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Network Modeling of Membrane Filtration: Prediction of Filter Performance from Membrane Morphological Measurements

机译：膜过滤网络建模：膜形态测量滤波器性能预测

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Efficient normal flow filtration steps are crucial to the success of any pharmaceutical process. It is therefore of economic interest to pharmaceutical manufacturers to conduct these filtrations in a short time and with the minimum necessary membrane area. A fundamental understanding of the membrane and fluid properties that control filter capacity and retention is thus essential. Yet filter capacities are most often predicted through only empirical correlations, and retention is understood exclusively through experimental measurements. To enhance our fundamental understanding of the filtration process, we have developed a three-dimensional network model from percolation theory to better study membrane capacity and retention. The initial model described here predicts fluid flow properties of the membrane (dispersion, tortuosity pressure drop) from direct of measurements filter properties (permeability, pore size distribution, porosity- each measured independently). These measurements allow the determination of pore connectivity and the number of layers of pores in each dimension. In this work we describe the development and validation of the network model and explore the relationship between microscopic model parameter values and macroscopic membrane properties related to flow.
机译：高效的正常流过滤步骤对于任何药物方法的成功至关重要。因此，药品制造商的经济利益在短时间内和最小必要的膜面积进行这些过滤。因此，对控制过滤器容量和保留的膜和流体性能的基本理解是必要的。然而，最常通过实验相关性预测过滤容量，并且通过实验测量专门地理解保留。为了提高对滤波过程的根本理解，我们从渗透理论开发了一个三维网络模型，以更好地研究膜容量和保留。这里描述的初始模型预测膜（分散，曲折压降）的流体流动性能从测量过滤器（渗透率，孔径分布，孔隙率）的直接来预测膜（分散性，曲折压降）的流体流动性质。这些测量允许确定每个尺寸中的孔连接和孔隙层的数量。在这项工作中，我们描述了网络模型的开发和验证，并探讨了与流动相关的微观模型参数值与宏观膜特性的关系。

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