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Machine Learning Supports Robust Operation of Thermosiphon Reboilers
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.
Machine Learning Supports Robust Operation of Thermosiphon Reboilers
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.
Machine Learning Supports Robust Operation of Thermosiphon Reboilers
Appelhaus, David (author) / Lu, Yan (author) / Schenkendorf, René (author) / Scholl, Stephan (author) / Jasch, Katharina (author)
Chemie Ingenieur Technik ; 93 ; 1976-1986
2021-12-01
11 pages
Article (Journal)
Electronic Resource
English
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