Gas treatment in trickle-bed biofilters: Biomass, how much is enough?

Gas Treatment in Trickle-Bed Biofilters:Biomass, How Much Is Enough?

Cristina Alonso,1 Makram T. Suidan,1 George A. Sorial,1 F. Lee Smith,1

Pratim Biswas,1 Paul J. Smith,2 Richard C. Brenner3

1Department of Civil and Environmental Engineering, University ofCincinnati, Cincinnati, Ohio 45221-0071; telephone: (513) 556-3695;fax: (513) 556-2599; e-mail: [email protected] Consultants, 1117 Perimeter Center West, Suite N415,Atlanta, Georgia 303383U.S. EPA, National Risk Management Research Laboratory,Cincinnati, Ohio 45268

Received 23 July 1996; accepted 15 November 1996

Abstract: The objective of this article is to define and biofilters are living pollution control systems and arevalidate a mathematical model that describes the physical subject to dynamic changes. This characteristic has hin-and biological processes occurring in a trickle-bed air dered the widespread application of biofiltration in thebiofilter for waste gas treatment. This model considers a

United States, where regulatory requirements typicallytwo-phase system, quasi-steady-state processes, uni-stipulate continuous compliance with emissions limita-form bacterial population, and one limiting substrate. The

variation of the specific surface area with bacterial growth tions or destruction efficiency. To help develop bio-is included in the model, and its effect on the biofilter filtration as a viable technology capable of meetingperformance is analyzed. This analysis leads to the con- regulatory constraints, researchers have focused on un-clusion that excessive accumulation of biomass in the

derstanding the fundamental physical, chemical, andreactor has a negative effect on contaminant removalefficiency. To solve this problem, excess biomass is re- microbiological processes that control reactor perfor-moved via full media fluidization and backwashing of the mance.biofilter. The backwashing technique is also incorporated Recently, biofilter models have been introduced thatin the model as a process variable. Experimental data

account for detailed representations of biofilm biodeg-from the biodegradation of toluene in a pilot system withradation mechanisms. Shareefdeen et al. (1993) pro-four packed-bed reactors are used to validate the model.

Once the model is calibrated with the estimation of the posed a model for a single-component waste stream thatunknown parameters of the system, it is used to simulate accounted for oxygen limitations in the biofilm. Smiththe biofilter performance for different operating condi-

(Smith, P. J. 1993. A fundamental approach to modelingtions. Model predictions are found to be in agreementthe treatment of VOC laden exhaust gases in biofilters,with experimental data. 1997 John Wiley & Sons, Inc.

Biotechnol Bioeng 54: 583–594, 1997. M.S. Thesis, University of Cincinnati) developed a two-Keywords: trickle-bed biofilter; mathematical model; vol- phase trickle-bed biofilter model that incorporated de-atile organic compound (VOC); waste gas treatment; cay and microbial shearing in determining the spatialbiofiltration

distribution of biomass. Deshusses et al. (1995a, b) de-veloped a dynamic model that calculates the variationof the contaminant concentration over time assumingINTRODUCTIONconstant biofilm thickness and includes multiple-sub-

Biofiltration, as a control technology for volatile organic strate degradation.compound (VOC) laden exhaust gases, continues to In this article, the conventional theoretical model ofreceive attention in the environmental field. A biofilter a synthetic media trickle-bed biofilter is enhanced withconsists of a packed bed of organic or synthetic material new features. The model describes the degradation ofon which microbial films are supported. Biodegradable one limiting substrate (VOC pollutant) in a homoge-pollutants present in waste gas pass through the biofilter, neous biomass by one type of microbial species. Thediffuse through the attached microbial film, and are dynamics of the system are characterized by a quasi-consumed. Since pollutant degradation occurs at normal steady-state term that accounts for biofilm growth. Astemperatures and pressures, biofiltration represents a a response to experimental observations, a new ap-potentially energy efficient technology when compared proach in the calculation of the specific surface area asto traditional physical and chemical control processes a function of biomass growth is presented. The experi-(e.g., incineration and carbon adsorption). However, mental data from the pilot system show an initial in-

crease and a subsequent drop in contaminant removalefficiency with time while biomass is accumulating inCorrespondence to: M.T. Suidan

1997 John Wiley & Sons, Inc. CCC 0006-3592/97/060583-12

Table I. Biofilter operating conditions.the system. The initial improvement in performance iseasily explained since an increase in the mass of microor- Empty-bedganisms results in increased contaminant removal ca- Toluene initial residence Loading

Biofilter conc. (ppmv) time (min) (kg COD/m3-day)pacity. To explain the subsequent drop in removal effi-ciency, the key variable is the reduction in the specific

A 500 1.33 6.2surface area of the biofilm that accompanies biomass B 250 0.67 6.2accumulation. In this model, the biomass is assumed to C 250 1 4.1

D 500 2 4.1grow in the void fraction between the packing solidssince the solids do not move to accommodate the newmicroorganisms. Therefore, available area for the con-taminant to diffuse into the biofilm and removal efficient), the biofilm biomass density, the biofilm/waterciency decreases. To remove the excess biomass that diffusivity ratio, and the initial biofilm thickness werecauses the contaminant removal efficiency to drop, the not known a priori and could not be measured. Hence,reactor is backwashed regularly with full media fluidiza- they were estimated together with the validation oftion. The backwashing technique parameters, duration the model.and frequency, also have a significant influence on theeffectiveness of the reactor and, therefore, are consid-

Performance Characteristics ofered as variables in the model. Trickle-Bed Biofilters

