MICROBIAL DEGRADATION KINETICS OF VOLATILE ORGANIC COMPOUND

MIXTURES IN A BIOFILTER

by

LI WANG

(Under the Direction of James R. Kastner)

ABSTRACT

Biofiltration degradation kinetics of an aldehyde mixture containing hexanal, 2-

methylbutanal, and 3-methylbutanal was investigated using a bench-scale, synthetic medium

based biofilter. The adsorption capacity of the synthetic medium for 3-methylbutanal was 10

times that of compost. Higher moisture content leads to higher removal efficiency. RTD analysis

showed no compaction or channeling. Kinetic analysis suggested an overall first order model

was more appropriate. In the range of 20-50 ppmv inlet each, hexanal had a significantly higher

reaction rate compared to the branched aldehydes. SEM analysis of the medium samples showed

microbial growth suggesting removal of the aldehydes could be attributed to biodegradation.

Methanethiol was added into the system 15 months later. Low removal of methanethiol was

observed, yet the reaction rates of the aldehydes increased. DMDS was formed along the reactor.

An external mass transfer model was fit to the data suggesting the overall reaction was limited by

mass transfer.

INDEX WORDS: Biofilter, synthetic matrix, kinetics, aldehyde, microorganisms, reaction

rate

MICROBIAL DEGRADATION KINETICS OF VOLATILE ORGANIC COMPOUND

MIXTURES IN A BIOFILTER

by

LI WANG

B.E., Beijing University of Chemical Technology, P.R. China, 2001

A Thesis Submitted to the Graduate Faculty of The University of Georgia in Partial Fulfillment

of the Requirements for the Degree

MASTER OF SCIENCE

ATHENS, GEORGIA

2006

© 2006

LI WANG

All Rights Reserved

MICROBIAL DEGRADATION KINETICS OF VOLATILE ORGANIC COMPOUND

MIXTURES IN A BIOFILTER

by

LI WANG

Major Professor: James R. Kastner

Committee: Mark A. Eiteman Keshav C. Das

Electronic Version Approved: Maureen Grasso Dean of the Graduate School The University of Georgia December 2006

iv

DEDICATION

To my mother Jifeng Jiang and my father Qinghua Wang for always being there for me

and encouraging me to work hard and accomplish my goals;

To my husband Chunbao Xu for his help and always believing in me;

To my lovely children, Wenjia and Wenduo, for being my sources of happiness in my

life.

v

ACKNOWLEDGEMENTS

I am grateful to those persons who helped me during my thesis research. I especially

would like to thank my major professor, Dr. James Kastner, for guiding me through every

obstacle I met during my research work. His creativity and tireless effort in researching

sustainable development make me learn a great deal.

I would like to extend my sincere thanks to my committee members, Dr. Mark Eiteman

and Dr. KC Das, for their helpful insights and positive encouragement.

Special acknowledgement goes to Joby Miller, who helped me with the experiment set-

up, sampling, and data analysis. Her expert mechanical and engineering skills, as well as

determination, were invaluable.

I would also like to thank Ph.D. candidate Praveen Kolar, who helped me overcoming

obstacles I met during his busy schedule. Without his help, this thesis would not have been

possible.

vi

TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS.............................................................................................................v

LIST OF TABLES....................................................................................................................... viii

LIST OF FIGURES ....................................................................................................................... ix

CHAPTER

1 FOREWORD ................................................................................................................ 1

2 INTRODUCTION AND LITERATURE REVIEW..................................................... 3

INTRODUCTION.................................................................................................... 3

LITERATURE REVIEW......................................................................................... 5

PROBLEM STATEMENT .................................................................................... 15

NOVELTY OF THIS RESEARCH ....................................................................... 16

OBJECTIVES ........................................................................................................ 17

REFERENCES....................................................................................................... 18

3 BIODEGRADATION KINETICS OF A GASEOUS ALDEHYDE MIXTURE

USING A SYNTHETIC MATRIX............................................................................. 25

ABSTRACT ........................................................................................................... 26

INTRODUCTION.................................................................................................. 27

MATERIALS AND METHODS ........................................................................... 29

RESULTS AND DISCUSSION ............................................................................ 34

CONCLUSIONS.................................................................................................... 45

vii

REFERENCES....................................................................................................... 47

4 EFFECT OF ORGANIC SULFUR ADDITION ON THE BIODEGRADATION OF

AN ALDEHYDE MIXTURE..................................................................................... 70

ABSTRACT ........................................................................................................... 71

INTRODUCTION.................................................................................................. 72

MATERIALS AND METHODS ........................................................................... 73

RESULTS AND DISCUSSION ............................................................................ 75

CONCLUSION AND FUTURE WORK............................................................... 78

REFERENCES....................................................................................................... 80

5 EXTERNAL MASS TRANSFER EFFECTS ON KINETICS OF DEGRADATION

IN A BIOFILTER ....................................................................................................... 92

ABSTRACT ........................................................................................................... 93

INTRODUCTION.................................................................................................. 94

MASS TRANSFER MODEL ................................................................................ 94

RESULTS AND DISCUSSION ............................................................................ 96

CONCLUSIONS.................................................................................................... 99

REFERENCES..................................................................................................... 100

6 CONCLUSIONS....................................................................................................... 107

APPENDIX................................................................................................................................. 109

viii

LIST OF TABLES

Page

Table 2.1. Summary of common biofilter materials properties (Devinny et al., 1999).................24

Table 3.1. Properties of BIOSORBENSTM medium and compost used in adsorption experiments51

Table 3.2. Moisture content (wt%) of the matrix along the reactor operating without direct water

addition and a single humidifier ....................................................................................51

Table 4.1. Reaction rate constants for aldehyde and methanethiol at different inlet concentrations

with 6 L/min flow rate...................................................................................................89

Table 4.2. Estimated first and zero order kinetics of aldehyde and methanethiol degradation in a

synthetic medium packed bed biofilter with 6 L/min flow rate ....................................91

Table 4.3. First order reaction rate constants of aldehyde before and after methanethiol addition91

Table 5.1. Mass transfer model parameters nomenclature and values ........................................105

Table 5.2. First order reaction rate constant comparison at two flow rate for aldehyde

biodegradation .............................................................................................................106

ix

LIST OF FIGURES

Page

Figure 2.1. The biophysical model for biofilm development on a non-porous medium (Swanson,

1997)..............................................................................................................................23

Figure 3.1. The schematic diagram of the bench scale biofilter design.........................................54

Figure 3.2. Comparison of the adsorption capacity of the synthetic matrix ( ) and the compost

( ) for 3-methylbutanal at 23°C...................................................................................55

Figure 3.3. Freundlich model for the compost (A) and the synthetic matrix (B), experimental data

( ) and the fitted model (line) ......................................................................................56

Figure 3.4 Langmuir model for the synthetic matrix, experimental data ( ) and the fitted model

(line) ..............................................................................................................................57

Figure 3.5. The pressure changes after the replacement of the supporting materials: glass wool

( ) and plastic disk ( )................................................................................................58

Figure 3.6. The pressure drop between inlet and outlet of the reactor with plastic disk support as

function of linear velocity at three different operating times: right after loading ( ), 20

days ( ), and 110 days ( )..........................................................................................59

Figure 3.7.Tracer analysis of the bioflter at start-up (A), after 6 months (B), 11 months (C), and

15 months (D) of operation ...........................................................................................60

x

Figure 3.8. The fractional conversion after start-up under two different moisture conditions. A:

20.7% initial moisture, one humidifier, did not add water regularly, 1.95% in the

middle of the reactor; B: 25% initial moisture, two humidifiers, add 60ml water into

the reactor twice a week, 29.37% in the middle of the reactor, Hexanal 3-

methylbutanal 2-methylbutanal ................................................................................61

Figure 3.9. Chromatographs of gas phase samples from the reactor: A) after 22 days operation,

inlet concentration: 33ppmv 3-MB, 48ppmv 2-MB, 27ppmv Hexanal, 4.7L/min flow

rate; B) after 78 days operation, inlet concentration: 56ppmv 3-MB, 70ppmv 2-MB,

712ppmv Hexanal, 4.7L/min flow rate .........................................................................63

Figure 3.10. The response of aldehyde fractional conversion to an increase in moisture content

after a significant decline in microbial activity (Q = 4.7 L/min, 16-39 ppmv hexanal,

25-67 ppmv 2-methylbutanal, 22-56 ppmv 3-methylbutanal) ......................................64

Figure 3.11. Concentration profile along the reactor after loading (A) and after 11 days (B) for

hexanal ( ), 3-methylbutanal ( ) and 2-methylbutanal ( ) – Z is position along the

reactor, 4.7 L/min flow rate...........................................................................................65

Figure 3.12. Kinetic analysis of 3-methylbutanal degradation results using a first order (A), zero

(B), and non-linear (C) model. Note, in this analysis t or time is the packing volume at

the sample position divided by the volumetric flowrate (Q = 4.7 L/min, 22-35 ppmv 3-

methylbutanal)...............................................................................................................66

Figure 3.13. Plot of the measured rate constant (e.g., k1st=Q/V ln(CAo/CA) versus the

associated fractional conversion (X = Cin-CA/Cin) (Q = 4.7 L/min, 22-35 ppmv 3-

methylbutanal)...............................................................................................................67

xi

Figure 3.14. SEM images of the original core (A) and original surface (C), and the core (B) and

the surface (D) after four months of operation treating a mixture of hexanal, 2-

methylbutanal, and 3-methylbutanal .............................................................................69

Figure 4.1. The schematic diagram of the bench scale biofilter design.........................................83

Figure 4.2. Fractional removal of VOC mixtures which include methanethiol ( ),3-

methylbutanal ( ),2-methylbutanal ( ), and hexanal (∇) for one month operation,

after addition of methanethiol to the biofilter at 6 L/min flow rate, 39 s residence time,

and 16-67 ppmv for each compound.............................................................................84

Figure 4.3. Concentration profile along the reactor for 3-methylbutanal ( ),2-methylbutanal

( ),hexanal ( ), methanethiol (∇), and dimethylsulfide ( ), time equals L/U, where

L is length of the reactor, U is linear velocity ...............................................................85

Figure 4.4. Typical inlet (A) and outlet (B) chromatograms of the biofilter showing peaks of

methanethiol (MT), 3-methylbutanal (3-MB), 2-methylbutanal (2-MB), and hexanal.

Internal standard peaks (IS1, IS2) are also shown ........................................................86

Figure 5.1. Concentration profile in stagnant film model............................................................101

Figure 5.2. Diffusion across stagnant film surrounding catalyst pellet .......................................101

Figure 5.3. External mass transfer limitation model for 3-MethylButanal after 1 month (A) and 4

months (B) operation, the actual concentration ( ) and the concentration predicted by

the external mass transfer limiting model ( ) ............................................................102

Figure 5.4. External mass transfer limitation model for 2-MethylButanal after 1 month (A) and 4

months (B) operation, the actual concentration ( ) and the concentration predicted by

the external mass transfer limiting model ( ) ............................................................103

xii

Figure 5.5. External mass transfer limitation model for Hexanal after 1 month (A) and 4 months

(B) operation, the actual concentration ( ) and the concentration predicted by the

external mass transfer limiting model ( ) ..................................................................104

1

CHAPTER 1

FOREWORD

Air pollution is one of the major environmental issues worldwide. Current air pollution

control technologies, such as wet scrubbers, catalytic oxidation, and regenerative thermal

oxidation, in many cases are either inefficient or highly cost, and sometimes introduce other

harmful chemicals. Biofiltration, on the other hand, has been increasingly applied for waste gas

treatment and is becoming the preferred way of treating large volume emissions that contain low

concentrations of contaminants. The reason for this is that biofiltration has low operational and

capital costs since it uses microorganisms to degrade pollutants. It is also an environmentally

friendly process since it only produces water, carbon dioxide, and mineral salts.

Volatile organic compounds (VOCs) such as aldehydes are important environmental

pollutants because they contribute to tropospheric ozone formation and odor generation, which

may lead to health problems. In this research, three industrially relevant aldehydes, 3-

methylbutanal, 2-methylbutanal, and hexanal, were chosen to study their biodegradation

behavior in a biofilter system. In the later phase of the research, methanethiol was added to the

reactor along with the aldehyde mixtures and the kinetic changes were analyzed. The main goal

of this research was to determine the biodegradation kinetics of the aldehyde mixture and the

effect of sulfur compound on the aldehyde degradation kinetics.

This thesis was conducted in the Bioengineering Lab located in the Driftmier Engineering

Center at the Athens campus of the University of Georgia. The thesis was organized as follows.

Chapter 2 is the Introduction and Literature Review, which includes the general background,

previous research, issues not addressed by previous research, an analysis of the problem,

2

objectives and the novelty of this research. Chapter 3 describes the biodegradation kinetics of

aldehyde mixtures. In this chapter, the degradation of a generated gaseous mixture, which

contains three aldehydes, was studied using a synthetic matrix biofitler. The operational

parameters, the matrix characterization, the adsorption phenomena, and the degradation kinetics,

as well as the factors which might affect the kinetics were measured and analyzed. Chapter 4

explores the effect of sulfur compound on the aldehyde biodegradation kinetics. Possible

pathway and reaction mechanism of sulfur compound were described. Chapter 5 explored

external mass transfer model of the biofilter and tested the effect of flow rate on the

biodegradation kinetics.

The standard curves for the three aldehydes and the sulfur compound used to calculate

the concentrations at each position of the reactor are presented in the appendices.

