Membrane technologies have become proficient alternatives for advanced wastewater treatment, ensuring high contaminant removal and sustainable resource recovery. Despite significant progress, ongoing research efforts aim to further optimize treatment performance. Among the challenges faced, membrane fouling persists as a relevant obstacle in membrane technologies, necessitating the development of more effective mitigation strategies. Mathematical models, widely employed for predicting treatment performance, generally exhibit low accuracy and suffer from uncertainties due to the complex and variable nature of wastewater. To overcome these limitations, numerous studies have proposed artificial intelligence (AI) modeling to accurately predict membrane technologies' performance and fouling mechanisms. This approach aims to provide advanced simulations and predictions, thereby enhancing process control, optimization, and intensification. This literature review explores recent advancements in modeling membrane-based wastewater treatment processes through AI models. The analysis highlights the enormous potential of this research field in enhancing the efficiency of membrane technologies. The role of AI modeling in defining optimal operating conditions, developing effective strategies for membrane fouling mitigation, enhancing the performance of novel membrane-based technologies, and improving membrane fabrication techniques is discussed. These enhanced process optimization and control strategies driven by AI modeling ensure improved effluent quality, optimized resource consumption, and minimized operating costs. The potential contribution of this cutting-edge approach to a paradigm shift toward sustainable wastewater treatment is examined. Finally, this review outlines future perspectives, emphasizing the research challenges that require attention to overcome the current limitations hindering the integration of AI modeling in wastewater treatment plants.

Wastewater treatment plant (WWTP) discharges are major contributors to the release of microplastics (MPs) into the environment. This research work aimed to assess the performance of the novel living membrane bioreactor (LMBR), which utilizes a biological layer as a membrane filter for the removal of polyethylene (PE) MPs from wastewater. The impact of an intermittently applied low current density (0.5 mA/cm²) on the reduction of MPs in the electrochemically enhanced LMBR (e-LMBR) has also been examined. The reactors were also compared to a conventional membrane bioreactor (MBR) and an electro-MBR (e-MBR). ¹H nuclear magnetic resonance spectroscopy (¹H NMR) was implemented for the MPs detection and quantification in terms of mass per volume of sample. The LMBR and MBR achieved comparable mean PE MPs reduction at 95% and 96%, respectively. The MPs mass reduction in the e-LMBR slightly decreased by 2% compared to that achieved in the LMBR. This potentially indicated the partial breakdown of the MPs due to electrochemical processes. Decreasing and inconsistent NH₄-N and PO₄-P removal efficiencies were observed over time due to the addition of PE MPs in the MBR and LMBR. In contrast, the integration of electric field in the e-MBR and e-LMBR resulted in consistently high values of conventional contaminant removals of COD (99.72-99.77 %), NH₄-N (97.96-98.67%), and PO₄-P (98.44-100.00%), despite the MPs accumulation. Integrating electrochemical processes in the e-LMBR led to the development of a stable living membrane (LM) layer, as manifested in the consistently low effluent turbidity 0.49 ± 0.33 NTU. Despite the increasing MPs concentration in the mixed liquor, applying electrochemical processes reduced the fouling rates in the e-LMBR. The e-LMBR achieved comparable efficiencies in contaminant reductions as those observed in the e-MBR, while using a low-cost membrane material.