RS Fellow - EPSRC grant (2014):Mathematical modelling of design strategies for membrane filtration.

RS 研究员 - EPSRC 资助（2014 年）：膜过滤设计策略的数学建模。

• 批准号: EP/N005147/1
• 负责人: Ian Griffiths
• 金额: $ 28.4万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FN005147%2F1
• 关键词: RS; Fellow; EPSRC; grant; 2014

Although water was once considered an abundant if not unlimited resource, population growth, drought and contamination are straining our finite water supplies, resulting in water quality and quantity concerns being one of the largest environmental issues facing the world today. Further, as a result of arsenic-contaminated groundwater, every day more than 100 million people, from developing countries such as Bangladesh to developed countries including the U.K. and U.S., drink water that contains arsenic levels above the World Health Organization's 0.01 mg/L safe concentration threshold [M. Argos et al. 2010, The Lancet, 376, 252]. As a result, the race to find new and effective strategies for the production of clean water is now more important than ever. Central to water purification is membrane filtration, in which contaminated water, or feed solution, is pushed through a porous medium that rejects the particulates, allowing only clean water to pass through. Particulates that are rejected at the membrane surface can often easily be removed, for example by reversing the flow for a short time, or by mechanical cleaning of the surface. However, for contaminants that penetrate deeper into the membrane structure and become lodged the removal becomes significantly more challenging. There are many features that play a role in particle trapping within a membrane. Recent experimental observations indicate that the pressures across a membrane as the fluid is pushed through cause deformations that lead to expansion of the pores. This allows particles that would usually be rejected at the surface to be transmitted deep into the membrane structure or even pass through the entire membrane entirely, both of which are undesirable. Despite these new observations, experimentation is currently limited to heuristic approaches to identify the most suitable membrane structure and operating regime for a given task. In addition, any dynamic experimental techniques are limited to measurement of macroscopic observables, such as the rate at which clean water is processed: any probing of the microstructure is necessarily invasive and thus can only be carried out at the end of an experimental run. Mathematical modelling is able to provide the key insight required into the microstructural behaviour during filtration, thus enabling us to connect this to the macroscropic observables. The behaviour on the microscale encompasses a broad range of complex phenomena, but homogenization techniques are able to smooth out these fine details to provide the essential link between the membrane microstructure and the resulting filtration behaviour. The result is to provide optimal membranes that minimize the energy used and maximize the rate of production of clean water. This research project will develop a mathematical model that is able to capture the behaviour of a membrane as it deforms due to the flow. Our model will describe the transport of particles through the membrane, which will allow us to determine how fast we can process the contaminated water without compromising the structural integrity of the membrane. The model will also allow us to predict the best strategy for cleaning the membrane ready for re-use. We will collaborate with key experimentalists at Princeton and Ryerson Universities, and the world-leading filtration and separation science industry Pall Corporation to ensure that the models we develop address the pressing issues faced in the filtration industry. In partnership with experimentalists and engineers, the development of new mathematical techniques will lead to new breakthroughs that will drive forward the technological boundaries to solve our current and future global challenges in water purification.

尽管水曾经被认为是一种丰富甚至无限的资源，但人口增长、干旱和污染正在使我们有限的水资源供应紧张，导致水质和水量问题成为当今世界面临的最大环境问题之一。此外，由于地下水砷污染，从孟加拉国等发展中国家到英美等发达国家，每天有超过1亿人饮用砷含量超过世界卫生组织规定的0.01毫克/升安全标准的水。浓度阈值[M。阿尔戈斯等人。 2010，《柳叶刀》，376, 252]。因此，寻找新的、有效的清洁水生产策略的竞赛现在比以往任何时候都更加重要。水净化的核心是膜过滤，其中受污染的水或进料溶液被推过多孔介质，该介质拒绝颗粒，只允许干净的水通过。在膜表面被截留的颗粒通常可以很容易地去除，例如通过短时间反向流动或通过表面的机械清洁。然而，对于渗透到膜结构深处并滞留的污染物，去除变得更加困难。有许多特征在膜内捕获颗粒中发挥作用。最近的实验观察表明，当流体被推动通过膜时，膜上的压力会引起变形，从而导致孔的扩张。这使得通常在表面被拒绝的颗粒被传输到膜结构深处，甚至完全穿过整个膜，这两种情况都是不希望的。尽管有这些新的观察结果，但实验目前仅限于启发式方法来确定给定任务的最合适的膜结构和操作方式。此外，任何动态实验技术都仅限于宏观可观测值的测量，例如清洁水的处理速度：任何对微观结构的探测都必然是侵入性的，因此只能在实验运行结束时进行。数学建模能够提供过滤过程中微观结构行为所需的关键洞察，从而使我们能够将其与宏观可观察结果联系起来。微观尺度上的行为涵盖了广泛的复杂现象，但均质化技术能够平滑这些细节，从而提供膜微观结构和最终过滤行为之间的重要联系。其结果是提供最佳的膜，最大限度地减少能源消耗并最大限度地提高清洁水的生产率。该研究项目将开发一种数学模型，能够捕获膜因流动而变形时的行为。我们的模型将描述颗粒通过膜的传输，这将使我们能够确定在不损害膜结构完整性的情况下处理污染水的速度。该模型还将使我们能够预测清洁膜以供重复使用的最佳策略。我们将与普林斯顿大学和瑞尔森大学的主要实验专家以及世界领先的过滤和分离科学行业颇尔公司合作，确保我们开发的模型能够解决过滤行业面临的紧迫问题。与实验学家和工程师合作，新数学技术的发展将带来新的突破，突破技术界限，解决我们当前和未来在水净化方面的全球挑战。

项目成果

Theoretical analysis of the viscosity correction factor for heat transfer in pipe flow

管流传热粘度修正系数的理论分析

• DOI: http://dx.10.1016/j.ces.2018.04.047
• 发表时间: 2018
• 期刊: Chemical Engineering Science
• 影响因子: 4.7
• 作者: Mondal S

Optimising Dead-End Cake Filtration Using Poroelasticity Theory

使用孔隙弹性理论优化死端滤饼过滤

• DOI: http://dx.10.3390/modelling2010002
• 发表时间: 2021
• 期刊: Modelling
• 作者: Köry J

Shrinking microbubbles with microfluidics: mathematical modelling to control microbubble sizes.

用微流体缩小微泡：控制微泡尺寸的数学模型。

• DOI: 10.1039/c7sm01418j
• 发表时间: 2017-11-29
• 期刊: Soft matter
• 影响因子: 3.4
• 作者: A. Salari;V. Gnyawali;I. Griffiths;R. Karshafian;Michael C. Kolios;S. Tsai
• 通讯作者: S. Tsai

Modelling the transport and adsorption dynamics of arsenic in a soil bed filter

模拟土壤床过滤器中砷的传输和吸附动力学

• DOI: http://dx.10.1016/j.ces.2019.115205
• 发表时间: 2019
• 期刊: Chemical Engineering Science
• 影响因子: 4.7
• 作者: Mondal R

The role of fouling in optimizing direct-flow filtration module design

污垢在优化直流过滤模块设计中的作用

• DOI: 10.1016/j.ces.2017.01.043
• 发表时间: 2017-05-18
• 期刊: Chemical Engineering Science
• 影响因子: 4.7
• 作者: Mylène Wang;S. Mondal;I. Griffiths
• 通讯作者: I. Griffiths