Solvent extraction as environmental benign separation technique can be modeled in physical detail by the population balance of the droplet size distribution. However, much information on droplet generation and coalescence is necessary for representative results. In this contribution, we present a comparison of AI-evaluated experimental and simulated data on the behavior of a stirred solvent extraction column with an inner diameter of 32 mm. Lab experiments were performed using the standard test system with n-butyl acetate, acetone, and deionized water. A digital camera is placed in front of the middle section as well as the head of the column. Droplet size evaluation is performed using a retrained neural net (Mask R-CNN). The stirred DN32 extraction column is modeled and simulated using a 1D CFD population balance software. The simulation allows for behavior analysis, trend comparison, and validation of the hydrodynamics and mass transfer performances.
Neuendorf, L., Hammal, Z., Fricke, A. and Kockmann, N. (2023), AI-Based Supervision for a Stirred Extraction Column Assisted with Population Balance-Based Simulation. Chemie Ingenieur Technik.
