Protein enrichment by foam Fractionation: Experiment and modeling. (20th July 2022)

• Record Type: Journal Article
• Authors: Keshavarzi, Behnam; Krause, Thomas; Sikandar, Sidra; Schwarzenberger, Karin; Eckert, Kerstin; Ansorge-Schumacher, Marion B.; Heitkam, Sascha
• Is Part Of: Chemical engineering science. Volume 256(2022)
• Publication Date: 2022-07-20
• Subjects: Protein -- Foam fractionation -- Flotation -- Modeling -- Dynamic adsorption -- Surface equation of state -- Flow-on-bubble; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; Chemical engineering; Periodicals; Electronic journals; 660
• DOI: 10.1016/j.ces.2022.117715
• Languages: English
• ISSNs: 0009-2509

Abstract

Graphical abstract: Highlights: BSA protein adsorbs irreversibly on the bubble interface. A surface equation of state was provided for BSA using surface compression tests. The rising bubble was emulated through flow-on-bubble experiments. Foam coalescence accelerates the liquid drainage leading to higher protein enrichment. A modeling was proposed to estimate recovery and grade in protein flotation. Abstract: This work investigates the enrichment of bovine serum albumin (BSA) protein through foam fractionation. Here, we performed experiments using BSA and measured the recovery and grade of the extract. Additionally, an unsteady-state simulation of the protein foam fractionation process was carried out by numerically solving the liquid drainage equation in the foam. Thereby, the extracted liquid volume and protein concentration were calculated. Required quantities such as foam stability, interface coverage or bubble size distribution were measured in corresponding experiments and were fed into the model. The experiments showed that the foam coalescence accelerates the liquid drainage leading to dryer extract and higher protein enrichment. The modeling also reproduced the liquid recovery and extract concentration of the foam fractionation tests within a reasonable error range. The modeling solely relies on experimental inputs and does not require any tuning parameters. It can be further used for optimization or up-scaling of protein foam fractionation.