Detecting flooding state in extraction columns: Convolutional neural networks vs. a white‐box approach for image‐based soft sensor development

Abstract

Among thermal separation methods, solvent extraction with countercurrent flow is one of the most energy-efficient processes. The separation efficiency is highest close to the flooding point, which characterizes the maximum possible liquid throughput. However, this point is the limiting parameter in the entire extraction process since the flow of at least one phase is stopped. The optical supervision of the equipment enables the efficient operation of the extraction column near the flooding state. This contribution presents an assessment of two approaches in detecting the undesirable flooding state through recorded images of a transparent extraction column. One approach is a feature-based space that is more interpretable in extracting features than a Support Vector Machine classifier. The second is a feature-extraction pipeline followed by a robust Convolutional Neural Network model. The analysis regarding the inference times shows that both models can be used as online classification tools for real-time applications.