Ivnitsky H. Biofouling Formation and Modeling in Nanofiltration Membranes Applied to Wastewater...

7/28/2019 Ivnitsky H. Biofouling Formation and Modeling in Nanofiltration Membranes Applied to Wastewater Treatment 2010

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Journal of Membrane Science 360 (2010) 165173

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Journal of Membrane Science

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Biofouling formation and modeling in nanoltration membranes applied towastewater treatment

Hanan Ivnitsky a , Dror Minz b , Larissa Kautsky b , Ami Preis a , Avi Ostfeld a ,Raphael Semiat c , Carlos G. Dosoretz a,a Faculty of Civil & Environmental Engineering and Grand Water Research Institute, Technion, IIT, Haifa, Israelb Institute of Soil, Water and Environmental Sciences, ARO, The Volcani Center, Bet-Dagan, Israelc Faculty of Chemical Engineering and Grand Water Research Institute, Technion, IIT, Haifa, Israel

a r t i c l e i n f o

Article history:Received 20 December 2009Received in revised form 26 April 2010Accepted 2 May 2010Available online 11 May 2010

Keywords:BiofoulingMembranesWastewaterNanoltrationBiolmModeling

a b s t r a c t

Biofouling development on nanoltration membranes treating tertiary efuents was studied at low(5 bar) and high (25 bar) pressures at different feedwater concentrations, temperatures and lengths of operation. The bacterial community prole composing the biofouling layer was characterized. Most of the bacterial species identied were Gram-negative, with Proteobacteria (approximately equally dividedbetween , and subdivisions) and Bacteroidetes beingthe prevalent groups. At high-pressure, scalingwas the primary source of fouling whereas at low-pressure, biofouling was dominant. For these condi-tions, an empirical approach to forecasting the contribution of biofouling resistance to total resistancewas derived based on the resistance in series theory. This approach showed that biofouling becomes adominating factor after approximately 20 L of permeate volume has been produced. A data-driven mod-eling algorithm for forecasting the reduction in permeate ux due to biofouling was also established.The reduction in permeate ux rates was related to the development of a fouling layer on the mem-brane. Pressure, total organic carbon, pH and conductivity of the feedwater were the most inuentialparameters. These results are novel in the area of model tree algorithms as they apply to forecasting thedevelopment of biofouling on membranes.

2010 Elsevier B.V. All rights reserved.

1. Introduction

Although membrane biofouling is one of the most undesirableeffects inpressure-drivenmembrane separation processes,very lit-tle is known about thefundamentalnature of theprocess [13] . Oneof the biggest challenges still facing the membrane industry is theprevention of biofouling on membranes, as uncontrolled growthof a biolm can eventually lead to clogging of the membranes andimpaired function of the system [4,5] .

Pressure-driven crossow membrane separation systemspresent a differential environment for biolm development. Thesurface of attachment is a selective porous substratum whichallows the transfer of certain molecules, whereas the concentra-tion polarization (CP) layer generates an environment of relativelyhigh nutrient and salt concentrations [69] . Herzberg and Elim-elech [7] f ound a higher permeate ux decline in reverse osmosis(RO) membranes with a biolm layer than in those fouled with

Corresponding author at: Faculty of Civil & Environmental Engineering, IsraelInstitute of Technology, Haifa 32000, Israel. Tel.: +972 4 8294962;fax: +972 4 8228898.

E-mail address: [email protected] (C.G. Dosoretz).

dead cells alone, which was mainly attributed to the increase inhydraulic resistance caused by the extra-cellular polymeric sub-stances (EPS) surrounding thebacterialcells. Fonseca et al. [11] alsonoted that the observed ux decline in nanoltration (NF) mem-branes is associatedwith theexcreted polymericsubstances,ratherthan the bacterial cells themselves. Permeate ux decline has alsobeen related to the application of high-pressure to the membranes,which generatesa greater foulant-depositionrate [10] . Huang et al.[12] f ound that membrane ux has a great impact on the predomi-nant populations in fouling biolms. The performance and foulingof NF andRO membranes seem to be controlled by feedwater com-position, physical and chemical properties of the membrane andsystem operating conditions. In general, the processes and interac-tions causingfoulingin membranesystemsare likely tobe complex.

