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.

The reuse of wastewater has been identified as an important initiative for the sustainable development of the environment; thus, the removal of secondary effluent organic matter (EfOM) to ensure the safety of reused wastewater is the key step and a subject of extensive research. In this study, Al₂(SO₄)₃ and anionic polyacrylamide were selected as coagulant and flocculant, respectively, for the treatment of secondary effluent from a food-processing industry wastewater treatment plant to meet the standard regulatory specifications for water reuse. In this process, the removal efficiencies of chemical oxygen demand (COD), components with UV₂₅₄, and specific ultraviolet absorbance (SUVA) were 44.61%, 25.13%, and 9.13%, respectively, with an associated reduction in chroma and turbidity. The fluorescence intensities (Fmax) of two humic-like components were reduced during coagulation, and microbial humic-like components of EfOM had a better removal efficiency because of a higher Log Km value of 4.12. Fourier transform infrared spectroscopy showed that Al₂(SO₄)₃ could remove the protein fraction of the soluble microbial products (SMP) of EfOM by forming a loose SMP protein complex with enhanced hydrophobicity. Furthermore, flocculation reduced the aromaticity of secondary effluent. The cost of the proposed secondary effluent treatment was 0.034 CNY t⁻¹ %COD⁻¹. These results demonstrate that the process is efficient and economically viable for EfOM removal to realize food-processing wastewater reuse.

Ion exchange membranes, especially cation exchange membranes (CEMs), are an important component in membrane-based energy generation and storage because of their ability to transport cations via the electrochemical potential gradient while preventing electron transport. However, developing a CEM with low areal resistance, high permselectivity, and stability remains difficult. In this study, electrospun graphene oxide/polyethersulfone (GO/PES) composite nanofibers were prepared with varying concentrations of GO. To fabricate a CEM, the pores of the electrospun GO/PES nanofiber substrates were filled with a Nafion ionomer. The pore-filled PES nanofiber loaded with 1% GO revealed a noticeable improvement in hydrophilicity, structural morphology, and mechanical properties. The 1% GO/PES pore-filled CEM was compared to a Nafion membrane of a varying thickness and without a nanofiber substrate. The CEM with a nanofiber substrate showed permselectivity of 85.75%, toughness of 111 J/m³, and areal resistance of 3.7 Ω cm², which were 12.8%, 4.3 times, and 4.0 times better, respectively, than those of the Nafion membrane at the same thickness. The development of a reinforced concrete-like GO/PES nanofiber structure containing stretchable ionomer-enhanced membrane surfaces exhibited suitable areal resistance and reduced the thickness of the composite membrane without compromising the mechanical strength, suggesting its potential application as a cation exchange membrane in electrochemical membrane-based systems.

Wastewater treatment plants (WWTPs) generate useful data, but effectively utilizing these data remains a challenge. This study developed novel ensemble tree-based models to enhance real-time predictions of chemical oxygen demand (COD) and total nitrogen (TN) concentrations, which are difficult to monitor directly. The effectiveness of these models, particularly the Voting Regressor, was demonstrated by achieving excellent predictive performance even with the small, volatile, and interconnected datasets typical of WWTP scenarios. By utilizing real-time sensor data from the anaerobic–anoxic–oxic (A2O) process, the model successfully predicted COD concentrations with an R² of 0.7722 and TN concentrations with an R² of 0.9282. In addition, a novel approach was proposed to assess A2O process performance by analyzing the correlation between the predicted C/N ratio and the removal efficiencies of COD and TN. During a one and a half year monitoring period, the predicted C/N ratio accurately reflected changes in COD and TN removal efficiencies across the different A2O bioreactors. The results provide real-time COD and TN predictions and a method for assessing A2O process performance based on the C/N ratio, which can significantly aid in the operation and maintenance of biological wastewater treatment processes.

The prediction of the chemical oxygen demand (COD) and total nitrogen (TN) in integrated anaerobic–anoxic–oxic (A2O) and anoxic–oxic (AO) processes (i.e., A2O+AO process) was achieved using a dynamic ensemble model that reflects the dynamics of wastewater treatment plants (WWTPs). This model effectively captures the variability in the influent characteristics and fluctuations within each reactor of the A2O+AO process. By employing a time-lag approach based on the hydraulic retention time (HRT), artificial intelligence (AI) selects suitable input (i.e., pH, temperature, total dissolved solid (TDS), NH₃-N, and NO₃-N) and output (COD and TN) data pairs for training, minimizing the error between predicted and observed values. Data collected over two years from the actual A2O+AO process were utilized. The ensemble model adopted machine learning-based XGBoost for COD and TN predictions. The dynamic ensemble model outperformed the static ensemble model, with the mean absolute percentage error (MAPE) for the COD ranging from 9.5% to 15.2%, compared to the static ensemble model’s range of 11.4% to 16.9%. For the TN, the dynamic model’s errors ranged from 9.4% to 15.5%, while the static model showed lower errors in specific reactors, particularly in the anoxic and oxic stages due to their stable characteristics. These results indicate that the dynamic ensemble model is suitable for predicting water quality in WWTPs, especially as variability may increase due to external environmental factors in the future.