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 (R2 = 0.85) and extreme gradient boosting model (R2 = 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.

中文翻译：

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用于预测正渗透和反渗透膜对有机污染物的去除并揭示去除机制的机器学习模型


虽然正渗透 （FO） 和反渗透 （RO） 工艺已被证明可有效去除有机污染物，但去除率在很大程度上取决于化合物和膜特性以及操作条件。本研究旨在建立机器学习 （ML） 模型，用于预测 FO 和 RO 对有机污染物的排斥，并深入了解潜在的排斥机制。在建立的 14 个 ML 模型中，随机森林模型 （R2 = 0.85） 和极端梯度提升模型 （R2 = 0.92） 分别成为 FO 和 RO 表现最好的模型。Shapley 加法解释 （SHAP） 分析确定化合物的长度、水通量和疏水性是影响 FO 模型的前三个变量。对于 RO，除了化合物的长度和操作压力外，包括 4 个分子描述符（例如 ATSC2m 和 Balaban J）和 3 个指纹图谱（例如 C=C 双键和羰基）在内的高级变量对预测有显着贡献。此外，这些排名靠前的变量与其 SHAP 值之间的关联阐明了截留机制，例如尺寸排斥、吸附、疏水相互作用和静电相互作用，并说明了 FO 渗透水通量和 RO 操作压力等操作参数在截留过程中的作用。这些发现为去除有机污染物提供了可解释的预测模型，并促进了对 FO 和 RO 过程中拒绝机制的机制理解。