The composition of membranefoulingbiolms has beenrepeat-edlyreported to include primarilyGram-negative bacterialspecies., and Proteobacteria (including Sphingomonas and Nitro-somonas species) and Bacteroidetes (including Flavobacterium andSphingobacterium species) have been found to be the dominantbacterial groups involved in fouling of membranes (MF, NF,RO) operated with fresh surface water and reclaimed efuents[9,1215,17] . Pang and Liu [16] , studying RO membranes appliedfor wastewater reclamation, retrieved bacterial phylotypes related

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to the order Rhizobiales, a group of bacteria that had not been pre-viously implicated in membranebiofouling. It appears thatbiolmsdeveloping on membrane separation systems follow a generic pat-tern despite the high diversity of bacterial species found, in whichmembers of Proteobacteria and Bacteroidetes are the predomi-nantspecies, witha minor contributionof Gram-positiveand otherGram-negative bacteria [9,12] . This prole appears to be relatedto a broad capacity for surface colonization in these organisms, aswell as adaptability to changing nutrients and ability to proliferateunder oligotrophic conditions.

Modeling the biofouling phenomenon, which consists of chem-ical, physical, and biological processes that interact on differentspatial and temporal scales, is exceptionally complex. Efforts havebeen made to create predictive models for permeate ux andother hydraulicparameters in dense membranes basedon a varietyof different approaches [11,1821] . The use of data-driven tech-niques for modeling this multifaceted phenomenon is employedherein. Development of these methods is motivated by the rapidadvances in information-processing systems along with increas-ing data availability, which have directed research towards thedevelopment of intelligent systems that evolve models of naturalphenomena automatically [2224] . This is the discipline of data-driven modeling, which is the study of algorithms that improveautomatically through experience. Applications of data-drivenmodeling range from data-mining schemes that discover gen-eral rules in large datasets, to information-ltering systems thatautomatically learn the interests of the user. The most utilizedtechniques of data-driven modeling are articial neural networks(ANNs), model trees, fuzzy-rule-based systems, and support vectormachines. In this study, model trees are applied for forecasting thereduction in the rate of permeate ux through membranes due tobiofouling formation [22,23,25] . Although researchers have usedsuch techniques in studies of membrane fouling [24,26] , they havenot dealt with estimations of the biofouling problem.

The overall objective of this work was to investigate biolmformation on the surface of NF membranes used for upgrad-ing the quality of tertiary efuents under different operating

conditions. The composition of the bacterial communities devel-oped in the biofouling layer was studied by polymeric chainreactiondenaturating gradient gel electrophoresis (PCRDGGE)and sequence analysis of 16S rRNA gene fragments from DGGEbands, as a function of temperature, pressure and length of opera-tion. An empiricalseriesof equations, based on permeate ux in NFmembranes treating tertiary wastewater, was derived to estimatethe contribution to resistance of the biofouling layer developed onthe membrane surface. In addition, a model algorithmfor forecast-ing the ux reduction rate in biofouled membranes and a methodfor estimating the biofouling layer resistance in NF membranestreating tertiary wastewater efuents are proposed.

2. Materials and methods

2.1. Experimental set-up and conditions

Nanoltration experiments were performed in a crossowapparatus equipped with a MIC-RO 240 module (PCI) comprisingone NF-polyamide tubular membrane (AFC 30, PCI) with a ltra-tion area of 120 cm 2 (30cm length 1.25 cm diameter) and 200 DaMWCO, as previously described [9,17] . Runs were conducted insemi-batch mode with full recirculation of the permeate and con-centrate into the feed tank, the contents of which were replaceddaily. Pressure was maintained at 5 bar or 25 bar and temperaturewas maintained at 20 C, 25 C, or 34 C, as indicated. Feedwaterconsisted of domestic efuents of tertiary quality, as described

below. Recirculation ow rate was maintained at 132.5 L/h (corre-

sponding to a linear velocity of 0.3m/s and Re of 5300) in all cases.Before each experiment, the system was intensively cleaned withalkaline sodium hypochlorite, profusely rinsed with tap water anda new membrane assembled. Following each run, the membraneswere dismounted and segmentedinto evenslices of approximately1 cm for the different microbiological and microscopic analysesdescribed further on.

