Liquid-liquid extraction column flooding
detection
A liquid-liquid extraction column serves the purpose of separation of
solvent mixtures with the help of a third solvent immiscible with the
carrier solvent. In the investigated case, a value component dissolved
in a light phase is contacted with the heavy phase, from which it can be
separated more easily in a following process step. Two operating states
within an extraction column can be identified. The regular operating
state, characterized by a high separation efficiency through a large
mass transfer of the solute from light to heavy phase. On the other
hand, the separation efficiency decreases significantly as flooding, the
second but undesired operating state occurs, and the volumetric
throughput breaks down.
As the laboratory extraction column is optically accessible, a different
approach based on computer vision is implemented for flooding detection.
Here, image or video data is fed into a deep learning algorithm,i.e. a convolutional neural network (CNN). The network’s
objective is to distinguish between the desired normal operating mode
and flooding of the column.