Department of Civil and Environmental Engineering, Michigan Technological University, 1400 Townsend Drive, Houghton, Michigan 49931, United States.
Trussell Technologies, Inc., San Diego, California 92075, United States.
Environ Sci Technol. 2020 Apr 21;54(8):5167-5177. doi: 10.1021/acs.est.9b06170. Epub 2020 Apr 3.
Reverse osmosis (RO) is a membrane technology that separates dissolved species from water. RO has been applied for the removal of chemical contaminants from water for potable reuse applications. The presence of a wide variety of influent chemical contaminants and the insufficient rejection of low-molecular-weight neutral organics by RO calls for the need to develop a model that predicts the rejection of various organics. In this study, we develop a group contribution method (GCM) to predict the mass transfer coefficients by fragmenting the structure of low-molecular-weight neutral organics into small parts that interact with the RO membrane. Overall, 54 organics including 26 halogenated and oxygenated alkanes, 8 alkenes, and 20 alkyl and halobenzenes were used to determine 39 parameters to calibrate for 6 different RO membranes, including 4 brackish water and 2 seawater membranes. Through six membranes, approximately 80% of calculated rejection was within an error goal (i.e., ±5%) from the experimental observation. To extend the GCM for a reference RO membrane, ESPA2-LD, 14 additional organics were included from the literature to calibrate nitrogen-containing functional groups of nitrosamine, nitriles, and amide compounds. Overall, 49 organics (72% of 68 compounds) from calibration and 7 compounds (87.5% of 8 compounds) from prediction were within the error goal.
反渗透（RO）是一种膜技术，可将溶解的物质与水分离。RO 已被应用于去除饮用水再利用应用中的水中的化学污染物。由于存在各种各样的进水化学污染物，以及 RO 对低分子量中性有机物的截留不足，因此需要开发一种模型来预测各种有机物的截留率。在这项研究中，我们开发了一种基团贡献法（GCM），通过将低分子量中性有机物的结构分解成与 RO 膜相互作用的小部分来预测传质系数。总体而言，使用了 54 种有机物，包括 26 种卤代和含氧烷烃、8 种烯烃和 20 种烷基和卤代苯，确定了 39 个参数来校准 6 种不同的 RO 膜，包括 4 种咸水膜和 2 种海水膜。通过这 6 种膜，大约 80%的计算截留率与实验观察值的误差在 5%以内。为了将 GCM 扩展到参考 RO 膜 ESPA2-LD，从文献中添加了 14 种额外的有机物来校准亚硝胺、腈和酰胺化合物中的含氮官能团。总体而言，来自校准的 49 种有机物（68 种化合物中的 72%）和来自预测的 7 种有机物（8 种化合物中的 87.5%）都在误差范围内。
