Modeling of a vapor-phase fungi bioreactor for the abatement of hexane: Fluid dynamics and kinetic...

Modeling of a Vapor-Phase FungiBioreactor for the Abatement ofHexane: Fluid Dynamics andKinetic Aspects

Giorgia Spigno, D. Marco De Faveri

Institute of Oenology and Food Engineering, Catholic Universityof Sacro Cuore, Via Emilia Parmense 84, 29100 Piacenza, Italy;telephone: + 39 0523-599181; fax: + 39 0523-599232;e-mail: [email protected]

Received 1 March 2004; accepted 9 September 2004

Published online 23 December 2004 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/bit.20336

Abstract: During some previous works, a packed-bed lab-scale biofilter (177 � 10�6 m3), inoculated with a selectedstrain of Aspergillus niger had been tested for the abate-ment of hexane vapors, showing a maximum eliminationcapacity of 200 g hexane/m3 reactor/h. A steady-statemathematical model taking into account axial dispersioneffect was applied to describe the process and predictexperimental results, but many model parameters couldnot be calculated from experimental data. The aim of thepresent work was to carry out further investigations toaccurately determine the dispersion coefficient and thekinetics parameters to verify the effective validity of themodel. Analysis of residential time distribution revealedthe presence of a certain degree of axial dispersion(dispersion coefficientD of 1.22 � 10�4 m2/s). Experimentaldata from kinetic trials carried out in reduced height reac-tors, together with data from full-scale runs, were elab-orated to estimate the kinetic saturation constant (Ks), thecoefficient yield (Y ), the maximum growth rate (Amax) andmaximum substrate degradation rate (rmax). All theseparameters were introduced into the model, which wasthen solved by simulation software finding a good corre-lation between experimental and theoretical results. B 2004Wiley Periodicals, Inc.

Keywords: biofiltration; fluid dynamics; kinetics; mathe-matical modeling; packed bed; VOCs

INTRODUCTION

Hexane is a common pollutant from many food and chem-

ical industries and, like the most of volatile organic com-

pounds (VOCs), is a very poorly water-soluble compound.

In the last decades, biological techniques for the treat-

ment of waste gases have been more and more popular

because, compared with traditional air pollution remedia-

tion techniques; they are relatively cheap, can show a good

operational stability, are environmentally friendly, require

ambient conditions for operation, and are very efficient

for the abatement of a large volume of air with low pol-

lutant concentrations.

However, conventional biofilters where the waste is

forced through a packed bed colonized by degrading bio-

mass and a liquid phase containing nutritive substances is

present, still or continuously recirculating co- or counter-

current, face problems with the elimination of hydrophobic

compounds that result in a poor absorption by the biofilms

(Devinny et al., 1999). To overcome these problems, bio-

filters with fungi on inert packing material have been

developed (Cox et al., 1993; Pagella et al., 2000, 2001; van

Groenestijn et al., 2001; Woertz et al., 2001). In fact, fungi,

in particular filamentous fungi, are tolerant to low water

activity and acid conditions, contain many species capable

of hydrocarbon degradation (hexane, a linear carbon chain

with less than ten atoms, is very difficult to be metabolized

by most of the bacteria) (April et al., 1992; Levi et al., 1979)

and, moreover, they develop aerial structures, hyphae,

which provide a large surface area so that a direct mass

transfer of the pollutant from the gas phase into the bio-

logical one is allowed (van Groenestijn et al., 2001).

Much research about VOCs biofiltration can be found

in literature (Acuna et al., 1999; Converti et al., 1997; Debus

et al., 1994; Delhomenie et al., 2002; Edwards and

Nirmalakhandan, 1996; Mohseni and Grant Allen, 2000;

Neal and Loehr, 2000; Zarook and Shaikh, 1997; Zarook

et al., 1993; Woertz et al., 2001), while little has been pub-

lished about hexane degradation (Silvestri et al., 1995).

Many researchers have modeled gas biofilters. The model

of Jennings et al. (1976) was first adapted to the gas-phase

biofilter by Ottengraf and van den Oever (1983). After that,

air biofilter models have been introduced that account for

more detailed representations of biofilm degradation

mechanism (Deshusses et al., 1995a, 1995b; Zarook and

Baltzis, 1994; Zarook et al., 1993, 1998a, 1998b). Also, dy-

namic models that study the effect of biomass accumulation

in the reactor have been developed (Alonso et al., 1998). The

more recent insights in this field consisted of a quantitative

B 2004 Wiley Periodicals, Inc.