3

CHAPTER 2

INTRODUCTION AND LITERATURE REVIEW

INTRODUCTION

Air pollution is a worldwide environmental issue today. In the USA, approximately 200

million tons of waste gases are released into the air annually (Mycock et al., 1995). According to

EPA, air pollution can not only cause health problems, but also damage the environment and

property. It has caused thinning of the protective ozone layer of the atmosphere, which is leading

to climate change. Increasing traffic, growing cities, rapid economic development, and

industrialization, etc. lead to the exacerbation of the air pollution. The federal Clean Air Act

Amendments (CAAA) required EPA to set National Ambient Air Quality Standards for

pollutants considered harmful to public health and environment. Six criteria air pollutants were

established: five primary and one secondary (Cooper and Alley, 2002). The five primary criteria

pollutants are carbon monoxide (CO), particulate lead, sulfur dioxide (SO2), nitrogen dioxide

(NO2), and particulate matter less than 10 µm in diameter (PM-10). The secondary criteria

pollutant is ozone (O3). Although volatile organic compounds (VOCs) and total reduced sulfur

compounds (TRS) are not listed as criteria pollutants, they are recognized as primary pollutants,

and sometimes as hazardous air pollutants because of their large emissions and toxic nature.

VOCs are organic compounds which can evaporate at ambient temperatures and exist in

the atmosphere in gaseous form or adsorbed on particles. It includes both saturated hydrocarbons

and partially oxidized hydrocarbons such as organic acids, aldehydes, and ketones. Most of the

VOCs are merely odorous; however some of them are acutely toxic. They can cause eye and

4

respiratory irritation, irritability, inability to concentrate, and sleepiness. VOCs are emitted from

manufactures of organic chemicals, polymers and herbicides, as well as rendering operations,

painting, printing, and metal degreasing. Certain VOCs can also react with oxides of nitrogen in

the present of sunlight to form photochemical oxidants, including ozone, a toxic compound

which must be controlled. According to Cooper and Alley (2002), 100 parts per billion parts

(ppb) of ozone and other oxidants can cause severe eye irritation, and 2 parts per million parts

(ppm) can cause severe coughing. The major VOCs that have been qualitatively identified as

potential emissions include organic sulfides, disulfides, C-4 to C-7 aldehyhdes, trimethylamine,

C-4 amines, C-3, C-4, C-5 and C-6 organic acids, etc.

Aldehydes are present in the emissions of many industries including poultry and red meat

rendering, wastewater treatment, particleboard and medium density fiberboard manufacturing

(Baumann et al., 2000), cooking operations (Andres et al., 2004), and fuel combustion. Although

aldehyde concentrations are low, aldehydes might still cause chronic toxic effects to both human

body and the environment (EPA). They can also contribute to local ozone and particulate matter

formation and are considered volatile organic compounds (VOCs).

Sulfur compounds are another category of VOCs that commonly exists in the pollutant

air. Numerous industrial operations including wastewater treatment, petrochemical refining,

rendering plant, food processing, fuel treatment, compost and paper manufacturing produce

gaseous sulfur compounds. For example, during the production of compost to be used as a

mushroom cultivation substrate, many sulfur compounds including H2S, COS, MeSH, CS2,

Me2S2, and Me2S3 were the main odorous compounds in the emitted gases ranging from 24 to

840 ppbv (Derikx et al., 1990).

5

LITERATURE REVIEW

Current air pollution control technologies

Current technologies for air pollution control are briefly described and their advantages

and disadvantages discussed too.

The non-biological processes to remove air pollutants include gas phase methods, liquid

phase methods, solid phase methods, and physical/chemical processes (Ottengraf, 1986).

Masking is a gas phase method of adding a strongly smelling component to mask the odor.

Chemical reaction with ozone is used to oxidize the waste gases with ozone, but it is no longer

used because of its harmful effects and the cost of process. For liquid phase methods, the

components are absorbed into a liquid phase to achieve the objective of elimination. However

there are two major problems: (1) It requires that the components are water-soluble; (2) post-

treatment is needed to remove the components from liquid phase, or incomplete reaction may

occur. Solid phase methods are used to contact the waste gas with a solid phase. The components

in the gas adsorb by physical adsorption or chemisorption. The disadvantage of this method is

the necessity of regeneration of adsorbent. Combustion burns the components into carbon

dioxide and water. The limitation of this approach is the high cost due to high combustion

temperature especially when the level of contaminates present in the air stream is low.

Regenerative thermal oxidation uses regenerative heat recovery for oxidizing HAPs and

CO to remove odorous compounds, destroy toxic compounds, and reduce the quantity of

photochemically reactive VOCs released to the atmosphere. Although it can help in the complete

elimination of the VOCs, the high operating costs and the production of large amounts of

greenhouse gases (i.e. CO2) both from thermal oxidation and burning of fuel make it an

uneconomical and environmentally unsustainable process. Wet scrubbers are used to treat

6

reduced sulfur fraction in many emissions (Seiwert, 1997) and to remove odor at various

rendering plants (Kastner and Das, 2002). However wet scrubbers is ineffective for aldehyde

removal (Kastner and Das, 2002). In addition, this process is costly, since oxidizing chemicals

like ClO2 or NaOCl are continuously required.

The biological methods typically include bioscrubbers, biotrickling filters, and biofilters.

A bioscrubber contains an absorption tower and bioreactor. The contaminant in the gas phase

transfers into the liquid phase and then is degraded by the microorganism in the bioreactor. In

both biotrickling filters and biofilters, the microorganisms are immobilized in the packing

material (Ottengraf, 1987). There is a continuous irrigation of the nutrient liquid in biotrickling

filter. Among these three processes, bioscrubbers require high water solubility compared to

biotrickling filters and biofilters (Kennes and Thalasso, 1998). Bioscrubbers and trickling filters

are more energy intensive than biofilters because of their water recirculation requirement. The

advantages of biofiltration are that it does not need extra energy as long as it can support the

survival of the microorganism, it can be operated at ambient temperature and pressure, and this

process does not give rise to other new environmental problems. Compared to the non-biological

processes, the biological technologies are more economical, more efficient, and environmentally

benign processes.

Biofiltration

In biofiltration, porous medium such as compost or peat are packed into a bioreactor.

When an appropriate environment is provided, the microorganisms will grow on the surface of

the particles and form a layer called biofilm. As the air stream which contains odorous or organic

compounds passes through the bioreactor, the components are transferred into this film and

degraded by the microorganisms. The principles governing biofiltration involve three steps as

7

shown in Fig. 2.1: (1) the chemicals cross the interface between gas flow and biofilm

surrounding the solid medium; (2) the chemicals diffuses through the biofilm to a consortium of

acclimated microorganisms; (3) the microorganisms use VOCs as an energy and carbon source,

or cometabolize them via nonspecific enzymes (Swanson, 1997).

The above model is for the gas-phase filter bed, in which the number of mass-transfer

units is generally much higher than that in liquid-phase filter bed. This means that interface

resistance in the gas phase can generally be neglected and therefore the biolayer concentration at

the interface may be assumed to be in equilibrium with the concentration of the bulk gas.

There are a lot of parameters which may affect the performance of biofiltration. The most

important parameters are explored here.

Moisture content is a key parameter in biofiltration because the presence of water is

essential to ensure optimal microbial activity (Atlas, 1989; VanDemark and Batzing, 1987).

However, too high moisture content could lead to the formation of stagnant zones with diffusion

limitation and possible anaerobic conditions (Ottengraf and Van den Oever, 1983) or increased

pressure drop (Van Langenhove, 1986 and Van Lith, 1990). Ottengraf (1986) suggested

maintaining the bed moisture content between 40% and 60% by weight. Leson and Winer (1991)

also mentioned that the biofilter bed should be kept at a moisture content of 40 to 60% to

maintain an environment that is moist enough to meet the requirements of the micro-organisms

and yet not wet enough to lead to the development of anaerobic conditions. Two ways to control

the moisture content are: (1) use a spray system dispersing water directly on the filter-bed and (2)

indirectly regulate the moisture content through humidification of the in-going polluted air.

Temperature can also affect the biofiltration performance. Ronald et al. (2002) reported

that the biofilter achieved a greater removal efficiency at higher temperatures, and the time to

8

achieve steady state increased from less than 1 day to 2 to 3 days as the temperature was

decreased from 25 to 15 oC. Some research indicates that quite low temperatures can be used

without significant microbial deactivation and an operational temperature range of 10-20 oC has

been reported (Cho et al., 1992). Chung et al. (1996) reported that the optimum temperature for

hydrogen sulfide removal using a biofilter was 30oC, and removal efficiency decreased

approximately 65% at 50oC. The removal efficiency will decrease if the temperature exceeds the

optimum temperature for microbial viability. The biodegradation process is exothermic which

can cause a decrease of the air stream humidity and provoke a significant filter-bed temperature

increase (Yang and Allen, 1994), thus dry out the filter-bed. Typically, the temperature is

between 20 to 40 oC.

In the process of biofiltration, air pollutants are degraded by microorganisms either as

energy/carbon source or as a co-metabolic substrate of key enzymes. The outcome is that they

are transformed into carbon dioxide, or partially oxidized products, hydrogen sulphide,

ammonia, etc. which can increase or decrease the pH of the filter-bed. Le Cloirec and et al.

(2001) observed a low pH varying from 3 to 5 during biofiltration of ethanol because one

degradation product of ethanol is acetic acid. This lower pH will inhibit some of the microbial

activity. So the regulation of the pH is another concern in biofiltration. Usually, it is controlled

around neutral.

Residence time represents the amount of time that an inert tracer spends in the reactor.

High flow rate and thus low residence time decreases the removal efficiency and elevates the

pressure drop along the reactor (Le Cloirec et al., 2001). Consequently, longer residence times

produce higher removal efficiencies; however, a design must minimize residence time to allow

9

the biofilter to accommodate larger flow rates. The typical residence time is from 30 to 60

seconds.

Loading rate is another parameter in biofiltration, which is used to define the amount of

air or contaminant that is being treated. A different loading rate will result in different

biodegradation pattern. Some biofilter studies have showed that a higher loading rate leads to

lower removal efficiency (Le Cloirec et al., 2001). There are different definitions for loading rate

(Devinny et al., 1999). Surface loading rate is defined as the volume of gas per unit area of filter

material per unit time (in metric units as m3 of gas per m2 of bed surface per hour). Volumetric

loading rate is defined as the volume of gas per unit volume of filter material per unit time (in

metric units as m3 of gas per m3 of filter material per hour). The mass loading rate (either surface

or volumetric) is the mass of the contaminant entering the biofilter per unit area or volume of

filter material per unit time, often expressed as grams per m2 or m3 of filter material per hour.

The pressure drop of the gas phase passing through the biofilter can contribute to the

treatment cost (Kennes and Thalasso, 1998). The gas flow rate, particle size and biomass are

factors that influence the pressure drop. There are several ways to decrease the pressure drop and

include: (1) minimize filter-bed height, (2) do not use a matrix of too small a particle size

because small particles create greater pressure drop (Yang and Allen, 1994); and (3) reduce or

minimize biomass growth (Holubar and Braun, 1995).

A lot of packing materials can be used in biofiltration, and include peat, compost, soil

beds, and engineered matrix, etc. According to Clark and Wnorowski (1992), almost any organic

material presenting a satisfactory structure and composition could be used. The most important

physical characteristics the medium should have are: (1) high surface area, for optimum

10

microbial development, (2) low bulk density for easiest and cheapest carrier operation, and (3)

high void fraction to limit pressure drop and clogging problem (Kennes and Thalasso, 1998).

Soil beds can offer a rich and varied microflora. However, they contain only a few

intrinsic nutrients and present low specific surface areas and high bulk density, which lead to

clogging and short circuiting, thus generate high pressure drop (Swanson and Loehr, 1997). Peat

is preferred as a support medium because of its absorption/adsorption properties, high cellulose

content, large moisture retention capacity and buffering capacity (Beerli and Rotman, 1989).

However, peat contains neither high levels of mineral nutrients nor a dense indigenous

ecosystem, and the resources of peat are limited (Guérin et al., 2001). Wood chips and barks

were also studied as filtering materials. Due to their low pH-buffering capacity, low specific

surface areas and low nutrient content, their performances in biofiltration were less satisfactory

than compost or peat (Smet et al., 1996). Compost has been widely used for its high air/water

permeability, high water holding capacity, high microbial population and low cost (Smet et al.,

1996). However, composts are often less stable than soils and peats because they tend to break

down and to become compacted, leading to the increase of pressure drop (Delhomenie and Heitz,

2005). Therefore, the filter bed usually requires blending in some inert materials like wood chips,

polystyrene, perlite to prevent compaction (Ottengraf and Konings, 1986) and has a typically 2-4

years lifetime (Devinny et al., 1999).

Compared with the conventional packing, the synthetic packing materials do not have the

problem of aging and compaction, but are expensive and must be inoculated before use.

Therefore, the choice between conventional and synthetic filter medium requires us to consider

their characteristics and effects on biofiltration performance comprehensively. A property

summary of common packing materials is included in table 2.1.

11

Different filter medium are different in their particle size, surface area, morphology, and

chemical characteristics, which lead to the different performances on moisture retention capacity,

buffering capacity, absorption/adsorption properties, air/water permeability, fraction, and

quantity of microbial population. For example, the adequate value of moisture content by weight

is 30-80% for peat, compost and wood subproducts, and 10-20% for soil-bed systems (Kennes

and Thalasso, 1998). While for pH, soil presents a higher buffering capacity than compost, which

is five times more buffered than wood bark, and peat has no buffering capacity at all (Smet et al.,

1996). For pressure drop, soil induces the highest pressure drop, followed by compost, peat and

finally wood bark (Kennes and Thalasso, 1998).

The moisture content, as mentioned before, is important for maintaining the growth of

microorganisms. The buffering capacity determines the stability of filter-bed pH which is

another requirement for microbial growth. Absorption/adsorption properties and air/water

permeability will decide how fast and easy the chemicals are transferred and diffuse into the

biofilm, which affects the kinetics directly. As to particle size, large particles will cause clogging

and thus slow down the mass transfer process. Therefore, all of these factors should be

considered to choose suitable packing materials in biofilter design.