Extensive description of the operating characteristics ofMATERIALS AND METHODSthe biofilters can be found in Sorial et al. (1995b). Theinitial phase of this research focused on the evaluationThe biofilter system used in this study consists of four

stainless steel reactors, each packed with pelletized dia- of three different types of biological attachment mediafor biofiltration: a peat mixture, a synthetic channelizedtomaceous earth biological support media (6 mm R-635

Celite) to a depth of 112 cm and internal diameter of medium, and a randomly packed pelletized medium.After the pelletized medium was shown to be the most14.6 cm. The pelletized medium was selected after initial

screening revealed it to be superior to two other candi- effective packing material, its performance characteris-tics were further evaluated. These investigations sug-date media (Sorial et al., 1995b). The average size of

the packing solids was 6 mm in diameter and 4 mm in gested that an increase in biofilter operating tempera-ture permitted a higher practical VOC loading and thatlength. They were represented in the model by equiva-

lent spheres of 6 mm diameter sized to have the same toluene removal efficiency decreases with a buildup inhead loss due to excessive accumulation of biomass involume as the packing solids. The sphericity factor, cal-

culated as the ratio of the surface of the equivalent the reactor. This experiment was conducted with tolu-ene loadings of 0.46, 0.92, 1.84, and 2.27 kg COD/m3sphere to the surface of the pellet, was 0.857. The poros-

ity of the clean packed bed was 0.34. The organic feed day, influent toluene concentrations of 50 ppmv, 100ppmv, and 250 ppmv, and the two different EBRTs ofto the biofilter consisted of toluene volatilized in the

influent air stream. Prior to the addition of toluene, the 1 and 2 min.The resulting contaminant removal efficiency andfeed air was purified and contained only oxygen and

nitrogen. The biofilters were fed 20 L of an aqueous pressure drop in the biofilter are shown in Figure 1. Thebiofilter loading was initially 0.46 kg COD/m3 day (50solution of nutrients per day. Nitrate was the sole nitro-

gen source. The temperature was maintained at 328C ppmv influent toluene concentration at 2 min EBRT),and the removal efficiency of the biofilter stabilized atthrough the biofilter length, and the outlet pressure was

very close to atmospheric pressure. The biofilters were more than 99.96%. On day 45, the EBRT was reducedto 1 min, doubling the toluene loading to 0.92 kg COD/operated in a co-current gas/liquid downward-flow

mode. A more detailed description of the experimental m3 day. The resulting removal efficiency dropped to andstabilized at 92%. Simultaneously with the decrease inapparatus can be found in previous publications (Smith

et al., 1994; Sorial et al., 1995a, b). the EBRT, the pressure drop began to increase.On day 82, the temperature was increased from 118CThe empty-bed residence time (EBRT) and the in-

fluent concentration for each biofilter are summarized in to 15.68C and the removal efficiency increased to over99%. The pressure drop across the biofilter did not de-Table I. Two different loadings [4.1 and 6.2 kg chemical

oxygen demand (COD)/m3 day], two different initial crease, however. On day 97, the inlet concentration wasincreased to 100 ppmv, resulting in a loading rate oftoluene concentrations (250 and 500 ppmv), and four

different values of the EBRT (2, 1, 1.33, and 0.67 min) 1.84 kg COD/m3 day. The removal efficiency droppedto 86%, and the pressure drop remained stable at aboutwere used. The experimental results obtained with this

system are described by Smith et al. (1995). The value 15 cm of water. The temperature was further increasedto try to improve performance. Nevertheless, after aof the kinetic parameters (Monod constant, yield coef-

ficient, maximum specific growth rate, and decay coeffi- brief period of increased removal efficiency, it dropped

584 BIOTECHNOLOGY AND BIOENGINEERING, VOL. 54, NO. 6 JUNE 20, 1997

Figure 1. Performance of the trickle-bed biofilter with backwashing.

to about 78%. During this period, the pressure drop After refinement of the biomass control strategy, ad-ditional experiments were conducted to evaluate theincreased rapidly. From this study, it was concluded that

high pressure drop due to excessive accumulation of effects of the COD volumetric loading rate and theduration and frequency of backwashing on the perfor-biomass in the system resulted in decreased removal of

toluene, and, therefore, a method for the control of mance of the trickle-bed biofilter (Smith et al., 1995;Sorial et al., 1995a). The four biofilters described in thebiomass accumulation within the system is essential in

sustaining high levels of biofilter performance. The se- previous section were operated using three differentbackwashing strategies that involved varying the fre-lected biomass control strategy employed backwashing

of the packed bed with full media fluidization, i.e., in situ quency and the duration of the full media fluidization.Table II summarizes these values for the three back-upflow washing with water. The second half of Figure 1

summarizes the results corresponding to this period. washing strategies. Each strategy is identified with anumber that will be used in the rest of the discussion.The biofilter was operated with an influent toluene con-

centration of 250 ppmv, an EBRT of 2 min, and a COD Characteristic performance of biofilter A during theperiod between backwashings is presented in Figure 2.volumetric loading rate of 2.27 kg/m3 day. After several

backwashing strategies that did not involve full media Biofilter A was operated with a COD volumetric loadingrate of 6.2 kg/m3 day, an influent toluene concentrationfluidization were evaluated, a procedure involving full

media fluidization was selected on day 73. The water of 500 ppmv, an EBRT of 1.33 min, and backwashingstrategy BW#1. Pollutant removal efficiency in the bio-flow required to achieve 40% bed expansion was 3.2

m3/h, involving a total water volume of 200 L. Of this filter is presented as a function of time following back-washing. The initial time corresponds to the momentvolume, 100 L was recirculated through the biofilter for

1 h, followed by rapid flushing of the fluidization water when the reactor was started after backwashing. Thefigure shows an initial increase and a subsequent dropwith 100 L of clean water.