Correspondence to: Giorgia Spigno

Contract grant sponsor: Catholic University of Sacro Cuore

Contract grant number: Research line D.3.2

structure–activity relationship model (Aizpuru et al., 2002),

and a cellular automation approach (Song and Kinney,

2002). Most of these models are very complex but also when

many simplifying assumptions are made, one of the main

concerns, which still remain is the determination of the un-

known model parameters, such as bacterial kinetic param-

eters and physical variables. While in certain cases these

values can be assumed or derived from published results, in

the general case, they have to be estimated using experi-

mental data and nonlinear techniques.

In other previous papers, the authors had investigated

the feasibility of a biofiltration process for decontamination

of hexane containing waste gases (Pagella et al., 2000;

2001; Spigno et al., 2003). After a selection procedure, a

strain of Aspergillus niger was isolated from soils near

gasoline stations (sites typically contaminated by hydro-

carbons) for its ability of growing on hexane and using it as

the sole carbon source. The fungus was inoculated on a lab-

scale bioreactor plant and different tests were carried out

to optimize the inoculation procedure, the choice of support

medium (finally expanded clay), the electrical/mechanical

devices set-up, and the flow rate to get the highest removal

efficiency. A steady-state model, including axial disper-

sion, was tested to describe the biodegradation process and

predict the plant performances. Anyway, many parameters

were still lacking (Spigno et al., 2003), that’s why the prin-

cipal aim of the present work was to characterize the lab-

scale from a fluid dynamic point of view (to determine the

real dispersion coefficient) and to complete the preliminary

kinetics experiments to calculate the degradation rate and

other kinetics parameters. The obtained results were then

used to test the mathematical model and further verify its

validity. On purpose, it was chosen as the most simple as

possible model to find an easy-to-use instrument for design

and management of biofilters.

MATHEMATICAL MODEL

As reported by Spigno et al. (2003), the tested mathemat-

ical model was a steady-state model taking into account

axial dispersion (Zarook et al., 1998a), and it was derived

using many commonly adopted simplifying assumptions

(Ottengraf, 1986; Zarook and Baltzis 1994):

� The biolayer is formed on the exterior surface of the

particles, not necessarily uniformly. There may be

patches of biofilm the extent of which is much larger

than its depth, hence diffusion/reaction in the biofilms

can be considered in a single direction only and not inside

the pores.� Biofilm thickness is small relative to the main curvature of

the solid particles and thus, planar geometry can be used.� Adsorption of the pollutant on the solid particles is at the

equilibrium in steady-state conditions.� Oxygen is in excess and hexane is the only limiting

substrate.

� Monod-type degradation kinetics with a term in the

denominator accounting for substrate inhibition (An-

drews kinetics).� The pollutant is depleted in a fraction of the actual bio-

layer, called effective biolayer.� Diffusivity of the pollutant in the biolayer is equal to that

of the same compound in water corrected by a factor

depending on biofilm density.� The biofilm density is constant and there is no biomass

accumulation in the filter bed so that the specific biolayer

surface area is constant.� There is direct contact between gas-phase and biofilm.� There is no gas-phase boundary layer at the air/bio-

film interface and hence, the gas-phase mass transfer can

be neglected.

The set of equations are expressed in dimensionless form,

where the dimensionless pollutant concentration in the gas-

phase, SG, is given relating concentration in the air at a po-

sition h along the biofilter height to that at the inlet of the

biofilter (CG/CG(0)); and the dimensionless pollutant con-

centration in the biofilm, SF, relating concentration at a

position u in the biolayer to the saturation constant (CF/Ks):

Mass balance in the gas phase—

CGð0ÞDv

H2

@2SG

@z2� CGð0Þ

Ug

H

@SG@z

þ KsDeaA

y�@SF

@xjx ¼ 0¼ 0

ð1Þ

Mass balance in the biological phase—

KsDe

y�2

@2SF

@x2� XFAmax

Y

SF

ð1 þ SF þ KsS2F=KIÞ

� XFmS ¼ 0 ð2Þ

Boundary and limit conditions—

ðaÞ Dv

UgH

@SG@zjz ¼ 0

¼ SGðz¼ 0Þ � 1;

ðbÞ @SG@zjz ¼ 1

¼ 0; ð3Þ

ðaÞ KsSFðx¼ 0Þ ¼SG

mCGð0Þ;

ðbÞ @SF@xjx ¼ 1

¼ 0; ð4Þ

Boundary conditions for the gas-phase account for axial

dispersion effects at the inlet and exit of the biofilter.

