GOALI: Predicting performance and fouling of membrane filters

膜过滤器——本质上是一种多孔介质薄片，用于去除悬浮在通过介质的流体饲料中的某些颗粒——在工业上得到广泛应用，仅在美国就代表着数十亿美元的产业。主要的跨国公司，如W.L. Gore &amp； Associates和Pall Corporation，生产了大量的膜基过滤产品，并对改进和优化其过滤器有着浓厚的兴趣。膜过滤用于各种应用，如水净化；放射性污泥的处理；生物技术行业的各种净化工艺；净化：空气或其他气体的净化；还有啤酒澄清。虽然潜在的应用可能会有很大的不同（气体过滤与液体过滤、小颗粒与大颗粒去除、慢通量与快通量、刚性颗粒与可变形颗粒），但广泛的工程挑战是相同的：在低功耗下实现精细控制的分离。但是，在过滤器的使用寿命中，膜的特性（以及过滤器的行为和性能）远不是恒定的：从进料中去除的颗粒沉积在过滤器内部和上面，污染过滤器并降低其性能。发生这种污染的过程是复杂的，并且在很大程度上取决于几个因素，包括膜的内部结构、进料溶液的流动动力学和进料颗粒的类型（它们的形状、大小和化学性质影响它们如何被膜去除）。在这个项目中，开发了新的数学模型来模拟膜过滤和污染，允许调整膜设计参数以获得最佳性能。特别注意内部膜形态的细节所起的作用，以便制定详细的设计指南。与工业伙伴（Pall Corporation的Anil Kumar博士）合作，可以最大限度地提高我们的理论成果转化为工业实践的机会。研究生也参与了该项目的工作。本课题研究膜过滤器的流动和污染问题，具有重要的工业应用价值。与一名博士生合作，PI和她的合作者制定了新的预测数学模型，描述了两种具有实际重要性的情况：(i)褶皱过滤筒内的流动和污染，以及（ii）内部非均质膜的膜污染模型。在每一种情况下，研究小组都建立了模型，考虑了进料溶液中任意粒径的分布，以及膜孔大小的分布。这些场景的第一原理理论研究对其他对此类系统进行基础理论和实验研究的人，以及那些寻求扩展当前应用范围和改进制造过程的人都很感兴趣。一个工业界的同事，Pall公司的Anil Kumar博士参与了这个项目。作为一名实验家，他与团队共享现有数据，根据需要生成测试模型所需的新数据，并充当工业顾问。这种交互确保了项目的重点，并确定和解决了与应用程序相关的问题。实验数据有助于确定模型中未知参数的适当范围，并检验不确定的建模假设。

Membrane filters -- essentially, thin sheets of porous medium that act to remove certain particles suspended in a fluid feed that passes through the medium -- are in widespread industrial use, and represent a multi-billion dollar industry in the US alone. Major multinational companies, such as W.L. Gore & Associates and Pall Corporation, manufacture a huge range of membrane-based filtration products and have a keen interest in improving and optimizing their filters. Membrane filtration is used in applications as diverse as water purification; treatment of radioactive sludge; various purification processes in the biotech industry; the cleaning of air or other gases; and beer clarification. While the underlying applications may vary dramatically (gas versus liquid filtration; small versus large particle removal; slow versus fast throughput; rigid versus deformable particles), the broad engineering challenge is the same: to achieve finely-controlled separation at low power consumption. But membrane characteristics (and hence the filter's behavior and performance) are far from constant over the filter lifetime: the particles removed from the feed are deposited within and on the filter, fouling it and degrading its performance. The processes by which this fouling occurs are complex and depend strongly on several factors, including the internal structure of the membrane, the flow dynamics of the feed solution, and the type of particles in the feed (their shape, size, and chemistry affect how they are removed by the membrane). In this project new mathematical models are developed to simulate membrane filtration and fouling, allowing membrane design parameters to be tuned for optimal performance. Particular attention is paid to the role played by the details of the internal membrane morphology, so that detailed design guidelines can be formulated. Collaboration with an industrial partner (Dr. Anil Kumar of Pall Corporation) maximizes the chances that our theoretical results translate into industrial practice. Graduate students are involved in the work of the project. This project studies flow and fouling in membrane filters, which are of significant interest for industrial applications. Working with a PhD student, the PI and her collaborator formulate new predictive mathematical models that describe two situations of practical importance: (i) Flow and fouling within pleated filter cartridges, and (ii) Membrane fouling models for internally heterogeneous membranes. In each scenario the team builds models that account for an arbitrary particle size distribution within the feed solution, and also for a distribution of membrane pore sizes. First-principles theoretical studies of these scenarios are of interest to others carrying out fundamental theoretical and experimental research on such systems, as well as to those seeking to extend the scope of current applications and improve manufacturing processes. An industrial colleague, Dr. Anil Kumar of Pall Corporation, collaborates on the project. An experimentalist, he shares his existing data with the team, generates new data as needed to test the models, and acts as industrial advisor. This interaction ensures that the project remains focused and that questions relevant to applications are identified and addressed. The experimental data help to determine appropriate ranges for unknown parameters in the models, and to test uncertain modeling assumptions.