Flux enhancers (FEs) have been successfully applied for fouling mitigation in membrane bioreactors. However, more research is needed to compare and optimise different dosing strategies to improve the filtration performance, while minimising the use of FEs and preventing overdosing. Therefore, the goal of this research is to develop an optimised control strategy for FE dosing into an AnMBR by developing a comprehensive integrated mathematical model. The integrated model includes filtration, flocculation, and biochemical processes to predict the effect of FE dosing on sludge filterability and membrane fouling rate in an AnMBR. The biochemical model was based on an ADM1, modified to include FEs and colloidal material. We developed an empirical model for the FE‐induced flocculation of colloidal material. Various alternate filtration models from the literature and our own empirical models were implemented, calibrated, and validated; the best alternatives were selected based on model accuracy and capacity of the model to predict the effect of varying sludge characteristics on the corresponding output, that is fouling rate or sludge filterability. The results showed that fouling rate and sludge filterability were satisfactorily predicted by the selected filtration models. The best integrated model was successfully applied in the simulation environment to compare three feedback and two feedforward control tools to manipulate FE dosing to an AnMBR. The modelling results revealed that the most appropriate control tool was a feedback sludge filterability controller that dosed FEs continuously, referred to as ∆R ₂₀_10. Compared to the other control tools, application of the ∆R ₂₀_10 controller resulted in a more stable sludge filterability and steady fouling rate, when the AnMBR was subject to specific disturbances. The simulation environment developed in this research was shown to be a useful tool to test strategies for dosing flux enhancer into AnMBRs.

Fouling is a major challenge for the economic feasibility and applicability of membrane bioreactors. Several strategies have been proposed for fouling control, and among them, addition of flux enhancers (FE) have shown promising results. Previous research revealed a high variability of the required optimal dosage of FE for different types of sludge; however, studies analysing the causes for such variability are lacking. Therefore, this research examines the effect of sludge characteristics on the optimal FE dosage required for sludge filterability improvement (D_opt). We applied the cationic polymer Adifloc KD451 as FE, and determined D_opt with short-term cross-flow filtration tests. Sludge samples were obtained from five different pilot and full-scale anaerobic membrane bioreactors (AnMBRs) treating municipal or industrial wastewater. Results showed that D_opt was significantly correlated with colloidal organic matter concentration, sludge filterability, capillary suction time, and concentration of soluble polysaccharides. Furthermore, we derived empirical models to predict D_opt based on the significantly correlated sludge characteristics as input variables. This research identifies the factors affecting the required dosage of FE for different sludges and provides guidelines for safe continuous dosing of FE in AnMBR for fouling control.

Cationic polymers have proven to be suitable flux enhancers (FEs) in large‐scale aerobic membrane bioreactors (MBRs), whereas in anaerobic membrane bioreactors (AnMBRs) research is scarce, and so far, only done at lab‐scale. Results from MBRs cannot be directly translated to AnMBRs because the extent and nature of membrane fouling under anaerobic and aerobic conditions are different. Our research focused on the long‐term effect of dosing the cationic polymer Adifloc KD451 to a pilot AnMBR, fed with source‐separated domestic blackwater. A single dosage of Adifloc KD451 at 50 mg L⁻¹ significantly enhanced the filtration performance in the AnMBR, revealed by a decrease in both fouling rate and total filtration resistance. Nevertheless, FE addition had an immediate negative effect on the specific methanogenic activity (SMA), but this was a reversible process that had no adverse effect on permeate quality or chemical oxygen demand (COD) removal in the AnMBR. Moreover, the FE had a long‐term positive effect on AnMBR filtration performance and sludge filterability. These findings indicate that dosing Adifloc KD451 is a suitable strategy for fouling mitigation in AnMBRs because it led to a long‐term improvement in filtration performance, while having no significant adverse effects on permeate quality or COD removal.

The application of cationic polymers to enhance membrane fluxes in anaerobic membrane bioreactors has been proposed by several authors. However, literature shows contradictory results on the influence of those chemicals on the biological activity. In this research, we studied the effect of a cationic polymer on the production of methane from acetate by acetoclastic methanogens. We assessed the effect of polymer concentration on the accumulated methane production (AMP) and the specific methanogenic activity (SMA) in batch tests. Batch tests results showed lower SMA values at higher concentrations of polymer and no effect on the final AMP. Different inhibition models were calibrated and compared to find the best fit and to hypothesize the prevailing inhibition mechanisms. The assessed inhibition models were: competitive (M1a), non-competitive (M2a), un-competitive (M3a), biocide-linear (M4a), and biocide-exponential (M5a). The parameters in the model related to the polymer characteristics were adjusted to fit the experimental data. M2a and M3a were the only models that fitted both experimental SMA and AMP. Although M1a and M4a adequately fitted the experimental SMA, M1a simulations slightly deviated from the experimental AMP, and M4a considerably underpredicted the AMP at concentrations of polymer above 0.23 gCOD L^-1. M5a did not adequately fit either experimental SMA and AMP results. We compared models a (M1a to M5a), which consider the inhibition by the concentration of polymer in the bulk liquid, with models b (M1b to M5b) considering the inhibition being caused by the total concentration of polymer in the reactor. Results showed that the difference between a and b models' simulations were negligible for all kinetic models considered (M1, M2, M3, M4, and M5). Therefore, the models that better predicted the experimental data were the non-competitive (M2a and M2b) and un-competitive (M3a and M3b) inhibition models, which are biostatic inhibition models. Consequently, the decreased methanogenic activity caused by polymer additions is presumably a reversible process.