Implementation of a Control Strategy for Hydrodynamics of a Stirred Liquid–Liquid Extraction Column Based on Convolutional Neural Networks
Laura Neuendorf, Fatemeh Z. Baygi, Pia Kolloch, Norbert Kockmann
Abstract
Online process supervision is enabled by smart sensor development, hence attracting increasing attention in pharmaceutical and chemical process engineering. Additional sensor data enable more precise process control as additional process parameters can be monitored. They are easy to integrate into modular plants, and their provided additional process parameters enable a more flexible operation of the apparatus due to the quick and more sensitive reaction to changing circumstances. An artificial intelligence-based optical sensor for the investigation of different operating states and droplet sizes within a liquid–liquid stirred DN32 extraction column in counter current flow is developed and presented in this work. Two operating states, the flooding state and the column's regular operating state, are differentiated as observable states. Additionally, the diameter of the rising liquid droplets of the disperse phase is categorized into different diameter classes. A control strategy for the extraction column is derived based on the results of the convolutional neural network-based image analysis. Thus, a robust soft sensor controlling the hydrodynamics of an extraction column was developed. The developed control strategy automatically leads the extraction column into a favorable hydrodynamically stable operation state.