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The gas-liquid mass transfer coefficient is a key parameter to the design and operation of biotrickling filters that governs the transport rate of contaminants and oxygen from the gas phase to the liquid phase where pollutant biodegradation occurs. Mass transfer coefficients are typically estimated via experimental procedures to produce empirical correlations, which are only valid for the bioreactor configuration and range of operational conditions under investigation. In this work, a new method for the estimation of the gas-liquid mass transfer coefficient in biotrickling filters is presented. This novel methodology couples a realistic description of the packing media (polyurethane foam without a biofilm) obtained using micro-tomography with computational fluid dynamics. The two-dimensional analysis reported in this study allowed capturing the mechanisms of the complex processes involved in the creeping porous air and water flow in the presence of capillary effects in biotrickling filters. Model predictions matched the experimental mass transfer coefficients (± 30%) under a wide range of operational conditions.
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1Mechanistic Description of Convective GasLiquid Mass Transfer in
2Biotrickling Filters Using CFD Modeling
3Patricio A. Moreno-Casas,
Felipe Scott,
JoséA. Abell,
Francisco Caicedo,
4Raúl Muñoz,
and Alberto Vergara-Ferná
Green Technology Research Group, Facultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, Santiago 7620001,
Facultad de Ingeniería, Universidad Mariana, San Juan de Pasto 520002, Colombia
Institute of Sustainable Processes, Universidad de Valladolid, Valladolid 47005, Spain
SSupporting Information
10 ABSTRACT: The gasliquid mass transfer coecient is a key parameter
11 to the design and operation of biotrickling lters that governs the
12 transport rate of contaminants and oxygen from the gas phase to the
13 liquid phase, where pollutant biodegradation occurs. Mass transfer
14 coecients are typically estimated via experimental procedures to
15 produce empirical correlations, which are only valid for the bioreactor
16 conguration and range of operational conditions under investigation. In
17 this work, a new method for the estimation of the gasliquid mass
18 transfer coecient in biotrickling lters is presented. This novel
19 methodology couples a realistic description of the packing media
20 (polyurethane foam without a biolm) obtained using microtomography
21 with computational uid dynamics. The two-dimensional analysis
22 reported in this study allowed capturing the mechanisms of the complex
23 processes involved in the creeping porous air and water ow in the
24 presence of capillary eects in biotrickling lters. Model predictions matched the experimental mass transfer coecients
25 (±30%) under a wide range of operational conditions.
26 Biotechnologies represent a cost-competitive and environ-
27 mentally friendly alternative to conventional physical/chemical
28 technologies for the treatment of malodorous, volatile organic
29 compounds (VOCs), greenhouse gases, and biogas.
Of them,
30 biotrickling ltration has become increasingly popular in the
31 past decade based on its low gas residence time of operation
32 (1540 s
) and the potential to control key environmental
33 parameters for microbial growth such as temperature, pH, and
34 the concentrations of nutrients and toxic inhibitory metabo-
35 lites.
Biotrickling lters (BTFs) are packed-bed units where
36 the packing material promotes an eective gasliquid contact,
37 while supporting biolm growth because of a continuous
38 supply of liquid medium.
Hence, the design of this technology
39 relies on the accurate description of both microbial biolm
40 kinetics and gasliquidsolid interactions.
However, while
41 the kinetics of pollutant biodegradation in biolms have been
42 consistently studied, the hydrodynamics of gas and liquid
43 circulation determining pollutant mass transfer in the packing
44 material of BTF are still poorly understood.
In fact, gas
45 liquid mass transfer in this bioreactor conguration is typically
46 characterized using empirical methodologies for the determi-
47 nation of the global volumetric mass transfer coecient (KLa)
48 based on simplied mathematical models.
The experimental
estimation of the volumetric mass transfer coecient in BTF,
as reviewed by Estrada et al.,
San Valero et al.,
and Dupnock and Deshusses,
is performed using VOC
concentration measurements in the gas and liquid phases and
CO2absorption in caustic water as the main experimental
techniques. Unfortunately, the aforementioned approaches do
not provide insights regarding the liquid and gas distribution
and channeling inside the packed column (wetted area,
velocity and pressure proles, preferential ows). Therefore,
new and more powerful techniques are required to describe all
complex phenomena determining the gasliquid pollutant
60mass transport in BTF.
In this context, the recent advances in computational uids
dynamics (CFD) along with the increase in computational
power over the past decades has enabled the use of this
powerful modeling tool for the design of o-gas treatment
biotechnologies, which represents a new application in this
In order to study the complex geometry of a porous
67matrix, three-dimensional (3D) digital imaging such as
Received: May 2, 2019
Revised: November 16, 2019
Accepted: December 2, 2019
Published: December 2, 2019
© XXXX American Chemical Society ADOI: 10.1021/acs.est.9b02662
Environ. Sci. Technol. XXXX, XXX, XXXXXX
*Unknown *|ACSJCA |JCA11.2.5208/W Library-x64 |research.3f (R4.2.i1:4953 |2.1) 2018/04/01 14:00:00 |PROD-WS-121 |rq_118395 |12/09/2019 04:33:23 |8|JCA-DEFAULT
68 computational tomography or microtomography (depending
69 on the resolution needed) can be used to assess the ow
70 dynamics and compute the parameters of interest. For
71 instance, the combination of 3D imaging and CFD techniques
72 can be employed to obtain pressures and velocities at the pore
73 scale. The coupling of CFD and computational micro-
74 tomography has been used in recent years to analyze the
75 ow through porous media,
thus allowing for the
76 characterization of the ow at the microscale. To the best of
77 our knowledge, these techniques have never been applied in
78 bioltration systems for air treatment. A recent work by Prades
79 and co-workers applied the CFD approach by using a
80 commercial code, where biological reactions were coupled to
81 ow equations in order to simulate the liquid velocities and
82 oxygen consumption in a at plate (rather than a porous
83 support) biolm bioreactor.
The latter CFD simulation
84 represented an important step forward toward the description
85 of gasliquid ow in porous media BTF.
86 The present work explores the potential of CFD for the
87 description of the gasliquid mass transport in an abiotic BTF
88 (not inoculated with microorganisms) using O2as a model gas
89 and a detailed description of the polyurethane foam (PUF)
90 support system obtained using 3D microtomography. The
91 predictions of this CFD modeling approach were compared
92 with the volumetric mass transfer coecients empirically
93 determined in a 6 L BTF operated under multiple operational
94 conditions typically encountered under industrial scale.