This method of backwash with full media fluidization in contaminant removal efficiency while biomass is accu-mulating in the system. Removal efficiency would typi-succeeded in controlling biomass accumulation in the

reactor with no measurable pressure drop betweenbackwashings and resulted in a consistent toluene re- Table II. Backwashing strategies.moval efficiency of 99%. The stable performance of the

Full media fluidizationbiofilter with backwashing using full media fluidizationis illustrated in Figure 1. On day 132, the recycle and Backwashing strategy No. Duration Frequencyflush water volumes were reduced to 70 L and 50 L,

BW#1 1 h every 84 hrespectively, without any apparent effect on system per-BW#2 2 h every 84 hformance. Regular backwashing with full media fluid-BW#3 1 h every 48 h

ization was practiced in all the remaining experiments.

ALONSO ET AL.: EFFECT OF BIOMASS ACCUMULATION ON BIOFILTER PERFORMANCE 585

and wall and end effects are neglected. The model con-siders two phases: a gaseous phase and the biofilm. Aliquid layer, present due to a small and intermittent flowof a solution of nutrients, was assumed to have minimalmass transfer resistance and is disregarded. The temper-ature in the biofilter and the physical properties of thegas and VOCs are assumed to be constant. The variablesof interest are the VOC concentration profiles in thebiofilm and gas phase and the thickness of the biofilmalong the reactor, i.e., the biomass profile.

The model is solved assuming quasi-steady-state con-ditions because the characteristic time of VOC transportand utilization is much smaller than the one for bacterialgrowth. This means that the concentration of contami-nant in the biofilm reaches steady state before thegrowth of the biofilm becomes significant. For each timestep, the concentration profile along the biofilter is cal-

Figure 2. Characteristic biofilter performance with time.culated assuming constant biofilm thickness. The calcu-lated concentration profile is then used to compute thechange in biofilm thickness during this time step. Thecally decrease after the first day of continuous operation.

This behavior is characteristic of the four biofilters, be- value of the time step is chosen so that the convergenceof the algorithm is assured. The solution obtained withing more pronounced in biofilters A and B with the

higher COD volumetric loading rates. Overall, back- this scheme is found to be identical to the exact solutionof the system of partial differential equations describingwashing strategy BW#3 produced the best performance

while backwashing strategy BW#1 resulted in the poor- the time-varying performance of the treatment system.In the formulation of mass balance equations aroundest performance (Smith et al., 1995). For biofilter A,

the average toluene removal efficiency during BW#1 the biofilter, the following assumptions are made: Thebiofilm is a stagnant phase with molecular diffusion aswas 91%. The efficiency increased to 95% during BW#2

and 97% during BW#3. the only transport mechanism; axial diffusion is negligi-ble; microbial growth is described by Monod kinetics;A mathematical model was developed to describe the

operation of the four pilot-scale trickle-bed biofilters. VOCs present are the only growth-limiting substances;and all kinetic parameters and the biofilm bacterial den-The model was calibrated against experimental data

collected in those biofilters for a variety of operating sity are constant along the biofilter. The biofilter geome-try is presented in Figure 3. The mass balance equationscenarios. A first set of experimental data was used to

estimate the unknown parameters of the biofilter using for the biofilm phase, expressed in spherical coordinatesto account for the curvature of the medium particles,a nonlinear parameter estimation technique. With these

parameter values, simulations were carried out for dif- and the corresponding boundary conditions areferent operating conditions and the model predictionswere compared with a second set of experimental data.The intent of the model is to facilitate the study of theeffects and interdependence of key system variablessuch as initial substrate concentration, residence time,pollutant properties, and system geometry on biofilterperformance.

THEORETICAL ASPECTS

This theoretical model describes a packed trickle-bedbiofilter employing uniformly shaped solids. Due to therandom packing, the flow path for the waste gas is tortu-ous and the gas is assumed to be well mixed across thebiofilter cross section. Consequently, the concentrationof contaminant in the bulk gas is considered to be uni-form at any given axial position. The packing mediumis modeled as a bed of equivalent spheres sized to havethe same volume as the actual pellets. All processes areassumed to be uniform across the biofilter cross section, Figure 3. Biofilm model representation.


Df Fd 2Cf

dr2 12r

dCf

dr G5mm Xf

Y F Cf

Ks 1 CfG dLf

dtXf 5 Srd Dw

dCf

dr Ur5R1Lg

D Y 2 Lf Xf b(4)

at t 5 0 Lf (z, t) 5 Lf 0(z)at r 5 R

dCf

dr5 0 (1)

where the initial time is considered to coincide with theat r 5 R 1 Lf Cg 5 HCf restart of the biofilter after backwashing and Lf 0(z) is

the initial biofilm thickness than can vary along thewhere Cf (M/L3) is the VOC concentration in the bio- reactor depth.film; Df (L2/t) is the contaminant diffusivity in the biofilm A variable of interest is the concentration of biomassand is assumed to be a fraction, rd , of the contaminant in the reactor, Xr (M/L3), since this concentration can bediffusivity in water, Dw; mm(t 21) is the maximum bacte- measured and it is an indication of the biofilm thickness.rial growth rate; Y (M biomass/M VOC) is the yield Here, Xr, which represents the mass of microorganismscoefficient; Ks (M/L3) is the Monod saturation constant; per volume of reactor, varies along the reactor depthand Xf (M/L3) is the film bacterial density. The boundary as contrasted with the density of biomass within theconditions have been formulated assuming that there is biofilm, which is considered constant. Assuming the bedno flux of contaminant into the surface of the packing volume remains constant with biomass growth, the bio-solids and that the concentrations of VOC in the biofilm mass concentration in the reactor can be calculated asand gas phase at the interface are in equilibrium as

