Predictive modeling of membrane lifetime for sustainable management of full-scale membrane bioreactors

Accurate prediction of membrane lifetime remains a critical challenge for the cost-effective and sustainable operation of full-scale membrane bioreactors (MBRs) for wastewater treatment. In this study, we developed a mathematical model, by integrating Darcy's law, resistance-in-series theory, and intermediate blocking model, to predict membrane lifetime over multiple operation cycles. Three parameters can be obtained through intra-cycle fitting, including J₀ (initial water permeance), J_ss (steady water permeance) and K (fouling constant). A temperature coefficient was introduced to quantify seasonal effects on membrane water permeance, and irrecoverable fouling resistance served as a bridge linking intra- and inter-cycle permeance variation. The model was calibrated and validated using 7 years of data from a 200,000 m³/day full-scale MBR, achieving high accuracy in predicting water permeance variation. Two replacement strategies were evaluated: a conventional replacement strategy without prediction model (5.5 years) incurred the higher cost ($346,088/tank), while the model-guided replacement strategy (6.9 years) reduced costs by 17.5 %. Sensitivity analysis revealed that membrane lifetime was quite sensitive to the fouling rate and irrecoverable foulant accumulation, highlighting the importance of material optimization and effective cleaning strategy. This work provides an interpretable framework for membrane lifetime prediction, enabling operators to optimize replacement timing, identify critical influencing factors and make sustainable membrane management decisions, while also supporting resource-efficient, low-carbon operation and the sustainable transformation of the water sector.