Exploring the Knowledge Attained by Machine Learning on Ion Transport across Polyamide Membranes Using Explainable Artificial Intelligence.

Recent studies have increasingly applied machine learning (ML) to aid in performance and material design associated with membrane separation. However, whether the knowledge attained by ML with a limited number of available data is enough to capture and validate the fundamental principles of membrane science remains elusive. Herein, we applied explainable artificial intelligence (XAI) to thoroughly investigate the knowledge learned by ML on the mechanisms of ion transport across polyamide reverse osmosis (RO) and nanofiltration (NF) membranes by leveraging 1,585 data from 26 membrane types. The Shapley additive explanation method based on cooperative game theory was used to unveil the influences of various ion and membrane properties on the model predictions. XAI shows that the ML can capture the important roles of size exclusion and electrostatic interaction in regulating membrane separation properly. XAI also identifies that the mechanisms governing ion transport possess different relative importance to cation and anion rejections during RO and NF filtration. Overall, we provide a framework to evaluate the knowledge underlying the ML model prediction and demonstrate that ML is able to learn fundamental mechanisms of ion transport across polyamide membranes, highlighting the importance of elucidating model interpretability for more reliable and explainable ML applications to membrane selection and design.

Predicting Micropollutant Removal by Reverse Osmosis and Nanofiltration Membranes: Is Machine Learning Viable?

Predictive models for micropollutant removal by membrane separation are highly desirable for the design and selection of appropriate membranes. While machine learning (ML) models have been applied for such purposes, their reliability might be compromised by data leakage due to inappropriate data splitting. More importantly, whether ML models can truly understand the mechanisms of membrane separation has not been revealed. In this study, we evaluate the capability of the XGBoost model to predict micropollutant removal efficiencies of reverse osmosis and nanofiltration membranes. Our results demonstrate that data leakage leads to falsely high prediction accuracy. By utilizing a model interpretation method based on the cooperative game theory, we test the knowledge of XGBoost on the mechanisms of membrane separation via quantifying the contributions of input variables to the model predictions. We reveal that XGBoost possesses an adequate understanding of size exclusion, but its knowledge of electrostatic interactions and adsorption is limited. Our findings suggest that future work should focus more on avoiding data leakage and evaluating the mechanistic knowledge of ML models. In addition, high-quality data from more diverse experimental conditions, as well as more informative variables, are needed to improve the accuracy of ML models for predicting membrane performance.

Contrasting Behaviors between Gypsum and Silica Scaling in the Presence of Antiscalants during Membrane Distillation.

Mineral scaling is a major constraint that limits the performance of membrane distillation (MD) for hypersaline wastewater treatment. Although the use of antiscalants is a common industrial practice to mitigate mineral scaling, the effectiveness and underlying mechanisms of antiscalants in inhibiting different mineral scaling types have not been systematically investigated. Herein, we perform a comparative investigation to elucidate the efficiencies of antiscalant candidates with varied functional groups for mitigating gypsum scaling and silica scaling in MD desalination. We show that antiscalants with Ca(II)-complexing moieties (e.g., carboxyl group) are the most effective to inhibit gypsum scaling formed via crystallization, whereas amino-enriched antiscalants possess the best performance to mitigate silica scaling created by polymerization. A set of microscopic and spectroscopic analyses reveal distinct mechanisms of antiscalants required for those two common types of scaling. The mitigating effect of antiscalants on gypsum scaling is attributed to the stabilization of scale precursors and nascent CaSO4 nuclei, which hinders phase transformation of amorphous CaSO4 toward crystalline gypsum. In contrast, antiscalants facilitate the polymerization of silicic acid, immobilizing active silica precursors and retarding the gelation of silica scale layer on the membrane surface. Our study, for the first time, demonstrates that antiscalants with different functionalities are required for the mitigation of gypsum scaling and silica scaling, providing mechanistic insights on the molecular design of antiscalants tailored to MD applications for the treatment of wastewaters containing different scaling types.