Please use this identifier to cite or link to this item:
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.
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
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

Files in This Item:
File Description SizeFormat 
  Until 2020-01-09
1.08 MBAdobe PDFView/Open    Request a copy

Items in DepositOnce are protected by copyright, with all rights reserved, unless otherwise indicated.