Equation (4a) states that the concentration of pollutant in

the gas is related to its concentration in the biofilm through

air/biofilm partition coefficient m. This air/biofilm partition

coefficient is significantly different from the convention-

ally used air/water partition coefficient and allows for a

greater partitioning of hydrophobic compounds to the

biofilm. Equation (4b) means that at the interface biofilm/

support, the pollutant gradient concentration gets to zero.

320 BIOTECHNOLOGY AND BIOENGINEERING, VOL. 89, NO. 3, FEBRUARY 5, 2005

The above-written equations were solved using a com-

puter code developed using the gPROMS Model Builder

program (v. 2.1.1 Process Systems Enterprise) according to

the method-of-lines family of numerical methods. This

involves discretization of the distributed equations with re-

spect to all spatial domains, which reduces the problem to

the solution of a set of differential and algebraic equations.

The axial domains of reactor column length and biofilm

thickness were discretized using centered finite differences

of second order over a uniform grid of 20 intervals.

The dispersion coefficient D represents the deviation

from the two idealized patterns, plug flow and mixed flow,

which could be caused by many factors: channeling of

fluid, recycling of fluid, creation of stagnant regions in the

vessel. The degree of non-ideality can be characterized by

residence time distribution (RTD) analysis.

According to the dispersion model (Levenspiel, 1999),

when an ideal pulse of tracer is introduced into the fluid

entering the reactor, the pulse spreads as it passes through

the reactor, and the dispersion coefficient D represents this

spreading process, while the dimensionless group D/UgH

characterizes the spread in the whole reactor.

The distribution of the times taken by the elements of

the fluid to leave the vessel is the exit age distribution E

function, or the RTD of fluid:

Z1

0

Edt ¼ 1 ð5Þ

For large deviations from plug flow, D/UgH > 0.01, the E

curve is not symmetrical, it can be constructed by nu-

merical methods and from its variance D/UgH can be de-

rived by graphical solution.

KINETICS INVESTIGATIONS

Often the growth and substrate utilization kinetics inside

the reactor are supposedly the same as those of the same

microbial culture when it is developed in separate batch

conditions (Mpanias and Baltzis, 1998), probably partly

due to difficulties in obtaining experimental kinetics data.

Actually, growth in a biofilm shows a disadvantage over

microbial growth in suspension since mass transfer (es-

pecially diffusional) limitation occurs in biofilms. This

implicates that microbial growth rates of the same micro-

organism in a biofilm can be lower, and cells physiology

and morphology may be different than in suspension

(Picioreanu et al., 1999). That’s why in the present re-

search, kinetics trials were performed in the same plant as

that employed for biofiltration experiments, to obtain param-

eters as realistic as possible.

Two different methods have been generally used to date

for growth kinetics determination, continuous and batch

culture. Continuous methods are preferred for determining

kinetics at low substrate concentrations, but their operation

is time consuming and labor intensive (Straube et al.,

1990). On the other hand, batch methods can be rapid, but

their application is limited to a relatively high concen-

tration of organic compounds for which the concentration

of the compound does not change significantly during the

logarithmic growth phase. Ferreira and Livingstone (1999)

developed a novel method for the determination of mi-

crobial growth kinetics on hydrophobic VOCs. A stirred-

tank reactor was operated as a fed-batch system to which

the VOC was continuously fed via the gas phase, thus

assuring a constant VOC concentration. Another commonly

employed technique is that of microcosms (Acuna et al.,

1999; Krimsky et al., 1995; Nielsen et al., 1996) where

the microbial activity is evaluated in closed environments.

As reported by Mohseni and Grant Allen (2000), Govind

et al. (1993) and Govind et al. (1997) carried out differ-

ential biofiltration experiments including a very small bio-

filter placed in a loop consisting of a large glass reservoir

and a pump for circulating air in the reservoir.