Biodegradation kinetics

Theoretical models have been developed for understanding the biodegradation processes

in biofilters. Early models were developed to explain the removal of only one single contaminant

which adopted the Monod type rate equation (Jennings et al., 1976). Then Ottengraf et al.

derived the design equations to predict the fractional removal based on two extreme conditions

of Monod type rate equation, zeroth and first order kinetics (Ottengraf, and Van den Oever,

12

1983). Deshusses et al. also developed design equations for a contaminant based on Michaelis-

Menten rate equation (Deshusses et al., 1995a; Deshusses et al., 1995b).

Many researchers have studied the biodegradation kinetics in a biofilter. Tang et al.

(1996) set up a simplified model, where mass transfer is assumed to take place in a wet biolayer

surrounding each packing particle. They found that the biodegradation rate tends to be

independent of contaminant concentration (zero-order) for all compounds when this

concentration is high, while the degradation rate is proportional to the concentration (first-order)

when the concentration is low. These two situations were reported previously by Ottengraf

(1986).

Some work has been completed on the multiple compounds biofiltration. Smet et al.

(1997) found that in biofiltration, the injection of isobutyraldehyde (IBA) will decrease the

elimination efficiency of dimethyl sulfide from 100% to 76% in compost biofilter, but IBA’s

elimination was not affected. While in the case of toluene and dimethyl sulfide, although the

elimination efficiency of dimethyl sulfide was not affected, toluene was not degraded at all.

Hwang et al. (2003) studied the effect of a different strain of bacteria on the inhibition of ethyl

acetate on toluene degradation. Mohseni and Allen (2000) observed that the presence of

methanol depressed the α-pinene removal because methanol is hydrophilic, thus easily

transformed into the biofilm and easily biodegraded. These results indicated that there may exist

microorganisms that can utilize both compounds, but preferentially utilize certain compounds.

Although many experiments have been conducted to study the biofiltration removal of a

single compound and the effect of one compound on the other (Smet et al., 1997; Hwang et al.,

2003; Mohseni and Allen, 2000), few have been performed on biodegradation of multiple

13

contaminants. More complicated models are needed to explain the biofiltration process where

multiple contaminants are used.

Biofiltration process involves three steps as shown in Fig.2.1 (Swanson and Loehr,

1997): transfer from gas phase to biofilm, diffusion in biofilm and biodegradation. Thus, the total

elimination rate depends on transfer, diffusion and degradation. Therefore, three aspects should

be considered in the study of biodegradation kinetics: (1) the growth of microorganisms, (2)

mass transfer from gas phase into liquid phase and the diffusion of the chemicals in biofilm, and

(3) the utilization by microorganisms. For gas mixtures, the potential inhibition and competition

mechanism between the multiple substrates should also be studied.

Biofiltration uses microorganisms to degrade chemical components as their carbon source

or energy source. Therefore the organism plays a very important role in biofiltration. A decrease

in microbial quantity will lead to a decrease in the removal rate, while over-growth can cause

clogging and a thick biofilm which may slow down the mass transfer rate of both VOCs and O2

and thus decrease the removal efficiency.

The growth of the microorganisms has been observed in a biofilter system. Acuna et al.

(1999) reported that in a toluene biofilter using peat as packing, the consortium was inoculated in

the biofilter with 7×107 bacteria and 3×105 yeast per gram of dry peat. After 12 days of

operation, the quantity increased to 3.6×1010 and 5.3×109 cfu/g, respectively. On 28th day of

operation, the microbial levels increased further up to 8.1×1011 and 7.9×109 after ammonia was

added as nitrogen source. On the 88th day, a slight increase in microbial levels was measured.

The relationship between the bacteria growth rate and the substrate concentration can be

formulated by Monod equation (1949), which was written as:

SKS

s += maxµ

µ (1)

14

Where µ is the specific growth rate of the bacterium, maxµ is its maximum specific growth rate

(which occurs at the higher range of substrate concentrations), S is the substrate concentration,

and sK is a constant that represents the substrate concentration at which the rate of growth is

half the maximum rate. Then the degradation rate can be written as:

XSKS

Xrs +

== maxµµ (2)

where X is the biomass, r is the degradation rate. If the assumption of constant biomass, which

means the non-growth phase, was made, then the degradation rate depends on the substrate level

S.

According to Ottengraf (1986), when skS ≤ , the rate expression approaches first order

kinetics in the substrate concentration, and when skS ≥ , zero order kinetics is obtained. The

differential forms are

SkdtdS

1=− (3)

2kdtdS

=− (4)

Ottengraf (1986) found that the reaction rate is zero-order and the elimination rate becomes

reaction-controlled when gas phase concentration is at high level, while the reaction rate is first-

order and the elimination rate becomes diffusion-controlled when at low gas phase concentration

or low water solubility of the contaminants. To obtain a high removal efficiency, mass transfer

and diffusion should be improved, which can be achieved by changing the inlet concentration,

employing different kind of matrix, using smaller size particle, increasing flow rate, etc.

15

Nutrient addition

The presence of nutrients in the biofiltration medium is required for the maintenance of

microbial activity and the consequent removal of air contaminants. Undersupply nutrition will

cause the slow degradation and thus a low removal efficiency, while oversupply of nutrition will

lead to biomass overgrowth with eventual clogging (Wubker et al., 1997). Acuña et al. (2002)

tested four different concentrations of base nutrient solution (KH2PO4, K2HPO4, MgSO4, CaSO4,

FeSO4, and (NH4)2SO4) and found that toluene consumption rates were delayed in a peat biofilter

medium amended with high nutrient concentration, but increased gradually reaching higher

values than those obtained with lower nutrient concentrations. However, the toluene

consumption decreased up to cell maintenance levels in all cases over long period (more than 60

days). Morgan-Sagastume et al. (2001) studied the effect of biomass growth on gas pressure drop

in biofilters. They found that higher biomass levels caused by excess nutrient addition leads to

higher pressure drop (2600 Pa/m vs. 550 Pa/m). Therefore, the amount of nutrients added and the

concentration, frequency and type of nutrients needed remain elusive in biofiltration.

PROBLEM STATEMENT

As stated before, the filter bed can affect biofiltration performances. In this research, two

packing materials, traditional (i.e. compost) and synthetic (product of Biorem company) matrix

were tested. Compared to compost, the synthetic matrix is more expensive, has a higher density

and a lower water holding capacity. However, much higher specific surface area, no compaction

and channeling, higher stability, and longer life span provide superiority in biofiltration. The

adsorption capacity is one of the most important properties of the packing materials. High

adsorption capacity will enhance mass transfer rate, thus increase removal efficiency. It can also

16

buffer inlet fluctuation. When the inlet concentration increases, more substrate will be adsorbed

into the medium; while the inlet concentration decrease, substrate will be desorbed which helps

maintain microbial activity. Therefore, the adsorption measurements of these two materials were

carried out and it was expected that the synthetic matrix has higher adsorption capacity than that

of compost.

The literature analysis indicates that a lot of studies have been done on biodegradation

kinetics, yet few of them were focused on the biodegradation kinetics of multiple substrates.

However, emissions from industries usually contain multiple compounds. The kinetics of

multiple compounds can be very different from that of a single compound. We propose to test

the biodegradation kinetics of an aldehyde mixture which contains 3-methylbutanal, 2-

methylbutanal, and hexanal. These compounds were identified by our research group from a

rendering plant, and little research has been performed on the microbial degradation of these

compounds.

Sulfur compounds were also found in waste gases. A lot of research has been done on the

biofilter degradation of sulfur compounds. Here, we will study methanethiol since it has been

detected in the rendering plant emissions along with aldehyde. The effect of methanethiol on the

aldehydes biodegradation kinetics and the possible mechanism will be studied.

NOVELTY OF THIS RESEARCH

The aldehyde mixture, which contains 2-methylbutanal, 3-methylbutanal, and hexanal,

include major compounds identified in the emissions from rendering processes. Typical

emissions from poultry rendering include dimethyl disulfide, methanethiol, and octane. The two

branched aldehydes, 2-methylbutanal and 3-methylbutanal, were by far the most consistent,

17

appearing in every sample and typically the largest fraction of the VOC mixture (Kastner and

Das, 2002). However, only limited studies on biodegradation kinetics of the aldehydes have been

reported, especially for multiple aldehyde biofiltration. This is the reason why the aldehyde

mixture was chosen as the target compounds here.

A synthetic medium was applied as the packing material in the biofilter . This medium

has higher surface area, more strength than those of compost.

OBJECTIVES

1. Compare properties of synthetic medium and conventional medium (i.e., compost) and

perform adsorption test

2. Measure biofilter parameters and evaluate

3. Find rate law of aldehyde mixtures in continuous biofiltration

4. Determine the effects of organic sulfur compound on aldehyde degradation kinetics

5. Set up external mass transfer model and study the effect of flow rate on degradation

kinetics

18

REFERENCES

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concentration on biofilm formation on peat and gas phase toluene biodegradation under

biofiltration conditions. Process Biochemistry. 38: 7-13

2. Acuna, M.E., F. Perez, R. Auria, S. Revah. 1999. Microbiological and kinetic aspects of a

biofilter for the removal of toluene from waste gases. Biotechnology and Bioengineering,

63(2): 175-184.

3. Andres, F., C.B. Angel, S. Sukh. 2004. Volatile aldehyde emissions from heated cooking

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4. Atlas, R. 1989. Microbiology, fundamentals and applications. MacMillan, New York.

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Jacksonville, FL:1-32

7. Cho, K., M. Hirai, M. Shoda. 1992. Enhanced removal efficiency of malodorous gases in a

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73: 46-50

8. Chung, Y.C., C. Huang, C. P. Tseng. 1996. Operation and optimization of thiobacillus

thioparus CH11 biofilter for hydrogen sulphide removal. J. Biotechnol., 52, 31.

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matrix formulations on the capacity and efficiency of odorant removal by an experimental

19

biofilter. Biotechniques for air pollution abatement and odour control policies, eds A.J.

Dragt & J.van Ham. Elsevier, Amsterdam, The Netherlands, 183-6

10. Cooper, C.D., and F.C. Alley. 2002. Air pollution control: A design approach (3rd edition).

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11. Delhomenie, MC., M. Heitz. 2005. Biofiltration of air: a review, Critical Reviews in

Biotechnology, 25: 53-72

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1990. Odorous sulfur compounds emitted during production of compost used as a substrate

in mushroom cultivation. Appl. Environ. Microbiol. 56: 176-180

13. Deshusses, M.A., G. Hamer, I.J. Dunn. 1995a. Behavior of biofilters for waste air

biotreatment. I. Dynamic model development, Environ. Sci. Technol. 29: 1048-1058

14. Deshusses, M.A., G. Hamer, I.J. Dunn. 1995b. Behavior of biofilters for waste air

biotreatment. II.Experimental evaluation of a dynamic model, Environ. Sci. Technol. 29:

1059-1068

15. Guérin, V., F. Lemaire, R. Caceres, F. Giuffrida. 2001. Growth of Viburnum tinus in peat-

based and peat-substitute growing medium. Scientia Horticulturae. 89: 129-142

16. Holubar, P., R. Braun. 1995. Biofiltration-bottlenecks in biological air purification and

possible future solutions. Meded. Fac. Landbouww. Univ. Gent. 60: 2303-12

17. Hwang, SC. J., CM. Lee, HC. Lee, H. F. Pua. 2003. Biofiltration of waste gases containing

both ethyl acetate and toluene using different combinations of bacterial cultures. J.

Biotechnol. 105:83-94

18. Jennings, P.A., V.L.Snoeyink, E.S.K. Chian. 1976. Theoretical model for a submerged

biological filter, Biotechnol. Bioeng. 18: 1249-1273

20

19. Kastner, J.R. and K.C. Das. 2002. Wet scrubber analysis of volatile organic compound

removal in the rendering industry. Journal of Air and Waste Management Association, 52:

459-469

20. Kennes, C., F. Thalasso. 1998. Waste gas biotreatment technology, J. Chem. Technol.

Biotechnol. 72: 303-319

21. Le Cloirec, P., P. Humeau, E.M. Ramirez-Lopez. 2001. Biotreatments of odours: control and

performances of a biofilter and a bioscrubber, Water Science and Technology. 44(9): 219-

226

22. Leson, G. and A.M. Winer. 1991. Biofibation: an innovative air pollution control technology

for VOC emissions. J. Air Waste Manage. Assoc. 41 (8): 1045-1054

23. Martin, R.W., J.H. Li, J.R. Mihelcic, J.C. Crittenden, D.R. Lueking, C.R. Hatch, P. Ball.

2002. Optimization of Biofiltration for Odor Control: Model Calibration, Validation, and

Applications. Water Environment Research 74:11-27

24. Mohseni, M., D.G. Allen. 2000. Biofiltration of mixtures of hydrophilic and hydrophobic

volatile organic compounds. Chemical Engineering Science. 55: 1545-1558

25. Monod, J. (1949) Annu. Rev. Microbiol. 3: 371-394

26. Morgan-Sagastume, F., B.E. Sleep, D.G. Allen. 2001. Effects of biomass growth on gas

pressure drop in biofilters. J. Envir. Engrg., 127(5): 388-396

27. Mycock, J.C., J.D. Mckenna, L. Theodore. 1995. Handbook of air pollution control of

engineering and technology. Lewis Publishers.