2.2. Feedwater

Tertiary quality efuents were generated in a continuous mem-brane bioreactor (MBR) operating at the Technion campus: rstthey were subjected to MF through at sheets with 8-m 2 ltrationarea and 0.2- m poresize (Kubota), as previously described [9] . TheMBR was fed with primary efuents from thesedimentation of rawwastewater collectedfrom theTechnioncampussewage-collectionsystem. MBR efuents were free of suspended solids (


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membrane surface with a sterile swab of approximately 1 cm inlength at selected time points during the experiment (up to 8days). The second consisted of elution of the accumulated bacte-ria at the end of eachexperimentfroma 1-cm 2 membrane coupon.In all cases, enumeration was performed by heterotrophic platecount (HPC) on R2A medium. Attached bacteria were expressedin CFU/cm 2 and bacteria suspended in the feedwater in CFU/mL.Plates were incubated at 30 C for 3 days and the colonies werethen counted.

Swab samples were takenfroma surface ofapproximately1 cm 2

at different places on the membrane by temporarily stopping thesystem and opening the module. Each sample was taken at a dif-ferent place on the membrane surface. The sampling site wasdetermined by measuring the depth of each entrance (1 cm, 2 cm,4 cm). The samplings were performed from both sides of the mem-brane.No area onthe membrane surface was sampled twice. Swabswere placed in phosphate-buffered saline (PBS)Tween solution(PBS+ 0.05% (v/v) Tween 20), vortexed for 1 min, and the suspen-sion was then diluted and plated. PBSTween solution contained8.0 g NaCl, 1.0g KH 2 PO4 , 14.5g Na 2 HPO4 12H 2 O and 0.5 mL Tween20, in 1000 mL DDW; pH was adjusted to 7.2.

At the end of the runs, membrane samples were subsectionedinto 1-cm 2 pieces and shaken (30min, 150 rpm) with sterile glassbeads (1mm in diameter)in PBS forextractionand resuspensionof the biofouling layer. The suspension was subsequently diluted andplated. Feedwater samples were diluted in sterile PBS and plated.

2.5. Model tree conguration

2.5.1. Theory of the model trees algorithmThe model trees algorithm builds a rule-based predictive model

using a top-down induction approach [25] . The tree is tted to atraining dataset by splitting the data into homogeneous subsetsbased on their attributes. Thereafter, the tree is constructed withall training cases being predicted by the tree leaves (i.e., each leaf isa linear regression model which predicts continuous values for thenumerical attributes). The tree is then pruned from the bottom upand transformed into a set of If Then rules, which simplify the treestructureand thus improve its ability to classify newinstances. Thepredictive ability of the tree is measured by the correlation coef-cients of the training and cross-validation datasets. The correlationcoefcient equals one in the case of a complete t between theactual values and the model predictions.

2.5.2. Model tree constructionThe goal is to construct a model that relates the target values of

the training cases to their values described by the input attributes.Theperformance of themodel is generallyassessedby the accuracywith which it predicts the target values of unseen cases (cross-validation dataset).

Step 1 . Computation of the standard deviation of the target val-ues of cases in dataset T . Unless T contains very few cases or theirvalues vary only slightly, it is split on the outcome of a test. Everypotential test is evaluated by determining the subset of cases asso-ciated with each outcome; let T i denote the subset of cases thathave the ith outcome of the potential test. Treating the standarddeviation sd( T i) of the target values of cases in T i as a measure of error, the expected reduction in error (SDR) as a result of this testcan be written as:

SDR = sd( T ) 1T

i

T i sd( T i) (1)

Step 2 . After examiningall possible tests,the modeltree choosesone that maximizes this expected error reduction (SDR). At this

stage,an initial model tree hasbeengrown anda multivariatelinear

model is constructed for the cases at each node of the model treeusing standard regression techniques. The tree is then transferredinto a simple set of If Then rules that simplify its structure.