Anyway, it must be said that the above-mentioned

studies regarded bacterial cultures, where biomass growth

is very fast and can be easily monitored continuously by

measuring optical density or by filtration of liquid samples

taken from the cultures. In the present research, the use of

Aspergillus niger comported very long cellular duplication

times (time-consuming experiments), together with a not

homogenous development in the reactor, so that biomass

could be evaluated only by dry weight of all the biomass

present onto the support. The ideal experimental design for

a complete kinetics characterization of Aspergillus niger

growth on hexane, should have involved trials at different

flow rates and hexane concentrations, monitoring the bio-

mass development along the time. Since, due to the above-

described problems, the plant had to be dismantled and

restarted each time the biomass was to be determined, we

chose to carry out the first experiments at the flow rate of

4 � 10�3 m�3 /h and with inlet concentration <8 g/m3 (the

optimal value established in the full-scale biofiltration runs)

to get kinetics parameters, which could be useful for the

mathematical model solution. Besides this, the reactor was

packed only up to 140 mm of height. The smaller length of

the biofilters allows for a low contaminant removal effi-

ciency that will keep the VOC concentrations at the inlet

and at the outlet of the biofilter similar in magnitude, thus

ensuring a somewhat uniform biomass growth along the

bed (Alonso et al., 2000).

Microbial growth often shows a lag-phase (E) in which the

specific growth rate begins at 0 and then increases up to a

maximum value Amax. In a final phase, growth decreases and

becomes 0 reaching an asymptote (B) (Zwietering et al.,

1990). When the growth curve is defined as the logarithm of

number of microorganisms vs. time, a sigmoid curve is ob-

tained. The Gompertz equation modified by Zwietering et al.,

(1990) is generally used to describe microbial growth data:

y ¼ B exp �expAmaxe

BðE� tÞ þ 1

h in oð6Þ

where y is the log(M/M0), with M the biomass at time t and

M0 the biomass at the beginning, and e is a constant 2.72.

SPIGNO AND DE FAVERI: VAPOR-PHASE FUNGI BIOREACTOR FOR THE ABATEMENT OF HEXANE 321

MATERIAL AND METHODS

Biofilter Set-Up and Start-Up

The lab-scale bioreactor (Spigno et al., 2003) consisted of a

jacketed glass column, or two identical columns connected

in series, each one of overall height 0.40 m, internal

diameter 25 mm, with a stainless steel net at 40 mm from

the bottom to sustain the packing material, and sampling

ports for the substrate and air supply and for the outlet gas

flow. The support was expanded clay in granular form

(average Ø 3–5 mm), and it was autoclaved at 121jC for

15 minutes prior the inoculation in the reactor. The con-

taminated airstream was artificially created by mixing two

distinct flows supplied by a compressor: the first one was

passed through an humidifying system; the second one was

made sparging air in a vessel containing liquid hexane at

30jC. By means of flow meters the superficial gas velocity

and its pollutant concentration could be regulated. Hexane

concentration in the inlet and outlet streams was monitored

with a Perkin-Elmer 8500 gas chromatograph according to

the analytical procedure described by Spigno et al. (2003).

The system worked at a constant temperature of 30jC,

optimal value for fungal growth. The contaminated air

stream was fed to the biofilter from the bottom while the

nutritive medium Malt Extract Broth (MEB), was given

and recirculated down flow from the top. The gas flow rate

was set 4 � 10�3 m3/h (Pagella et al., 2001).

The inoculation procedure and frequency of nutritive

medium supply have already been described in Spigno et al.

(2003). The choice of a nutritive medium containing an

additional carbon source besides hexane (MEB), was due to

the results from preliminary kinetics experiments (Spigno

et al., 2003) which had shown a better development and

elimination capacity (EC) with MEB than with a nutritive

medium lacking of carbon sources. The system was oper-

ated continuously for 2 months; the second column was

connected to the first one on the 12th day, while after the

first 34 days the supply of both air and nutrients was in-

terrupted for 2 weeks to simulate any adverse conditions or

sudden industrial interruption.

Residence Time Distribution Analysis

The set-up used for the RTD analysis was the same as that

described above, but in abiotic conditions, that is with the

packing material soaked with sterilized water instead of

spores suspension in MEB. The influence of the presence of

support was investigated carrying out trials on both empty

and packed columns. Inlet flow rate was 4 � 10�3 m3/h, the

same as that employed for kinetics and biofiltration experi-

ments. Hexane was selected as tracer as the removal of the

same compound was studied. According to the procedure

described for the pulse experiment type (Levenspiel, 1999),

an amount of tracer (1, 10, and 25 AL) was instantaneously

introduced into the fluid entering the reactor and the con-

centration-time of tracer leaving the reactor was recorded.