95 2.1. Mathematical Model. The eect of capillarity should
96 be considered when assessing the ow of liquid and gas (two-
97 phase ow) in a porous material with suciently small pores
98 because liquid meniscus attached to the porous material can
99 impact the ow of both phases. This can be achieved by adding
100 a term to the uid linear momentum or NavierStokes (NS)
101 equations and by applying the Volume of Fluids (VOF
102 technique.
The NS equations (eq 1) can be coupled to the
103 continuity equation (eq 2) for each uid (liquid and gas) to
104 obtain the pressure and velocity elds, while the liquidgas
105 interphase can be tracked by using the transport equation for
106 the volume fraction of the two phases (eq 3). Both uids were
107 considered to be Newtonian, incompressible, isothermal, and
108 immiscible.
=+ +∇ +
tUU p g U F
109 (1)
110 (2)
+∇· +∇· − =
tUU() ((1 ))
111 (3)
112 where, ρis the uid density (kg m3), Uis the velocity vector
113 (u,v,w) for the x,y, and zdirections (m s1), respectively, gis
114 the acceleration of gravity (m s2), prepresents the pressure
115 vector in space (Pa), μdenotes the dynamic viscosity of the
uid (kg·m1·s1), the operator
, and FSis
117 the surface tension force (N·m1). The variable αis the VOF
118 indication function, which can be dened as the quantity of
119 liquid per unit volume at each computational cell (i.e., if α=1,
120 the cell contains only liquid, if α= 0, the cell contains only gas,
121 else there will be a mixture of both phases). Finally, the last
122 term in eq 3 is a mathematical expression required to avoid
123excessive numerical diusion, where UCrepresents the
124convenient velocity eld to compress the gasliquid
125interphase. The above equations were solved in OpenFOAM,
126where solutions to eqs 1 and 2were obtained by applying the
127well-known predictorcorrector technique pressure-implicit
128with splitting of operators algorithm,
while the mathematical
129tracking of the interphase was achieved by solving eq 3
130discretized in the interFoam solver.
1312.1.1. Determination of the Volumetric Mass Transfer
132Coecient (KLa). The volumetric mass transfer coecient, KLa
133(s1or h1), is the product of the mass transfer coecient, KL
134(m s1), and the specic surface area, a(m2m3), where the
135mass transfer occurs. The specic surface area is the ratio of
136the surface S(m2) of contact between the two phases, or
137interfacial area, and the volume V(m3) of the bioreactor.
138Several theories are typically used to determine KL:lm
139theory, penetration theory, surface renewal theory, and
140boundary layer theory.
However, only the boundary
141layer theory takes into account the hydrodynamic character-
142ization of the system and provides a more realistic
143interpretation of the mass transfer phenomena occurring at
144the boundary layer.
145The concentration distribution of a species A, CA, within the
146air boundary layer is a function of its location, CA=CA(x,y)
147and its thickness, δm, and it also depends on the distance from
148the plate leading edge.
In this regard, the momentum
149diusivity and the mass diusivity play a key role in the overall
150mass transfer phenomena, which can be accounted for with the
151Schmidt number, Sc. Whenever the momentum diusivity is
152larger than the mass diusivity, Sc is larger than 1
and δ/δm=
153Sc1/3. Moreover, an expression for an average Sherwood
154number, Shav, can be developed by connecting the average
155plate Reynolds number, Rel=ρVl/μ,(V, is the air free
156stream velocity, right above the end of the boundary layer) and
157an average Schmidt number, Scav, along a at plate of length l
DRe Sc0.664( ) ( )
1/2 1/
159where KLis the average mass transfer coecient along the
160plate length, l, and DAB is the diusion coecient between
161uids A and B (air and water, 2 ×109m2s1
). Considering
162that Sc =ν/DAB, an expression for the computation of the mass
163transfer coecient can be obtained.
KlV D0.664( ) ( ) ( ) ( )
1/2 1/2 1/6
165where νis the kinematic viscosity of the gas phase (1.51 ×105
166m2s1for air). In the present study, the at plate stands for the
167liquidgas interphase, which is not at. However, it will be
168assumed to be nearly at for computational purposes. The
169estimation of the specic area per unit volume of reactor is
170deferred to Section 3.2. All constants applied in this study
171assume a temperature of 22 °C in order to be able to compare
172numerical and experimental results.
1732.2. PUF Packing 3D Microtomography. In order to
174solve the NS equations, a detailed and realistic description of
175the boundary conditions at the uidsolid interface is needed,
176which requires a highly resolved 3D image of the porous
177media. Nowadays, it is possible to construct such image by
178using X-ray computed microtomography (μCT). In the
179present study, a SKYSCAN 1272 high resolution X-ray
180microtomography scanner from Bruker was used with a
181maximum resolution of 0.35 μm. Because of the high
Environmental Science & Technology Article
DOI: 10.1021/acs.est.9b02662
Environ. Sci. Technol. XXXX, XXX, XXXXXX
182 resolution needed to obtain images of the PUF support, a small
183 sample of the PUF support of the cylindrical reactor was
184 scanned in the μCT. The height, width, and depth of the
f1 185 sample were 1.58, 1.58, and 0.76 cm, respectively (Figure 1A).
186 The 3D image was saved in stl (stereolithography) format and
187 later used in OpenFOAM.
188 A two-dimensional (2D) slice of the original 3D digitalized
189 image was used in the present study because of the high
190 computational cost required to numerically solve the ow (see
191 Figure 1). The 2D image used in the simulations was 1.58 cm
192 ×1.58 cm. From the 2D image, a ne grid was generated in
193 OpenFOAM in order to discretize the porous voids within the
194 PUF, where the liquid (water) and gas (air) were allowed to
195 ow. A sample of the 2D mesh is shown in Figure 1, where the
void spaces indicate the presence of the foam, and the
discretized surfaces show the areas where the water and air will
ow in the xyplane (where yis vertical). In order to nd a
sound grid resolution (number of cells) to simulate all the
cases of interest in the present work, a grid independence
analysis was carried out (see Figure S1 in the Supporting
Because the computational domain is much smaller than the
complete BTF, the digitalized PUF was assumed to be far away
from the BTF inlet and outlet and far away from the column
inner walls. In this way, the velocity conditions of the
digitalized PUF at the top and bottom were maintained from
the experimental setup, while on the sides, cyclic/periodic
209 f2
eects were used to mimic BTF operation (see Figure 2). The
results from each simulation were considered to be
representative of the average behavior of the BTF, while wall
eects (air and water ow interaction with the reactor inner
walls) were assumed to be negligible. The velocity boundary
conditions, for the 2D grid, were left and right boundaries had
a periodic condition; upper and lower boundaries were dened
by the inlet and outlet water and air velocities according to
each case of study. Wherever there is PUF support, the
condition was dened as nonslip or zero velocity condition.
