Desalination 277 (2011) 414–419

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Online monitoring of membrane fouling in submerged MBRs

H. Monclús a, G. Ferrero b, G. Buttiglieri b, J. Comas a, I. Rodriguez-Roda a,b,⁎a Laboratory of Chemical and Environmental Engineering (LEQUiA), Institute of the Environment, University of Girona, Girona E17071, Spainb ICRA (Catalan Institute for Water Research), Science and Technology Park of the University of Girona, H2O Building, c/Emili Grahit 101, Girona E17003, Spain

Abbreviations: FR, Fouling rate (dTMP/dT); HF, HolloMBR, Membrane bioreactor; iMBR, Immersed MBR; Msolids; SADm, Specific aeration demand (membrane); TUF, Ultrafiltration.⁎ Corresponding author at: Laboratory of Chemical a

(LEQUiA), Institute of the Environment, University ofTel.: +34 972183244.

E-mail addresses: hector@lequia.udg.cat (H. Monclú(G. Ferrero), gbuttiglieri@icra.cat (G. Buttiglieri), quim@ignasi@lequia.udg.cat, irodriguezroda@icra.cat (I. Rodrig

0011-9164/$ – see front matter © 2011 Elsevier B.V. Adoi:10.1016/j.desal.2011.04.055

a b s t r a c t

a r t i c l e i n f o

Article history:Received 15 March 2011Received in revised form 11 April 2011Accepted 21 April 2011Available online 13 May 2011

Keywords:FoulingMembrane bioreactorOnline signalSlopeTMP

Membrane fouling is the most serious problem affecting membrane bioreactors (MBR). It decreasesmembrane permeability and consequently increases energy consumption. Various methods and techniques tomeasure fouling rates (FR) can be found in the literature, but few provide online data, and most are expensiveor too invasive. The aim of this short communication is to present a new online method for monitoringand estimating the fouling in an MBR, based on the use of the derivative of the transmembrane pressure(TMP) per cycle, for which hourly and daily mean values can be obtained. This online method (FR per cycle)was experimentally evaluated at a lab-scale MBR plant, where fouling conditions were induced by theaddition of a protein (albumin) and a polysaccharide (glucose). Finally, we compared the new method tostandard methods, such as the flux-step method. Similar results for fouling rates were found, but the newmethod showed increased sensitivity. At the same time, the method seems to be useful in characterizing thenature of fouling.

w fibre; J, Flux; Jc, Critical flux;LSS, Mixed liquor suspendedMP, Transmembrane pressure;

nd Environmental EngineeringGirona, Girona E17071, Spain.

s), gferrero@icra.catlequia.udg.cat (J. Comas),uez-Roda).

ll rights reserved.

© 2011 Elsevier B.V. All rights reserved.

1. Introduction

Membrane bioreactors (MBR) are well-known wastewater treat-ment systems that produce a clarified and substantially disinfectedeffluent. In addition, they concentrate the biomass and, in doing so,reduce the necessary tank size and also increase the efficiency of thebiotreatment process. Hence, MBR technology reduces the environ-mental footprint of the plants (for example, secondary clarifiers aresubstituted by membranes). Furthermore, MBRs are a very attrac-tive option for communities requiring small and compact treatmentfacilities. High quality effluent discharges are obtained with MBRs dueto the very low concentration of suspended solids, which improvesmetal separation [1] and results in good biological nutrient removal[2,3]. In addition, bacterial indicators are better retained [4,5] andsome micropollutants, such as pesticides, pharmaceuticals andpersonal care products, may be removed more efficiently than withconventional activated sludge systems [6,7]. However, the mainlimitation of MBRs is their high operating costs due to membraneaeration and the fouling phenomena [8,9]. Membrane fouling is

responsible for the decrease in permeability and the consequentincrease in energy consumption [9].

Various techniques to evaluate andmonitormembrane fouling canbe found in the literature. Bouhabila et al. [10] characterize fouling bymeasuringfiltration resistance. Huyskens et al. [11] based their studieson a dead-end filtrationmethod. Xu et al. [12] applied ultrasonic time-domain reflectometry to monitor fouling online. Another methodto characterize fouling is the flux-step method, which assesses thefouling propensity for a given operating condition and provides anestimate of the critical flux [13]. Cho and Fane [14] and Ognier et al.[15] evaluated in a continuous filtration operation the changes on theTMP evolution related with the availability of the membrane pores.

