Nanocomposite Polymeric Membranes for Organic Micropollutant Removal: A Critical Review

Predicting micropollutant removal through nanopore-sized membranes using several machine-learning approaches based on feature engineering

Predicting the efficacy of micropollutant separation through functionalized membranes is an arduous endeavor. The challenge stems from the complex interactions between the physicochemical properties of the micropollutants and the basic principles underlying membrane filtration. This study aimed to compare the effectiveness of a modest dataset on various machine learning tools (ML) tools in predicting micropollutant removal efficiency for functionalized reverse osmosis (RO) and nanofiltration (NF) membranes. The inherent attributes of both the micropollutants and the membranes are utilized as input factors. The chosen ML tools are supervised algorithm (adaptive network-based fuzzy inference system (NF), linear regression framework (linear regression (LR)), stepwise linear regression (SLR) and multivariate linear regression (MVR)), and unsupervised algorithm (support vector machine (SVM) and ensemble boosted tree (BT)). The feature engineering and parametric dependency analysis revealed that characteristics of micropollutants, such as maximum projection diameter (MaxP), minimal projection diameter (MinP), molecular weight (MW), and compound size (CS), exhibited a notably positive impact on the correlation with removal efficiency. Model combination with key variables demonstrated high prediction accuracy in both supervised and unsupervised ML for micropollutant removal efficiency. An NF-grid partitioning (NF-GP) model achieved the highest accuracy with an R 2 value of 0.965, accompanied by low error metrics, specifically an RMSE and MAE of 3.65. It is owed to the handling of the complex spatial and temporal aspects of micropollutant data through division into consistent subsets facilitating improved identification of rejection efficiency and relationships. The inclusion of inputs with both negative and positive correlations introduces variability, amplifies the system responsiveness, and impedes the precision of predictive models. This study identified key micropollutant properties, including MaxP, MinP, MW, and CS, as crucial factors for efficient micropollutant rejection during real-time filtration applications. It also allowed the design of pore size of self-prepared membranes for the enhanced separation of micropollutants from wastewater.

Understanding Rejection Mechanisms of Trace Organic Contaminants by Polyamide Membranes via Data-Knowledge Codriven Machine Learning

Data-driven machine learning (ML) provides a promising approach to understanding and predicting the rejection of trace organic contaminants (TrOCs) by polyamide (PA). However, various confounding variables, coupled with data scarcity, restrict the direct application of data-driven ML. In this study, we developed a data-knowledge codriven ML model via domain-knowledge embedding and explored its application in comprehending TrOC rejection by PA membranes. Domain-knowledge embedding enhanced both the predictive performance and the interpretability of the ML model. The contribution of key mechanisms, including size exclusion, charge effect, hydrophobic interaction, etc., that dominate the rejections of the three TrOC categories (neutral hydrophilic, neutral hydrophobic, and charged TrOCs) was quantified. Log D and molecular charge emerge as key factors contributing to the discernible variations in the rejection among the three TrOC categories. Furthermore, we quantitatively compared the TrOC rejection mechanisms between nanofiltration (NF) and reverse osmosis (RO) PA membranes. The charge effect and hydrophobic interactions possessed higher weights for NF to reject TrOCs, while the size exclusion in RO played a more important role. This study demonstrated the effectiveness of the data-knowledge codriven ML method in understanding TrOC rejection by PA membranes, providing a methodology to formulate a strategy for targeted TrOC removal.

Mitigation of reverse osmosis membrane fouling by coagulation pretreatment to remove silica and transparent exopolymer particles

The dissolution of silica and transparent exopolymer particles (TEP) can deposit on the membrane surface and cause serious membrane fouling in reverse osmosis (RO) technology. Coagulation, as a common pretreatment process for RO, can effectively intercept pollutants and alleviate membrane fouling. In this study, FeCl3 and AlCl3 coagulants and polyacrylamide (PAM) flocculants were used to explore the optimal coagulation conditions to reduce the concentration of silica and TEP in the RO process. The results showed that the two coagulants had the best removal effect on pollutants when the pH was 7 and the dosage was 50 mg/L. Considering the proportion of reversible fouling after coagulation, the removal rate of pollutants, and the residual amount of coagulation metal ions, the best PAM dosage was 5 mg/L for FeCl3 and 1 mg/L for AlCl3. After coagulation pretreatment, the Zeta potential decreased, and the particle size distribution increased, making pollutants tend to aggregate, thus effectively removing foulants. The removal mechanisms of pollutants by coagulation pretreatment were determined to be adsorption, electric neutralization and co-precipitation. This study determined the best removal conditions of silica and TEP by coagulation and explored the removal mechanism.

Pressure-driven membrane filtration technology for terminal control of organic DBPs: A review

Disinfection by-products (DBPs), a series of undesired secondary contaminants formed during the disinfection processes, deteriorate water quality, threaten human health and endanger ecological safety. Membrane-filtration technologies are commonly used in the advanced water treatment and have shown a promising performance for removing trace contaminants. In order to gain a clearer understanding of the behavior of DBPs in membrane-filtration processes, this work dedicated to: (1) comprehensively reviewed the retention efficiency of microfiltration (MF), ultrafiltration (UF), nanofiltration (NF) and reverse osmosis (RO) for DBPs. (2) summarized the mechanisms involved size exclusion, electrostatic repulsion and adsorption in the membrane retention of DBPs. (3) In conjunction with principal component analysis, discussed the influence of various factors (such as the characteristics of membrane and DBPs, feed solution composition and operating conditions) on the removal efficiency. In general, the characteristics of the membranes (salt rejection, molecular weight cut-off, zeta potential, etc.) and DBPs (molecular size, electrical property, hydrophobicity, polarity, etc.) fundamentally determine the membrane-filtration performance on retaining DBPs, and the actual operating environmental factors (such as solute concentration, coexisting ions/NOMs, pH and transmembrane pressure) exert a positive/negative impact on performance to some extent. Current researches indicate that NF and RO can be effective in removing DBPs, and looking forward, we recommend that multiple factors should be taken into account that optimize the existed membrane-filtration technologies, rationalize the selection of membrane products, and develop novel membrane materials targeting the removal of DBPs.

