Image analysis for design and operation of gravity separators with coalescing aids. (18th July 2022)

• Record Type: Journal Article
• Authors: Schäfer, Jan; Hlawitschka, Mark W.; Bart, Hans‐Jörg
• Is Part Of: Canadian journal of chemical engineering. Volume 100:Number 9(2022)
• Page Start: 2331
• Page End: 2346
• Publication Date: 2022-07-18
• Subjects: coalescing aids -- convolutional neuronal network -- horizontal gravity settler -- image analysis; Chemical engineering -- Periodicals; Technology -- Periodicals; 660.05
• DOI: 10.1002/cjce.24503
• Languages: English
• ISSNs: 0008-4034

Abstract

Abstract: In gravity separators, also known as settlers, two immiscible liquid phases separate due to differences in density. In extraction mixer‐settler units, a dispersion needs to be separated within the separator unit. In order to overcome the hitherto purely experimental design, a knitted mesh adapted model as well as an automated test facility were developed in this work, which easily enable a scale‐up to industrial units. An automation allows for a controlled investigation of knitted meshes as coalescing aids in settlers, and this was achieved via photo‐optical probes with an optimized image analysis technique. It overcomes the limitations of neuronal network training based on manually annotating images using computer‐generated image data. Therefore, the new methodology and setup are explained in detail, and the derivation and application of a new model to design separators with knitted meshes as coalescing aid is presented and compared to experimental results using meshes of different structures and materials. Finally, case studies and scale‐up are discussed.