28. Ottengraf, S.P.P. 1986. Exhaust gas purification. Biotechnology, VCH Verlagsgesellschaft,

Weinheim. 8: 427-452

21

29. Ottengraf, S.P.P. and A.H.C. Van den Oever. 1983. Kinetics of organic compound removal

from waste gases with a biological filter. Biotechnol. Bioeng. 25: 3089-102

30. Ottengraf, S.P.P. and J.H.G. Konings. 1986. Bioprocess Eng. 1: 61-69

31. Seiwert, J.J. Pulp Mill TRS/VOC/HAPs reductions (HVLC NCGs) using regeneratibe

thermal oxidation (RTO) technology. The 1997 Environmental Conference and Exhibit part

2, Minneapolis, MN. TAPPI PROC ENVIR CONF EXHIB, TAPPI PRESS, NORCROSS,

GA, USA.1: 67-68

32. Smet, E., G. Chasaya, H. Van Langenhove, W. Verstraete. 1996. The effect of inoculation

and the type of carrier material used on the biofiltration of methyl sulphides. Appl.

Microbiol. Biotechnol. 45: 293-8

33. Smet, E., H. Van Langenhove, W. Verstraete. 1997. Isobutyraldehyde as a competitor of the

dimethyl sulfide degrading activity in biofilters. Biodegradation 8: 53-59

34. Swanson, W.J., R.C. Loehr. 1997. Biofiltration: fundamentals, design and operations,

principles, and applications. Journal of environmental engineering 123: 538-546

35. Tang, H.M., S.J. Hwang, S.C. Hwang. 1996. Waste gas treatment in biofilters. J. Air

&Waste Manage. Assoc. 46: 349-354

36. VanDemark, P., B. Batzing 1987. The Microbes: An introduction to their nature and

importance. Benjamin/Cummings, Menlo Park, California.

37. Van Lith, C., S.L. David, R. Marsh. 1990. Design criteria for biofilters. Trans IChemE. 68:

127-32

38. Wubker, S.M., A. Laurenzis, U. Werner, C. Friedrich. 1997. Controlled biomass formation

and kinetics toluene degrading in a bioscrubber and in a reactor with periodically moved

tickle-bed. Biotechnol Bioeng. 55: 686-92

22

39. Yang, Y. and E.R. Allen. 1994. Biofiltration control of hydrogen sulfide. 2. Kinetics,

biofilter performance and maintenance. J. Air & Waste Management Assoc. 44: 1315-21

23

Figure 2.1. The biophysical model for biofilm development on a non-porous medium (Swanson, 1997)

24

Tab

le 2

.1. S

umm

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of c

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on b

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199

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25

CHAPTER 3

BIODEGRADATION KINETICS OF A GASEOUS ALDEHYDE MIXTURE USING A

SYNTHETIC MATRIX1

1 Wang, L., P. Kolar, J.R. Kastner. To be submitted to J. Air & Waste Manage. Assoc.

26

ABSTRACT

Biofiltration degradation kinetics of an aldehyde mixture containing hexanal, 2-

methylbutanal, and 3-methylbutanal was investigated using a bench-scale, synthetic medium

based biofilter. The adsorption capacity of the synthetic medium for a model VOC, 3-

methylbutanal, was 10 times that of compost. Periodic residence time distribution analysis (over

the course of one year) via a tracer study (84-99% recovery), indicated plug flow without

channeling in the synthetic medium and lack of compaction in the reactor. Simple first-order and

zero-order kinetic models both equally fit the experimental data, yet analysis of the measured

rate constants versus fractional conversion suggested an overall first order model was more

appropriate. Kinetic analysis indicated that hexanal had a significantly higher reaction rate (k1st

order = 0.0998 ± 0.0059 1/s; 18-28 ppmv) compared to the branched aldehydes (k1st order =

0.0505±0.0188 1/s; 21-46 ppmv). After 3 months of operation, all three compounds reached

100% removal (50 sec residence time, 18-46 ppmv inlet). Medium samples withdrawn from the

biofilter and observed under SEM analysis indicated microbial growth, suggesting removal of

the aldehydes could be attributed to biodegradation.

Key Words: biofiltration, aldehyde, kinetics, adsorption, isotherm, microorganism

27

INTRODUCTION

Aldehydes are present in the emissions of many industries including animal rendering,

wastewater treatment, particleboard and medium density fiberboard manufacturing (Melissa et

al., 2000), cooking operations (Andres et al., 2004), and fuel combustion. Aldehydes are known

to contribute to ozone and particulate matter formation, and even low concentrations can cause

health problems, such as asthma (EPA).

Increasing concerns about air quality and more stringent national and international

regulations have led to the development and improvement of air pollution control processes for

volatile organic compounds (VOCs). Traditional methods used to eliminate VOCs from

industrial emissions primarily include physical and chemical methods. Physical methods (e.g.,

absorption, adsorption) have two disadvantages: the VOCs are not eliminated, they are just

transferred from one phase to another, and the sorbents have to be regenerated. Thermal

oxidation can eliminate a wide range of VOCs, but requires high energy input and emit

additional carbon dioxide (for low concentration VOC emissions). Chemical wet scrubbers

require costly oxidizing chemicals (e.g., ClO2, NaOCl) and can produce chlorinated

hydrocarbons if not properly controlled. On the other hand, biofiltration is based on the

biodegradation of VOCs by microorganisms immobilized on the surface of a medium at ambient

temperatures (Ottengraf and Van den Oever, 1983; Leson and Winer, 1991). Compared to the

non-biological processes, the biological technologies are more economical, efficient, and

environmentally benign.

Most biofilters use either natural organic medium or synthetic medium. Organic medium

typically include soil beds, peat, and compost, which are abundant and low-cost (Beerli and

Rotman, 1989; Smet et al., 1996). However, organic medium (e.g. compost) are prone to

28

compaction or clogging and thus can cause channeling or an increased pressure drop of the filter

bed (Morgan-Sagastume et al., 2003). Compared with the organic medium, synthetic packing

materials (e.g. activated carbon, ceramic pellets) do not age and undergo compaction, but are

more expensive and need inoculation before use. However, synthetic medium have many desired

physical and chemical properties, such as a higher adsorption capacity, controlled particle size,

and strength, which might enhance removal rates and increase reactor longevity. These

advantages were verified by Hirai et al. (2003) as they found that NH3 removal capacities highly

depended on the physical and chemical properties of the inorganic matrix, i.e., medium with high

porosity, maximum water content, and suitable mean pore diameter showed excellent removal

capacity.

Theoretical models have been developed for understanding the biodegradation processes

in biofilters. Early models were developed to explain the removal of only one contaminant which

adopted the Monod type rate equation (Jennings et al., 1976). Then Ottengraf et al. (1983)

derived the design equations to predict the fractional removal based on two extreme conditions

of Monod type rate equation. Deshusses et al. (1995a and 1995b) also developed design

equations for a contaminant based on Michaelis-Menten rate equation. Although many

experiments have been conducted to study the biofiltration removal of a single compound and

the inhibition mechanism (Smet et al., 1997; Mohseni and Allen, 2000; Hwang et al., 2003), few

have been performed on biodegradation of multiple contaminants. More complicated models are

needed to explain the biofiltration process where there are multiple contaminants.

The objective of this research was to determine the biodegradation kinetics of an

aldehyde mixture containing 3-methylbutanal, 2-methylbutanal, and hexanal. These VOCs were

chosen based on previous analysis indicating that the major compounds in the emissions from a

29

poultry rendering plant included hexanal, 2-methylbutanal, and 3-methylbutanal (Kastner and

Das, 2002). This previous study also found that the two branched aldehydes, 2-methylbutanal

and 3-methylbutanal, were by far the most consistent, appearing in every sample and typically

the largest fraction of the VOC mixture.

MATERIALS AND METHODS

Reactors

120ml Amber glass bottles with Mininert® valves (Supelco Park, Bellefonte, PA) were

used to perform batch adsorption experiment.

The reactor used in the continuous experiment has three sections: biofilter body, inlet

cap, and outlet cap. There were three sample ports with body, one with inlet cap, and one with

outlet cap, totally five sample ports. The reactor was 0.1m in diameter and 0.5m in length. The

effective distances between the sample ports from the top of the packing were 10cm, 22cm,

34cm, and 52cm.

Experimental Setup

The experiments were conducted in a continuous flow, packed-bed reactor illustrated in

Figure 3.1. The inlet sample port was 21cm from the packing. The distances from the top of the

packing and the other four sample points were 9.5 cm, 21.5 cm, 33.5 cm, and 68.5 cm,

respectively. The actual height of the packing was 49 cm and the reactor was 0.1 m in diameter

and 0.55 m in total length. 4.7 L/min flow rate gave a 49 s residence time. The medium was

initially contacted with water to generate a 60% dry basis water content and the mass of the

medium was also recorded.

30

The compressed air was pressure regulated and filtered with a water trap to eliminate oils

and water. The flow rate was controlled using a mass flow controller (URS-40, Celerity, INC.

Yorba Linda, CA). A 10L/min flow meter (Dwyer Instruments, INC. Michigan) was used to

verify the flow rate. The air was humidified by passing through two bubble columns in series to

reach 84.2% relative humidity (RH) at the outlet of the first humidifier and 92% RH at the outlet

of the second humidifier. After humidification and VOC introduction, the contaminated air

passed through a column filled with small glass beads to provide mixing and was subsequently

passed downward across the medium as indicated in Figure 1. All columns were sealed by

threaded Teflon plugs with O-ring (ACE Glass incorporated, Vineland, NJ) and tubing was 6.35

mm in diameter.

The addition of the contaminants was accomplished by a syringe pump (Cole- Parmer

74900-30, Vernon Hills, Illinois). The contaminants were added to the air stream as a neat liquid

through a stainless steel Swagelok T-fitting with septum. The T-fitting and the liquid mixture

were heated (Thermolyne 45500, Barnstead International, Dubuque, Iowa) to accelerate

evaporation.

Medium Characterization

The matrix used as the biofilter packing material in this experiment is a synthetic matrix

which is a product of the Biorem® Technologies Inc. According to their description

(Shareefdeen and Herner, 2005), this medium included a plurality of grains, where each grain

includes a porous hydrophilic nucleus and a hydrophobic coating. The coating was made of a

metallic material, microorganisms, nutrients, organic carbon, an alkaline buffer, a bonding agent,

an adsorptive agent, and a hydrophobic agent. This material is considered to have long life, high

31

surface area and low compaction. Several characteristics of compost and this synthetic matrix

were determined as described below.

1. Moisture Content

Approximately 10 g of sample were placed in aluminum weighing dishes, and then into a

100°C oven for 24 h. Weights of the samples before and after were compared to determine

percent moisture content. Samples from the tested columns were taken from the external surface

of the column cores at 0m, 0.15m, 0.25m, and 0.35m along its length.

2. Bulk Density

The bulk density was calculated from the mass of a given volume of dry matrix. The

sample was first dried in a 100°C oven for 24 hours and then the dry weight was measured. The

volume of the matrix was determined by displacement in water.

3. pH

The pH value for the synthetic matrix was determined by mixing about 2 grams of the

sample with 30 ml distilled and deionized water in a 50 ml beaker and using a calibrated ORION

pH meter (model 520A) to determine the pH of the solution. As with moisture analysis, samples

from the reactor were taken from the external surface of the column cores at 0m, 0.15m, 0.25m,

and 0.35m along its length.

4. Surface Area

A High Speed Gas Sorption Analyzer (NOVA3000, Quantachrome corporation, Boynton

Beach, FL) was used to measure the specific surface area. Surface area was calculated from N2

adsorption isotherms at -196°C using the 6 point Brunauer-Emmett-Teller (BET) method using

N2. Original samples (0.18 – 0.26 g) were heated to 200°C and degassed under vacuum (10-5

Torr) to constant pressure (12 hours) before surface area analysis.

32

Gas Sampling and Measurement

Hewlett Packard 5890 series II Gas Chromatograph (coupled to an FID) equipped with an

SPB-1 capillary sulfur column (30m×0.32µm, Alltech Associates, Inc. Deerfield, IL) and helium

as the carrier gas was used for measuring the contaminant concentration along the reactor. A split

ratio of 30:1 was used with a column head pressure of 9psi and the flow rates of the purge vent,

split vent and the column were 4 ml/min, 60 ml/min, and 2 ml/min respectively. The

temperatures of the oven, injection port, and detector were 80, 250, and 250°C, respectively. A

standard curve was generated prior to the experiments by generating at least five gas samples

with known concentrations in the range from 3 ppmv to 70 ppmv (Appendix A). The samples

were analyzed in triplicate by GC/FID, and the standard was periodically checked for linearity

and drift.

Pressure Measurement

Pressure measurements were made using a Dwyer inclined and vertical portable

manometer (Dwyer Instruments, Inc., Michigan City, IN) with 0-1” H2O and 0-2” H2O ranges.

The pressure differences between inlet and outlet of the column were measured by connecting

the two tees at the inlet and outlet of the biofilter system with the manometer.

Adsorption Capacity Studies

The adsorption capacities of the synthetic matrix and compost were conducted in 120 mL

Amber glass serum bottles at room temperature (23°C) equipped with a Mininert® valves

(Supelco Park, Bellefonte, PA). The model VOC used was one of the previously identified

aldehydes, 3-methylbutanal. The diameter of the matrix chosen was less than 15 mm to fit the

opening of the serum bottles. The mass of the matrix or compost used the equilibrium adsorption

experiments ranged from 0.4 to 9 g. The bottles with the matrix were sterilized at 121°C for 20

33

minutes. The time for equilibrium to occur was first determined by injecting 3-methylbutanal and

sampling every hour until the gas phase concentration did not change any more, and the time was

recorded which was 24 h. Then various known amounts of 3-methylbutanal, neat liquid were

injected into the bottles. After 24 h, 500 µl of gas headspace was sampled for GC analysis (with

a 2.5 ml Gastight® syringe, Hamilton co. Reno, Nevada). Adsorption capacity was measured for

a series of gas phase concentrations using synthetic matrix, compost, and a blank was used as a

control; each experiment was conducted in triplicate. The adsorption capacity was calculated

from a mass balance at equilibrium

ssggVOC MCVCM += (1)

Where MVOC is the mass of the VOC neat liquid added into the bottle, Cg is the

equilibrium gas phase concentration (g/m3), Vg is the volume of gas in the bottle (m3), Cs is the

equilibrium adsorption capacity (mg VOC/g-matrix), and Ms is the mass of the matrix (g).