The initial conditions for velocity and pressure, for the air and
water ows, were dened in accordance with each numerical
trial, which in turn was connected to a particular experimental
222 t1
condition (see Table 1). The time step for all simulations was
xed at 0.001 s, and data were also saved every 0.001 s. The air
and water ow in the BTF occurred in the ydirection (vertical
direction). Thus, water entered the biolter from the top and
moved downward, while the air entered from the bottom of
the biolter and moved upward. A zero-pressure gradient
condition was imposed in the support, whereas for the right
and left borders, the boundary condition was set to cyclic/
periodic. For the inlet and outlet boundaries, the pressure was
computed according to the velocity at each boundary cell by
applying a total pressure set to p0= 0, while as the velocity U
233changed, the pressure was adjusted as p=p0+ 0.5|U|2.
Figure 1. (A) Digitalized PUF image using μCT. (B) PUF image
showing the computational domain (slice right at the PUF center).
(C) Computational domain used for simulations. (D) Mesh zoom.
White areas indicate the presence of PUF. Only PUF void areas were
Figure 2. Schematization of the boundary conditions for the 2D microscale computational domain. BC stands for boundary conditions.
Environmental Science & Technology Article
DOI: 10.1021/acs.est.9b02662
Environ. Sci. Technol. XXXX, XXX, XXXXXX
234 2.3. Experimental Determination of the Volumetric
235 Mass Transfer Coecient in the BTF. The KLavalues for
236 O2were empirically determined in a 6 L polyvinyl chloride
237 absorption column (0.08 m diameter ×1mheight)
238 interconnected to a 1.5 L glass stirred tank reactor (magneti-
239 cally stirred at 300 rpm). The absorption column was packed
240 with a 4 L PUF cylinder, while the liquid level in the stirred
241 tank was maintained at 1 L. The concentration of dissolved
242 oxygen (DOC) was measured in the stirred tank reactor using
243 a polarographic DOC probe coupled to an O2transmitter 4100
244 (Mettler Toledo GmbH, Urdolf, Germany), which according
245 to the manufacturer exhibited a response time of 90 s to
246 achieve 98% of the equilibrium concentration in a step change
247 from an air-saturated solution to an oxygen-free aqueous
248 solution at 25 °C. Distilled water was used as a model liquid
249 medium in the BTF to avoid any interference of the salt
250 concentrations. (A) A Watson Marlow 520 peristaltic pump
251 was used to recycle the liquid medium from the stirred tank to
252 the top of the absorption column, which was equipped with a
253 cylindrical spray tubing (0.3 cm tip diameter ×10.5 cm length)
254 located 4.5 cm above the PUF packed bed. Figures 2 and S2
255 (Supporting Information) (B) illustrate a schematic of the
256 experimental BTF and water irrigation system, respectively.
257 The volumetric gasliquid mass transfer coecients for O2
258 were determined using the gassing-out method at empty bed
259 gas residence times (EBRTs) of 17, 36, 60, and 240 s and
260 liquid velocities of 2, 4, 11, and 17 m h1at each EBRT. The
261 gassing-out method was selected because of its simplicity, the
262 absence of dangerous chemicals, and our previous expertise
263 using it.
Prior to the determination of the KLa,the DOC in
264 the recirculating liquid medium was depleted with helium
265 supplied from the bottom of the BTF counter-currently with
266 the trickling liquid medium (at the corresponding liquid
267 velocity and EBRT). Then, the helium stream was replaced
268 with air at the target operational conditions and the DOC
269 monitored to saturation. The empirical determinations of the
270 KLawere conducted in duplicate at 22 ±1°C (controlled
271 using a thermostatic water bath) using O2mass balances in the
272 BTF and stirred tank reactor (6), and the experimental data
273 obtained in the test above are described.
The abiotic BTF was
274modeled as 10 interconnected continuous stirred tank reactors
275(CSTRs) as follows
tKa C
d() ( )
LO L,out
L,in L, out
tKa C
( ) 2, ..., 9
tKa C
L,in L
280where CL,in and CL,outjstand for the dissolved O2concentration
281(g m3) at the inlet and outlet of each CSTR representing the
282absorption column (the rst CSTR is at the top of the abiotic
283BTF); His Henrys law constant for O2(dimensionless), QL,
284the recirculating liquid velocity (m3h1), VC, the packed bed
285volume (m3), and VT, the volume of the stirred tank (m3). In
286the estimation of KLavalues in CSTRs, it is necessary to
287account for the response time of the electrode when the
288response time of the probe is in the same order of magnitude
289as 1/KLa.
This requirement arises because the delay in the
290electrode response produces a delayed DOC concentration
291measurement and thus an underestimation of the KLavalue.
292However, in our system the concentration of DOC in the
293CSTR changes in small increments as oxygen-rich water
294owing out of the abiotic BTF enters the CSTR, where the
295electrode is positioned. Moreover, the dynamic of ow
296circulation in the abiotic BTF and in the CSTR already
297introduced delays that are accounted for in the model.
Table 1. Experimental KLa, and Estimated KL,a, and KLaUsing CFD Simulations for the Experimental Conditions Tested
condition water velocity
(m h1)EBRT
(s) estimated a
(m1)estimated KL
(m h1)experimental
error in CL,in predictions
(g m3)
case 1 2 240 233.59 0.476 112.58 ±3.16 0.09
case 2 4 240 190.53 0.700 144.04 ±6.46 0.07
case 3 11 240 180.35 0.693 125.16 ±2.93 0.12
case 4 17 240 227.42 0.699 156.60 ±7.26 0.24
cse 5 2 60 223.82 0.567 122.29 ±3.84 0.09
case 6 4 60 215.07 0.675 112.13 ±4.88 0.21
case 7 11 60 205.19 0.748 167.86 ±4.23 0.11
case 8 17 60 193.27 0.688 167.21 ±7.91 0.23
case 9 2 36 234.54 0.579 173.39 ±6.82 0.08
case 10 4 36 141.97 0.832 183.68 ±5.59 0.08
case 11 11 36 218.09 0.742 232.89 ±12.37 0.18
case 12 17 36 209.07 0.905 259.24 ±15.66 0.21
case 13 2 17 190.72 0.696 189.28 ±7.78 0.09
case 14 4 17 150.05 1.009 212.50 ±6.73 0.07
case 15 11 17 210.37 1.062 249.60 ±16.15 0.20
case 16 17 17 208.76 1.154 380.87 ±29.72 0.19
Estimated KLavalue from the experimental information and its 95% condence interval.
Average error calculated as the mean value of the
absolute value dierences between measured and predicted (CL,in in eq 9) DOC concentrations.
