Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-8388
Main Title: Digitalization in Thermodynamics
Author(s): Forte, Esther
Jirasek, Fabian
Bortz, Michael
Burger, Jakob
Vrabec, Jadran
Hasse, Hans
Type: Article
Language Code: en
Abstract: Digitalization is about data and how they are used. This has always been a key topic in applied thermodynamics. In the present work, the influence of the current wave of digitalization on thermodynamics is analyzed. Thermodynamic modeling and simulation is changing as large amounts of data of different nature and quality become easily available. The power and complexity of thermodynamic models and simulation techniques is rapidly increasing, and new routes become viable to link them to the data. Machine learning opens new perspectives, when it is suitably combined with classical thermodynamic theory. Illustrated by examples, different aspects of digitalization in thermodynamics are discussed: strengths and weaknesses as well as opportunities and threats.
URI: https://depositonce.tu-berlin.de/handle/11303/9315
http://dx.doi.org/10.14279/depositonce-8388
Issue Date: 8-Jan-2019
Date Available: 11-Apr-2019
DDC Class: 540 Chemie und zugeordnete Wissenschaften
660 Chemische Verfahrenstechnik
Subject(s): digitalization
machine learning
Pareto optimization
thermodynamic models
uncertainty propagation
License: http://rightsstatements.org/vocab/InC/1.0/
Journal Title: Chemie - Ingenieur - Technik
Publisher: Wiley
Publisher Place: Weinheim
Volume: 91
Issue: 3
Publisher DOI: 10.1002/cite.201800056
Page Start: 201
Page End: 214
EISSN: 1522-2640
ISSN: 0009-286X
Appears in Collections:FG Thermodynamik und Thermische Verfahrenstechnik » Publications

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