Residence Time Distribution (RTD) Analysis

The RTD analysis was used to confirm plug flow and identify any channeling effects in

the reactor. It was conducted without a VOC present and with just air flowing through the

reactor. Helium was used as tracer and 10 ml of 99.999% helium was injected into the column

using a pulse injection technique (Levenspiel, 1972). The injection was made via a tee fitting at

the inlet of the reactor, 21cm away from the packing. Immediumtely after the injection, the outlet

concentration was monitored and recorded with a MGD-2002 Multigas Detector

(Radiodetection, Bridgton, ME). The sensitivity and range of this instrument for helium was

from 25 to 1,000,000 ppmv (in 25 ppmv increments).

34

Scanning Electron Microscopy (SEM)

After five months of operation, triplicate samples of the biofilter matrix were collected at

different depths of the reactor. The samples were dissected into 1-3 mm cubes with a grease-free

razor blade and fixed in 2 % glutaraldehyde in a 0.1 M cacodylate buffer (pH 7.2) at 4°C for 90

minutes. After the samples were washed two times with a 0.1 M cacodylate buffer for 15 minutes

each, the samples were fixed secondarily with a 1 % osmium tetroxide in 0.2 M cacodylate

buffer at 4°C for 90 minutes. The samples were rinsed twice for 15 minutes with 0.2 M

cacodylate buffer before dehydrating with increasing concentrations of ethanol at 30 %, 50 %, 70

%, 85 %, 95 %, 100%, and 100% for 15 minutes each. The dehydrated samples immersed in

ethanol were dried with a critical point drier (model 780-A, Tousimis Inc, Rockville, MD). The

dried samples were subsequently mounted on an aluminum stub with an adhesive carbon sticky

tab. The specimen stub was sputter coated with ~ 150 °A of gold using sputter coater (model

SPI, SPI supplies, West Chester, PA). The observations of the samples were carried out in a

digital scanning electron microscope (ZEISS 1450EP, Carl Zeiss Micro Imaging, Thornwood,

NY). An accelerating voltage of 20 keV was used and a secondary electron detector was used for

imaging the samples. The images obtained from the SEM were processed for publication using

Adobe Photoshop (version 7).

RESULTS AND DISCUSSION

Properties of the Synthetic Matrix and the Compost

The comparison of properties between the synthetic matrix and a traditional organic

matrix (i.e. compost) are shown in Table 3.1. The synthetic matrix had higher strength which

35

results in less compaction. It also had much higher surface area leading to a higher adsorption

capacity.

Comparison of Adsorption Capacity

For biodegradation to occur in biofiltration, VOCs must be transferred from the gas phase

to the biofilm, the contaminants adsorbed by the medium, and then metabolized by the

microorganisms. Therefore, if microbial degradation rates are high enough, high adsorption

capacity may enhance VOC removal. Also, medium with high adsorption capacities can adsorb

high concentrations of substrates and slowly release them for microbial degradation (Khaled et

al., 1996), and thus can buffer against inlet shocks or pulses of VOCs. Therefore, prior to the

continuous flow experiments, the adsorption studies were performed to compare the adsorption

capacity of synthetic matrix and compost. The headspace concentration was measured

periodically and found to approach equilibrium within 24 hours. Previous adsorption studies

using peat got similar results (Acuna et al., 1999). These results indicated that the synthetic

matrix had an adsorption capacity 10 times higher than that of compost (Figure 3.2). The high

adsorption capacity was potentially due to the high surface area of the synthetic matrix which

was nearly 16 times higher than that of compost (Table 3.1). These results indicate one potential

advantage of using an engineered synthetic matrix as biofilter medium.

The equilibrium data were also analyzed using Freundlich (Freundlich and Zeitschrift,

1906) and Langmuir (Langmuir and Am, 1916) isotherm equations which were explained as

follows:

Freundlich isotherm is an empirical adsorption isotherm for non ideal adsorption on

heterogeneous surfaces as well as multiplayer adsorption and is expressed by the equation:

n

eFe CKq /1= (2)

36

)ln(1)ln()ln( eFe Cn

Kq += (3)

where eq is the adsorption density (mg of VOC per g of adsorbent), FK and n/1 are Freundlich

constants, eC (mg/L) is the VOC concentration in the fluid at equilibrium. It was derived by

assuming an exponentially decaying adsorption site energy distribution. The limitation is that it

does not follow the fundamental thermodynamic basis since it does not reduce to Henry’s law at

lower concentrations. Equation (3) is anther form of equation (2) which is used to make the

regression and get Freundlich constants.

Langmuir isotherm is a theoretical equilibrium isotherm relating the amount of solute

sorbed on a surface to the concentration of solute. Two assumptions were made that the forces of

interaction between sorbed molecules are negligible and once a molecule occupies a site and no

further adsorption takes place. Based on these assumptions, in theory, a saturation value is

reached beyond which no further adsorption takes place. The saturated monolayer adsorption

capacity can be represented by the following equation:

eL

eLme CK

CKqq+

=1 (4)

m

e

Lme

e

qC

KqqC

+=1

(5)

where mq is the maximum adsorption capacity corresponding to complete monolayer coverage

(mg of solute adsorbed per g of adsorbent), LK is the Langmuir constant (liters of adsorbent per

mg of VOC). Equation (5) is anther form of equation (4) which is used to make the regression

and get Langmuir constants.

37

At lower VOC levels (<1000 ppmv), the data were fit using the Freundlich equation

(Figure 3.3). The Freundlich constant FK was found to be 0.037 for compost (R2=0.9418) and

1.3 for the synthetic media (R2=0.8874), respectively. The Freundlich constant n was found to

be 0.91 for compost and 1.31 for the synthetic media, respectively. The Langmuir isotherm was

also fit to the entire data set for the synthetic matrix (Figure 3.4) and the Langmuir constant was

found to be 0.43 L/mg and the maximum adsorption capacity corresponding to complete

monolayer coverage was 4.95 mg/g (R2=0.9895). These results suggested higher adsorption

capacity for the synthetic matrix comparing to compost. Acuna et al. (1999) reported Freundlich

constant for toluene adsorption on peat to be 0.459. Benkhedda et al. (2000) studied adsorption

behavior of toluene onto activated carbon and reported Freundlich constants FK to be 2.43 and

n to be 8.38 at 298.15K; Langmuir maximum adsorption capacity to be 510.4 mg/g. Again,

these isotherm constants confirm the higher adsorption capacity of the synthetic matrix for the

aldehydes, probably due to its hydrophobic nature and high surface area relative to compost.

pH

The pH of the original material was 9.04 ± 0.04. After the biofilter had been operated for

a year, the overall pH of the packing became 8.98 ± 0.14, which was derived from the average

pH of samples from the reactor. A two sample independent t test showed that there was no

significant difference between pH of the original sample and pH of used sample. Individual pH

value for inlet, number2, 3, and 4 ports were recorded, which were 8.81 ± 0.05, 9.15 ± 0.05, 8.96

± 0.06, 9.00 ± 0.08. Usually, a near-neutral pH is required for the greatest spectrum of bacterial

activity (Devinny et al., 1999). The usual pH for the packing materials is from 6 to 8, although a

pH as low as 2 to 4 was observed when treating reduced sulfur compounds (Furusawa et al.,

1984; Webster et al., 1996).

38

Pressure drop analysis

When the rector was initially loaded with matrix, glass wool was placed at the bottom of

the reactor to prevent build up of the outlet. After the experiment has been operated for nearly

one year, a large pressure drop along the reactor was observed. Specifically, with superficial gas

velocities vary from 7.6-53.5 m3/m2 h, the pressure loss between inlet and the number 4 port was

from 14.2 to 71.2 Pa/m, while pressure loss between inlet and the outlet was from 1,743.6 to

22,168.9 Pa/m. Since water has been added through the top all the time, some small particles of

the matrix were flushed away and stayed on the glass wool which causes this huge pressure drop.

Therefore, a plastic disk with uniformly distributed holes (about 5 mm diameter) was placed at

the bottom of the reactor instead; this significantly decreased the pressure drop (Figure 3.5).

Within the same range of superficial gas velocities range, the pressure loss between inlet and

outlet was from 19.9 to 254.1 Pa/m. After the replacement of the supporting material and

reloading of the reactor, the pressure drop was monitored regularly. A slight increase of pressure

drop with time was observed (Figure 3.6).

The pressure drop through a biofilter bed typically ranges from 20 to 100 Pa/m, however

can sometimes go up to 980 Pa/m, with typical superficial gas velocities ranges from 5 to 500

m3/m2 h (Devinny et al., 1999). Leson and Smith (1997) reported that for the system with

adequate moisture control and a porous medium containing bulking agents will typically have a

pressure loss less than 900-1,700 Pa/m. Comparing to these data, the synthetic medium can lead

to low pressure drop even with adequate water addition. The slight increase of the pressure drop

with time may attribute to water or biomass accumulation.

39

Verification of Plug Flow (Tracer Analysis)

To determine the biodegradation kinetics of the target compounds, the reactor design and

rate equations are needed. Since our reactor was assumed to be of the plug flow type (PFR),

residence time distribution (RTD) analysis was carried out to determine if the flow hydraulics

were plug flow. The RTD curves were analyzed by a dispersion model which is used for a non-

ideal PFR and in which axial dispersion of the material occurs. The Peclet number characterizes

the level of dispersion in a reactor and is defined as,

RatediffusionDispersionconvectionateTransportR

DULPe

er )(

)(== (6)

where, U is the superficial molar average velocity through the bed (m/s), L is the length of the

reactor (m), De is the effective dispersion coefficient. The Peclet number was calculated from the

following equations:

∫∞=

0

)(

)()(dttC

tCtE (7)

∫∞

=0

)( dtttEτ (8)

∫∞

−=0

22 )()( dttEt τσ (9)

)1(2222

2rPe

rr

ePePe

−−−=τσ

(10)

where E(t) is the residence time distribution function, τ is mean residence time, σ is the second

moment of the mean, and t is time. In the limiting cases when Per = 0 (very high dispersion) we

40

have a complete mixing regime and when Per = ∞ (no dispersion) we have a perfect plug flow

reactor.

The RTD experiments were replicated and performed after initially loading the reactor,

and after 1, 6, 11, and 15 months of operation. Tests were conducted at air flow rates of 4.5, 5, 5,

and 5 L/min respectively resulting in 44.79 s, 30.77 s, 35.98 s, and 33.94 s mean residence times

based on the RTD analysis (Figure 3.7). The recoveries of the tracer were 99.36%, 83.72%,

92.28%, and 109% respectively. The calculated Peclet numbers were 15.57, 25.26, 37.6, and

24.36 respectively, indicating the assumption of a plug flow reactor was reasonable. Usually a

Peclet number of 500 indicates small amount of dispersion, 40 indicates intermediumte amount

of dispersion, and 5 indicates large amount of dispersion (Fogler, 2006). The RTD results also

demonstrated that channeling or bypassing of the medium did not occur, indicative of limited

compaction and ageing of the synthetic matrix. Contrarily, Morgan-Sagastume et al. (2003)

observed channeling in a compost based biofilter using the RTD technique, which may have

been due to the low compression strength of the compost and/or degradation of the organic

medium. Therefore, these results indicate that the lack compaction and channeling is an

advantage of the synthetic medium over the traditional medium.

Biodegradation kinetic analysis

Initially the biofilter was operated without directly adding water and only one

humidification reactor. After three days of operation, the biofiter reached more than 80%

aldehyde removal, but after the fifth day, the fractional conversion began to decrease (Figure

3.8A). A limited number of matrix samples were then taken from the reactor to measure the

water content and a significant decrease in moisture content was observed (Table 3.2). Water

content is known to be very critical in biofiltration (Auria et al., 1998), and Acuna et al. (1999)

41

reported that initial water contents between 55% and 70% give optimum degradation. The results

of this experiment indicated a dramatic decrease of water content (lower than 1%), which

explains the reduced degradation rates and fractional conversion of the aldehydes. Therefore,

during all subsequent experiments, water was added from the top of the reactor twice a week (60

ml), and the humidifiers (two humidifiers in series were used at this point) were filled with water

twice a week to maintain the moisture level in the biofilter. An increase in aldehyde degradation

was observed due to this improvement (Figure 3.8B).

During the continuous biofiltration experiments, gas samples were taken from the reactor

via the five sample ports and analyzed using the GC/FID. Figure 3.9A shows a typical

chromatograph from the different positions along the reactor. Clearly, concentrations of the three

contaminants decrease along the reactor due to biodegradation activity. An unknown peak (after

22 days of operation and the new moisture addition campaign) was found to be present in all of

gas samples from the reactor, except the inlet sample, and the concentration of the unknown

increased along with the reactor (based on an increase in peak area), which suggested that this

unknown was formed from the metabolism of the aldehydes. With continued operation, this

unknown disappeared even at very high loading rates (Figure 3.9B). This may have been due to

an initial limited number and diversity of microorganisms, such that the biodegradation capacity

was low (i.e., any intermediates formed during metabolism of the aldehydes were not degraded

before leaving the reactor). After the reactor had been operated for 78 days, the microbial

population probably increased as the microorganisms were continuously provided with air (O2),

water, and carbon sources. Therefore, the biodegradation capacity increased toward any potential

metabolic intermediates in aldehyde degradation.

