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Machine Learning Supports Robust Operation of Thermosiphon Reboilers
Author(s) -
Appelhaus David,
Lu Yan,
Schenkendorf René,
Scholl Stephan,
Jasch Katharina
Publication year - 2021
Publication title -
chemie ingenieur technik
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 36
eISSN - 1522-2640
pISSN - 0009-286X
DOI - 10.1002/cite.202100063
Subject(s) - thermosiphon , reboiler , process (computing) , computer science , work (physics) , machine learning , engineering , mechanical engineering , heat exchanger , operating system
The analysis of process and equipment operational data in chemical engineering regularly requires a high level of expert knowledge. This work presents a Machine Learning‐based approach to evaluate and interpret process data to support robust operation of a thermosiphon reboiler. By applying an outlier detection, potentially interesting and unstable operating conditions can be identified quickly. A multidimensional regression allows to forecast the circulating mass flow. The results obtained fit well into the current state of research and manual evaluation of thermosiphon reboilers.