Step 3 . The accuracy of the model is estimated on unseen cases(i.e.,usually 70% of the data is used fortrainingand forconstructingthe tree while 30% of the cases are put aside for cross-validation).The predictive ability of the tree is measured by r 2 , dened as theproportion of the initial variance accounted for by the model (cor-relation coefcient of the model represented by the model tree):

r 2 =F 20 F

2

F 20(2)

where F 2 is the residual variance, i.e., the sum of squares of thedifferences between computed and observed output, and F 20 is thesum of squares of the differences of the observed output from itsmeanvalue. The correlation coefcient is computedfor the trainingdataset and forthecross-validationdataset aswell.If thereis a com-plete match between the model values and the actual measuredvalues, then the correlation coefcient is equal to one.

Step 4 . In this last step, the over-elaborated model tree struc-ture is simplied, pruned from the bottom up, and smoothed,thereby improving its ability to classify new datasets (i.e., cross-

validation data). The model tree is simplied mainly by removingvariablesthat contribute little to themodel; in somecases, thealgo-rithm removes all variables,leavingonlya constant. The smoothingprocess is performed to compensate for discontinuities that willinevitably occur between adjacent linear models at the leaves of the pruned tree, particularly for some models constructed from asmaller number of training examples. In smoothing, the adjacentlinear equationsare updatedsuchthat thepredicted outputs for theneighboring inputvectors correspondingto the differentequationsbecome closer in value.

3. Results

3.1. Characteristics of the biofouling layer

3.1.1. Bacterial communityThe composition of the bacterial community developed on the

fouling layer of NF membranesfed with tertiary efuents wasstud-ied in 25 independent experiments by PCRDGGE. A total of 26bands were successfully excised from the DGGE gels, re-amplied,cloned and sequenced. The sequences were compared to pub-lished 16SrRNA sequencesanda phylogenetictree wasconstructed(Fig. 1).

The microbial populations identied by sequence homologycomplemented our previous report comparing synthetic andreal wastewater efuents [9] . Bacteria phylogenetically associ-ated with Saprospiraceae (10245, 10632), Flavobacterium (6522,10855, 10631), Pseudomonas (4315, 5218, 5519), Legionella (3012),

Ralstonia (3713, 10246), Hydrogenophaga (3514, 9418), Bacillus(4716,6220), Nitrospira (106291), Delftia (6421), Sphingomonas (42,83, 10249, 111), Novosphingobium (10226), Dyella (84), Acidovo-rax (4917), Limnobacter (9418) and Phaeospirillum (10148) weredetected (see dendogram for clone numbers and accession num-bers, Fig. 1).

Most species identied by sequence analysis were Gram-negative (24 of 26 sequences); the only two Gram-positive speciesbelonged to the Firmicutes. Proteobacteria were found to be theprevalent group in all cases (18 of 26 sequences), followed by theBacteroidetes (5 sequences). Among the Proteobacteria, specieswere divided more or less equally between the subdivision(7 sequences), the subdivision (6 sequences) and the subdi-vision (5 sequences). Three dominant Proteobacteria subgroups,

related to Pseudomonas (3 sequences), Ralstonia (2 sequences) and


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Fig. 1. Phylogenetic tree of sequences obtained from the biofouling layer on NF membranes treating tertiary efuents under different environmental conditions, based on530-bp 16S rRNA gene sequences. The topology of the dendogram was generated using the neighbor-joining method included in the ARB phylogenetic package. Sequenceswere obtained from bands excised from PCR-DGGE gels. Bar indicates an estimated 10% sequence divergence.

Sphingomonas (4 sequences), were found most often. No speciceffects of the ltration conditions studied, i.e., duration, temper-ature and pressure, on bacterial community composition (DGGEbanding patterns) could be strictly identied. The microbial popu-lations identied in the recirculation uid were not always foundin the biolm, and not all of the predominant populations on themembranes could be found in the recirculation uid at the time of sampling. The only exception was the Pseudomonas -afliated bac-terial species represented by clone number 4315, which prevailedin both the recirculation uid and the membranes.

3.1.2. Enumeration of live bacteriaFig.2 shows a typical time course of counts of live bacteria found

onthe membranesurfaceduring the rst 8 days ofoperation atlow-

pressure (5 bar), as determined by swabbingthe membranesurfaceduring a single run and culturing on R2A medium. During the rst24 h, a two orders of magnitude increase in bacterial counts wasnoted (from 1.0 10 3 CFU/cm 2 at3hto1.6 10 5 CFU/cm 2 at 24h).Over the next 6 days, HPC increased to 1.8 10 6 CFU/cm 2 (48h),1.2 10 7 CFU/cm 2 (4 days) and 2.3 10 7 CFU/cm 2 (7 days). As canbe seen from Fig.2 , the increasing bacterialcounts in the biofoulinglayer with time corresponded well to the decrease in permeability.