42

From the first day of start-up of the reactor to approximately three months after

operation, the fractional removal of hexanal (nearly 100%) was always higher than that of 3-

methylbutanal and 2-methylbutanal (Figures 3.10 and 3.11). The reason for this preferential

pattern may be that the straight chain aldehyde was more easily metabolized by the

microorganisms in the biofilter than the branched chain aldehydes. Similar metabolic patterns

have been observed between straight chain and branched alkanes (Watkinson et al., 1990; Olson

et al., 1999).

The kinetic analysis was performed by using the plug flow design equation (eq. 10) with

the appropriate rate law (-r = kCn , where r is degradation rate of the VOC). Assuming a

homogeneous system, constant volume, constant pressure, constant temperature, and O2 in

excess, the design equation can be derived as

00

0 ==+− ∫V

jjjj dt

dNdVrFF (11)

jj r

dVdF

= (12)

where Fj0 is the inlet flow rate (moles/time), Fj is the outlet flow rate (moles/time), V is reactor

volume, rj is rate of consumption per unit volume, Nj is the number of moles of j in the system, j

is substrate. Using an empirical approach to model the reaction rate and substituting the

expressions of Aj QCF = and nAj kCr −= (with n = 0 or 1) and

A

Aj Ck

Ckr2

1

1+−= into equation 12

we obtain a power rate model to predict VOC concentration profiles (Hamaker, 1972),

nA

A kCdt

dC=− (1st and zero order) or

A

AA

CkCk

dtdC

2

1

1+=− (13)

43

where CA is the substrate concentration, t is time or position along the reactor, k, k1 and k2 are

rate constants for VOC disappearance, and n is the overall reaction order. The reaction rate and

rate constant were calculated assuming the order of the reaction and fitting the subsequent model

to the concentration profile along the reactor. Using a mechanistic model including diffusion and

reaction in a biofilm, previous authors found that when the gas phase concentration is high, the

reaction rate is zero-order and the elimination rate becomes reaction-controlled, while at low gas

phase concentrations or low water solubility, the reaction rate is first-order and the elimination

rate becomes diffusion-controlled (Ottengraf, 1986). During this experiment, gas samples were

withdrawn from different positions along the reactor and the concentration profile quantified

(Figure 3.11). Then zero, first order, non-linear models were assumed for each data set and the

reaction rate and rate constants were calculated, as shown in equations 14, 15, and 16.

QVk

CC

A

Ao =⎟⎟⎠

⎞⎜⎜⎝

⎛ln , first order model (14)

kQVCC AoA −= , zero order model (15)

AAoAAo

A

Ao

CCQVk

kCC

CC

−−−=

−

⎟⎟⎠

⎞⎜⎜⎝

⎛1

2

ln

, non-linear model (16)

In a second method to analyze the reaction kinetics, the resultant rate constants

determined from equations 14 and 15 were plotted against the measured fractional VOC

conversion to determine if k remained constant. A systematic change in k suggests an incorrect

model (Fogler, 2006).

44

Both first and zero order kinetic models appeared to fit the experimental data for

aldehyde degradation versus position (or V/Q) along the reactor. As demonstrated in Figure 3.12,

both 1st and zero models resulted in reasonable goodness of fit values. The non-linear model

(equation 16), which should accurately predict the non-linear portion of the degradation rate

versus inlet concentration curve, did not fit the data over the range of VOC concentrations tested.

When the calculated first and zero rate constants were plotted against the measured fractional

conversion a systematic change in zero order constant was observed suggesting the zero order

model did not fit the data either (Figure 3.13). Thus, the kinetic analysis suggests an overall first

order model is most appropriate to predict the degradation of hexanal, 2-methylbutanal, and 3-

methylbutanal from 10 to 50 ppmv. Similar to our results, butanal degradation kinetics appeared

to follow first order kinetics in a wood bark based biofilter at an inlet concentration of 10 ppmv

(Weckhuysen et al., 1993) and isobutanal degradation was first order in a compost based biofilter

up to 300 ppmv (Sercu et al., 2005). Regardless of the model, overall removal or reaction rates

increased with time and hexanal had significantly higher removal rates compared to the branched

aldehydes (Table 3.3).

Using the first order model and the reaction rate constants of three aldehydes were

estimated within six months period (Table 3.4). Comparing the kinetics of aldehyde degradation,

the measured degradation rates in this work were similar to those previously reported for butanal

and isobutanal (2-methylpropanal) for wood bark and compost based biofilters. For example,

first order rate constants for hexanal, 2-methylbutanal, and 3-methylbutanal were 0.0998 ±

0.0059, 0.0543 ± 0.0188, and 0.0468 ± 0.0209 1/s (18-46 ppmv), respectively, compared to

0.091/s for butanal (10 ppmv, Weckhuysen et al., 1993) and 0.033 1/s for isobutanal (300 ppmv,

Sercu et al., 2005).

45

Based on the higher adsorption capacity of the synthetic medium, the reaction rate of our

biofilter system should be higher than that of compost. The synthetic medium has a much higher

surface area than that of compost, as well as a higher adsorption capacity, which may result a

higher contaminant surface concentration. However, compared to the literature, the reaction rate

for this synthetic medium and for compost is approximately same. This may due to the low

microbial level in the synthetic matrix since no nutrients were added and just humidified air and

VOCs were added to the reactor.

Microbial analysis

Samples of the support were withdrawn from different positions of the biofilter and

observed under SEM. The structure of the original synthetic matrix core was of a porous nature

with a limited number of microorganisms (Figure 3.14A). Samples from the biofilter after 4

months of operation treating a mixture of hexanal, 2-methylbutanal, and 3-methylbutanal showed

evidence of microbial growth and biofilm formation. Qualitatively, the core sample appeared to

have a large number of bacteria, compared to the surface in which fungi were primarily observed

(Figure 3.14). The hydrophilic nature of the core may account for presence of bacteria which

can’t tolerate water activity as low as fungi. Also fungi require oxygen to a greater extent than

bacteria. Therefore, the presence of fungi on the surface may merely be due to oxygen levels.

CONCLUSIONS

Medium characterization was performed for both the synthetic matrix and the compost.

The higher surface area of the synthetic media suggested it would have a high VOC adsorption

capacity which can significantly affect biofiltration performance. Therefore, an adsorption

46

capacity experiment was carried out and the results indicate that the adsorption capacity of the

synthetic medium for a model VOC, 3-methylbutanal, was 10 times that of compost.

Residence time distribution (RTD) analysis via a tracer study was performed at the

beginning and after 6, 11, and 15 months, respectively. The flow pattern and the Peclet number

indicate no channeling and lack of compaction, and thus plug flow could be assumed.

Pressure drop analysis and pH measurement were also carried out. After modification of

the reactor, the pressure loss was significantly decreased and was in the lower range of the

typical value required for biofilter operation at full scale. The pH value of the media, which was

around 9, did not change after one year of operation.

Simple first-order and zero-order kinetic models both equally fit the experimental data,

yet analysis of the measured rate constants versus fractional conversion suggested an overall first

order model was more appropriate. Kinetic analyses indicated that hexanal had a significantly

higher reaction rate (k1st order = 0.0998 ± 0.0059 1/s; 18-28 ppmv) compared to the branched

aldehydes (k1st order = 0.0505±0.0188 1/s; 21-46 ppmv). After 3 months of operation, all three

compounds reached 100% removal (50 sec residence time, 18-46 ppmv inlet).

After 5 months of operation, medium samples were withdrawn and observed under SEM

analysis which indicated microbial growth, suggesting removal of the aldehydes could be

attributed to biodegradation.

47

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51

Table 3.1. Properties of BIOSORBENSTM medium and compost used in adsorption experiments

Properties BIOSORBENSTM medium Compost

Bulk Density, dry 1447 kg/m3 730 kg/m3

Water Holding Capacity 25% (dry basis) a 116.5~461.8% (dry basis) b

Particle Size 5~25mm a 5~20mm c

Surface Area d 47.6 m2/g 3.6 m2/g

Life Expectancy Permanent a ~5 years

a Shareefdeen and Herner, 2005 b. H.K.Ahn et.al. Laboratory Determination of Compost Modeling Parameters, 2004. c. Delhomenie et al. (2002) d. The specific surface area was measured by High Speed Gas Sorption Analyzer (NOVA2200, Quantachrome Corporation)

Table 3.2. Moisture content (wt%) of the matrix along the reactor operating without direct water addition and a single humidifier

After 20 days run (07/19/05~08/08/05) Beginning

inlet #1 #2 #3 outlet

20.7% 1.32% 1.79% 1.84% 1.95% 0.864%

52

Tab

le 3

.3. R

eact

ion

rate

con

stan

ts. C

alcu

late

d de

grad

atio

n ra

tes a

t diff

eren

t inl

et c

once

ntra

tions

and

ope

ratio

n tim

es

Ope

ratio

n T

ime

Six

days

ope

ratio

n O

ne m

onth

ope

ratio

n

Ald

ehyd

es

Cin

a

(ppm

v)

Zero

(g/m

3 /h)

R2

Firs

t

(g/m

3 /h)

R2

Cin

(ppm

v)

Zero

(g/m

3 /h)

R2

Firs

t

(g/m

3 /h)

R2

Hex

anal

18

.5

7.96

58

0.92

1 29

.08

0.94

2 23

.71

13.6

34

0.97

1 63

.49

0.74

8

2-M

B

33.6

3 7.

4052

0.

907

14.9

5 0.

839

46.2

5 10

.328

0.

876

26.8

9 0.

988

3-M

B

24.1

3 5.

2862

0.

976

10.7

81

0.91

8 34

.94

7.73

87

0.86

8 20

.900

0.

999

a. a

vera

ge in

let a

ldeh

yde

gas p

hase

con

cent

ratio

n

2-

MB

: 2-m

ethy

lbut

anal

3-

MB

: 3-m

ethy

lbut

anal

53

T

able

3.4

. Fir

st o

rder

rea

ctio

n ra

te c

onst

ants

at d

iffer

ent c

once

ntra

tions

and

ope

ratio

n tim

es

Bio

filte

r w

as st

arte

d on

7/1

9/20

05 a

nd o

pera

ted

with

two

hum

idifi

ers a

t 4.7

L/m

in fl

ow r

ate

Ope

ratio

n T

ime

7/24

/200

5 9/

22/2

005

10/7

/200

5 12

/30/

2005

1/

18/2

006

Ald

ehyd

es

Cin

a

(ppm

v)

Firs

t k

(1/s

)

Cin

(ppm

v)

Firs

t k

(1/s

)

Cin

(ppm

v)

Firs

t k

(1/s

)

Cin

(ppm

v)

Firs

t k

(1/s

)

Cin

(ppm

v)

Firs

t k

(1/s

)

Hex

anal

18

.5

0.10

7

(r2 =0

.942

)27

.93

0.09

28

(r2 =1

) 23

.35

0.10

13

(r2 =0

.847

) 25

.07

0.09

81

(r2 =0

.952

)22

17.6

10.

3736

(r2 =0

.917

)

2-M

B

33.6

3 0.

035

(r2 =0

.839

)28

.41

0.07

83

(r2 =0

.93)

46.2

5 0.

0589

(r2 =0

.988

) 35

.17

0.04

48

(r2 =0

.984

)32

0.74

0.

1688

(r2 =0

.959

)

3-M

B

24.1

3 0.

024

(r2 =0

.918

)22

.22

0.06

83

(r2 =0

.91)

34.9

4 0.

0603

(r2 =0

.999

) 20

.98

0.03

46

(r2 =0

.899

)77

.45

0.30

32

(r2 =1

)

a. a

vera

ge in

let a

ldeh

yde

gas p

hase

con

cent

ratio

n

2-

MB

: 2-m

ethy

lbut

anal

3-

MB

: 3-m

ethy

lbut

anal

54

Figu

re 3

.1. T

he sc

hem

atic

dia

gram

of t

he b

ench

scal

e bi

ofilt

er d

esig

n

Syri

nge

Pum

p

Mai

n A

ir S

ourc

e Flow

Con

trol

ler

Hum

idifi

er

Mix

er

Flow

Met

er

To

Fum

e H

ood

Rea

ctor

Hea

ter

1 (in

let)

2 3 4

5 (o

utle

t)

55

Equi

libriu

m G

as P

hase

Con

cent

ratio

n, p

pmv

010

0020

0030

0040

0050

0060

00

Equilibrium Adsorption Density, mg.g

01234

Figu

re 3

.2. C

ompa

riso

n of

the

adso

rptio

n ca

paci

ty o

f the

synt

hetic

mat

rix

() a

nd th

e co

mpo

st (

) for

3-m

ethy

lbut

anal

at 2

3°C

56

Equilibrium gas phase concentration Ce, mg/L

0 1 2 3 4

Equi

libriu

m a

dsor

ptio

n de

nsity

qe

mg

VOC

/g m

ediu

m

0.00

0.05

0.10

0.15

0.20

Equilibrium gas phase concentration Ce, mg/L

0.0 0.5 1.0 1.5 2.0 2.5

Equi

libriu

m a

dsor

ptio

n de

nsity

qe

mg

VOC

/g m

ediu

m

0.0

0.5

1.0

1.5

2.0

2.5

Figure 3.3. Freundlich model for the compost (A) and the synthetic matrix (B), experimental data ( ) and the fitted model (line)

A

B

57

Equilibrium gas phase concentration Ce, mg/L 0 10 20 30 40 50 60

Equi

libriu

m a

dsor

ptio

n de

nsity

qe

mg

VOC

/ g

med

ia

0

1

2

3

4

5

Figure 3.4 Langmuir model for the synthetic matrix, experimental data ( ) and the fitted model (line)

