Optimising the Flux Enhancer Dosing Strategy in a Pilot-Scale Anaerobic Membrane Bioreactor by Mathematical Modelling

Abstract

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 ∆R20_10. Compared to the other control tools, application of the ∆R20_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.

Keywords: anaerobic membrane bioreactor (AnMBR); control tool; flux enhancer; integrated model; sludge filterability.

Figure 1

Modelling approach scheme. Between square brackets is the number of compared alternate models to select the most appropriate model. Abbreviations: ADM1, anaerobic digestion model No. 1; AnDFCm, anaerobic Delft filtration characterization method; RIS, resistance-in-series; SCR, specific cake resistance; TMP, transmembrane pressure; D1a, D1b, D1c, D2, and D3 are alternate deposition submodels; ${\alpha }_{\mathrm{c},1}$, ${\alpha }_{\mathrm{c},1p}$, ${\alpha }_{\mathrm{c},2}$, ${\alpha }_{\mathrm{c},2p}$, ${\alpha }_{\mathrm{c},3}$, ${\alpha }_{\mathrm{c},3p}$, and ${\alpha }_{\mathrm{c},4}$ are alternate SCR submodels; and FR1 to FR6 are alternate empirical FR models.

Figure 2

Long-term biochemical-flocculation model calibration. Sludge characteristics during operational period of pilot AnMBR plant dosed with flux enhancer on day 16: (A) mean particle diameter; (B) total suspended solids; (C) colloidal COD.

Figure 3

Validation of the alternate AnMBR empirical fouling rate (FR) models which (A) exclude and (B) include floc size as input variable. The grey vertical areas represent the representative dataset (iD1 to iD8 from left to right) used for model calibration. Imposed limits between 0 and 60 in the y-axis.

Figure 4

Validation of the alternate FR_RIS AnMBR filtration models combining the different deposition submodels (D1a, D1b, D1c, and D2) with the Carman–Kozeny based specific cake resistance submodels: (A) ${\alpha }_{\mathrm{c},1}$; (B) ${\alpha }_{\mathrm{c},1p}$; (C) ${\alpha }_{\mathrm{c},2}$; (D) ${\alpha }_{\mathrm{c},2p}$. The grey-vertical areas represent the representative dataset (iD1 to iD8 from left to right) used for model calibration. Imposed limits between 0 and 60 in the y-axis.

Figure 5

Validation of the alternate AnDFCm filtration models that combine the different deposition submodels (D1c, D2, and D3) with the non-compressible specific cake resistance submodels: (A) ${\alpha }_{\mathrm{c},1}$; (B) ${\alpha }_{\mathrm{c},2}$; (C) ${\alpha }_{\mathrm{c},3}$. The grey area represents the in situ data used for model calibration.

Figure 6

Simulated pilot-scale AnMBR behaviour with different feedback (FB) and feedforward (FF) control tools for manipulating the flux enhancer (FE) dosage. Compared variables: (A) Fouling rate with empirical model FR6; (B) fouling rate with RIS model D1c ${\alpha }_{\mathrm{c},1p}$; (C) sludge filterability expressed as $∆R_{20}$; (D) mean particle diameter; (E) colloidal material concentration; (F) particulate material concentration; (G) total FE concentration inside the reactor; (H) cumulative mass of FE dosed; (I) cumulative mass of FE removed with permeate flow. The vertical lines indicate applied disturbances on: TSS setpoint (dotted) and $f_{\mathrm{C},\mathrm{WS}}$ (continuous).