Bacteria were enumerated on the membrane surface atthe end of a series of multiple runs (membrane autopsy).For tertiary efuents at high-pressure (25bar), bacterial countswere in the range of 4.1 10 5 CFU/cm 2 to 9.9 10 7 CFU/cm 2 ,average 2.9 10 7 CFU/cm 2 . At low-pressure (5 bar), the aver-age was 8.0 10 6 CFU/cm 2 , ranging from 3.0 10 5 CFU/cm 2 to


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Fig.2. Typical timecourseof liveculturable bacteria found onthe membranesurfaceat 5 barfeedwater pressure.Samples were taken by swabbing themembrane at theindicated times. Counts were performed on R2A plates. Note that the left ordinateis logarithmic. Runs were performed in semi-batch mode with full recirculation of bothpermeate andconcentrateinto thefeedtank, thecontentof whichwas reneweddaily (TOC was maintained at 8.6 0.6 mg/L throughout the entire period).

2.4 10 7 CFU/cm 2 . A summary of the HPC on R2A of bacteriaextracted from different membranes treating tertiary efuent asa function of the corresponding TOC is presented in Fig. 3. HPCin the recirculation uid was in the range of 1.6 10 5 CFU/mL to 9.0 10 6 CFU/mL (average 1.62 10 6 CFU/mL) at low-pressure(5 bar), and in the range of 1.0 10 5 CFU/mL to 2.4 10 7 CFU/mL (average 5.7 10 6 CFU/mL) at high-pressure (25 bar). Slightlyhigher bacterial counts appeared in the high-pressure tests, proba-blydue to enhanced accumulationof organic matter anddrag forcetowards the membrane surface. The development of the biofoul-ing layer to a convergent countable magnitude of 10 7 CFU/cm 2

regardless of pressure,time or temperature applied, appeared to bethe result of a steady nutrient supply to the biolm and the shearforces developed on the membrane surface.

3.2. Contribution of biolm development to membrane hydraulic

properties

A typical effect of biofouling on membrane performance can beseen inthe uxprolesof membranesat either5 bar (low-pressure)or 25 bar (high-pressure) as a function of time ( Fig. 4) or perme-ation volume ( Fig. 5). As depicted in Fig. 4, a moderate ux declinewas observed at 5 bar, reaching 16% after 24 h, 23% after 48 h, 32%after 72h and 42% after 96 h. At 25 bar, the ux decline was verypronounced with an 80% decrease after 24 h in most of the experi-ments. Thereafter, the ux decreased at a lower rate (82% decrease

Fig. 3. Summary of heterotrophic bacterial enumeration on the membrane surfaceas a function of TOC concentration in the feedwater. 5 bar and 25 bar denote theinletpressureconditions. Platecounts wereperformed on R2A of bacteria extracted

from membranes after different runs.

Fig. 4. Summary of relative permeate ux as a function of time for experimentsperformed at 5 bar and 25 bar.

at 48 h, 85% at 72 h and 88% at 96 h). A similar ux decline prolewas obtained from the plot of permeate ux vs. permeation vol-ume ( Fig. 5). Once again, a moderate ux decline was observed atlow-pressure, reaching an asymptotic value of 35% ux decreaseat approximately 2030L of permeate. At high-pressure, the uxdecline was very sharp, reaching a minimum value corresponding

to 20% of the original ux at less than 20 L permeate volume. Simi-lar temporal behavior as a function of operating pressure has beenreported in RO membranes [10,29] .