Porous polymer magnetic adsorbents for dye wastewater treatment

Dye wastewater discharged from industries has caused serious environmental problems. The recent decade has witnessed adsorption technology emerging as an advanced dye wastewater treatment method with great potential Therefore, we fabricated two kinds of magnetic porous adsorbents (HSF and HSVF) with different specific surface areas and activity sites. Both of which exhibit excellent performance with remarkable dye adsorption capacities, especially HSVF. We further investigated their adsorption kinetic and isotherm in detail. Therein, HSVF showed a nice desorption capacity, and it could be recycled rapidly by magnetism, which exhibited the advantages of effective, easy operation, and low cost. In addition, their adsorption kinetic and isotherm were further studied and compared in detail. The results revealed that introducing strong active sites could improve both the adsorption capacity and rate effectively even though sacrificing part of specific surface areas, indicating that active sites might play a dominant role during the dye adsorption process.

Development of a Green Polymeric Membrane for Sodium Diclofenac Removal from Aqueous Solutions

Water-soluble polymers provide an alternative to organic solvent requirements in membrane manufacture, aiming at accomplishing the Green Chemistry principles. Poly(vinyl alcohol) (PVA) is a biodegradable and non-toxic polymer renowned for its solubility in water. However, PVA is little explored in membrane processes due to its hydrophilicity, which reduces its stability and performance. Crosslinking procedures through an esterification reaction with carboxylic acids can address this concern. For this, experimental design methodology and statistical analysis were employed to achieve the optimal crosslinking conditions of PVA with citric acid as a crosslinker, aiming at the best permeate production and sodium diclofenac (DCF) removal from water. The membranes were produced following an experimental design and characterized using multiple techniques to understand the effect of crosslinking on the membrane performance. Characterization and filtration results demonstrated that crosslinking regulates the membranes' properties, and the optimized conditions (crosslinking at 110 °C for 110 min) produced a membrane able to remove 44% DCF from water with a permeate production of 2.2 L m^-2 h^-1 at 3 bar, comparable to commercial loose nanofiltration membranes. This study contributes to a more profound knowledge of green membranes to make water treatment a sustainable practice in the near future.

Boosting the Performance of Nanofiltration Membranes in Removing Organic Micropollutants: Trade-Off Effect, Strategy Evaluation, and Prospective Development

In view of the high risks brought about by organic micropollutants (OMPs), nanofiltration (NF) processes have been playing a vital role in advanced water and wastewater treatment, owing to the high membrane performance in rejection of OMPs, permeation of water, and passage of mineral salts. Though numerous studies have been devoted to evaluating and technically enhancing membrane performance in removing various OMPs, the trade-off effect between water permeance and water/OMP selectivity for state-of-the-art membranes remains far from being understood. Knowledge of this effect is significant for comparing and guiding membrane development works toward cost-efficient OMP removal. In this work, we comprehensively assessed the performance of 88 NF membranes, commercialized or newly developed, based on their water permeance and OMP rejection data published in the literature. The effectiveness and underlying mechanisms of various modification methods in tailoring properties and in turn performance of the mainstream polyamide (PA) thin-film composite (TFC) membranes were quantitatively analyzed. The trade-off effect was demonstrated by the abundant data from both experimental measurements and machine learning-based prediction. On this basis, the advancement of novel membranes was benchmarked by the performance upper-bound revealed by commercial membranes and lab-made PA membranes. We also assessed the potentials of current NF membranes in selectively separating OMPs from inorganic salts and identified the future research perspectives to achieve further enhancement in OMP removal and salt/OMP selectivity of NF membranes.

Direct Contact Membrane Distillation for Treatment of Mixed Wastewater of Humic Acid and Reactive Dye: Membrane Flux Decline and Fouling Analysis

The main waste stream from the textile industry is its wastewater with high color, organic matters, and other contaminants. This study aims to investigate the effect of humic acid in mixed wastewater of humic acid and reactive dye on the treatment performance and permeate flux of a direct contact membrane distillation (DCMD) system. In this research, feed temperature and humic acid concentration were the main input parameters for the analysis of DCMD system operation. The fouling resistances significantly increased with higher humic acid concentrations in the mixed wastewater. As compared with the DI water test, 23% of flux decline occurred when the humic concentration in the wastewater was increased up to 20 mg/L. After the DCMD treatment, the 25 ADMI residual color was detected in the permeate when the mixed wastewater contained 20 mg/L humic acid. The mathematical model, based on the Antione equation, was proposed to predict the membrane flux decline of the DCMD system. The reduced pore size of the cake layer by a dimensionless constant β from the Kelvin equation was also considered for the fouling calculation to describe the transport mechanism.