Tayara Adel, Shang Chii, Zhao Jing, Xiang Yingying
Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon 000, Hong Kong Special Administrative Region of China.
Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon 000, Hong Kong Special Administrative Region of China; Hong Kong Branch of Chinese National Engineering Research Center for Control & Treatment of Heavy Metal Pollution, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon 000, Hong Kong Special Administrative Region of China.
Water Res. 2024 Nov 15;266:122363. doi: 10.1016/j.watres.2024.122363. Epub 2024 Aug 30.
While forward osmosis (FO) and reverse osmosis (RO) processes have been proven effective in rejecting organic pollutants, the rejection rate is highly dependent on compound and membrane characteristics, as well as operating conditions. This study aims to establish machine learning (ML) models for predicting the rejection of organic pollutants by FO and RO and providing insights into the underlying rejection mechanisms. Among the 14 ML models established, the random forest model (R = 0.85) and extreme gradient boosting model (R = 0.92) emerged as the best-performing models for FO and RO, respectively. Shapley additive explanations (SHAP) analysis identified the length of the compound, water flux, and hydrophobicity as the top three variables contributing to the FO model. For RO, in addition to the length of the compound and operating pressure, advanced variables including four molecular descriptors (e.g., ATSC2m and Balaban J) and three fingerprints (e.g., C=C double bond and carbonyl group) significantly contributed to the prediction. Besides, the associations between these highly ranked variables and their SHAP values shed light on the rejection mechanisms, such as size exclusion, adsorption, hydrophobic interaction, and electrostatic interaction, and illustrate the role of the operating parameters, such as the FO permeate water flux and RO operating pressure, in the rejection process. These findings provide interpretable predictive models for the removal of organic pollutants and advance the mechanistic understanding of the rejection mechanisms in the FO and RO processes.
虽然正向渗透（FO）和反渗透（RO）工艺已被证明在去除有机污染物方面有效，但去除率高度依赖于化合物和膜的特性以及操作条件。本研究旨在建立机器学习（ML）模型，用于预测FO和RO对有机污染物的去除率，并深入了解潜在的去除机制。在所建立的14个ML模型中，随机森林模型（R = 0.85）和极端梯度提升模型（R = 0.92）分别成为FO和RO表现最佳的模型。Shapley值分解法（SHAP）分析确定化合物长度、水通量和疏水性是对FO模型贡献最大的前三个变量。对于RO，除了化合物长度和操作压力外，包括四个分子描述符（如ATSC2m和巴拉班J）和三个指纹（如C = C双键和羰基）在内的高级变量对预测有显著贡献。此外，这些排名靠前的变量与其SHAP值之间的关联揭示了去除机制，如尺寸排阻、吸附、疏水相互作用和静电相互作用，并阐明了操作参数，如FO渗透水通量和RO操作压力，在去除过程中的作用。这些发现为有机污染物的去除提供了可解释的预测模型，并推进了对FO和RO过程中去除机制的机理理解。