Membrane surface resistance due to fouling can be describedby a series of resistance expressions [30] , all of which contribute topermeate ux decline. The permeate ux can be described by:

J =P

Rt(3)

and

Rt = RS + Rm (4)

RS = RSo + RSf + Rbf (5)

RS=

RSo+

RSf (6)where Rt is the total membrane resistance, RS is the membranesurface resistance (hydraulic resistance), RSo is CP resistance, RSf is the surface fouling resistance (clogging, adsorption, resistancedue to gel formation), Rbf is the biofouling resistance, Rm is themembrane resistance, is the osmotic pressure difference, J isthe permeate ux, P is the transmembrane pressure (TMP), and

is the dynamic viscosity of the tertiary efuents.calculated during the experiments was of low magni-

tude (


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expression then becomes:

J =P

(Rm + RS)(7)

The major contribution to the ux decline observed at high-pressure within the rst 24h can be attributed to RS , i.e., theresistance developed on the membrane surface due to the CP, gelformation and deposition (Eq. (6) ). These processes are very weakat low-pressure [31] and their contribution to membrane foulingseems to be less signicant. This leads to a few assumptions: (a)RS contributes less to ux decline at low-pressure and (b) at low-pressure, the majorcontributionto membranesurface resistance isRbf .Basedonthesendings, Rbf canbe expressedfrom low-pressureexperiments ( Fig. 5).

RS = RSo + RSf is very low and can be neglected ( 0), RS Rbf and replacing in Eq. (7) :

Rbf =P J

Rm (8)

Rbf can be found from low-pressure test experiments.Based on experimental data at 5 bar, the expression of the per-

meate ux decline can be described by an equation of exponentialform: J J o

= a eb Vc (9)

a =0.88; b = 0.0104. J o is the initial permeate ux with tertiary wastewater and Vc

[L] is the cumulative permeate volume. The expression for Rbf thenbecomes:

Rbf =P

J o a eb Vc Rm (10)

The membrane resistance, Rm , was derived from tests withdistilled water. The resultant experimental values of Rm were4.08 10 13 m 1 to 1.15 10 14 m 1 (Rm = 7.69 2.7 10 13 m 1 ).Similar results have been foundin studies with NF membranes [11] .

Rm canbe calculated fromexperiments withdistilled water foreachmembrane and substituted into Eq. (10) . J o is measured in the rsthour (at 20 min) of the test with wastewater.

The results of this study suggest that the biofouling layer devel-ops to a relatively convergent magnitude under a broad range of conditions ( 1.0 10 7 CFU/cm 2 at both low and high-pressures).These ndings lead to the assumption that biofouling formationand its development (biolm on membrane) are similar at low andhigh-pressures. Therefore, biofouling resistance can be calculatedbased on experimental data from the low-pressure test.

Data at 25 bar yielded the following empirical equation for per-meate ux: J J o

= k (m e n Vc ) J = J o k (m e n Vc ) (11)

k = 0.82; m = 0.22; n = 0.175.Substituting Eq. (11) into Eq. (3) , Rt becomes:

Rt =P

J o k (m e n Vc )(12)

Substituting Eqs. (5) and (6) into Eq. (4) and then into Eqs.(10) and (12) , assuming that the same resistance due to biofoulingderived at low-pressure occurs at high-pressure, RS becomes:

RS = Rt Rm Rbf =P

J o1

k (m e n Vc )

1a eb Vc

(13)

Substitution of the constants into Eq. (13) gives:

RS =P

J o1

0.18 + 0 .82e 0 .175 Vc

10.88e 0 .0104 Vc

(14)

Fig. 6. Estimation of biofouling layer resistance ( Rbf ) on a NF membrane operatingat 25 bar. The difference between the lines ( Rt Rm ) and RS represents the value of Rbf . Rt and RS were calculated according to Eqs. (12) and (13) , respectively. Rm was7.69 2.7 10 13 m 1 .

A comparison of ( Rt Rm ) and RS is presented in Fig. 6; the dif-ference between ( Rt Rm ) and RS is Rbf . Rbf begins to dominatethe membrane surface resistance with time, due to biolm devel-opment, after 1520L of permeate volume has passed throughthe membrane. At 25bar, the major initial contribution to surfaceresistance is probably CP resistance and surface fouling resistance(clogging, adsorption, resistance due to gel formation), with bio-fouling contributing less. After 20 L permeation, the biolm beginsits rapid development ( Fig.6 ) andbiofouling startsto dominate themembrane surface resistance.

3.3. Implementation of the model trees technique to forecast uxreduction rate through the membranes due to biofouling

The dataset used for this application utilizes physical, chem-ical and biological measurements obtained from experiments onbiofouling development on wastewater treatment membranes.