Xr 5 Xf (e0 2 ef ) (5)defined by Henry’s law. The use of Henry’s law is justi-fied due to the high water content of the biofilm. where e0 and ef are the clean packed-bed porosity and

Because the gas velocity and the VOC concentration the porosity of the bed with biofilm, respectively.are uniform radially across the bed, plug flow can beassumed for the gas phase. Assuming that there is no

Water/Biofilm Diffusivity Ratiocontaminant degradation in the gas phase and the con-centration of contaminant in the gas phase at the inlet The contaminant molecular diffusivity in the biofilm hasof the biofilter is uniform, the mass balance equation been assumed to be a fraction of its diffusivity in water.at the biofilm–gas interface and the initial condition can This fraction is called the water/biofilm diffusivity ratiobe expressed as and is defined as

dCg

dz5

2Jaf

u0 (2)rd 5

Df

Dw(6)

at z 5 0 Cg 5 Cg0

Shear/Decay Coefficient Calculationwhere Cg (ppmv) is the VOC concentration in the gasphase, J(M/L2 t) is the flux of VOC into the biofilm, Following the formulation of Rittmann (1982), the com-af (L21) is the biofilm–gas interfacial surface area per bined shear/decay coefficient represents the effects ofunit bed volume, u0(L/t) is the approach velocity of the biomass loss and combines biomass decay and physicalgas, i.e., the gas flow rate divided by the total cross- shearing. The specific decay coefficient, bd, is assumedsectional area of the biofilter, and Cg0 is the initial VOC to be constant, and the specific shear rate, bs, is assumedconcentration in the gas phase. The flux of VOC out of to be a function of the biofilm thickness:the gas phase is equal to its flux into the biofilm surface:

b 5 bs 1 bd (7)

at r 5 R 1 Lf J SPMv

Rg TD5 Jf 5 rd DwdCf

dr(3) The different existing expressions to define the rate of

biofilm detachment suggest that this process is not verywell understood (Peyton and Characklis, 1993). Here,where R (L) is the radius of the sphere equivalent ofthe shear rate is assumed to be proportional to the shearthe packing medium, Lf (L) is the biofilm thickness, Pstress, t(L/t), which is proportional to the interpore gas(atm) is the system pressure, Mv (M/mol) is the molecu-velocity, u(L/t), u 5 u0/ef :lar weight of the VOCs, Rg (atm L3/mol K) is the univer-

sal gas constant, and T (K) is the system temperature.t 5 b Su

efD5 b Su0

e2fD (8)Since the biofilm thickness along the biofilter is not

constant, another equation is needed to characterize itsvariation. The variation in the thickness of the biofilm The proportionality constant, b, is chosen to be thewith time is due to bacterial growth and decay. If b (t21) default shear rate coefficient, b0

s , corresponding to theis the specific combined shear/decay coefficient, this default shear stress, t0, when the bed is clean and no

biofilm is present. Thenequation is


t0 5 b Su0

e20DR b0

s a b Su0

e20D (9)

Eliminating the constants, the final expression for b is

b 5 b0s Se0

efD2

1 bd (10)

Specific Surface Area and Porosity Calculation

The biofilm–gas phase interfacial surface area per unitvolume of clean biofilter, a0 (L21), is given by

a0 53(1 2 e0)

fR(11)

where f is the packing solids sphericity and e0 is theporosity of the clean reactor bed without biofilm. Tocalculate the specific surface area with biofilm growth,

Figure 5. Schematic of the packing solids covered with the biofilm:af (L21), the experimental results shown in Figure 2 andAL and VL are the area and volume of biofilm lost in each contact

discussed in the introduction have to be considered. point as compared with no contact point between solids.Experimental observations showed that the biofilter re-moval capacity decreases usually after 1 day of continu-ous operation when biomass has begun to accumulate in

be formulated in the context of the second case. Thethe biofilter. Monod kinetics and mass transfer modelsvalues of these two parameters will depend on the num-alone cannot explain the behavior observed in Figureber of spheres initially in contact with a given sphere,2. Actually, these models predict an increase in contami-n. This situation is illustrated in Figure 5. The terms ALnant removal if the quantity of biomass increases.and VL are, respectively, the biofilm surface area andTherefore, the solution to this problem depends on reac-the biofilm volume lost with each point of contact. Thentor characteristics, particularly on the changes in bio-the specific surface area isfilm–gas phase surface area available for contaminant

transport into the biofilm.Traditionally, it has been assumed that the packing af 5

4p(R 1 Lf)2 2 n AL

43

p R3

(1 2 e0)

(12)solids will move to fit the growing biomass, i.e., solidparticles that were in contact when the biofilter wasclean will now separate to fit the developing biofilm that

53(1 2 e0)

2R S1 1Lf

RDS(2 2 n)Lf

R1 2Dwill completely cover them. However, in practice, the

solids that were in contact when the biofilter was cleanwill remain in contact and, therefore, the biomass will The bed porosity with biofilm, ef , can be calculated ingrow only in the void space left between the solids. the same way:This assumption implies that the bed volume remainsconstant during system operation.