Kinetics Experiments

Kinetics experiments were carried out in the same pilot

plant as that described above, in to reproduce the same

conditions of the biofiltration runs, but with the columns

packed only up to 140 mm of height to avoid excessive

hexane concentration drop and gradient along the reactor, so

that uniform conditions could be assumed for all the

biomass. The 140 mm was also the minimum height to

allow for a reliable biomass weight determination. To in-

vestigate the influence of nutrient type on hexane degra-

dation and mass development, two columns were inoculated

with a similar amount of spores and fed in parallel with two

similar air streams (flow rate 4 � 10�3 m3/h, hexane con-

centration 6–7 g/m3), but one column was fed with MEB,

while the other one with yeast nitrogen base (YNB), which

does not contain any carbon source. After the first 4–5 days

elimination capacity was monitored for a period of 2, 4, and

6 weeks. At the end of each trial the biomass in each column

was determined by dry weight and samples of support were

observed under the Scanning Electron Microscope (SEM

Hitachi S-2300). Samples were progressively dried through

passages in alcohol at increasing absolute volume, dried in

a critical point dryer, and gold-coated. They were then pho-

tographed extensively to ensure that representative images

of the sample were obtained.

RESULTS AND DISCUSSION

Biofiltration Experiments

Biofiltration runs (Spigno et al., 2003) revealed a global

removal efficiency of the two reactors connected in series

exceeding the 80%, while the first column had always a

removal efficiency RE (average 50–60%) lower than the

second column (average RE 70%) due to the higher inlet

pollutant concentration (Fig. 1). The bulk elimination

capacity increased with increasing hexane concentration

(mass transfer limitation) until an asymptote value (kinetics

limitation) of 150 g/m3reactor/h in correspondence of 12 g

hexane/m3 air (that corresponded to an hexane load of

Figure 1. Removal efficiency (RE) of the biofilters as a function of inlet

hexane concentration (CG(0)) during the 2-month full-scale run.


300 g/m3reactor/h), while the RE showed a decreasing

trend with the pollutant load. The first column reached a

maximum EC of 200 g/m3/h.

The system started to be efficient after about 2 weeks

(12 days), which can be considered as the adaptation period

of the Aspergillus to the new environmental conditions.

This is a long lag phase due to the well-known long fungal

replication times. After this period, biomass development

was not visually observed anymore and steady-state con-

ditions were assumed. After the 2 weeks interruption in

air and nutrient supply (from the 820th h to the 1150th h

in Figure 2), the biomass present was still able to degrade

the pollutant without any reduced efficiency. This great

stability could be explained by the capability of the fungi to

stay in a latent state as demonstrated by the large amount of

spores always observed under SEM (see figures in Pagella

et al., 2001; Spigno et al., 2003).

Residence Time Distribution Analysis

The experimental E curves were not symmetrical, meaning

there was a certain deviation from the ideal plug flow.

Presence of support obviously increased the dispersion

(Fig. 3) and from these E curves the dispersion coefficient

for the packed bed was calculated in a mean value of 1.22 �10�4 m2/s. The dimensionless group D/UgH resulted 0.18,

that is much beyond the limit of 0.01 indicated for small

deviations from plug flow (Levenspiel, 1999). Zarook et al.

(1998) found an almost double value than ours for a bio-

filter with an inner diameter of 5 cm and a height of 68 cm.

Kinetics Experiments

As already stated in some preliminary experiments (Spigno

et al., 2003) MEB allowed a better development and elim-

ination capacity than YNB. Although a lag phase of about

12 days was always observed, the EC in the next phase was

not the same in all runs (Fig. 4), which testifies the low

repeatability of a biological system such as a biofilter, and

particularly it was higher than in the full-scale runs, prob-

ably due to the constant and lower hexane inlet concen-

tration of 5–7 g/m3. It was also very difficult to obtain the

same inoculum for all the trials (Table I) since Aspergillus

niger spores are highly hydrophobic so that it is hard to

collect them, count, and inoculate onto the filter bed. Data

have been interpolated by linear regression analyses (SPSS

software 2.1) according to the Zwietering Eq. (6) with a

good regression coefficient (0.999) and obtaining:

B ¼ 0:51; Amax ¼ 0:001 h�1; E ¼ 116 h

These values indicate a long lag-phase before biomass

starts developing (about 5 days) and a very low maximum

specific growth.

SEM observations of samples from columns fed with

MEB and YNB did not reveal any substantial difference

due to nutritive medium. Aspergillus niger could develop

an abundant mycelium with a high coverage percentage of

support surface (Fig. 5) and aerial structures (Fig. 6). As

already observed in previous works (Pagella et al., 2001;

Spigno et al., 2003), there was always an extraordinary

number of spores whose role in the hexane degradation has

not yet been clarified and that, in some cases, formed a sort

of compact mat (Fig. 7), probably due to the production of

extrapolymeric substances, in particular near the gas en-

trance, where biomass had to form a denser and more com-

pact biofilm to defend itself from gas flow detachment.