The input attributes include the following parameters:

Discrete parameters:

Temperature ( C): 20, 25, 34.Pressure (bar): high, 25; low, 5.

Continuous parameters:TOC (mg/L).EC feed (mS/cm) (represented by Cb).Chemical parameters (mg/L): Na, K, NH 4 , Ca, Mg, PO4 , SO4 , andB.

The output attribute includes the following parameter:Normalized ux reduction rate through the membrane:

1 J t = 0 24(h)

( J t = 0 J t = 24 h ) normalizationfactor (15)

where J istheuxthroughthemembraneinL/m 2 h; J t =0 istheuxat

the startof the experiments ( t = 0 h)this is the highest uxvalue asbiofoulingdevelopment has notyet begun on the membrane; J t =24his the ux after 24h of the experiment ( t = 24 h)this is the timepoint at which the value of the ux becomes relatively stable (themajor ux reduction as a result of fouling has already occurred);the normalization factoris a constant used to keep the outputvaluewithin the same range as the input values. This factor assists thedata-driven technique in producing more robust solutions (e.g., inthis application the normalization factor= 10 000).

The number of cases obtained from the experiments was about30;thislownumber isobviously notsufcientto implementa data-driven technique. Therefore, a synthetic set of cases was added tothe real ones. Each real case was used to produce 10 syntheticcasesby adding noise to the real input and output measurements. The

noise was added by Monte Carlo simulations which produced syn-


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Fig. 7. Set of rules produced for predicting the normalized ux reduction rate in24 h through the membranes.

thetic cases similar to the real ones. At the end of this process, thetotal numberof cases wasapproximately 350, a suitable dataset fordata-driven modeling using the model trees technique.

The model trees technique was implemented using 67% of thedata for training and constructing the model tree and 33% of thedata for cross-validation. The set of rules produced for predictingthe normalized ux reduction rate in 24h through the membranesis presented in Fig. 7: the most important input parameters withregards to ux reduction rate were pressure, TOC, pH and EC. Thecorrelation coefcient for the prediction of the cross-validationdataset was relatively high (e.g., 93%), showing that the proposeddata-driven methodology is capable of modeling the biofoulingphenomenon by simulating a surrogate parameter such as ux

reduction rate. Fig. 8 describes the accuracy of the predictionsthrough a scatter plot that graphs the real target values of thecross-validation cases against the values predicted by the model.

A thorough examination of the set of rules presented in Fig. 7shows that a high concentration of inorganic ions (as EC), rep-resented by high levels of conductivity (Cb> 1505mS/cm, rule 9)

Fig. 8. Scatter plot of thecorrelation between real andmodeled valuesin thecross-

validation.

at high-pressure is one of the more inuential factors causing ahigh rate of ux reduction. Rule 6 suggests that at high-pressure,TOC > 10 mg/L and high levels of conductivity, there is a signicantreductionin permeate uxrate.In general, theset of acceptedrulesfrom the model tree shows that a substantialincrease in uxreduc-tion rate is caused by higher levels of TOC (over 10mg/L), highlevels of conductivity (over 1505 mg/L) and high-pressures.Similarresults were observed in the evaluation of permeability reductionrates, i.e., a high rate of permeability reduction caused by higherlevels of TOC (over 9.38 mg/L), high levels of conductivity (over1564mg/L), and high-pressures.

4. Discussion

The development of biofouling on NF membranes treating ter-tiary wastewater was studied at low- and high-pressures, andvarious feedwatercompositions, temperatures andlengthsof oper-ation. Proteobacteria, followed by the Bacteroidetes, were theprevalent groups found in all fouled membranes, with speciesbelonging to Pseudomonas , Ralstonia and Sphingomonas most oftenfound. In agreement with other studies [1316,3237] , the domi-nantspeciesfoundin ourworkwere Gram-negative bacteria,whichdisplay wide metabolic and physiological diversity, broad surfacecolonization ability and adaptability to the environment, regard-less of separation technology (NF or RO) or source of the appliedfeedwater. It should be noted that the specic role of each type of bacteria in the physiology of the biolm, as well as in membranebiofouling, remains to be elucidated.