Environmental Science & Technology Article
DOI: 10.1021/acs.est.9b02662
Environ. Sci. Technol. XXXX, XXX, XXXXXX
298 The KLavalues for the 16 experiments shown in Table 1
299 were estimated by nonlinear tting to the experimental data (in
300 triplicate) to the model described by equations (6) using
301 MATLABs nlint function with default options. The 95%
302 condence intervals for the estimated KLavalues were
303 calculated using MATLABs nlparci function. The comparison
304 between model predictions and DOC concentration exper-
305 imental data is provided in Supporting Information.
306 3.1. Experimental Mass Transfer Coecients in a BTF
307 with PUF as Packing Material. The experimental results
308 obtained for KLaof oxygen dissolving into a trickling aqueous
309 solution under the 16 operational conditions tested are shown
310 in Table 1. Higher values of KLawere obtained at higher
311 trickling medium velocities and lower EBRTs (i.e., higher air
312 velocities). From a uid mechanics perspective, the water
313 moving downward due to gravity interacts with the air moving
314 upward, causing shear at the waterair interface. Hence, two
315 mass transfer mechanisms may occur simultaneously: (i)
316 diusion of O2into water due to dierences in O2
317 concentration between the two phases, and (ii) diusion of
318 O2into water due to turbulence (momentum exchange) or
319 shear between the moving uids at the interface. The
320 magnitude of the shearing interaction between the two uids
321 at the interface depends on the local Reynolds number of the
322 uid lm in each section of the wetted-column.
323 two boundary layers are formed: a concentration boundary
324 layer and a velocity or momentum boundary layer.
325 Table 1 showsthattheempiricalKLadecreased by
326 approximately 50% when the EBRT increased by a factor of
327 14, regardless of the trickling liquid velocity. On the other
328 hand, the increase in KLawhen the trickling liquid velocity
329 increased from 2 to 17 m h1depended on the EBRT, with
330 increases of 200, 300, 240, and 230% at EBRTs of 17, 36, 60,
331 and 240 s, respectively. A similar behavior was reported by
332 Lebrero et al.
and Estrada et al.
for toluene and methane
333 KLain BTF. Estrada et al.
reported KLavalues for oxygen in
334 the range 30300 h1in an abiotic BTF with PUF as the
335 support, using liquid velocities between 0.5 and 5.0 m h1and
336 EBRTs between 12 and 250 s.
337 3.2. Simulation of a 2D PUF Slide of the BTF Using
338 CFD and Comparison of Predicted and Experimental O2
339 Mass Transfer Coecients. A2DCFDnumerical
340 simulation with a detailed description of the porous media
341 was used in order to elucidate the physical mechanisms of O2
342 gasliquid mass transfer in a BTF at laboratory scale. 2D
343 simulations were chosen over the 3D approach because of their
344 model simplicity and the signicant reduction in computa-
345 tional costs. Before all, operational conditions were simulated
346 and a sensitivity analysis for mesh independence was carried
347 out (see Figure S1 in the Supporting Information). The
348 analysis of the computational results was based on steady-state
349 conditions, which were reached with real time simulations of
350 10 s. In addition, it should be stressed that one of the main
351 objectives of these simulations was to obtain a quantitative
352 measure of the specic surface area, where O2is dissolved into
353 water, that is, the gasliquid interphase. Once the resulting
354 distribution of the two phases was identied, the waterair
355 interphase area (WAIA) was computed (see Figure S3 in the
356 Supporting Information).
f3 357 The results obtained under steady state are shown in Figure
f3 358 3. The simulation results are displayed in terms of the
359distribution of water, air, and air velocity vectors. The air
360velocity vector arrows graphically show the locations where
361preferential ow occurs as a result of the distribution of water
362patches. Preferential ow spots are likely to occur when two
363large patches of water separated by a small distance where air
364ows through are formed by the ow.
365The numerical simulations conducted also showed that the
366volumetric mass transfer coecient was greatly aected by the
367variations in EBRT and water velocity. As shown in Figure S4
368(Supporting Information), the variations in KLawere more
369signicant at low EBRTs, that is, changes in water feeding
370velocity greatly impacted the mass transport of oxygen into
371water at lower EBRT (higher air velocities). While at the
372highest EBRT analyzed (EBRT 240 (s)), the variations in
373water velocity exhibited a lower impact on KLa. This can be
374explained by the increase of shear stresses near the waterair
375interphase, causing an increase in the O2mass transfer rate, a
376phenomenon that can be described using the boundary layer
377theory. This was represented in the numerical computations as
378an increase in the relative velocity dierence between the
379waterair interphase and the free stream velocity of the air,
381The BTF water velocities directly impacted the diusion of
382oxygen from the air into the trickling aqueous solution at the
383microscale level. The air free stream velocity (V) gradually
384increased when increasing the trickling water velocity (see
385Figure S5 in the Supporting Information) at EBRTs of 60, 36,
386and 17. According to eq 7,KLincreases as the square root of
387V, and therefore, more oxygen is dissolved into water because
388of the increase of air ow momentum near the airwater
389interface. In addition, when the air ow was too low (EBRT
390240), this variable did not aect the mass transport process.
391On the contrary, at low EBRT, the variations in water
Figure 3. Water and air fraction results for the 2D PUF simulations.
Dark gray color indicates the presence of water and light gray
indicates the presence of air. White areas indicate the presence of
PUF. Each row shows results for four dierent EBRT, from top to
bottom: 240, 60, 36, and 17 s. Each column shows results for four
dierent water velocities, from left to right: 2, 4, 11, and 17 m h1.
Arrows indicate velocity vectors (m s1), with velocity values
indicated in the color bar. Cases are numerated from 1 to 16 in
accordance with Table 1.
Environmental Science & Technology Article
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Environ. Sci. Technol. XXXX, XXX, XXXXXX
392 velocities determined the distribution of water blobs, hence
393 modifying the ow conditions of the air ow near each blob
394 and therefore changing V.
395 The WAIA, the boundary layer, and the velocities of air
396 moving along the waterair interphase can be estimated using
f4 397 postprocessing (see Figure 4). Data from the cells conforming
398 such interface can be extracted from the simulation and the
399 location, length, and area of the waterair interphase. From
400 each cell conforming the airwater interface, a computational
401 algorithm was used to estimate the air velocity of each cell
402 above and perpendicular to the interface computational cells.
