Adaptation of Dynamic Data‐Driven Models for Real‐Time Applications: From Simulated to Real Batch Distillation Trajectories by Transfer Learning
Abstract: In the absence of knowledge about challenging dynamic phenomena involved in batch distillation processes, e.g., complex flow regimes or appearing and vanishing phases, generation of accurate mechanistic models is limited. Real plant data containing this missing information is scarce, also limiting the use of data‐driven models. To exploit the information contained in measurement data and a related but inaccurate first‐principles model, transfer learning from simulated to real plant data is analyzed. For the use case of a batch distillation column, the adapted model provides more accurate predictions than a data‐driven model trained exclusively on scarce real plant data or simulated data. Its enhanced convergence and lower computational cost make it suitable for optimization in real‐time.
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
Adaptation of Dynamic Data‐Driven Models for Real‐Time Applications: From Simulated to Real Batch Distillation Trajectories by Transfer Learning ; day:29 ; month:03 ; year:2023 ; extent:10
Chemie - Ingenieur - Technik ; (29.03.2023) (gesamt 10)
- Creator
- DOI
-
10.1002/cite.202200228
- URN
-
urn:nbn:de:101:1-2023033015113306435492
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 10:59 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Rihm, Gerardo Brand
- Schueler, Merlin
- Nentwich, Corina
- Esche, Erik
- Repke, Jens-Uwe
Other Objects (12)
Adaptation of Dynamic Data‐Driven Models for Real‐Time Applications: From Simulated to Real Batch Distillation Trajectories by Transfer Learning
Optimization of batch trajectories under scarcity of measured information by dynamic data-driven surrogate models
Optimal process operation under uncertainty using transient data-driven probability distributions
Development of a rate-based model for multi-component distillation in a rotating packed bed based on mass transfer experiments
A pressure-driven, dynamic model for distillation columns with smooth reformulations for flexible operation
Control of reactive distillation columns for dimethyl ether production: a simulative and experimental study
Uncertainty Analysis for Data-Driven Chance-Constrained Optimization
Direct Numerical Simulations of Liquids on Microstructured Surfaces: Analysing the Fluid Dynamics on Packing
Controlling Sliding Droplets with Optimal Contact Angle Distributions and a Phase Field Model
Optimal design and evaluation of a biogas-based oxidative coupling of methane process
Influence of the Distance between two Catalysts for CO 2 to Dimethyl Ether Tandem Reaction
Optimal Control of Surfactant containing Multiphase Systems – Challenges and Solution Strategies for a stable Mini-Plant Operation
Adaptation of Dynamic Data‐Driven Models for Real‐Time Applications: From Simulated to Real Batch Distillation Trajectories by Transfer Learning
Optimization of batch trajectories under scarcity of measured information by dynamic data-driven surrogate models
Optimal process operation under uncertainty using transient data-driven probability distributions
Development of a rate-based model for multi-component distillation in a rotating packed bed based on mass transfer experiments
A pressure-driven, dynamic model for distillation columns with smooth reformulations for flexible operation
Control of reactive distillation columns for dimethyl ether production: a simulative and experimental study
Uncertainty Analysis for Data-Driven Chance-Constrained Optimization
Direct Numerical Simulations of Liquids on Microstructured Surfaces: Analysing the Fluid Dynamics on Packing
Controlling Sliding Droplets with Optimal Contact Angle Distributions and a Phase Field Model
Optimal design and evaluation of a biogas-based oxidative coupling of methane process
Influence of the Distance between two Catalysts for CO 2 to Dimethyl Ether Tandem Reaction
Optimal Control of Surfactant containing Multiphase Systems – Challenges and Solution Strategies for a stable Mini-Plant Operation
Adaptation of Dynamic Data‐Driven Models for Real‐Time Applications: From Simulated to Real Batch Distillation Trajectories by Transfer Learning
Optimization of batch trajectories under scarcity of measured information by dynamic data-driven surrogate models
Optimal process operation under uncertainty using transient data-driven probability distributions
Development of a rate-based model for multi-component distillation in a rotating packed bed based on mass transfer experiments
A pressure-driven, dynamic model for distillation columns with smooth reformulations for flexible operation
Control of reactive distillation columns for dimethyl ether production: a simulative and experimental study
Uncertainty Analysis for Data-Driven Chance-Constrained Optimization
Direct Numerical Simulations of Liquids on Microstructured Surfaces: Analysing the Fluid Dynamics on Packing
Controlling Sliding Droplets with Optimal Contact Angle Distributions and a Phase Field Model
Optimal design and evaluation of a biogas-based oxidative coupling of methane process
Influence of the Distance between two Catalysts for CO 2 to Dimethyl Ether Tandem Reaction