Review of intelligent fouling control in membrane bioreactors with a systems-level assessment of deployment readiness

Membrane bioreactors have emerged as a leading technology for advanced wastewater treatment; however, membrane fouling remains the principal obstacle to their reliable, energy-efficient, and cost-effective operation. Although artificial intelligence (AI)-driven fouling-control strategies have advanced considerably, existing reviews largely emphasise predictive performance while providing limited evaluation of deployment readiness, operational resilience, and systems-level integration. This review systematically synthesises 150 peer-reviewed studies published between 2020 and 2026 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 framework, complemented by methodological robustness assessment and Technology Readiness Level (TRL) evaluation. Unlike previous reviews, this study adopts a systems-engineering perspective that integrates predictive intelligence with operational implementation, deployment maturity, and lifecycle asset management. The synthesis reveals that operational benefits consistently diminish as validation progresses from simulation to full-scale applications, while fewer than 15% of reported studies demonstrate full-scale implementation and none achieve sustained autonomous operation (TRL 9). These findings indicate that the principal challenge is no longer improving predictive accuracy but translating intelligent control into robust, scalable, and deployment-ready wastewater treatment systems. Accordingly, this review provides a structured implementation framework that bridges the gap between algorithm development and engineering practice, offering practical guidance for the design, evaluation, and deployment of resilient intelligent MBR systems.