Acoustic wave separation (AWS) is gradually emerging as an effective method for continuous clarification of Chinese Hamster Ovary (CHO) cells. Here we present a mechanistic model for AWS based on population balance model (PBM) along with acoustic agglomeration kernel to predict cell size distribution as a function of time. Cell trajectory in the acoustic zone has been analysed numerically based on inertial and acoustic forces. The results indicate a significant contribution of flowrate and acoustic power on cell dynamics in the acoustic zone. Using this understanding, PBM has been used to determine the agglomeration rate and cell separation efficiency (CSE) based on the flowrate, acoustic power, load cell density and cell diameter. Results show that more than 90% CSE was achieved for flowrates &lt; 5 mL min<sup>-1</sup> and acoustic power &gt; 12.5 W. Higher load cell densities also resulted in enhanced separation owing to the increase in inter-cell collisions. Multiple chambers increased the residence time of cells and led to an increase in the overall CSE. Experimental results confirm that the mechanistic model offers an accurate prediction and a better understanding of the CHO cell agglomeration process using the novel AWS technology. Furthermore, a generalized correlation has been developed using the Buckingham Pi theorem between the dimensionless numbers composed of relevant process and geometrical variables to predict the agglomeration efficiency. To our knowledge, this is the first such study to model the agglomeration dynamics of CHO cells in acoustic standing waves.

Mechanistic modelling of Chinese hamster ovary cell clarification using acoustic wave separator

ACS Fall 2021

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