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Some Aspects of Combining Data and Models in Process Engineering

 
: Heese, Raoul; Nies, Julia; Bortz, Michael

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Volltext ()

Chemie- Ingenieur- Technik 92 (2020), Nr.7, S.856-866
ISSN: 0009-286X
ISSN: 1522-2640
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer ITWM ()

Abstract
Observing phenomena under defined conditions and building mathematical models to make further predictions are essential ingredients of natural and engineering sciences. Recent technological and methodical advances make large and high‐dimensional simulation data accessible to model building and therefore to optimization. In this article, selected machine learning methods are highlighted and applied to example data from simple flow sheet simulations. Furthermore, the essential outcomes of the workshop dealing with combination of data and models during the Tutzing Symposium 2019 are summarized.

: http://publica.fraunhofer.de/dokumente/N-593143.html