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Main Title: | Digitalization in Thermodynamics |
Author(s): | Forte, Esther Jirasek, Fabian Bortz, Michael Burger, Jakob Vrabec, Jadran Hasse, Hans |
Type: | Article |
URI: | https://depositonce.tu-berlin.de/handle/11303/9315 http://dx.doi.org/10.14279/depositonce-8388 |
License: | http://rightsstatements.org/vocab/InC/1.0/ |
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. |
Subject(s): | digitalization machine learning Pareto optimization thermodynamic models uncertainty propagation |
Issue Date: | 8-Jan-2019 |
Date Available: | 11-Apr-2019 |
Language Code: | en |
DDC Class: | 540 Chemie und zugeordnete Wissenschaften 660 Chemische Verfahrenstechnik |
Journal Title: | Chemie - Ingenieur - Technik |
Publisher: | Wiley |
Volume: | 91 |
Issue: | 3 |
Publisher DOI: | 10.1002/cite.201800056 |
Page Start: | 201 |
Page End: | 214 |
EISSN: | 1522-2640 |
ISSN: | 0009-286X |
TU Affiliation(s): | Fak. 3 Prozesswissenschaften » Inst. Prozess- und Verfahrenstechnik » FG Thermodynamik und Thermische Verfahrenstechnik |
Appears in Collections: | Technische Universität Berlin » Publications |
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