403 When the air velocity remained constant, the boundary layer
404 thickness position was identied (red dots in Figure 4), and
405 the air free-stream velocity information was recorded and used
406 to compute the average V. At this point, all information for
407 the computation of KLwas available (i.e., interface length, free-
408 stream velocity, uid kinematic viscosity, and the airwater
409 diusion coecient). Then, the average KLfor each segment of
410 the airwater interface was computed. The diusion
411 coecient of O2into water (2 ×109m2s1
412 was used. In addition, the total WAIA for that case was divided
413 by the computational domain volume (4.93 ×108m3)in
414 order to obtain a(m2m3), which allowed the estimation of
415 KLa. The KLavalues estimated using CFD simulations for
416 operational conditions 116 are shown in Table 1. The WAIA
417 estimated from the simulations range from 142 to 235 m2m3.
418 The WAIA did not strongly correlate with the improvement of
419 KLabut it does correlate with V(see Figures S5 and S6 in the
420 Supporting Information). This suggests that under the
421 operational conditions tested in this study, the enhancement
422 in the air ow momentum near the WAIA played a key role in
423 increasing the oxygen mass transfer in the BTF.
424 The simulation results agree (within ±30%) with the
f5 425 experimental values of KLabelow 300 h1(Figure 5). Dorado
426 et al.
determined the mass transfer coecient for four
427packing materials, including PUF, and compared the results
428obtained with several literature correlations. None of the
429existing correlations provided an accurate description of the
430gasliquid mass transfer coecient for PUF. Among the
431correlations evaluated by Dorado et al.,
the equation
432reported by Van Krevelen & Hoftijzer
and the correlation
433proposed by Kim and Deshussees
predicted mass transfer
434coecients nearly 1 order of magnitude lower than the
435experimental results. An attempt of tting our experimental
436results using the constants and equations reported by Kim and
and Van Krevelen and Hoftijzer,
produced on
438average values representing only 23.5 and 18.9% of the
439experimental values, respectively. At this point, it must be
440stressed that other correlations for the estimation of KLain
441packed columns are typically not suitable in PUF-packed BTF
442because relevant parameters, such as the packing equivalent
443diameter, are not available.
444The dierences between the experimental and predicted KLa
445shown in Figure 5 may be due to the fact that this is a 2D
446microsimulation of a limited sample of PUF (0.0158 m ×
4470.0158 m). In addition, neither 3D nor wall eects (the latter
448entailing a local velocity reduction and channeling because of
449the presence of the column BTF inner wall) were considered
450in this simulation. At this point, it should be also stressed that
451the boundary layer theory used to compute the average KL
452values was capable of capturing the dynamics of the system.
453Other theories such as the lm theory, penetration theory, and
454surface renewal theory provided KLavalues one or more orders
455of magnitude lower than their experimental counterparts, likely
456due to the fact that the latter techniques did not include the
457dynamic eects of the moving uids (data not shown). The
458results here obtained highlighted the potential of the CFD
459modeling approach used to describe the volumetric mass
460transfer coecients for dierent air and water ow conditions,
461despite all simplications made in the simulations and the
462small computational domain used to mimic the operation of a
4633D BTF column. A signicant contribution of the present
464study to the eld of gas treatment arises from the detailed
465description of the distribution of water patches formed because
466of the inuence of surface tension in the PUF structure. The
467air ow in the BTF was not sucient to overcome the water
468surface tension, even at the highest air velocities applied in the
Figure 4. Graphical computation of the interfacial area, boundary
layer thickness, and air free stream velocity under the steady state.
Waterair interface is shown as black dots (one dot represents one
computational cell). White patches indicate the presence of water.
Red dots represent the boundary layer interface where the air free-
stream velocity, V, is reached. Blue arrows indicate air velocity
Figure 5. Comparison between simulated (Sim) and experimental
(Exp) results of KLa. Diagonal broken lines limit the match between
experimental and simulation results. White circles show the actual
simulated vs experimental results for the 16 operational conditions
tested. Error bars represent the 95% condence interval for the KLa
values estimated from the experiments in Table 1.
Environmental Science & Technology Article
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Environ. Sci. Technol. XXXX, XXX, XXXXXX
469 experimental and computational runs. However, the combina-
470 tion of water moving downward and air owing upward was
471 capable of breaking the water bubblesdown and help gravity
472 to break the water patches into smaller ones. In this context,
473 the presence of a large number of small patches of water
474 creates a much larger airwater interphase than a large water
475 patch containing the same amount of water. Similarly, a larger
476 airwater interphase mediates a larger specic surface area for
477 O2to dissolve into water, and therefore a higher KLa.
478 Although more experimental validation and CFD model
479 renement are required to attain a realistic description of the
480 system, the CFD modeling platform here developed allows
481 obtaining key operational data at any point of the BTF. For
482 instance, the determinations of the actual gas velocities inside
483 the BTF column are very dicult to obtain experimentally
484 without perturbing the natural ow patterns but could be easily
485 recorded via CFD simulations. Similarly, the inuence of key
486 operational parameters on the interfacial area and free-stream
487 velocities can be easily determined using this novel modeling
488 approach.
490 *
SSupporting Information
491 The Supporting Information is available free of charge at
493 Grid independence analysis for the selection of the grid
494 discretization, schematic of the experimental apparatus
495 used for the determination of the mass transfer
496 coecient, calculation example of the WAIA, analysis
497 of the modeled KLabehavior as a function of the
498 experimental variables, and model tting versus exper-
499 imental data of the DOC concentration in the 16
500 experiments for the determination KLa(PDF)
502 Corresponding Author
503 *E-mail: Phone: + 562 2618 1441.
505 Raúl Muñoz: 0000-0003-1207-6275
506 Alberto Vergara-Ferná
ndez: 0000-0002-6075-9137
507 Notes
508 The authors declare no competing nancial interest.
510 The present work has been sponsored by the CONICYT
511 Chile (National Commission for Scientic and Technological
512 Research) project Fondecyt 1190521. The nancial support
513 from the Regional Government of Castilla y León is also
514 gratefully acknowledged (UIC71 and CLU-2017-09). J.D.
515 thankfully acknowledges funding from projects Fondecyt
516 1180685, CONICYT Basal FB0008, and from Fondo de
517 Ayuda a la Investigacion (FAI), Universidad de los Andes,
518 INV-IN-2017-05.
520 CL,in dissolved O2concentration measured by the electrode,
521 CL,outjdissolved O2concentration of the jth CSTR in the BTF
model, g m3
522 DAB diusion coecient of oxygen in water, m2s1
523 gacceleration of gravity, m s2
524HHenrys law constant for O2
525KLavolumetric mass transfer coecient, h1
526QLrecirculating liquid ow, m3h1
527ppressure vector in space, Pa
528RelReynolds number
529Sc Schmidt number
530Sh Sherwood number
531Uvelocity vector, m s1
532VCpacked bed volume, m3
533VTstirred tank reactor volume, m3
534δmboundary layer thickness, m
535ρuid density, kg m3
536μdynamic viscosity of a uid, kg·m1·s1
537νkinematic viscosity of the gas phase, m2s1
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... Many researchers studied the mathematical and CFD modelling of different masstransfer systems [14,31]. To our best knowledge, however, there is no research on the modelling of the HYSPEC degasser [32], which is crucial equipment in the sulfur production industry, in the literature. ...