Figure 2. Removal efficiency (RE) of the biofilters as a function of time

during the 2-month full-scale run.

Figure 3. E curves showing experimental data obtained from pulse tracer

input with both empty and packed-bed reactor.

Figure 4. Elimination capacity in kinetics experiments carried out for

15–30– 45 days (column fed with MEB).


Some strange and crystalline structures have been observed

throughout the reactor, whose chemical nature could not be

identified (Fig. 8). The nature of fungal mycelium, with its

very long and thin structures, suggested the adoption of a

simple one-dimensional (1D) biofilm model.

Mathematical Model

The final equations of the considered model [(1) and (2)]

contain many parameters, some of which are very difficult

to accurately determine. The effective diffusion coefficient

of hexane in the biofilm has been assumed as 40% of that

in water (Mohseni and Grant Allen, 2000; Zarook et al.,

1994) and then set at 0.4 � 1.85 � 10�9 m2/s, where 1.85 �10�9 m2/s is the diffusivity of hexane in water estimated

using the empirical correlation of Wilke and Chang (Red

et al., 1988). The air/biofilms partition coefficient (m) can

be about 3 orders of magnitude lower than the air/water

partition coefficient, for hydrophobic VOCs, due to the

presence of organic matter and bacteria in the biofilms.

Spigno et al. (2003) had assumed a theoretic m of 0.01,

while in the present work m was derived during the sim-

ulation of the model through a trial and error approach, ob-

taining the final value of 0.2, which indicates a not so high

compound solubility in the biofilm.

In relation to the biomass parameters, it was not possible

to measure exactly either the fraction of support surface

covered by the fungi or the biofilm thickness. Moreover,

the biomass did not develop as a homogenous layer, but as

a mycelium mat with abundant aerial cylindrical structures

and spores. On the basis of the numerous SEM photographs

taken in this work and previous ones (Pagella et al., 2001;

Spigno et al., 2003), the average mycelium thickness of

5 Am (variable from 1 to 10 Am) was assumed as the ef-

fective biofilm thickness and y*, since mycelium is the met-

abolically active part of the fungus. Porosimeter analysis of

the support particles (Spigno et al., 2003) had revealed

that only 8.6% of the pores has a mean diameter >10 Am,

33.1% between 1 and 10 Am, and 58.3% < 1 Am. On the

basis of these dimensions, of the SEM photos and of the

filamentous structures of Aspergillus, it could be concluded

that pores were hardly colonizable by mycelium.

Kinetics experiments described above were not sufficient

to accurately determine the parameters of biomass density,

XF, and yield coefficient, Y. That is why, as suggested by

Mohseni and Grant Allen (2000), all the parameters were

included into a single parameter on the basis of full scale

biofiltration runs (XF � Amax /Y). The experimental data of

EC as a function of inlet hexane concentration were inter-

preted according to the general and commonly employed

Monod Kinetics model (Monod, 1942). Since the adopted

Figure 5. Biomass coverage of the support (SEM photograph).

Figure 6. Aerial structures of biomass (SEM photograph).

Figure 7. Tightly compact layer of spores and mycelium over the sup-

port inside the reactor (SEM photograph).

Table I. Biomass development for kinetics experiments with MEB.

Days

15 30 45

Inoculated biomass Mi (g) 156.2 166.9 172.9

Final biomass Mf (g) 312.0 514.9 563.2

Mass increment (g) 155.8 348.0 390.3

Mf/Mi 2 3.08 3.25


model was a steady-state model, only the CG data after the

adaptation phase (300 h) were used. To evaluate the satu-

ration constant Ks and maximum substrate degradation rate

rmax, the Michaelis-Menten equation was rearranged in a

linear form, according to the Linewear-Burke diagram

(Dunn et al., 1992) substituting the degradation rate and its

maximum, which are expressed as g hexane m3 biomass h,

with the EC (g hexane/m3 reactor/h):

1

EC¼ Ks

ECmax

� 1

CGð0Þþ 1

ECmax

ð7Þ

Points from column 1 and 2 were put together as data

from an unique reactor (Fig. 9) and the following param-

eters were calculated for the system of two reactors con-

nected in series:

Ks ¼ 16 g=m3; and ECmax ¼ 290 g hexane=m3reactor=h:

The saturation constant calculated following the ap-

proach described above is supposed to be more correct than

the same parameter derived by Spigno et al. (2003).

