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Machine and Deep Learning Approaches for Flooding Prevention in Distillation and Extraction Columns
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  • Jonas Oeing,
  • Laura Neuendorf,
  • Lukas Bittorf,
  • Waldemar Krieger,
  • Norbert Kockmann
Jonas Oeing
Technische Universität Dortmund
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Laura Neuendorf
TU Dortmund University
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Lukas Bittorf
Technische Universität Dortmund Fakultät Bio- und Chemieingenieurwesen
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Waldemar Krieger
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Norbert Kockmann
TU Dortmund
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Abstract

Machine Learning (ML) algorithms can be combined with the modular automation protocol (MTP) and recognize the flooding behavior of laboratory fluids separation columns. Hence, artificial intelligence (AI) tools with deep learning (DL) offer a high potential for the process industry and allow to capture operating states that are otherwise difficult to detect or model. However, the advanced methods are only hesitantly applied in practice. This article provides an overview on how artificial intelligence-based algorithms can be implemented in existing laboratory plants. Process sensor data as well as image data are used to model the flooding behavior of distillation and extraction columns and the system is adapted to the existing modular automation standard of the Module Type Package (MTP).