A schematic representation of these two cases isef 5 1 2

43

p(R 1 Lf)3 2 n VL

43

p R3

(1 2 e0)

(13)shown in Figure 4. The equations for the specific surfacearea and bed porosity in the reactor with biofilm will

5 1 2 (1 2 e0) FS1 1Lf

RD3

2n4 SLf

RD2S2Lf

R1 3DG

With this formulation of the problem, the actual specificsurface area and bed porosity are functions of the num-ber of packing solids in contact with a given one andthe ratio of the biofilm thickness to the radius of theequivalent sphere. The variation of the specific surfacearea with the dimensionless biofilm thickness, Lf /R, fordifferent values of n is plotted in Figure 6. It can beobserved that for values of n , 4, the specific surfaceFigure 4. Representation of two different models for biomass

growth. area increases with increasing biofilm thickness. For val-


z* 5zL

r* 5rR

t* 5 t(b0s 1 bd)

C*g 5Cg

Cg0C*f 5

H Cf

Cg0

L*f 5Lf

RL*f0 5

Lf0

Ra*f 5

af

a0

(15)

b* 5b

b0s 1 bd

J* 5 J S R P Mv Hrd Dw Rg T Cg0

DNumerical Solution

The dimensionless form of Equation (2) is solved usingan Adams–Moulton finite difference scheme. The solu-tion is found by marching axially through the biofilter.At each axial step, the flux, biofilm concentration pro-file, biofilm thickness, and packed-bed characteristicsFigure 6. Variation of the surface area of the biofilter with the

number of contact points. are evaluated. The dimensionless form of Equation (1)is solved with a second-order two-point boundary valueproblem direct method. Two levels of iteration are re-quired in each axial step to handle the nonlinear termin Equation (1) and to calculate the biofilm thicknessues of n $ 4, the specific surface area decreases whengiven by the linearization of the dimensionless form ofthe biofilm grows. In our case, the number of contactingEquation (4).spheres is assumed to be 10 and the specific surface

The thickness of the biofilm has two physical limitsarea decreases very quickly when the biofilm thicknesswhen the bed is clogged, i.e., when the porosity getsincreases. This explains why biomass performance dete-too small or the specific surface area is close to zero,riorates when biomass accumulates beyond a certainwhichever occurs first. Close to clogging, the assumptionthickness. After a certain biofilm thickness has beenof spherical packing solids with a shell of biofilm is notattained, the benefit due to additional biomass is over-valid. These two conditions define the maximum biofilmshadowed by the decreasing specific surface area avail-thickness enforced in each iteration.able for contaminant transport.

The value of n has been obtained from the valueof the clean-bed porosity (Dullien, 1979). For regular RESULTS AND DISCUSSIONpacking, the number of points of contact between a

The validation of the mathematical model was carriedgiven sphere and the adjacent spheres is called the coor-out in two stages: estimation of the model parametersdination number. The most common regular packingand testing of the model predictions. Thus, two differentsystems are cubic, n 5 6, and rhombohedral or compact,sets of data were needed. The first set was collectedn 5 12, with porosities 0.476 and 0.25, respectively. Forfrom an experiment conducted on a different biofilterregular packing, a relation exists between the value ofrun, which will be referred to as model biofilter (biofilterthe clean-bed porosity and the coordination numberM) and the second one from the four biofilters duringthat can be expressed as follows (Dullien, 1979):normal operation. Biofilter M was operated with aninfluent toluene concentration of 250 ppmv and ane 5 1.072 2 0.1193n 1 0.004312n2 (14)EBRT of 1 min.

Although this reactor does not have regular packing, n After 190 days of stable performance, an experimentcan be estimated using the previous equation and the was conducted on biofilter M to collect the data neededvalue of the clean-bed porosity. For a porosity value of for the estimation of the unknown system parameters.0.34, the corresponding value of n is between 9 and 10. Biofilter M was operated continuously for 9 days with-Because it yields better results in the validation of the out any biomass removal. Air scour was used with back-model, n 5 10 is used. washing to leave the biofilter scrupulously clean and

The packed-bed biofilter model is defined by Equa- justify the assumptions that the initial concentration oftions (1)–(7) and (10)–(13). These equations are solved biomass within the reactor is uniform with depth, andin dimensionless form for mathematical simplicity and this biomass is limited to what is sheltered within theto reduce the number of model parameters. The new pores of the particles. The biofilter was then operated

for 9 days, after which the biomass concentration profiledimensionless variables are


along the biofilter was measured, both prior to and after in water and the toluene Henry constant used in themodel are also included in Table III.backwashing. Samples of the media were collected at

five different depths and analyzed for volatile suspended To determine the most critical parameters in the esti-mation process, a sensitivity analysis of the model wassolids (VSS).