An average biomass density of 14.6 kg biomass/m3

reactor was calculated from the values measured in the two

columns, respectively 2.13 g in the first column and 3.04 g

in the second (Spigno et al., 2003). Substituting the pre-

vious calculated Amax of 0.001 h�1, it yields:

ECmax ¼290g hexane

m3reactor � h

¼ 14600g Biomass

m3reactor� 0:001 h�1

Y¼) Yj

¼ 0:05g Biomass

g hexane

A coefficient yield of 0.05 confirms the low biomass

increase of Aspergillus niger growth on hexane observed in

our own experiments.

The biofiltration model was applied to experimental data

from biofilters considering the two reactors as a unique

reactor so that hexane concentrations at the inlet of the

second column could be assumed as sample points at half

height of the reactor. The model equations were slightly mod-

ified substituting the term XF � Amax/Y with ECmaxVreactors /

Vbiomass and a � A with a � Vbiomass /y. The biomass volume

was set to 1 � 10�5 m3, considering the reactor volume free

of packing material and SEM photographs, from which

space occupied by biomass appears. The most critical point

was to fix a value for a, since it was very hard to establish

the fraction of biomass present as aerial mycelium and, as a

consequence, the effective biomass surface available for

hexane diffusion. Again, with the aid of SEM images, an

average a = 0.55 was assumed. Considering the abundance

of spores in the reactor (Figs. 5–8), the amount of active

biomass in hexane degradation (mycelium) to be used in

the model, was calculated as 3 g instead of the total mea-

sured 5.14 g. Finally, the correspondence between pre-

dicted and experimental data appeared to be satisfactorily

good (Fig. 10) assuming mS = 0.0001 h�1 and the absence

of a substrate inhibition effect (KI = 1000). Concentration

profiles of hexane in the biofilm revealed a small concen-

tration gradient due to the small thickness (Fig. 11). The

regime resulted diffusion limited on the basis of the criti-

cal value of the Thiele number calculated as indicated by

Ottengraf (1986).

Figure 9. Ks and ECmax from experimental data elaborated according to

the Monod kinetics model.

Figure 8. Crystalline structures observed among the biomass (SEM

photograph).

Figure 10. Hexane concentration profiles along the biofilters for dif-

ferent experimental pollutant load (ex 4.4 � 19 g/m3) and their comparison

with a model predicted profile (th 4.4 � 19).


The same model was applied to the single columns, and

the following distinct parameters were calculated from

Figure 9 separately interpolating data for column 1 and 2:

Column 1 :Ks ¼ 14 g=m3;

ðXF �Amax=YÞ ¼ 79650 g=m3

biomass=h;

ECmax ¼ 270 g=m3

reactor=h;

Column 2 :Ks ¼ 17:4 g=m3;

ðXF�Amax=YÞ ¼ 95000 g=m3

biomass=h;

ECmax ¼ 322 g=m3

reactor=h:

The final RE could well be predicted for both the first and

second column, with an average error percentage of �0.75%

and �0.38%, respectively.

Model Sensitivity

Since some parameters were estimated partly through a trial

and error simulation approach and partly from theoric

supposition (m, mS, a, KI, y and active biomass), their effect

on the predictive results of the model was assessed carrying

out a sensitivity analysis. It was also found that an accurate

estimation of the partition coefficient, the maintenance

coefficient, and the available specific surface greatly

influenced the final removal efficiency of the biofilter. In

particular, lower m values mean higher biofilm solubility

and as a consequence a higher removal efficiency, unless

an inhibition effect takes place (Fig. 12a). As concerns the

effect of specific surface area (Fig. 12b), it is quite evident

that higher area increases the RE, that is intuitively

expected because, for a given biofilm thickness, increased

surface area increases the reaction volume and area for

mass transfer (Amanullah et al., 1999). The maintenance

coefficient assumed in the present work did not have any

significance since the results obtained with a coefficient

equal to zero are the same, while an increase of 2 orders of

magnitude enormously increases the removal efficiency

(Fig. 12c). On the other hand, a further increase in the bio-

film thickness is not beneficial (not reported data), which

is indicative of a diffusion-controlled system when the

Figure 12. Effect of the gas-biofilm partition coefficient (a), of the fraction of surface area covered by the biofilm (b), of the maintenance coefficient (c),

and of the inhibition constant (d) on the exit gas concentration for CG(0) = 11.6 g/m3.

