Design Principles, Analytical Methods, and Membrane Development for High-Pressure Reverse Osmosis

在拟议的工作中，我们与一家水工程公司合作，主要目标是开发扩大反渗透(RO)操作范围的膜材料。在反渗透中，施加的液压必须超过溶液的渗透压，渗透压是溶液盐度的函数。目前的膜通常最大压力为~80巴(~1200磅/平方英寸)，最大盐度为~1.4M氯化钠。我们的目标是开发可工作到~150巴(~2200磅/平方英寸)的膜，使可获得的盐度几乎翻一番，达到~2.7M氯化钠。这种高压反渗透(HPRO)将为加拿大自然资源行业的水管理带来革命性的变化，包括采矿、石油和天然气以及纸浆和造纸。HPRO将有效地最大限度地实现水的循环利用，同时最大限度地减少处置废物。我们的工作将分为四个主要目标。首先，我们将评估当前反渗透膜在压力高达150bar时的性能(和局限性)，特别是要了解“压实”对性能的影响。在这个过程中，由于施加的液压，膜的多孔结构变得更致密(不那么多孔)。压实对运输的影响仍未得到充分研究。在第二个目标中，我们将使用先进的显微镜工具来详细评估压实如何影响膜的孔结构。在第三个目标中，我们将使用机器学习来开发一个工具，该工具将基于2D电子显微图像生成统计的3D孔结构。这种工具将能够快速生成重要的孔隙结构数据，目前只能使用高度耗时的分析工具进行评估。该工具将有助于研究压实作用。在第四个目标中，我们将使用高通量实验来优化膜合成，目标是创造出耐压实的膜。通过这一研究范围，我们希望开发创新，这些创新将转化为商业HPRO产品，供加拿大资源行业使用。

In the proposed work, we are partnering with a water-engineering firm with the primary aim of developing membrane materials that extend the operating range of reverse osmosis (RO). In RO, the applied hydraulic pressure must exceed the solution's osmotic pressure, which is a function of the salinity of the solution. Current membranes have typical maximum pressures of ~80 bar (~1200 psi), enabling a maximum salinity of ~1.4 M NaCl. We aim to develop membranes that operate up to ~150 bar (~2200 psi), almost doubling the attainable salinity to ~2.7 M NaCl. This high-pressure RO (HPRO) would be revolutionary for water management in the Canadian natural-resource industries, including mining, oil & gas, and pulp & paper. HPRO would efficiently maximize water recycling while minimizing waste for disposal. Our work will be divided into four main aims. In the first, we will assess the performance (and limitations) of current RO membranes at pressures up to 150 bar, particularly aiming to understand how performance is impacted by "compaction." In this process, the porous structure of the membrane becomes denser (less porous) because of the applied hydraulic pressure. The effect of compaction on transport remains understudied. In the second aim, we will use advanced microscopy tools to assess in detail how compaction impacts the pore structure of the membranes. In the third aim, we will use machine learning to develop a tool that would generate statistical 3D pore structures based on 2D electron micrographs. Such a tool would enable rapid generation of important pore structural data, which currently can only be assessed using highly time-intensive analytical tools. The tool will be useful for studying compaction. In the fourth aim, we will use high-throughput experimentation to optimize membrane synthesis, with the goal of creating membranes that will be resistant to compaction. Through this research scope, we hope to develop innovations that will be translated into commercial HPRO products for use by Canadian resource industries.