The biomass generated during this period can be cal- performed. Simulations were run for each estimatedparameter modifying the nominal value by 10% andculated as the difference between the concentration

after 9 days and the initial concentration or the concen- keeping the other parameters constant. The impact ofthe variation in the parameter value in the model predic-tration after the final backwashing, since the initial states

are assumed not to vary. The toluene removal efficiency tions was measured as the difference between the nomi-nal model predictions for the toluene removal efficiencyalong the biofilter was measured 1 day after the resump-

tion of operation. This variable was obtained from mea- and biofilm thickness along the biofilter (Fig. 7) and theresults obtained when each parameter was modified.surements of the substrate concentration in the bulk gas

phase at four different depths. Observed and model The measurement used as the difference between twofunctions was the numerically calculated area be-predicted values for the biomass produced and toluene

removal efficiency along the biofilter are presented in tween them.The results of this study are presented in the thirdFigure 7. The values are plotted against the biofilter

depth normalized to the total length of the packed me- and fourth columns of Table III, where the calculatedarea is termed as parameter impact. For each parameterdium, L. Here, z is the axial coordinate; therefore, z 5

0 corresponds to the top of the packed medium. The fit the value of this area is given for the two predictedvariables, the toluene removal efficiency and the bio-of the mathematical model solution to the experimental

data is reasonably good. mass distribution with depth. The most influential pa-rameters are the ones with the highest impact values.Arcangeli and Arvin (1992) reported estimated values

for the kinetic parameters that describe the biodegrada- For the predicted toluene removal efficiency these pa-rameters are the yield coefficient, Y, the biomass den-tion of toluene in a fixed film reactor using nitrate as the

nitrogen source. These values were used as the starting sity, Xf , the ratio between VOC diffusivities in biofilmand water, rd , and the maximum growth rate, mm. Thepoint in the nonlinear parameter estimation process.

Eight unknown parameters were estimated: the yield yield coefficient, Y, the biomass density, Xf , and thedecay rate, bd , are the ones that have more impactcoefficient, Y; the biofilm/water diffusivity ratio, rd ; the

maximum growth rate, mm; the Monod constant, Ks; the in the predicted biomass. This analysis indicates whichparameters should be more accurately evaluated.default shear rate, bs0; the decay rate coefficient, bd ; the

biomass density, Xf ; and the initial biofilm thickness, The model was then tested in the prediction of thedynamic performance of the four biofilters previouslyLf 0. The parameter estimation technique used was the

weighted least-squares errors method. The kinetic pa- described. During the periods between backwashings,toluene removal efficiency in each reactor was regularlyrameters that resulted in the best fit of the mathematical

model solution to the experimental data and those pa- determined over 242 days of operation. Data were col-lected during the first 5 h and at the end of the first,rameters reported by Arcangeli and Arvin (1992) are

given in Table III. The values of the toluene diffusivity second, and third day (if applicable) after the restart ofthe reactor for the three different backwash strategies.The averages of observed values at different times andthe model predictions are shown in Figure 8. For back-washing strategy BW#3, the efficiency was measuredonly the first and the second day after the restart becausethe frequency of backwashing was every 48 h. The errorbars in Figure 8 represent the percentage coefficient ofvariation of the sample, i.e., the standard deviation ofthe sample divided by the mean times 100.

Although the efficiency of the reactor was measuredeach day, the analysis of the first 5 h was done only onsome selected days, so the number of observations issmaller. For backwashing strategy BW#2 in biofilter D,only one set of observations was taken, and, therefore,there are no error bars for this plot. The predictions ofthe model are in most cases within the error bars. Asthe biomass removal process was not included in themathematical description of the system, the biomassdensity in the biofilm and the initial biofilm thicknessFigure 7. Performance of biofilter when air scouring was used dur-

ing backwash. were assumed to be variables dependent on the back-


Table III. Biofilter parameters.

Parameter ParameterArcangeli and Arvin impact on impact on

Parameter Estimated value value efficiency biomass

Maximum growth rate, mm 3.0 day21 3.5 day21 0.115 0.788Decay rate coefficient, bd 0.432 day21 0.6 day21 0.014 2.228Default shear rate coefficient, bs0 0.005 day21 0.001 0.106Monod kinetic constant, Ks 0.15 mg VOC/L 0.028 0.161

0.47 mg COD/L 0.6 mg COD/LYield coefficient, Y 0.84 mg biomass/mg VOC 0.167 2.469

0.4 mg COD/mg COD 0.5 mg COD/mg CODRatio between VOC diffusivities in 0.9 0.9 0.119 0.675

biofilm and water, rd

Biomass density, Xf 17000 mg biomass/L0.157 1.421

24140 mg COD/LInitial biofilm thickness, Lf 0 0.0042 cm — 0.014 0.516Number of contacting spheres, n 10Toluene diffusivity in water, Dw 10.8 3 1026 cm2/sToluene Henry constant, H 104.03 ppmv/(mg/L)

Figure 8. Variation of the toluene removal efficiency after backwashing in four biofilters operating in different conditions and using threedifferent backwashing strategies.


Table IV. Density of biomass in the biofilter and initial biofilm thickness.

Biofilter A Biofilter B Biofilter C Biofilter D

Backwashing Xf Lf 0 Xf Lf 0 Xf Lf 0 Xf Lf 0

technique (g/L) (cm) (g/L) (cm) (g/L) (cm) (g/L) (cm)

BW#1 15.2 0.0042 17.5 0.0028 16.5 0.0027 13.0 0.0036BW#2 17.0 0.0039 22.0 0.0025 18.0 0.0023 13.0 0.0031BW#3 17.0 0.0042 23.5 0.0028 25.0 0.0021 15.0 0.0036

washing strategy used. Thus, the values of these parame- number of observations. MSE1 corresponds to the datacollected in the first 5 h, and MSE2 to the data collectedters were estimated for each biofilter and backwashing

strategy pair to best fit the experimental data. in the first, second, and third days. These values areincluded for comparison. The MSE for each biofilterThe estimated values of the biomass density and the

initial biofilm thickness are presented in Table IV. Al- (MSE biofilter in Table V) and for each backwashingstrategy (MSE backwash in Table V) have also beenthough these two parameters were selected to fit the

data, correlations between them and the operational calculated. The value of MSE backwash has been ob-tained grouping the data corresponding to each back-variables of the biofilter can be inferred from Table IV.

