Flooding Prevention in Distillation and Extraction Columns with Aid of Machine Learning Approaches |
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| Authors: | Jonas Oeing Laura Maria Neuendorf Lukas Bittorf Waldemar Krieger Norbert Kockmann |
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| Affiliation: | 1. TU Dortmund University, Department of Biochemical and Chemical Engineering, Laboratory of Equipment Design, Emil-Figge-Straße 68, 44227 Dortmund, Germany;2. TU Dortmund University, Department of Biochemical and Chemical Engineering, Laboratory of Equipment Design, Emil-Figge-Straße 68, 44227 Dortmund, Germany
All authors contributed equally. |
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| Abstract: | Flooding of separation columns is a severe limitation in the operation of distillation and liquid-liquid extraction columns. To observe operation conditions, machine learning algorithms are implemented to recognize the flooding behavior of separation columns on laboratory scale. Besides this, the investigated columns already provided the modular automation interface Module Type Package (MTP), which is used for data access of necessary sensor data. Hence, artificial intelligence (AI) tools with deep learning offer 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 due to complex combination of operational sensing, data analysis, and active control of the equipment. This article provides an overview on how AI-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 for stable and robust operational conditions. |
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| Keywords: | Clustering Convolutional neural networks Flooding Process monitoring Time series forecasting |
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