Platform-specific fouling control strategies for membrane bioreactors in biomanufacturing

Membrane fouling is a primary operational bottleneck in membrane-based bioproduct manufacturing, driving membrane replacement, productivity losses, and process downtime. The central premise is that fouling control in biomanufacturing cannot be inferred from wastewater membrane bioreactor (MBR) practice, because the two applications differ in their most basic configuration. Wastewater MBRs predominantly use membranes submerged in the bioreactor, where aeration supplies the shear that limits cake deposition. Biomanufacturing instead relies almost exclusively on external tangential flow filtration (TFF) and its alternating tangential flow (ATF) variant, where crossflow or cyclical reverse flow controls accumulation. These distinct hydrodynamic regimes, rather than cells alone, govern which mitigation strategies are applicable. Beyond hydrodynamics, bioproduction faces constraints absent from water treatment: product-sensitive biomolecules, organisms spanning robust microbial to mammalian cultures, and good manufacturing practice (GMP) requirements that often make cleaning and reuse impractical, so membranes are validated as single-use and discarded rather than regenerated. We establish a platform-specific mechanistic framework centered on foulant chemistry: microbial systems exhibit extracellular polymeric substances (EPS)–cation fouling, largely reversible by mechanical cleaning; mammalian culture develops protein–DNA gel layers managed through hydrodynamic (ATF/TFF) and single-use prevention; and microalgae require hybrid approaches addressing transparent exopolymer particle (TEP) compressibility. A critical need in the field is the development of mechanistic decision frameworks enabling rational strategy selection based on foulant chemistry; this review provides that framework, supported by literature-derived quantitative metrics and a real-time adaptive control architecture integrating process analytical technology (PAT) with machine learning (ML) for predictive fouling management.