There is a relationship between the influent contaminant washing strategy.The biofilters are ranked according to the value ofconcentration and the biomass density and initial biofilm

thickness: Biofilters A and D with 500 ppmv influent the MSE: biofilter D (MSE 5 2.63, EBRT 5 2 min)being the best fit, followed by biofilter A (MSE 5 6.9,concentration have lower values of biomass density and

higher values of initial thickness than biofilters B and EBRT 5 1.33 min), biofilter C (MSE 5 9.87, EBRT 51 min), and biofilter B (MSE 5 17.39, EBRT 5 0.67C with 250 ppmv influent concentration. When the bio-

filters with the same influent toluene concentration are min). When the backwashing strategies are compared,the best fit corresponds to BW#3 (MSE 5 7.08), fol-compared, the biomass density and the initial biofilm

thickness are higher in the biofilters loaded with 6.2 kg lowed by BW#1 (MSE 5 10.67) and BW#2 (MSE 513.87). It is interesting to note that lower EBRT corres-COD/m3-day than in the ones loaded with 4.1 kg COD/

m3-day. Biomass density also appears to be related to ponds with lower goodness of fit. Decreasing EBRTmay raise the importance of other factors neglected inthe backwashing technique: it was found to be lower

for backwashing strategy BW#1 than for BW#3. BW#1 the model, such as oxygen limitations, mass transferresistance of the liquid layer, and concentration gradientis less aggressive than BW#3: 1 h every 84 h for BW#1

compared to 1 h every 48 h for BW#3. Figure 8 illustrates in the bulk gas. In general, removal efficiencies pre-dicted by the model are higher than the measured ones,that the variation of the contaminant removal efficiency

of the biofilter can be reasonably predicted over time. due probably to the aforementioned factors not incorpo-rated in the model.In all cases, the removal efficiency increases and then

drops after the first day of operation. It also reveals that Overall, the model is more accurate in predicting thebiofilter performance after the first 24 h rather thanthe differences in the backwashing strategy employed

has a great influence on biofilter performance. during the initial stages; in 8 cases of 12, MSE2 is lowerthan MSE1. This is because the system is disrupted afterThe value of the mean-square error (MSE) can be

used to measure the accuracy of the model predictions. backwashing and takes time to stabilize. In fact, thepoints where the model is the furthest away from theMSE values are presented in Table V for the 12 different

experimental treatments, a treatment here being a bio- data are after the first 20 and 60 min of operation follow-ing backwashing. Another factor to consider is that thefilter with a backwashing strategy. The MSE is calcu-

lated as the sum of the squared residuals divided by the backwashing process has not been included in the math-

Table V. Mean-square error for the model.

Backwashing MSEtechnique Biofilter A Biofilter B Biofilter C Biofilter D backwash

BW#1 MSE1 8.83 10.11 6.53 19.68 21.53 9.90 3.55 1.51 10.67MSE2 11.03 10.11 28.21 19.68 4.09 9.90 0.55 1.51 10.67

BW#2 MSE1 7.15 5.18 14.02 21.19 32.19 14.82 23.15 10.14 13.46MSE2 3.64 5.18 25.98 21.19 3.25 14.82 0.37 10.14 13.46

BW#3 MSE1 1.79 4.40 14.10 12.28 21.09 7.98 4.37 2.07 7.08MSE2 5.85 4.40 11.24 12.28 0.11 7.98 0.01 2.07 7.08

MSE biofilter 6.9 6.9 17.39 17.39 9.87 9.87 2.63 2.63


ematical formulation of the model but has been incorpo- tial contaminant concentration, the EBRT, and thebackwashing technique. Time after backwashing wasrated in the estimation of the initial biofilm thickness

and biomass density. also a factor. The model agreement was better (lowervalues of MSE) after 1 day when the biofilter was stable.Simultaneously, some problems have been identified.

CONCLUSIONS The method used to remove excess biomass in the sys-tem had a big influence on the performance of the bio-filter but currently is not included in the mathematicalA mathematical model that describes the biotreatment

of a waste gas in a trickle-bed reactor has been proposed description. However, it is considered in the determina-tion of the biomass density and the initial biomass thick-and validated. The model considers a two-phase system,

uniform bacterial population, one limiting substrate, ness. Future models should mathematically account forbiofilter backwashing. They should also include moreand quasi-steady-state processes. In the model analysis,

the unknown biofilter parameters have been estimated than one limiting substrate, such as oxygen and nitrate,and nonhomogeneous biomass species, such as activeand the model has been tested against experimental

data. A new approach describing the variation in the and inactive biomass. The model presented in this articleprovides a greater understanding of the VOC degrada-biofilter specific surface area with microbial growth has

been included. Variations in specific surface area can tion process in a biofilter and identifies fundamentalquestions to be addressed in the development of fu-explain experimental results that exhibit a decrease in

contaminant removal efficiency in the biofilter while ture models.biomass is growing. This fact leads to the question inthe title of the article—How much biomass is enough?—which has to be answered considering not only the

NOMENCLATUREamount of biomass in the system but also how accessibleis its biomass. The most important conclusion of this

a0 clean bed surface area per unit volume (cm21)article is that the performance of the biofilter depends af surface area per unit volume when there is biofilm growthnot only on the amount of biomass but also on the in the bed (cm21)

AL biofilm area lost in each contact point between equivalentamount of biomass that can be readily accessed by thespheres (cm2)diffusing contaminant.