... Thus, k L a C can be calculated using Equations (27), (28), (31) and (32) [17]. Substituting k L a C into Equation (25) then can obtain the dissolved gas concentration at outlet. ...
... . Equation (24) The prediction of the CSTR using Equation (31) is in good agreement with the experimental data, as indicated by the discrepancy within ±3.3%. The degassing performance of BC is strongly associated with the type of sparger, which is different from CSTR. ...
Full-text available
This paper presents and compares the mathematical models and computational fluid dynamics (CFD) models for degassing of oxygen from water in a laboratory-scale multi-function gas-liquid contactor under various operating conditions. The optimum correlations of the overall volumetric liquid-phase mass transfer coefficient (kLa) are determined by the mathematical models of specific contactors. Both the continuous-reactor model and semi-batch model can evaluate the degassing efficiency with relative errors within ±13%. Similarly, CFD models agree with experimental data with relative errors of ±10% or less. Overall, the mathematical models are deemed easy to use in engineering practice to assist the selection of efficient contactors and determine their optimum operation parameters. The CFD models have a wider applicability, and directly provide the local mass transfer details, making it appropriate for harsh industrial scenarios where empirical correlations for important quantities are unavailable. Combining these two types of models can effectively guide the design, optimization, and operation of the high-throughput degassing system.
... CFD is based on numerical methods to analyze fluids moving throughout packed bed columns. Recently, CFD coupled with computational tomography (CT) has been used to study preferential flows in a packed bed biofilter [6] and to describe the gas-liquid mass transfer coefficient in a biotrickling filter [7], unveiling the potential of these techniques in the future design of gas-phase bioreactors. Despite the potential of CFD-CT, validation against experimental data becomes crucial to generate a reliable, reproducible, and scalable model [8]. ...
... Despite the potential of CFD-CT, validation against experimental data becomes crucial to generate a reliable, reproducible, and scalable model [8]. For instance, these models were validated against experimental data based on pressure drops [6] and gas-liquid mass transfer coefficients [7]. However, none of the above models could validate the modeled gas flow distributions inside the reactor, which is relevant to improving the design and selecting the proper operating conditions to avoid flow maldistribution and axial dispersion. ...
In this work, the validation of a Computational Fluid Dynamics (CFD) model coupled with a 3D computational tomography (CT) bed description of the gas flow field inside a packed column used for gas biofiltration was conducted using a low-cost metal oxide sensor. The validation was carried out in terms of gas residence time distribution (RTD), which was constructed from the sensor measurements using a mathematical model to filter the transient signal behavior. The coupled CFD-CT model was used to obtain the steady-state velocity field inside the packed bed by numerically solving the incompressible Navier-Stokes equation. Later, the tracer injection was simulated over the obtained velocity field by solving a transport equation for a passive scalar. Finally, the experimental and simulated RTD were compared to validate the model. The comparison between the experiments and the CFD simulations showed good agreement between both shapes of the RTD distribution with a relative difference of 4.167% for the mean RTD, denoting the potential of the proposed methodology to validate the CFD model and predict the moments of the RTD. This methodology can become a very useful tool for the validation of CFD simulations with the final purpose of studying the processes at the microscale undergoing inside packed bed biofiltration reactors.
Full-text available
Organic pollutants in the air have serious consequences on both human health and the environment. Among the various methods for removing organic pollution gas, biotrickling filters (BTFs) are becoming more and more popular due to their cost-effective advantages. BTF can effectively degrade organic pollutants without producing secondary pollutants. In the current research on the removal of organic pollutants by BTF, improving the performance of BTF has always been a research hotspot. Researchers have conducted studies from different aspects to improve the removal performance of BTF for organic pollutants. Including research on the performance of BTF using different packing materials, research on the removal of various mixed pollutant gases by BTF, research on microbial communities in BTF, and other studies that can improve the performance of BTF. Moreover, computational fluid dynamics (CFD) was introduced to study the microscopic process of BTF removal of organic pollutants. CFD is a simulation tool widely used in aerospace, automotive, and industrial production. In the study of BTF removal of organic pollutants, CFD can simulate the fluid movement, mass transfer process, and biodegradation process in BTF in a visual way. This review will summarize the development of BTFs from four aspects: packing materials, mixed gases, micro-organisms, and CFD, in order to provide a reference and direction for the future optimization of BTFs.
A two-phase partitioning microbial fuel cell (TPPMFC) was first time constructed to enhance the mass transfer and removal efficiency of hydrophobic volatile organic sulfur compounds by adding silicone particles as solid non-aqueous phase. The results indicated that the output voltage was gradient increased with the concentration of selected mode propanethiol increased from 100 mg/L to 500 mg/L, but the complete degradation cycle of PT was extended from 12 h to 78 h. The obvious enhancement of microbial fuel cell performance was achieved with 2% (v/v) 0.4 mm silicone particle. The output voltage increased by 95.6% ± 12%, the removal efficiency of 500 mg/L of propanethiol increased from 67% ± 3% to 91% ± 4% within 24 h, and the maximum power density was 26.23 mW/m² (72.5% improvement). The live microorganism on the anodic biofilm was maintained at 95% after a long-term operation, which was benefited from the fluid shear force caused by agitated anolyte and solid non-aqueous phase. Moreover, the dominant microbial in the TPPMFC were Pseudomonas, sulfate-reduced Aquamicrobium, and Acinetobacter, and the propanethiol was biodegraded by them via two different pathways compared with the traditional ones. Finally, the mechanisms of propanethiol removal and power generation in the TPPMFC were analyzed. It is believed that the results provide insight into the application of two-phase distribution technology for the removal of hydrophobic volatile organic compounds in bioelectrochemical systems.
