Waseem Raja, Pratik Kumar
Rapid urbanisation and industrialisation have significantly strained global water resources, resulting in increased wastewater generation across sectors. Membrane Bioreactors (MBRs), which integrate biological degradation with membrane-based separation, have emerged as a robust solution for advanced wastewater treatment. This review presents a comprehensive and critical synthesis of recent advancements in MBR technology, spanning membrane materials, module configurations, fouling mechanisms, energy demands, microbial community dynamics, integration with artificial intelligence and machine learning, and the circular economy. It highlights the strengths and limitations of aerobic, anaerobic, and hybrid MBR systems, with comparative insights into pollutant removal efficiencies, lifecycle costs, and energy footprints. Special emphasis is placed on state-of-the-art fouling mitigation strategies, integration with machine learning for real-time monitoring, and circular economy applications, including energy and resource recovery. The review underscores the evolving role of microbial genomics and their role in optimising MBR performance. A detailed comparison table and future research framework are presented to bridge existing knowledge gaps, such as scalability, operational resilience, and sustainable deployment in decentralised systems. The review aims to support researchers, engineers, and policymakers in advancing MBR applications toward achieving water reuse, sustainability goals, and United Nations SDG 6 on clean water and sanitation.Abbreviations: ABC: algal bacterial consortia; ABS: acrylonitrile butadiene styrene; AEM: anion exchange membrane; AFCMBR: anaerobic fluidised ceramic membrane bioreactor; AGMBR: anaerobic granular membrane bioreactor; AHL: Acyl Homoserine Lactone; AI: Artificial intelligence; AI-2: Autoinducer-2; Al₂O₃: aluminium oxide; AMRUT: Atal Mission for Rejuvenation and Urban Transformation; AnMBR: anaerobic membrane bioreactor; ANN: artificial neural network; AOB: ammonia-oxidising bacteria; ASP: activated sludge process; BOD: biochemical oxygen demand; BOD₅: biochemical oxygen demand (5 days); BPC: biopolymer clusters; CAS: conventional activated sludge; CASP: conventional activated sludge process; CFV: cross-flow velocity; C-MBR: ceramic membrane bioreactor; CNN: convolutional neural network; COD: chemical oxygen demand; CSTR: continuous stirred tank reactor; CTA: cellulose triacetate; DM: dynamic membrane; DO: dissolved oxygen; DOC: dissolved organic carbon; EC: electrocoagulation; EDTA: Ethylenediaminetetraacetic Acid; EMBR: expanded membrane bioreactor; EPS: extracellular polymeric substances; FI: filament index; FO: forward osmosis; FOG: fats, oils, and grease; FTIR: Fourier-transform infrared spectroscopy; GHG: greenhouse gases; GO: graphene oxide; GRNN: general regression neural network; HFM: hollow fibre membrane; HRT: hydraulic retention time; HRT: hydraulic retention time; LSTM: long short-term memory; MBR: membrane bioreactor; MD: membrane distillation; ML: machine learning; MLD: million litres per day; MLP: multilayer perceptron; MLSS: mixed liquor suspended solids; MLVSS: mixed liquor volatile suspended solids; NaOH: sodium hydroxide; NF: nanofiltration; NOB: nitrite-oxidising bacteria; NOM: natural organic matter; OLR: organic loading rate; PAN: polyacrylonitrile; PES: polyethersulfone; PTFE: Polytetrafluoroethylene; PVDF: Polyvinylidene fluoride; RF: random forest; RMSE: root-mean-square error; RO: reverse osmosis; RSM: response surface methodology; SDG-6: Sustainable Development Goal 6 (Clean Water and Sanitation); SMP: soluble microbial products; SRT: sludge retention time; SS: suspended solids; SVM: support vector machine; TDS: total dissolved solids; TKN: Total Kjeldahl Nitrogen; TM: tubular module; TMP: transmembrane pressure; TN: total nitrogen; TOC: total organic carbon; TP: total phosphorus; TS: total solids; TSS: total suspended solids; UF: ultrafiltration; USD: US dollars; UV: ultraviolet; VOC: volatile organic compounds; VSS: volatile suspended solids; XGB: Extreme Gradient Boosting (XGBoost)