58

Linear Velocity, m/s

0.000 0.002 0.004 0.006 0.008 0.010 0.012 0.014 0.016

Pres

sure

Dro

p, P

a/m

0

5000

10000

15000

20000

25000

Pres

sure

Dro

p, P

a/m

0

100

200

300

400

500

Figure 3.5. The pressure changes after the replacement of the supporting materials: glass wool ( ) and plastic disk ( )

59

Linear Velocity, m/s

0.000 0.002 0.004 0.006 0.008 0.010 0.012 0.014 0.016

Pres

sure

Dro

p, P

a/m

0

50

100

150

200

250

300

350

400

Figure 3.6. The pressure drop between inlet and outlet of the reactor with plastic disk support as function of linear velocity at three different operating times: right after

loading ( ), 20 days ( ), and 110 days ( )

60

Tim

e, s

020

4060

8010

012

0

Concentration, g/m3

0.0

0.2

0.4

0.6

0.8

Tim

e, s

020

4060

8010

0

Concentration, g/m3

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

Tim

e, s

020

4060

8010

0

Concentration, g/m3

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Tim

e, s

020

4060

8010

0

Concentration, g/m3

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

Fi

gure

3.7

.Tra

cer

anal

ysis

of t

he b

ioflt

er a

t sta

rt-u

p (A

), af

ter

6 m

onth

s (B

), 11

mon

ths (

C),

and

15 m

onth

s (D

) of o

pera

tion

A B

D

C

61

Time, day

0 2 4 6 8 10 12

Frac

tiona

l con

vers

ion,

%

0

20

40

60

80

100

120

Time, day

0 2 4 6 8 10 12

Frac

tiona

l con

vers

ion,

%

0

20

40

60

80

100

120

Figure 3.8. The fractional conversion after start-up under two different moisture conditions. A: 20.7% initial moisture, one humidifier, did not add water regularly,

1.95% in the middle of the reactor; B: 25% initial moisture, two humidifiers, add 60ml water into the reactor twice a week, 29.37% in the middle of the reactor, Hexanal

3-methylbutanal 2-methylbutanal

A

B

62

time(

min

)

02

46

810

1214

Instensity

0

200

400

600

800

A

2-m

ethy

lbut

anal

3-

met

hylb

utan

al

unkn

own

Hex

anal

inle

t

30.5

cm fr

om in

let

42.5

cm fr

om in

let

54.5

cm fr

om in

let

89.5

cm fr

om in

let

63

Tim

e, m

in0

24

68

1012

14

Intensity

0

2000

4000

6000

8000

B

inle

t

30.5

cm fr

om in

let

42.5

cm fr

om in

let

54.5

cm fr

om in

let

89.5

cm fr

om in

let

Figu

re 3

.9. C

hrom

atog

raph

s of g

as p

hase

sam

ples

from

the

reac

tor:

A) a

fter

22

days

ope

ratio

n, in

let c

once

ntra

tion:

33p

pmv

3-M

B,

48pp

mv

2-M

B, 2

7ppm

v H

exan

al, 4

.7L

/min

flow

rat

e; B

) aft

er 7

8 da

ys o

pera

tion,

inle

t con

cent

ratio

n: 5

6ppm

v 3-

MB

, 70p

pmv

2-M

B,

712p

pmv

Hex

anal

, 4.7

L/m

in fl

ow r

ate

64

Time, day

60 80 100 120

Frac

tiona

l con

vers

ion,

%

0.0

0.2

0.4

0.6

0.8

1.0

1.2

3-MethylButanal2-MethylButanalHexanal

Figure 3.10. The response of aldehyde fractional conversion to an increase in moisture content after a significant decline in microbial activity (Q = 4.7 L/min, 16-39 ppmv

hexanal, 25-67 ppmv 2-methylbutanal, 22-56 ppmv 3-methylbutanal)

65

Figure 3.11. Concentration profile along the reactor after loading (A) and after 11 days (B) for hexanal ( ), 3-methylbutanal ( ) and 2-methylbutanal ( ) – Z is position along

the reactor, 4.7 L/min flow rate

Z, m

0.0 0.1 0.2 0.3 0.4 0.5 0.6

Con

cent

ratio

n, g

/m3

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

Z, m

0.0 0.1 0.2 0.3 0.4 0.5 0.6

Con

cent

ratio

n, g

/m3

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18B

A

66

Figure 3.12. Kinetic analysis of 3-methylbutanal degradation results using a first order (A), zero (B), and non-linear (C) model. Note, in this analysis t or time is the packing

volume at the sample position divided by the volumetric flowrate (Q = 4.7 L/min, 22-35 ppmv 3-methylbutanal)

0 10 20 30 40 50 60

Ln(C

Ao/C

A)

0.00.51.01.52.0

Time, s0 10 20 30 40 50 60

CA

020406080

100

t/(CAo-CA)0.5 0.6 0.7 0.8 0.9 1.0

ln (C

Ao/C

A)/(

CA

o-CA

)

0.000.010.020.030.040.05

R2=0.97

R2=0.96

R2=0.39

B

C

A

67

Figu

re 3

.13.

Plo

t of t

he m

easu

red

rate

con

stan

t (e.

g., k

1st=

Q/V

ln(C

Ao/

CA

) ver

sus t

he a

ssoc

iate

d fr

actio

nal c

onve

rsio

n (X

= C

in-

CA

/Cin

) (Q

= 4

.7 L

/min

, 22-

35 p

pmv

3-m

ethy

lbut

anal

)

Frac

tiona

l Con

vers

ion

0.2

0.4

0.6

0.8

1.0

First Order Rate Constant, 1/s

0.00

0.01

0.02

0.03

0.04

0.05

0.06

Zero Order Rate Constant, mg/m3/s

0.0

0.5

1.0

1.5

2.0

68

A

B

69

Figure 3.14. SEM images of the original core (A) and original surface (C), and the core (B) and the surface (D) after four months of operation treating a mixture of hexanal, 2-

methylbutanal, and 3-methylbutanal

D

C

D

70

CHAPTER 4

EFFECT OF ORGANIC SULFUR ADDITION ON THE BIODEGRADATION OF AN

ALDEHYDE MIXTURE

71

ABSTRACT

Biofiltration degradation kinetics of methanethiol and an aldehyde mixture containing

hexanal, 2-methylbutanal, and 3-methylbutanal was investigated using a bench-scale, synthetic

medium based biofilter. Simple first-order and zero-order kinetic models were both fit the

experimental data, and the correlation coefficients suggested an overall first order model was

more appropriate. Kinetic analysis indicated that hexanal had a significantly higher reaction rate

(k1st order = 0.113 ± 0.029 1/s; 11-82 ppmv) compared to the branched aldehydes (k1st order =

0.083 ± 0.021 1/s, 20-94 ppmv). Also the reaction rate for each aldehydes increased after

methanethiol was introduced into the biofilter. Methanethiol had a very low degradation rate

(k1st order = 0.016 ± 0.004 1/s, 11-30 ppmv). DMDS was found to be formed in the reactor and

its concentration increased along the reactor.

Key Words: biofiltration, aldehyde, methanethiol, kinetics, DMDS

72

INTRODUCTION

High volume low concentration (HVLC) emissions of VOCs and reduced sulfur

compounds are odorous, toxic, and can contribute to smog formation (Devai and DeLaune,

1999). Many industries, e.g. pulp and paper industry, composting operations, and wastewater

treatment facilities, release large volume of waste gases which contain a range of reduced sulfur

compounds, such as hydrogen sulfide, methanethiol, dimethyl sulfide and dimethyl disulfide.

Two primary air pollution control technologies currently used to treat reduced sulfur compound

in the waste gases are regenerative thermal oxidation (RTO) and wet scrubbers (Seiwart, 1997;

Kastner and Das, 2002A). RTOs have high operating costs because of the high temperature (800

– 1000 oC) for the oxidation to occur. RTOs produce greenhouse gases (CO2) due to combustion

of an external carbon source at high temperatures, and also require SO2 scrubbing if sulfur is

present. Wet scrubbers require costly oxidizing chemicals (e.g. ClO2 or NaOCl) and can produce

chlorinated hydrocarbons if not properly controlled (Kastner and Das, 2002A). More efficient,

economic, and environmentally benign air pollution control technology for treating reduced

sulfur compounds is required.

In recent years, biofiltration has been considered as one of the main efficient odor

removal technologies. Much research has been performed on sulfur compound biodegradation.

Shareefdeen et al. (2002) noted the removal of low-level sulfur compounds (i.e., 0.56 ppm of

methanethiol) is difficult with wood-based biofilter. However, hydrogen sulfide was efficiently

removed when a synthetic medium was used as the packing material (Shareefdeen et al., 2003).

Many mathematical models have been developed for volatile organic compounds, nevertheless,

only a few models were appropriate for sulfur compound biofiltration (Yang and Allen, 1994).

73

The major compounds identified in the emissions of a rendering plant were methanethiol,

hexanal, 2-methylpropanal, 2-methylbutanal, and 3-methylbutanal, and the branched aldehydes

were by far the most consistent, appearing in every sample and typically the largest fraction of

the mixture (Kastner and Das, 2005A). Compounds inconsistently detected included hexanal and

methanethiol. Results at two other rendering facilities indicated consistent present of hexanal and

methanethiol (Barnes RD and MacLeod, 1982; Kastner and Das, 2002A). Therefore, the

objective of this research is to study the biodegradation kinetics of VOC mixture which contains

hexanal, 2-methylbutanal, 3-methylbutanal, and methanethiol. In this study, the synthetic

medium based packed bed biofilter was exposed to aldehyde mixture first, then methanethiol was

introduced into the system. The effect of methanethiol on the aldehyde degradation was studied.

MATERIALS AND METHODS

Medium characterization

Synthetic medium used in this experiment is the same as used for aldehyde degradation

study. The physical and chemical characteristics of the medium were determined previously

which include bulk density, surface area (BET using N2, Nova 3000 Quantachrome, Boynton

Beach. FL), and pH (ORION pH meter, model 520A). The bulk density, surface area, pH, and

water content of the synthetic medium were 1746.5 ± 37.2 kg/m3, 47.6 ± 2.3m2/g (for ~7mm

diameter particles), 9.042 ± 0.038, and 20.7% (dry basis), respectively.

Pressure loss was measured using Dwyer inclined and vertical portable manometer

(Dwyer Instruments, Inc., Michigan city, IN) with 0-1” H2O and 0-2” H2O ranges. Typical

pressure drop within the packing bed was 19.9 to 254.1 Pa/m with superficial gas velocities vary

from 7.6 to 53.5 m3/(m2 h).

74

The RTD analysis was also performed before starting the experiments using a pulse

injection technique (Levenspiel, 1972). Helium was used as the tracer, and its concentration was

monitored using a MGD-2002 Multigas Detector (Radiodetection, Bridgton, ME) with

sensitivity from 25 to 1,000,000 ppmv.

Analytical methods

The VOC concentration in the reactor was analyzed using a bench top GC/MS unit

(Hapsite Inficon, East Syracuse, NY) equipped with a SPB-1 sulfur column (Supelco, Bellefonte,

PA). Columns were standard 30m capillary columns with 0.32 µm internal diameter and 1 µm

film. Injection volume was controlled by a sample loop volume. The mass spectrometer

consisted of an ionizer (70 eV), a mass selector (1-300 AMU), and an ion detector (scan rate

1000 AMU/sec @ 10 points per AMU). Two internal standards, 1, 3, 5-tris (trifluoromethyl)

benzene (100 ppmv) and bromopentafluorobenzene (50 ppmv) were used to tune the Hapsite

GC/MS and were injected with each gas sample. A non-evaporable getter pump was used to

generate the required vacuum, which necessitated the use of nitrogen as the carrier gas (3.5-3.7

ml/min flow rate). Standard curves were prepared from liquid standards for 2-methylbutanal, 3-

methylbutanal, and hexanal, and from gas standard for methanethiol in the range of 0-80 ppmv.

The standard curves were periodically analyzed to confirm the GC sensitivity and the reliability

of the curve. The Hapsite GC/MS was tuned before each analysis and was operated at 70 oC

column temperature and 50 oC probe temperature. An internal standard with calibrate mass plots

of 69 and 117 were used and a scanning range from a m/z 45 to 250 was used. Gas samples were

withdrawn using Tedlar bags from tees at different ports of the reactor and were analyzed at 70

oC isothermally.

75

Experimental procedure

The experimental setup was same as described in chapter 3. Compressed air was first

passed through two bubble columns. Then 2-methylbutanal, 3-methylbutanal, and hexanal liquid

mixture was injected into the compressed air flow via a syringe pump. The vaporized aldehyde

was further mixed with air in a mixer which was filled with glass balls. After that, the air

containing aldehydes was mixed with methanethiol (~5000 ppmv) using a mass flow controller

(Figure 4.1).

RESULTS AND DISCUSSION

After 15 months of continuous operation of the biofilter treating an aldehyde mixture,

methanethiol was added into the system in gas phase. Removal efficiencies for the biofilter were

determined by measuring inlet and outlet individual VOC concentrations using a portable

GC/MS. The removal efficiency, defined as [Cgin-Cgout]/Cgin, ranged from 92% to 100% for 3-

methylbutanal and 2-methylbutanal, while hexanal removal remained at 100%. However, for

methanethiol, the fractional removal only reached as high as 42% and was in the range from 24%

to 42% (Figure 4.2). The concentration profile along the reactor showed that the decrease of

methanethiol concentration was limited at the top of the reactor compared to that in the lower

part of the reactor (Figure 4.3), however the total removal was very small. Aldehydes were

getting continuously removed along the reactor and were completely removed by the outlet

(Figure 4.3). An unknown peak was observed at the second port of the reactor which was

identified as dimethyl disulfide (DMDS) by the GC/MS with a retention time at 6.53 minutes

(Figure 4.4). The concentration profile showed that the methanethiol concentration decreased

along the reactor, while DMDS concentration increased along the reactor (Figure 4.5).