In this study, the effect of rhamnolipids (RL) on m-dichlorobenzene (m-DCB) removal and biofilm was investigated in two biotrickling filters (BTF) (BTF1: blank control; BTF2: RL addition). The critical micelle concentration (CMC) value of RL was 75.6 mg L⁻¹, and the RL could significantly improve the solubilization of m-DCB. The results showed that the optimal concentration of RL was 180 mg L⁻¹. The removal efficiency (RE) of m-DCB dropped by 42.4% for BTF1 no fed with RL and only 28.2% for BTF2 fed with RL when the inlet concentration increased from 200 to 1400 mg m⁻³ at an empty bed time (EBRT) of 60 s. RL increased the secretion of extracellular polymers (EPS) and the ratio of Protein/Polysaccharide, which improved the mass transfer of m-DCB to the biofilm. RL also had a facilitating effect on catechol-1,2-dioxygenase (C12O) enzyme activity. Furthermore, RL increased Zeta potential and facilitated microorganisms to form biofilm. The dominant microorganisms of microbial community were increased and the application of RL promoted the enrichment of them.
Emissions of n-alkanes are facing increasingly stringent management challenges. Biotrickling filtration in the presence of surfactants is a competitive alternative for the enhanced removal of n-alkanes. Herein, sodium dodecyl benzene sulfonate (SDBS) was added into the liquid phase feeding a biotrickling filter (BTF) to enhance the removal of various short-chain n-alkanes from n-hexane (C6) to methane (C1). The removal performance of C6-C1 and microbial response mechanisms were explored. The results showed that the removal efficiency (RE) of n-alkanes decreased from 77 ± 1.3 to 35 ± 5.6% as the carbon chain number of n-alkanes decreased from C6 to C1, under the conditions of an n-alkane inlet load of 58 ± 3.0 g/m3·h and EBCT of 30 s. The removal performance of n-alkanes was enhanced significantly by the introduction of 15 mg/L SDBS, as the RE of C6 reached 99 ± 0.7% and the RE of C1 reached 74 ± 3.3%. The strengthening mechanisms were that the apparent Henry's law coefficient of n-alkanes decreased by 11 ± 1.4-30 ± 0.3%, and the cell surface hydrophobicity of microorganisms improved from 71 ± 5.6 to 87 ± 4.0% with the existence of SDBS. Moreover, the presence of SDBS promoted the succession and activity of the microbial community. The activities of alkane hydroxylase and alcohol dehydrogenase were 5.8 and 5.9 times higher than those without SDBS, and the concentration of the cytochrome P450 gene was improved 2.2 times. Therefore, the addition of SDBS is an effective strategy that makes BTF suitable for the removal of various n-alkanes from waste gas streams.
In this study, a single particle model coupled with a computational fluid dynamics (CFD) model was developed to simulate the mass transfer-biodegradation process in a packed-bed bioreactor for the removal of H2S. Sensitivity analyses of particle size and diffusion coefficient were performed to evaluate their effects on internal diffusion on a single particle scale. The role of the liquid phase was evaluated on the reactor scale. The developed CFD coupled model was then validated by comparing the simulation results to experimental data in terms of removal efficiency at different inlet loads of H2S. A gradual reduction of the internal diffusion effect was observed with increase in the inlet H2S concentration, and internal diffusion had a minor influence on the removal efficiency when the particle size was less than 2.5 mm. The proposed CFD coupled model is potentially a useful tool to investigate the mass transfer-biodegradation behaviors in packed-bed bioreactors with complicated multiscale properties.
Full-text available
Biotrickling filters (BTFs) for hydrophobic chlorobenzene (CB) purification are limited by mass transfer and biodegradation. The CB mass transfer rate could be improved by 150 mg/L rhamnolipids. This study evaluated the combined use of Fe³⁺ and Zn²⁺ to enhance biodegradation in a BTF over 35 day. The effects of these trace elements were analysed under different inlet concentrations (250, 600, 900, and 1200 mg/L) and empty bed residence times (EBRTs; 60, 45, and 32 sec). Batch experiments showed that the promoting effects of Fe³⁺/Zn²⁺ on microbial growth and metabolism were highest for 3 mg/L Fe³⁺ and 2 mg/L Zn²⁺, followed by 2 mg/L Zn²⁺, and lowest at 3 mg/L Fe³⁺. Compared to BTF in the absence of Fe³⁺ and Zn²⁺, the average CB elimination capacity and removal efficiency in the presence of Fe³⁺ and Zn²⁺ increased from 61.54 to 65.79 g/(m³⋅hr) and from 80.93% to 89.37%, respectively, at an EBRT of 60 sec. The average removal efficiency at EBRTs of 60, 45, and 32 sec increased by 2.89%, 5.63%, and 11.61%, respectively. The chemical composition (proteins (PN), polysaccharides (PS)) and functional groups of the biofilm were analysed at 60, 81, and 95 day. Fe³⁺ and Zn²⁺ significantly enhanced PN and PS secretion, which may have promoted CB adsorption and biodegradation. High-throughput sequencing revealed the promoting effect of Fe³⁺ and Zn²⁺ on bacterial populations. The combination of Fe³⁺ and Zn²⁺ with rhamnolipids was an efficient method for improving CB biodegradation in BTFs.
Full-text available
There is growing interest in using advanced imaging techniques to describe the complex pore-space of natural rocks at resolutions that allow for quantitative assessment of the flow and transport behaviors in these complex media. Here, we focus on representations of the complex pore-space obtained from X-ray microtomography and the subsequent use of such ‘pore-scale’ representations to characterize the overall porosity and permeability of the rock sample. Specifically, we analyze the impact of sub-resolution porosity on the macroscopic (Darcy scale) flow properties of the rock. The pore structure of a rock sample is obtained using high-resolution X-ray microtomography (Formula presented.). Image analysis of the Berea sandstone sample indicates that about 2 % of the connected porosity lies below the resolution of the instrument. We employ a Darcy–Brinkman approach, in which a Darcy model is used for the sub-resolution porosity, and the Stokes equation is used to describe the flow in the fully resolved pore-space. We compare the Darcy–Brinkman numerical simulations with core flooding experiments, and we show that proper interpretation of the sub-resolution porosity can be essential in characterizing the overall permeability of natural porous media.
Full-text available
A dynamic model describing physical-chemical and biological processes for the removal of high loads of H2S from biogas streams in biotrickling filters (BTFs) was developed, calibrated and validated for a wide range of experimental conditions in a lab-scale BTF. The model considers the main processes occurring in the three phases of a BTF (gas, liquid and biofilm) in a co-current flow mode configuration. Furthermore, this model attempts to describe accurately the intermediate (thiosulfate and elemental sulfur) and final products (sulfate) of H2S oxidation.. A sensitivity analysis was performed in order to focus parameters estimation efforts on those parameters that showed the highest influence on the estimation of the H2S removal efficiency, the accumulated mass of sulfur and the sulfate concentration in the liquid phase. Biofilm and liquid layer thicknesses, specific growth rate of biomass over elemental sulfur and the H2S global mass transfer coefficient were the parameters that showed the highest influence on model outputs. Experimental data for model calibration corresponded to the operation of the BTF under stepwise increasing H2S concentrations between 2000 and 10000 ppmv. Once the model was calibrated, validation was performed by simulating a stationary feeding period of 42 days of operation of the BTF at an average concentration of 2000 ppmv and a dynamic operation period were the BTF was operated under variable inlet H2S concentration between 1000 and 5000 ppmv to simulate load fluctuations occurring in industrial facilities. The model described the reactor performance in terms of H2S removal and predicted satisfactorily the main intermediate and final products produced during the biological oxidation process.