TheHPCs in the foulant layer reported here arein general agree-ment with previous studies [9,17,38,39] . Interestingly, slightlyhigher average counts were detected at high compared to low-pressure. Moreover, HPC increased by approximately two ordersof magnitude with the increase of initial organic carbon levelin the feedwater in the range of 525mgTOC/L. This is inagreement with the accepted notion that feedwater quality is adecisive factor in biofouling development [3,40] . Fonseca et al.

[11] f ound that regardless of the surface-averaged polysaccharidedensities observed, culturable cell densities (5 10 7 CFU/cm 2 to5 10 8 CFU/cm 2 ) recovered from nal membrane autopsies werestatistically similar ( P < 0.05). Their reported surface cell densi-ties, calculated from cultured samples of homogenized membranebiolms, were also within the ranges reported by us and otherresearchers [9,17,32,38,39] .

An empirical approach to forecasting the contribution of bio-fouling resistance to the total membrane resistance to efuentsin the entire range of studied pressures (525 bar) was derivedbased on the resistance in series theory. This approach showedthat biofouling becomes the dominant factor after approximately20 L of permeate volume has passed through the membrane. Athigh-pressure, this pattern was preceded by a drastic decrease in

permeate ux (>80%) within the rst 24 h of operation, suggestingthat surface fouling resistance is the dominant effect at high-pressure. Experimental dataat low-pressure (5 bar) in our previouspublications [9,17] showed a detectable settlingof microorganismson the membrane surface after 8 h, along with the initiation of biolm layer development, and bacterial growth became signi-cant at 4872h. Thus, whereas at low-pressure biofouling appearsto be a major contributor to the fouling layer from the start,its contribution to the initial increase in total membrane resis-tance at high-pressure appears to be minor. The main aim of this empirical approach was to evaluate the direct contribution of each resistance factor to the total membrane resistance. In addi-tion to previous empirical methods published elsewhere [8,11,29] ,our approach allows specic estimation of membrane biofouling

resistance among all other series of resistances. However, further


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research with different membrane congurations, environmentalconditions and efuent types is needed to improve the accuracy of this empirical forecasting approach.

A data-driven technique for forecasting the reduction in per-meate ux through the membranes was also developed anddemonstrated. The values predicted by the model were in goodagreement with experimental measurements. Pressure, TOC, pHand conductivity of the tertiary wastewater treated by the mem-brane were the most inuential parameters. The reduction inpermeate ux rates was related to the development of a foulinglayer on themembrane. These results arenovel in thearea of modeltree algorithms and ANNs as they apply to forecasting the devel-opment of a biofouling layer on membranes. Previous work hasnot dealt with estimations of biofouling [22,24,26] . Nevertheless,for future applications, the number of real measurements needsto be increased and the number of synthetic ones reduced: thelarger the dataset, the higher the likelihood of obtaining accu-rate predictions. In particular, increasing the number of traininginstances for the proposed model is expected to improve its pre-diction accuracy. Research challenges for extending this study liein more closely incorporating the physical, chemical and biologicalprocesses involved in membrane separation systems. This can beaccomplished, for example, by using a physically, chemically andbiologically based model in conjunction witha data-driven model-ing technique. An evolutionary algorithm could tie both together,but the computational effort needed to run such a set-up is likelyto be very high.

Biolm development in dense membrane systems is a criticalmode of fouling that is complex to resolve and model, and mayhave a synergistic effect with other forms of fouling that neg-atively affect membrane performance. Moreover, the direct linkbetween bacterial communities and membrane performance, aswell as the implication of microbial diversity on long-term appli-cation of membranes, is still unclear and more insight into thebiofouling phenomenon is needed for its effective control. Follow-ing van der Brugen et al. [5] , not much work has been done onmodeling fouling in nanoltration and the inuence of membrane

fouling onux is one ofthe main aspects that remainto be modeled.The utilization of a data-driven modeling scheme shows promis-ing results. Enhancement of this approach can be accomplished bytailoring a physical model in the overall algorithm framework toselect the most dominant input parameters and utilizingstatisticalor fuzzy-rule-based systems to enlarge the training dataset.

Acknowledgements

This work was funded by the Chief Scientist of the Ministry of Agriculture, the Grand Water Research Institute and the Fund forPromotion of Research at the Technion.

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