AI‐Based Supervision for a Stirred Extraction Column Assisted with Population Balance‐Based Simulation

Abstract: Solvent extraction as environmental benign separation technique can be modeled in physical detail by population balance of the droplet size distribution. However, much information on the 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, trends comparison, and validation of the hydrodynamics and mass transfer performances.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: AI‐Based Supervision for a Stirred Extraction Column Assisted with Population Balance‐Based Simulation ; day:25 ; month:04 ; year:2023 ; extent:13 ; Chemie - Ingenieur - Technik ; (25.04.2023) (gesamt 13)

Contributors, Places and Time

• Creator: Neuendorf, Laura ; Hammal, Zakariae ; Fricke, Armin ; Kockmann, Norbert

Further information

• DOI: 10.1002/cite.202200241
• URN: urn:nbn:de:101:1-2023042515575142068481
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 14.08.2022, 10:48 AM CEST