AI‐based supervision for a stirred extraction column assisted with population balance-based simulation

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dc.contributor.authorNeuendorf, Laura
dc.contributor.authorHammal, Zakariae
dc.contributor.authorFricke, Armin
dc.contributor.authorKockmann, Norbert
dc.date.accessioned2025-01-23T08:57:36Z
dc.date.available2025-01-23T08:57:36Z
dc.date.issued2023-04-25
dc.description.abstractSolvent 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.en
dc.identifier.urihttp://hdl.handle.net/2003/43377
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-25209
dc.language.isoen
dc.relation.ispartofseriesChemie - Ingenieur - Technik; 95(7)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectLiquid-liquid extractionen
dc.subjectMask R-CNNen
dc.subjectPopulation balance modelingen
dc.subjectProcess simulatoren
dc.subjectSolvent extractionen
dc.subject.ddc660
dc.titleAI‐based supervision for a stirred extraction column assisted with population balance-based simulationen
dc.typeText
dc.type.publicationtypeResearchArticle
dcterms.accessRightsopen access
eldorado.secondarypublicationtrue
eldorado.secondarypublication.primarycitationNeuendorf, 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, 95: 1134-1145. https://doi.org/10.1002/cite.202200241
eldorado.secondarypublication.primaryidentifierhttps://doi.org/10.1002/cite.202200241

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