Membrane fouling remains a major challenge in full-scale membrane bioreactor (MBR) systems, reducing operational efficiency and increasing maintenance needs. This study introduces a predictive and analytic framework for membrane fouling by integrating artificial intelligence (AI)-driven feature engineering and explainable AI (XAI) using real-world data from an MBR treating food processing wastewater. The framework refines the target parameter to specific flux (flux/transmembrane pressure (TMP)), incorporates chemical oxygen demand (COD) removal efficiency to reflect biological performance, and applies a moving average function to capture temporal fouling dynamics. Among tested models, CatBoost achieved the highest predictive accuracy (R² = 0.8374), outperforming traditional statistical and other machine learning models. XAI analysis identified the food-to-microorganism (F/M) ratio and mixed liquor suspended solids (MLSSs) as the most influential variables affecting fouling. This robust and interpretable approach enables proactive fouling prediction and supports informed decision making in practical MBR operations, even with limited data. The methodology establishes a foundation for future integration with real-time monitoring and adaptive control, contributing to more sustainable and efficient membrane-based wastewater treatment operations. However, this study is based on data from a single full-scale MBR treating food processing wastewater and lacks severe fouling or cleaning events, so further validation with diverse datasets is needed to confirm broader applicability.

Ion transport characteristics during nanofiltration (NF) were investigated by measuring effective osmotic pressure and diffusivity experimentally. Effective osmotic pressure and diffusivity were measured through lab-scale transport and diffusion tests. First, it was shown that the water flux across NF membranes decreased dramatically with increasing ionic strength because of the noticeable increase in effective osmotic pressure. Second, the results from transport experiments showed that the ion selectivity values, which were derived from the thermodynamic model, decreased with increasing ionic strength. Third, by analysing the data of measured osmotic pressure and diffusivity, it was demonstrated that the former and the latter increased and decreased, respectively, as ionic strength increased. The experimentally determined osmotic pressure across NF membranes was much lower than that calculated theoretically. At best, around 5% or less of the theoretical osmotic pressure was obtained under the experimental conditions investigated. The effective osmotic pressure and ion rejection decreased in the presence of nano-colloids. The influence of nano-colloids on ionic transport was found to be dependent on the concentration and size of the nano-colloids. Therefore, ion transport characteristics across NF membranes can be determined practically by measuring effective osmotic pressure and diffusivity as these reflect both feed water and membrane propertiesopen6

While Rwanda is aiming at environmental pollution resilience and green growth, some industries are still discharging untreated effluent into the environment. This study gives a general overview of the compliance level of industrial effluent discharge in Rwanda and the linked negative environmental impacts. It comprises qualitative and quantitative analyses of data obtained from wastewater samples collected from five selected industries in Rwanda. The selected industries had previously been audited and monitored by the Rwanda Environment Management Authority (REMA), due to complains from neighboring residents. The study found that the effluent discharge from wastewater treatment plants (WWTP) for all concerned industries failed to comply with (i) oil and grease (O&G) national and international tolerable parameter limits or the (ii) fecal coliforms national standard. In addition, a compliance level of 66.7% was observed for key water quality monitoring parameters (pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), and heavy metals (i.e., lead (Pb), cadmium (Cd), and chromium (Cr)). Following these study findings, one industry was closed by the REMA for deliberately discharging untreated effluent into an adjacent river. This study recommends the adoption of the best available technology for effluent treatment, installation or renovation of existing WWTPs, and the relocation to industrial zones of industries adjacent to fragile environments.

Fouling mechanisms associated with membrane-based polysaccharide enrichment were determined using a dense ultrafiltration (UF) membrane. Dextran with different molecular weights (MWs) was used as a surrogate for polysaccharides. The influence of dextran MW on fouling mechanisms was quantified using the Hermia model. Flux data obtained with different dextran MWs and filtration cycles were plotted to quantify the more appropriate fouling mechanisms among complete pore blocking, standard pore blocking, intermediate pore blocking, and cake filtration. For 100,000 Da dextran, all four mechanisms contributed to the initial fouling. As the filtration progressed, the dominant fouling mechanism appeared to be cake filtration with a regression coefficient (R²) of approximately 0.9519. For 10,000 Da, the R² value for cake filtration was about 0.8767 in the initial filtration. Then, the R² value gradually decreased as the filtration progressed. For 6000 Da, the R² values of the four mechanisms were very low in the initial filtration. However, as the filtration progressed, the R² value for cake filtration reached 0.9057. These results clearly show that the fouling mechanism of dense UF membranes during polysaccharide enrichment can be quantified. In addition, it was confirmed that the dominant fouling mechanism can change with the size of the polysaccharide and the duration of filtration.