Membrane filtration is commonly used in biorefineries to separate cells from fermentation broths containing the desired products. However, membrane fouling can cause short-term process disruption and long-term membrane degradation. The evolution of membrane resistance over time can be monitored to track fouling, but this calls for adequate sensors in the plant. This requirement might not be fulfilled even in modern biorefineries, especially when multiple, tightly interconnected membrane modules are used. Therefore, characterization of fouling in industrial facilities remains a challenge. In this study, we propose a hybrid modeling strategy to characterize both reversible and irreversible fouling in multi-module biorefinery membrane separation systems. We couple a linear data-driven model, to provide high-frequency estimates of trans-membrane pressures from the available measurements, with a simple nonlinear knowledge-driven model, to compute the resistances of the individual membrane modules. We test the proposed strategy using real data from the world's first industrial biorefinery manufacturing 1,4-bio-butanediol via fermentation of renewable raw materials. We show how monitoring of individual resistances, even when done by simple visual inspection, offers valuable insight on the reversible and irreversible fouling state of the membranes. We also discuss the advantage of the proposed approach, over monitoring trans-membrane pressures and permeate fluxes, from the standpoints of data variability, effect of process changes, interaction between module in multi-module systems, and fouling dynamics