However, these methods have certain limitations: some cannotbe obtained online, some are too invasive and require subsequentchemical cleaning, and others are very expensive methods that re-quire new instrumentation.

The aim of this study is to present a new online method for themonitoring and estimatingmembrane fouling in anMBR, based on theuse of the derivative of the transmembrane pressure (TMP) per cycle,fouling rates (FR), for which hourly and daily mean values can beobtained. The method has been experimentally evaluated at a lab-scale MBR plant, where fouling conditions were induced by theaddition of proteins and polysaccharides.

This short communication is organized into five sections. First,in Section 2, we describe the MBR pilot plant where the study wascarried out and the work plan for the experiment. In Section 3 weprovide a full description of the newmethod. In Section 4, we presentthe results of the experiments to evaluate this new method and adiscussion. Finally, in Section 5 we provide our conclusions.

415H. Monclús et al. / Desalination 277 (2011) 414–419

2. Materials and methods

2.1. Experimental system

The lab-scale plant (Fig. 1) comprises a bioreactor (total volume of30 L, working volume 25 L) with a ZW-10 submerged ultrafiltrationhollow fibre (UF-HF) membrane module (Zenon/General Electric)with a nominal pore size of 0.04 μm. The total membrane area is0.93 m2. Raw urban wastewater was collected twice a week from thewastewater treatment plant at Quart (Girona; NE Spain) and storedunder mixing conditions in a 150 L buffer tank at 4 °C. This urbanwastewater contained 390±40 mg COD L−1, 69.9±12 mg TKN-NL−1, 0.09±0.01 mg NOx

−–N L−1 and 4.99±2.5 mg PO43−–P L−1,

achieving a conventional urban C:N:P ratio of 100:16.9:1.42. The MBRlab-scale plant was working under conventional operational condi-tions [16], maintaining the SRT on 25±2 days, wasting around 0.9±0.1 L of sludge per day and 15±0.5 hours as a HRT. The MLSS con-centration was previously fixed at 5 g·L−1.

The lab-scale plant is located in the Science and Technology Park ofthe University of Girona (Girona; NE Spain). The wastewater waspumped from the buffer tank into the reactor using a peristaltic pump(Watson-Marlow). The permeate was obtained using a gear pump(Micropump) controlled by a pressure transducer to create a vacuumpressure drop over the membranes. The TMP values were stored in asupervisory control and data acquisition system. All data are remotelyaccessible (www.colmatar.es) [8].

2.2. Flux-step method

The flux-step method was used to compare in different time thepropensity of the membrane to be fouled at different fluxes and underdifferent conditions. Critical flux, below which permeability declineis considered negligible [9], was also estimated. TMP was monitoredto measure the slope of the TMP in each permeation cycle, applyingdifferent fluxes in steps over a fixed period of time; then the slope ofthe TMP was represented against the flux for each step [17].

Generally, in the classical flux-step methodology, the flux isincremented in steps of 2–5 L m−2 h−1 (LMH) and for periods of 15–60 min, and the TMP response is observed [17]. With the applied flux,many authors report a roughly exponential increase in the rate ofincrease in TMP (dTMP dt−1, usually referred to as the fouling rate). Inthis study, the critical flux was assumed to be the flux at which thefouling rate, dTMP dt−1, exceeded a threshold of 0.5 mbar min−1

(0.0083 mbar s−1) [18].

Fig. 1. Schematic diagram of the lab-scale membrane bioreactor.

2.3. Cleaning protocol

The membrane module was systematically cleaned prior to eachphase. Before the experiments, the membrane was cleaned ex-situ bysoaking it in a hypochlorite solution of 0.05% wt. Three flux-stepsreplicates were carried out in each phase (after and before each test);between replicates, a chemical backwashwith 0.05% hypochloritewasapplied: five backwashing steps of 15 s each at 40 LMH were appliedwith five minutes of membrane relaxation between steps [18].

2.4. Working plan

Two experiments, each consisting of two phases, were carried outfollowing the working plan shown in Table 1. In both experiments,Phase 1 was dedicated entirely to achieving the stabilization of thefiltration process, to obtaining and to maintaining the design MLSSconcentration (5 g·L−1).