This study investigated the hydrogen mass transfer limitations in a biotrickling filter inoculated with hydrogenotrophic methanogens for biogas upgrading. A highly sensitive dissolved hydrogen probe allowed measuring concentrations in real-time. Experiments were conducted to test the mass transfer resistance in the gas and liquid films. Results demonstrated that the main resistance resides in the trickling liquid film and that promoting direct gas-biofilm mass transfer could improve upgrading performance by about 20%. Increasing the gas velocity (keeping a constant gas contact time) lowered the upgrading capacity. This was explained by the lowering of the concentration to the average concentration throughout the bed, which resulted a lower reaction rate. At extended gas contact times, the bioreactor shifted from microbial to diffusion limitation, causing lower upgrading capacities. Methane-containing biogas mimics (H2/CH4/CO2) were successfully upgraded to natural gas pipeline standards (>97% methane) with only minor performance reduction compared to upgrading just a H2/CO2 mixture.
This chapter describes the mass transfer characteristics in gas/water/organic solvent systems. The ability of an organic solvent (called NAP, i.e., Non Aqueous Phase) to influence the gas transfer of solutes (Volatile Organic Compounds, VOCs, or oxygen) from the gas phase to the aqueous phase and to affect the gas/liquid interface and the volumetric mass transfer coefficient K L a is considered. The objective of the chapter is to summarize the current knowledge in the comprehension of mass transfer mechanisms. The influent parameters and the possible mass transfer mechanisms are described. Theoretical mass transfer enhancements that could be reached are quantified and compared with experimental data. Moreover, new insights based on the “Equivalent Absorption Capacity” concept are given. Coupled with the ɛ-NTU method, this concept could be used for the determination of the overall mass transfer coefficient K L a in multiphasic gas/water/NAP systems.
A three-phase dynamic mathematical model based on mass balances describing the main processes in biotrickling filtration: convection, mass transfer, diffusion, and biodegradation was calibrated and validated for the simulation of an industrial styrene-degrading biotrickling filter. The model considered the key features of the industrial operation of biotrickling filters: variable conditions of loading and intermittent irrigation. These features were included in the model switching from the mathematical description of periods with and without irrigation. Model equations were based on the mass balances describing the main processes in biotrickling filtration: convection, mass transfer, diffusion, and biodegradation. The model was calibrated with steady-state data from a laboratory biotrickling filter treating inlet loads at 13-74 g C m⁻³ h⁻¹ and at empty bed residence time of 30-15 s. The model predicted the dynamic emission in the outlet of the biotrickling filter, simulating the small peaks of concentration occurring during irrigation. The validation of the model was performed using data from a pilot on-site biotrickling filter treating styrene installed in a fiber-reinforced facility. The model predicted the performance of the biotrickling filter working under high-oscillating emissions at an inlet load in a range of 5-23 g C m⁻³ h⁻¹ and at an empty bed residence time of 31 s for more than 50 days, with a goodness of fit of 0.84.
The removal of cyclohexane from gaseous emissions was studied using a biotrickling filter packed with polyurethane foam. Acivodorax sp. CHX100 was chosen as inoculum due to its ability to use cyclohexane as carbon source. Performance was evaluated by means of different resident times from 18 s to 37 s and concentration levels of 60, 90, 120, 160, 320, 480 and 720 mg C m⁻³, respectively. Removal efficiencies of 80%–99% and elimination capacities in the range of 5.4 g C m⁻³ h⁻¹–38 g C m⁻³ h⁻¹ were achieved for concentrations among 60 mg C m⁻³–480 mg C m⁻³. The removal efficiency decreased to 40% at concentrations of cyclohexane of 720 mg C m⁻³. The dynamics of the microbial population showed the strain CHX100 as predominant during the different operational process of biotrickling filter. The results of this study propose a novel approach for cleaning waste air containing cyclohexane by means of a biotrickling filter.
Rigorous modeling of transport phenomena is essential to reproduce accurately biofiltration systems performance. In this sense, the aim of this study was to investigate the effect of integrating fluid flow dynamics in the development of these bioreactor models, mimicking their hydrodynamics and behavior in a fixed biofilm reactor. 2D bioreactor models were developed using three different well-established tools for modeling bioreactors (AQUASIM, MATLAB, and Computational Fluid Dynamics – CFD), considering from ideal flow patterns to more complex fluid dynamics. A detailed comparison was performed among the results, taking into account the simulation of dissolved oxygen profiles in the liquid phase, inside the biofilm and in the boundary layer along a bioreactor. These models were validated by comparing the simulations with direct measurements obtained by means of dissolved oxygen microsensors of high spatial resolution. In all cases, deviations were below 6%, nevertheless CFD predictions obtained the lowest deviations below 3.5%. Thus, these results underline that CFD techniques are appropriate to model more accurately the performance of fixed-bed biofilm reactors, allowing the study in detail of all the hydrodynamics variables involved in the process. In addition, a 3D CFD model, combining hydrodynamics and biological reactions, was developed and solved to simulate local transient flow and dynamic behaviors of oxygen consumption in the bioreactor. The results of CFD simulations were evaluated in order to know the effect of mass transport phenomena (advection and diffusion) by characterizing hydrodynamics and, finally, to predict the oxygen degradation along the bioreactor.
Understanding the mechanisms of filtration through porous media is relevant in many engineering applications ranging from waste water treatment and aquifer contamination in environmental engineering to estimating the permeability reduction in near wellbore region during drilling or water re-injection in petroleum engineering. In this paper we present a pore-scale approach that models straining through the pore structures extracted from X-ray tomographic images of rock and grain pack samples from the first principles, enabling the examination of current macroscopic models. While continuum models are widely used for fast prediction of the retention profiles and permeability of the host porous medium, they require a number of phenomenological parameters which are derived from matching experimental results. One of these parameters is the rate of entrapment, which is the sink term in the advection-diffusion equation. Here we find the constitutive relationship for the rate of entrapment as a product of the filtration coefficient, velocity, and concentration and validate it by comparing with core flood experiments. Results show that the pore-scale simulation gives close approximations of filtration coefficient when pore bridging and straining are the main particle capture mechanisms.