogate modeling; liquid?liquid equilibrium; parameterization; numerical continuation; optimization; multistage extraction
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dc:subject: surrogate modeling; liquid?liquid equilibrium; parameterization; numerical continuation; optimization; multistage extraction
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