The duration of Phase 1 in each experiment depended on thestabilization requirements.

Phase 2 in each experiment consisted of adding albumin (protein)in Experiment 1 and glucose (polysaccharide) in Experiment 2 tothe influent buffer tank in order to observe the effect on fouling[19]. Albumin and glucose were added directly to the influent tank(concentrations in table 1), just after the wastewater collection fromthe WWTP. The length of the second phases depended on theevolution of the fouling. Throughout the study, the working permeateflux was set at 25 LMH.

3. Online method for fouling monitoring

Our new online monitoring method for monitoring and estimatingthe membrane fouling using fouling rates (FR) entails three mainsteps that are summarized below:

(i) Data are recorded for processing in each permeation cycle(9 min), and the permeate flow rate and TMP are recordedonline every 10 s. The initial data from the first minute of thecycle are not taken into account in order to avoid non-representative values after backwashing or relaxation. Afterthat, data are filtered to remove outliers [8]. Once the data arefiltered, flux and permeability are automatically calculated.

(ii) A fouling rate (FR) per cycle is obtained as the slope of all valuesof TMP per cycle according to the following calculation:

FR = dTMP.dt

� �cycle

in mbars·s−1� �

Fig. 2 shows how a FR value is automatically calculated everycycle.

(iii) Mean hourly and daily values for FRs per cycle can be obtained.A warning alarm indicating significant fouling is set to triggerwhen the current hourly FR is higher than 0.0083 mbar·s−1

(critical flux) [18].

Table 1Working plan.

Experiment 1 Experiment 2

Phase 1 Phase 2 Phase 1 Phase 2

Operational days 56 days 15 days 13 days 20 daysPhase characteristic Stabilization Albumin addition Stabilization Glucose additionFlux (J) 25 LMH 25 LMH 25 LMH 25 LMHConcentration 333 mg L−1 333 mg L−1

Fig. 2. Schematic representation of the online fouling rates (FR) monitoring method.

416 H. Monclús et al. / Desalination 277 (2011) 414–419

4. Evaluation of the results of the new online foulingmonitoring method

The first experiment lasted 71 days and the second 33 days. Phase1 of Experiment 1 lasted 56 days due to the high initial concentrationof MLSS (~10 g·L−1) and the need to achieve stable conditions atdesign MLSS (~5 g·L−1). In the second experiment, the first phase

Fig. 3. A) Permeability and TMP evolution through the second phase of each experiment. Bexperiment. C) TMP evolution with mean hourly values for three consecutive days (day 7consecutive days (day 7 to 9).

was shorter (13 days) because the initial MLSS concentration wasalready stabilized. The second phase of the first experiment lasted15 days due to the rapidly obtained high TMP, while in the secondexperiment it lasted 20 days. In both second phases, the initial MLSSconcentration was approximately 5 g MLSS L−1.

Fig. 3A shows the mean daily values of both the permeability andTMP trends. Fig. 3B shows the evolution of the mean daily values of

) New online method (FR per cycle) with mean daily values during the phases of eachto day 9). D) New online method (FR per cycle) with mean hourly values for three

Table 2Permeability and TMP values before (Phase 1) and after (Phase 2) the external addition.

Experiment Parameter Phase 1 (stabilization) Phase 2 (external addition) Units

Mean Phase 1 Initial value Final value Initial value Final value

Experiment 1(albumin)

Permeability 133.8±20.8 172.43 125.63 129.9 61.77 LMH bar−1

TMP 0.166±0.033 145 201 180 405 barExperiment 2(glucose)

Permeability 128.87±8 129.85 119.32 138.63 88.27 LMH bar−1

TMP 0.201±0.011 193 210 192 288 bar

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the online method (FR per cycle). Fig. 3C and Fig. 3D show,respectively, details of three consecutive days from Fig. 3A and Bwith mean hourly values.

In both cases, the initial values of permeability in the secondphases ranged between 130 and 138 LMH bar−1 (Fig. 3A). After theaddition of albumin and glucose, the permeability decreased at thesame rate until day 6. After that, the permeability dropped fasterwith albumin than with glucose. As can be seen in Fig. 3B, the dailymean values of the FR (mbars s−1) of each permeation cycle duringExperiment 1 (albumin) increased considerably after day 6 whenthe fouling phenomenon was high. In Experiment 2 (glucose), thepermeability decreased until values reached approximately 90 LMHbar−1 (Fig. 3A) and the FR was relatively constant during the assay, atlower than or close to 0.004 mbars s−1 (Fig. 3B).

