(485c) Applications of Computational Fluid Dynamics for Prediction of Buffer Dissolution in Mixing Tanks for Biopharmaceutical Processes

In the quest for efficiency and precision in biopharmaceutical manufacturing, the rate and homogeneity of buffer solutions within mixing tanks emerges as a critical parameter for engineering and qualification runs and influencing the quality and consistency of the final product. This presentation delves into the innovative application of Lattice Boltzmann based computational fluid dynamics (CFD) as a predictive tool for understanding and optimizing the dissolution process of buffers in mixing tanks. Leveraging the power of MStar-CFD, we dissect the complex interplay of fluid mechanics and mass transfer to provide a detailed visualization and quantitative analysis of the mixing process, including the identification of potential dead zones and the prediction of dissolution rates under various operational conditions.

Through a series of simulations, this study reveals how variations operating conditions affect the efficiency of buffer dissolution, offering insights into optimal design and operational strategies to achieve uniform dissolution. The application of CFD allows for the a priori prediction of mixing outcomes, facilitating the move from process development to clinical and commercial manufacturing that can significantly reduce time and resources related to trial-and-error experimentation during this phase.

Moreover, this presentation highlights the role of CFD in scaling up processes from laboratory to industrial scale, ensuring that buffer mixing remains efficient and consistent across different production volumes. By integrating CFD simulations into the development and optimization of mixing processes, biopharmaceutical manufacturers can enhance product quality, reduce production costs, and accelerate the time-to-patient for vital therapeutic products. This study not only underscores the utility of CFD in advancing biopharmaceutical manufacturing but also sets the stage for its broader application in the optimization of various bioprocesses.