b shear/decay rate coefficient (s21)Four biofilters with different operating conditionsb0 default shear/decay rate coefficient (s21)

were used in the validation process. To improve biofilter bd decay rate coefficient (s21)performance, biomass accumulated in the biofilter was bs shear rate coefficient (s21)

b0s default shear rate coefficient (s21)periodically removed using three different backwashing

Cf biofilm VOC concentration (mg/L)techniques. Two sets of observations have been em-Cg gas phase VOC concentration (ppmv)ployed in the nonlinear estimation of the parameters,Cg0 inlet gas phase VOC concentration (ppmv)

the biofilter treatment efficiency profile and the biomass Df VOC diffusivity in the biofilm (cm2/s)concentration. These variables have been calculated and Dw VOC diffusivity in water (cm2/s)

H Henry’s law constant [ppmv/(mg/L)]compared with experimental data from a model bio-J VOC flux in gas phase (ppmv cm/s)filter, biofilter M. The biofilter was continuously oper-Jf VOC flux in biofilm (mg cm/L s)ated during 9 days, and air scouring was used duringKs Monod saturation constant (mg/L)

the backwash at the start and at the end of the period. L biofilter packing media length (cm)The estimated parameters are the kinetic constants: Mo- Lf biofilm thickness (cm)

M/L3 mass/length3nod constant, maximum growth rate, yield coefficient,Mv VOC molecular weight (g/mol)decay and shear rate coefficients, the ratio between then number of characteristic packing spheres in contact with aVOC diffusivity in the biofilm and water, the biomass

given spheredensity, and the initial biofilm thickness. The biomass P pressure in the biofilter (atm)density and the initial biofilm thickness are assumed to r radial coordinate (cm)

rd ratio between VOC diffusivities in biofilm and waterbe dependent on the biofilter operating conditions. TheR characteristic packing sphere radius (cm)model has been tested in the prediction of the variationRg universal gas constant (cm3 atm/mol K)of toluene removal efficiency of the four biofilters witht time (s)

time. The initial time is considered to be the restart of T system temperature (K)the biofilter after backwashing. u interpore velocity (cm/s)

u0 approach velocity to the biofilter (cm/s)Prediction of biofilter performance over time is suc-VL volume of biofilm lost in each contact point between equiva-cessful when the variation of the specific surface area

lent spheres (cm3)with biofilm growth is included in the model. To evalu-Xf biomass density (mg/L)

ate the accuracy of the model, the MSE values were Xr biomass concentration in the biofilter (mg/L)used as a measure of goodness of fit. It was found that Y yield coefficient (mg biomass/mg VOC)

z axial coordinate (cm)the accuracy of the predictions depended upon the ini-


Greek letters effects of substrate utilization and shear stress on the kinetics ofbiofilm detachment. Biotechnol. Bioeng. 41: 728–735.

b stress proportionally constantRittmann, B. E. 1982. The effect of shear stress on biofilm loss rate.

e porosity of a packed bedBiotechnol. Bioeng. 42: 501–506.

e0 clean bed porosityShareefdeen, Z., Baltiz, B. C., Oh, Y. S., Bartha, R. 1993. Biofiltration

ef porosity in bed with biofilmof methanol vapor. Biotechnol. Bioeng. 41: 512–524.

f sphericity of packing solidsSmith, F. L., Suidan, M. T., Sorial, G. A., Biswas, P., Brenner, R. C.

mm maximum growth rate (s21)1994. Trickle bed biofilter performance: Biomass control and N-

t shear stress (dyn/cm2)nutrient effects. Presented at the 67th Annual Conference andExposition of the Water Environmental Federation, Chicago, IL,October 15–19, 1994.References

Smith, F. L., Sorial, G. A., Suidan, M. T., Pandit, A., Biswas, P., Bren-ner, R. C. 1995. Further evaluation of trickle bed biofilter perfor-Arcangeli, J. P., Arvin, E. 1992. Modeling of toluene biodegradationmance as a function of loading, residence time and biomass control.and biofilm growth in a fixed biofilm reactor. Water Sci. Technol.Presented at the 68th Annual Conference and Exposition of the26: 617–626.Water Environmental Federation, Miami, FL, October 21–25, 1995.Deshusses, M. A., Hamer, G., Dunn, I. J. 1995a. Behavior of biofilters

Sorial, G. A., Smith, F. L., Pandit, A., Suidan, M. T., Biswas, P., Bren-for waste air biotreatment. 1. Dynamic model development. Envi-ner, R. C. 1995a. Performance of trickle bed biofilters under highron. Sci. Technol. 29: 1048–1058.toluene loading. Paper no. 95-TA9B.04. Presented at the 88th An-Deshusses, M. A., Hamer, G., Dunn, I. J. 1995b. Behavior of biofiltersnual Meeting and Exhibition of the Air and Waste Managementfor waste air biotreatment. 2. Experimental evaluation of a dynamicAssociation, San Antonio, TX, June 18–23, 1995.model. Environ. Sci. Technol. 29: 1059–1068.

Sorial, G. A., Smith, F. L., Suidan, M. T., Biswas, P., Brenner, R. C.Dullien, F. A. L. 1979. Porous media. Fluid transport and pore struc-ture. Academic, New York. 1995b. Evaluation of trickle bed biofilter media for toluene removal.

J. Air Waste Manag. Assoc. 45: 801–810.Peyton, B. M., Characklis, W. G. 1993. A statistical analysis of the


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Gas treatment in trickle-bed biofilters: Biomass, how much is enough?