A comparison between Figs. 3C and D shows that FR monitoringis more sensitive to changes in the fouling process than to TMPmonitoring or permeability. While the TMP was still evolving at a

Fig. 4. TMP evolution (i.e., Fouling Rates) during consecutive

constant rate (Fig. 3C), the FR slope (Fig. 3D) began to change,indicating a change in the fouling phenomenon.

When only the TMP or the permeability are monitored (Fig. 3A), itis not possible to appreciate any change or increase in the foulingphenomena, since the slope of the daily TMP values increases at aconstant rate. In contrast, when the FRs are monitored, the increase infouling is detected more easily and earlier. Fig. 3D also shows that theFRs increasedmore after albumin addition than after glucose addition.A warning alarm was set to trigger when the FR increased to morethan 0.0083 mbar·s−1 (critical flux values, see also Fig. 3B). This valueis a reference value at which it can be assumed the system is workingunder critical flux [18]. The TMP value can be set to trigger the alarmbut it depends on the membrane status or membrane configuration(i.e., HF or FS).

Table 2 presents the mean, initial and final values of permeabilityand TMP during the Phase 1 (stabilization) and the initial and finalvalues during Phase 2 (external addition of albumin and glucose). As

cycles at the end of each phase in Experiments 1 and 2.

Fig. 5. A) Fouling rates at the end of Phase 2 for each experiment. B) Fouling rates at the end of Phase 2 for each experiment using a logarithmic scale.

Table 3Comparison of the fouling rates and critical fluxes using the flux-step method in bothexperiments.

Experiment 1 Experiment 2 Units

Phase 1(stabilization)

Phase(albumin)

Phase 1(stabilization)

Phase 2(glucose)

Fouling ratesestimatorsvia flux-stepmethod

0.1046 0.2178 0.0787 0.1529 mbar s−1

LMH −1

Jc estimation 24.5 9.0 25.5 12.0 LMH

418 H. Monclús et al. / Desalination 277 (2011) 414–419

the assay was carried out on a new module of membranes, thepermeability in the first phase of Experiment 1 decreased from 170LMH bar−1 down to a stable value of, approximately, 125 LMH bar−1.In the first phase of Experiment 2 (glucose), the permeability wasalready stable at around 125 LMH bar−1 (Table 2).

With the addition of albumin (Experiment 1, Phase 2), perme-ability decreased rapidly from nearly 130 LMH bar−1 to nearly 62LMH bar−1 in 13 days – a 53% loss in permeability – while in the caseof glucose addition (Experiment 2, Phase 2), permeability decreasedfrom over 138 LMH bar−1 to over 88 LMH bar−1 in 19 days – apermeability loss of about 36% (Table 2).

Fig. 4 provides further visual information of the TMP slopes (visualFRs). It shows the different TMP cycles at the end of each phase.During Phase 1 (stabilization) in each experiment, the TMP slopes(FRs) were relatively flat (Fig. 4A and C). After the external additions,TMP increased in both cases. However, a higher increase of the FR canbe appreciated after the addition of albumin (Fig. 4B) than in theglucose addition (Fig. 4D).

4.1. Comparison with flux-step method

Flux-step method [17,18] were carried out to observe the foulingrates at different fluxes and compare themwith the results of our newonline method for monitoring fouling rates. The tests were conductedat the end of each phase of each experiment. The values obtainedwereused to compare the fouling rates under different working conditions.If the fouling phenomenon increases, it is directly reflected by theFR [17].

Fig. 5A shows the results for the flux-step method and Fig. 5Bshows the same using a logarithmic Y-axis. The flux-step methoddemonstrated that the fouling rate was higher after albumin addition(Exp. 1) than after glucose addition (Exp. 2). The slope can be ob-tained using an exponential regression. The fouling rate increased by0.2178 mbar s−1 LMH−1 compared to 0.1529 for added glucose.During the stabilization phases the slopes of the fouling rates were0.1046 and 0.0787 respectively

In the case of albumin addition, the estimated critical flux (Jc) inthe second phase was 9 LMH, while during the stabilization phase ithad been 24.5 LMH. In the second experiment (glucose) the estimatedJc in the second phase was 12 LMH while during stabilization ithad been 25.5 LMH (Table 3). The Jc values found during Phase 2 ineach experiment were much lower than normal Jc values, which aregenerally between 20 and 25 LMH [17].