Figure 11. Dimensionless concentration profile of hexane in the biofilm

at half height in the biofilter.


contaminant fails to reach the depths of the biolayer. To

change the active biomass from 1 to 5.14 g (the exper-

imentally measured final dry biomass) did not affect the

exit concentration as well. Finally, a change in the in-

hibition constant KI influenced the removal efficiency for

inlet concentrations higher than 10 g/m3, that is when, for a

given partition coefficient, hexane levels inside the biofilm

could become toxic (Fig. 12d).

CONCLUSIONS

In the present work an axial dispersion mathematical model

describing removal of hexane vapor in a biofilter, has been

quite successfully tested with data from a lab-scale plant,

despite the many simplifying assumptions made in deriving

this model. Some of the many parameters included in the

model could be valued after experimental trials, such as the

axial dispersion coefficient (by residential time analysis)

and kinetics parameters of fungal growth on hexane (by

both full- and reduced-scale biofilter runs). Anyway, as far

as kinetics parameters are concerned, it is worth noting that

Aspergillus niger development and its metabolic activity

have been extremely variable in the present work (different

performances between the two reactors connected in series

and between the kinetics trials), so that saturation constant,

coefficient yield, specific growth, and maximum degrada-

tion rate cannot be assumed constant but should be better

estimated case by case.

Two other factors, the specific surface area and the air/

biofilm partition coefficient strongly affect the removal

efficiency of the system, as shown by a model sensitivity

analysis. The specific surface area should be known from

experimental observations of biomass development onto the

support inside the reactor, but in our case, in which aerial

mycelium is present together with a large amount of spores,

the role of which in hexane degradation has not yet been

understood, it was very hard to determine it accurately. From

our results the assumption of a 1D biofilm for filamentous

fungi development appeared to be a quite good approx-

imation. The air/biofilm partition coefficient is very difficult

to experimentally determine as well, since inactivation of

biomass and reproduction of the exact biofiltration con-

ditions are required. However, in the present work m has

been obtained through a trial and error approach during

model simulation. It can then be concluded that the

biofiltration process can be modeled even with quite

simplified models, but many uncertainties about parameters

estimation are still to be faced. Further experiments are

necessary and being carried out to assess if a bigger biofilter

could give more constant and reproducible performances so

that the tested model will become reliable enough for the

designing and scaling-up of industrial plants.

NOMENCLATURE

A biolayer surface area per unit volume of the reactor (m�1)

Ar reactor section (m2)

B asymptote value in growth curve

CG concentration of the pollutant in the

air at position h along the biofilter (g/m3)

CG(0) concentration of the pollutant in the

air at the inlet of the biofilter (g/m3)

CF concentration of the pollutant at

a position u in the biolayer at a

point h along the column (g/m3)

D dispersion coefficient in the reactor (m2/h)

De effective diffusion coefficient of the

pollutant in the biolayer (m2/h)

E exit age distribution function

EC elimination capacity (g-hexane/m3reactor/h)

h position in the column; h = 0 at the

entrance, h = H at the exit

H reactor height (m)

KI inhibition constant (g/m3)

Ks saturation constant in the specific

growth rate expression of a culture

growing on the pollutant (g/m3)

m pollutant air/biofilm

distribution coefficient

mS maintenance coefficient (g-hexane/g-biomass/h)

Q gas flow rate (m3/h)

rmax maximum degradation rate (g-hexane/m3biomass/h)

RE removal efficiency

RTD residence time distribution

SG dimensionless concentration in the

gas phase = CG/CG(0)

SF dimensionless concentration in the

biolayer = CF/Ks

Ug superficial gas velocity (m/h)

Vbiomass biomass volume (m3)

Vreactor reactor volume (m3)

x dimensionless position in the

biolayer = u/y*

XF biofilm density (g-dry cells/m3biofilm)

Y yield coefficient of a culture

on pollutant j (g-biomass/g-compound)

z dimensionless height = h/H

Greek letters

a fraction of A covered by the biofilm

y* effective biolayer thickness (m)

E lag time (h)

u position in the biolayer (m), u = 0 at the air/biofilm

interface; u = y* at the biofilm/support interface

Amax maximum specific growth rate (h�1) in Monod kinetic;

kinetic constant in Andrews kinetic (Monod-type

equation with substrate inhibition)

r bed porosity

The authors wish to thank Dr. S. Arisi and Dr. F. Fusca for their

precious laboratory contribution.

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