4.2. Summary of results and further discussion

Table 4 provides a comparison of the different methods used tomonitor fouling. Permeability and TMP evolution are now standardtools for monitoring membrane fouling. However, our online FR

monitoring method (TMP slope per cycle, with mean hourly and dailyvalues) detects fouling earlier and with improved sensitivity.

The flux-step test and the here presented online method bothcomplement, even if differently, other standard tools. Nonetheless,although the flux-step test is a useful and widely used tool, in orderto carry out the test, the MBR operation must be stopped and themembranes forced at high fluxes, which probably results in changesto the membrane characteristics. Furthermore, after each test,chemical cleaning is required to reproduce the same initial conditions.In continuous operation, or in a full-scale facility where haltingoperations is a highly complicated procedure, the flux-step methodcannot be used. In contrast, since it is calculated online, monitoringthe FR per cycle can be easily integrated into a closed-loop controller,complementing and enhancing other control modules [8,20]. The FRmethod provides, online, information about the attainment of mem-brane critical condition and the necessity of a recovery control action.

This method, together with the monitoring of the evolution ofpermeability or TMP, would appear to be useful in distinguishingdifferent kinds of fouling. Preliminary results illustrate that it can beused to characterize the type of fouling and to establish which kind ofcleaning process would be more efficient.

Changes in permeability performance and FR could be due to poreblocking (irremovable fouling) in the albumin phase [13,21]. Theincrease in FRs after each cycle is related to a reduction in the numberof availablemembrane pores. In these cases, physical cleaning, such asbackwashing, is not enough to recover the previous status of themembranes. In Phase 2 of the albumin experiment, the accumulatedfouling increased the FR (slope of the TMP per cycle) from 6.3×10−4

to 1.38×10−2 mbar s−1 and rapidly decreased the permeability from130 to 61.71 LMH bar−1 (Fig. 3B).

In Experiment 2 a different behaviour from Experiment 1(albumin) and no significant fouling effect was detected. The glucosepresumably provoked an increase in only removable fouling (such ascake layer). Consequently, physical cleaning was quite successfullyapplied after each cycle in terms of maintaining the FRs. Permeabilitydid, in fact, decrease as well but more slowly (139 to 88 LMH bar−1)

Table 4Summary of results for fouling monitoring during the whole study.

Permeability TMP Online FR method Flux-step method

Initial value Final value Initial value Final value Initial value Final value End of Phase 1 End of Phase 2

Exp. 1 (albumin) 129.9 61.77 180 405 6.3×10−4 1.38×10−2 0.1046 0.2178Exp. 2 (glucose) 138.63 88.27 192 288 2.42×10−3 4.28×10−3 0.0787 0.1529Units LMH bar−1 mbar mbar s−1 mbar s−1 LMH−1

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and the online FR measurements did not detect an increase in foulingsince the evolution of the slope was relatively constant (from2.42×10−3 to 4.28×10−3 mbar s−1; Fig. 3D). We can assume, there-fore, that fouling due to albumin was significantly different fromfouling due to glucose. Further experiments are being conducted tocorroborate these results.

5. Conclusions

A new online method to monitoring the fouling using FR has beenpresented and shown to be a better tool for monitoring the fouling ina UF-HF membrane bioreactor. Comparative results showed that thisonline method detected an increase fouling more easily and earlierwith respect to the TMP and permeability monitoring alone.

More conventional but intrusive flux-step methodology repro-duced the same results as this online method. However, the newonline method for monitoring FRs is more sensitive and moreresponsive (i.e., it detects fouling earlier) than the more conventionalmethods used until now (TMP, permeability and flux-step test). It isalso less aggressive since it does not require halting operations orcleaning the membranes after each test.

Acknowledgements

This researchwas funded by the SpanishMinistry of Education andScience (CTM2009-1-14742-C02-01) and (CONSOLIDER-CSD2007-00055). The authors would like to thank Íngrid Ferrer and SaraGabarrón (LEQUiA-UdG) for their support during the experimentalstudy.

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