76

These results indicate that methanethiol is transformed to DMDS. The question is

whether this transformation is due to biodegradation or an abiotic reaction? A possible pathway

of methanethiol microbial metabolism is shown in Figure 4.6 (Lomans et al., 2002; White,

1995). According to this pathway, degradation of DMDS can give rise to methanethiol

formation. Methanethiol is subsequently oxidized by methanethiol oxidase, which is

accomplished by methanethiol oxidase EC 1.8.3.4 (also known as methyl mercaptan oxidase).

The reaction leads to the formation of formaldehyde, hydrogen peroxide, and sulfide (Eq. 1).

CH3–SH + O2 + H2O → HCHO + H2S + H2O2 (1)

Part of the formaldehyde will be incorporated into cell mass through the serine pathway.

Sulfide will be oxidized and yield sulfuric acid. However, in an aerobic system like a biofilter,

biodegradation of methanethiol to DMDS may be possible. Another possibility was an abiotic

reaction since previous work has showed that methanethiol oxidation can give rise to DMDS

formation through chemical reaction. According to Kelly and Smith (1991), methanethiol can be

chemically oxidized to DMDS. Kastner et al. (2003) also found that in a wood fly ash packed

bed reactor, methanethiol can be catalytically oxidized to form DMDS.

Meeyoo and Trimm (1997) suggested an oxidation mechanism of hydrogen sulfide and

ethanethiol when transition metal ions were present:

R − SH → R − S- + H+

2R − S- +M3+ → R − SS − R +M+

R − SS − R + 21 O2 → H2O + S2, if R = H

M+ + O2 → M3+ + O22-

Kastner et al. (2002B and 2003) suggested that if the R group is CH3, oxygen is not

capable of reacting with R − SS − R and thus no further reaction occurs beyond this point. The

77

coating of the synthetic medium used in our experiments was made of a metallic material,

microorganisms, nutrients, organic carbon, an alkaline buffer, a bonding agent, an adsorptive

agent, and a hydrophobic agent (Shareefdeen and Herner, 2005). Therefore, the metal ion in the

coating may result in the formation of dimethyl disulfide from methanethiol in the presence of

oxygen.

The experiment of biodegradation of methanethiol and aldehyde was operated for one

month. The low removal of methanethiol was consistently observed. The reason that caused this

low removal may be that the microbial populations in the reactor preferentially utilize aldehydes

over methanethiol. Therefore, with more time for the microorganisms to adapt, or at lower

aldehydes concentration, methanethiol might be biodegraded.

A kinetic analysis was performed by using the plug flow design equation (eq. 10 in

chapter 3) with the appropriate rate law (-r = kCn , where r is degradation rate of the VOC).

Assumptions of a homogeneous system, constant volume, constant pressure, constant

temperature, and O2 in excess were made and the same kinetic procedures were performed as in

chapter 3. Both first order and zero order models were fit to the data and first order appeared

more appropriate based on larger R-square (Table 4.1). Utilizing all the data obtained within one

month operation period, first and zero order rate constants were calculated for methanethiol, 3-

methylbutanal, 2-methylbutanal, and hexanal (Table 4.2). Based on this limited analysis, among

the aldehydes, 3-methylbutanal had the lowest rate constant and appeared to be the rate limiting

compound. Methanethiol had a much lower rate constant than those of the aldehydes.

Using the first order model and comparing the kinetics of aldehyde and methanethiol

degradation, the measured degradation rates in this work were higher than that prior to the

methanethiol addition (Table 4.3) but still similar to those previously reported for butanal and

78

isobutanal (2-methylpropanal) for wood bark and compost based biofilters. For example, the first

order rate constants for hexanal, 2-methylbutanal, and 3-methylbutanal were 0.113, 0.084, and

0.082 1/s (17-100 ppmv), respectively. Before methanethiol was added, those constants were

0.06, 0.032, and 0.033 1/s (30-165 ppmv), respectively. Weckhuysen et al. (1993) reported a first

order rate constant of 0.091/s for butanal (10 ppmv,) and Sercu et al. (2005) reported a first order

rate constant of 0.033 1/s for isobutanal. First order reaction rate for methanethiol was 0.0013 g-

MT/kg-matrix/h. Previous study on removal of H2S (50 ppm), MT (30 ppm), and DMS (25 ppm)

in peat biofilter reported the degradation rate of methanethiol was 0.048 g-MT/kg-matrix/h (Cho

et al., 1991). Therefore, the degradation rate of methanethiol in our biofilter was much lower.

The reason for the low degradation rate, as mentioned earlier, may be the preferential utilization.

The increase of reaction rate constant of aldehydes may due to the reaction between methanethiol

and aldehyde.

CONCLUSION AND FUTURE WORK

A synthetic medium based packed bed biofilter treating an aldehyde mixture and

methanethiol was investigated. The biofilter system had been continuously utilizing 3-

methylbutanal, 2-methylbutanal, and hexanal for 15 months and approached 92% to 100%

removal efficiencies for all compounds at 6 L/min flow rate. Subsequently, methanethiol was

introduced into the reactor and the overall removal efficiencies for the aldehydes were

approximately same. However, after first order kinetic model was applied to the data, the

resulting first order rate constants were higher than before. Removal efficiency for methanethiol

ranges from 24% to 42% with a first order rate constant. An unknown substance (identified as

dimethyl disulfide with GC/MS analysis) was formed with an increasing concentration along the

79

reactor as methanethiol decreased. Further analysis on the mechanism from both a biotic and

abiotic point of view indicated that the reaction from methanethiol to DMDS may be due to

catalytic oxidation of methanethiol by the synthetic matrix.

More experimental work is required to understand the effect of methanethiol addition on

aldehyde biodegradation and methanethiol degradation itself. Long term continuous operation is

needed and the changes in degradation behavior of methanethiol should be monitored. If the

microbial population present in the reactor can only utilize aldehydes, another source of

microorganism is needed to achieve methanethiol biodegradation. Another way is to extract the

microbial population from the reactor and incubate in culture medium (i.e., see if one can isolate

and enrich for methanethiol degraders). After methanethiol is added into the system, the

concentration of methanethiol would be monitored. If the concentration decreases, then

methanethiol can be utilized by the microorganism present in the reactor. Batch reactors with

sterilized medium could be used to verify whether catalytic oxidation of methanethiol is possible.

80

REFERENCES

1. Barnes RD and MacLeod AJ. 1982. Analysis of the composition of the volatile malodorous

emissions from six animal rendering factories. Analyst 10:711–715

2. Cho, KS, M. Hirai, M. Shoda. 1991. Degradation characteristics of hydrogen sulfide,

methanethiol, dimethyl sulfide and dimethyl disulfide by Thiobacillus thioparus DW44

isolated from peat biofilter. Journal of fermentation and bioengineering. 71(6): 384-389

3. Devai, I. and R.D. DeLaune. 1999. Emissions of reduced malodorous sulfur gases from

wastewater treatment plants. Water Environmental Research. 71: 203-208.

4. Kastner, J.R. and K.C. Das. 2005A. Comparison of chemical wet scrubbers and biofiltration

for control of volatile organic compounds using GC/MS techniques and kinetic analysis. J.

Chem. Technol Biotechnol 80:1170-1179

5. Kastner, J.R. and K.C. Das. 2002A. Wet scrubber analysis of volatile organic compound

removal in the rendering industry. Journal of Air and Waste Management Association, 52:

459-469

6. Kastner, J.R., K.C. Das, N.D. Melear. 2002B. Catalytic oxidation of gaseous reduced sulfur

compounds using coal fly ash, Journal of Hazardous Materials, B95: 81-90

7. Kastner, J.R., Q. Buquoi, R. Ganagavaram, K.C. Das. 2005B. Catalytic Ozonation of

Gaseous Reduced Sulfur Compounds Using Wood Fly Ash., Environmental Science and

Technology, 39(6): 1835-1842.

8. Kastner, J.R., K.C. Das Q. Buquoi, N. Melear, 2003. Low Temperature Catalytic Oxidation

of Hydrogen Sulfide and Methanethiol Using Wood and Coal Fly Ash., Environmental

Science and Technology, 37: 2568-2574.

81

9. Kelly, D.P. and N.A. Smith. 1991. Organic sulfur compounds in the environment:

biochemistry, microbiology and ecological aspects. Adv. Microb. Ecol. 11: 345-385

10. Lomans, B.P., C. van der Drift, A. Pol, and H.J.M. Op den Camp. 2002. Microbial cycling

of volatile organic sulfur compounds. CMLS, Cell. Mol. Life Sci. 59: 575-588

11. Meeyoo, V. and D.L. Trimm, 1997. J. Chem. Technol. Biotechnol. 68: 411.

12. Seiwert, J.J. Pulp Mill TRS/VOC/HAPs reductions (HVLC NCGs) using regeneratibe

thermal oxidation (RTO) technology. The 1997 Environmental Conference and Exhibit part

2, Minneapolis, MN. TAPPI PROC ENVIR CONF EXHIB, TAPPI PRESS, NORCROSS,

GA, USA.1: 67-68

13. Sercu, B., K. Demeestere, H. Baillieul, and H.V. Langenhove. 2005. Degradation of

isobutanal at high loading rates in a compost biofilter. J. Air & Waste Manage. Assoc. 55:

1217-1227

14. Shareefdeen, Z.M. and B.P. Herner. Applicant: Biorem Technologies NC (CA). 2005.

Biological Filter. IPC:B01D53/85; B01D53/84; (IPC1-7): B01D39/02 (+6) Publication

info:WO2005037403-2005-04-28

15. Shareefdeen, Z.M., B.P. Herner, S. Wilson. 2002. Biofiltration of nuisance sulfur gaseous

odors from a meat rendering plant. J. of Chemical Technology and Biotechnology, 77(1): 1-4

16. Shareefdeen, Z.M., B.P. Herner,D. Webb, S. Wilson. 2002. Hydrogen sulfide removal in

synthetic medium biofilters. Environmental Progress. 22(3): 207-213

17. Weckhuysen, B., L. Vriens, and H. Verachtert. 1993. The effect of nutrient supplementation

on the biofiltration removal of butanal in contaminated air. Appl Microbiol Biotechnol, 39:

395-399

18. White D. 1995. The physiology and biochemistry of prokaryotes. Oxford University Press.

82

19. Yang, Y. and E.R. Allen. 1994. Biofiltration control of hydrogen sulfide, 2. Kinetics,

biofilter performance and maintenance. Journal of the Air and Waste Management

Association, 44: 1315-1321

83

Figu

re 4

.1. T

he sc

hem

atic

dia

gram

of t

he b

ench

scal

e bi

ofilt

er d

esig

n

Syri

nge

Pum

p

Mai

n A

ir S

ourc

e Flow

Con

trol

ler

Hum

idifi

er

Mix

er

Flow

Met

er

To

Fum

e H

ood

Rea

ctor

Hea

ter

1 (in

let)

2 3 4

5 (o

utle

t)

MT

Sou

rce

84

Time, days0 5 10 15 20 25 30

Frac

tiona

l rem

oval

, %

0

20

40

60

80

100

120

Figure 4.2. Fractional removal of VOC mixtures which include methanethiol ( ),3-methylbutanal ( ),2-methylbutanal ( ), and hexanal (∇) for one month operation, after

addition of methanethiol to the biofilter at 6 L/min flow rate, 39 s residence time, and 16-67 ppmv for each compound

85

Time, s0 10 20 30 40 50

CO

ncen

trat

ion,

ppm

v

0.0

0.5

1.0

1.5

2.0

2.5

Figure 4.3. Concentration profile along the reactor for 3-methylbutanal ( ),2-methylbutanal ( ),hexanal ( ), methanethiol (∇), and dimethylsulfide ( ), time equals

L/U, where L is length of the reactor, U is linear velocity

86

Figure 4.4. Typical inlet (A) and outlet (B) chromatograms of the biofilter showing peaks of methanethiol (MT), 3-methylbutanal (3-MB), 2-methylbutanal (2-MB), and hexanal.

Internal standard peaks (IS1, IS2) are also shown

MT

MT

IS1

DMDS

IS2

A

Hexanal

IS1

2-MB

3-MB

B

IS2

87

Reaction position1 2 3 4 5

Peak

are

a

0.0

5.0e+4

1.0e+5

1.5e+5

2.0e+5

2.5e+5

Figure 4.5. Peak area change as a function of position for methanethiol ( ) and dimethyl disulfide ( ).

88

Figu

re 4

.6. O

xida

tion

path

way

for

met

hane

thio

l Sy

mbo

ls: 1

. met

hane

thio

l oxi

dase

; 2. s

ulfu

r ox

idas

e; 3

. sul

fite

oxid

ase;

4. A

PS r

educ

tase

; 5. A

DP

sulfu

ryla

se.

(Lom

ans e

t al.,

200

2; W

hite

, 199

5)

DM

DS

O2

H2O

H

CH

O

H2O

2

MT

H2S

SO

42-

[S]

SO32-

APS

??

2e-

3H2O

6H

+ +4e-

H2O

AM

P

AD

P

Pi

2e-

2H+ +2

e-

1 2

3

4 5

89

Tab

le 4

.1. R

eact

ion

rate

con

stan

ts fo

r al

dehy

de a

nd m

etha

neth

iol a

t diff

eren

t inl

et c

once

ntra

tions

with

6 L

/min

flow

rat

e

Inle

t con

cent

ratio

n

3-m

ethy

lbut

anal

20

.18p

pmv

0.07

g/m

3

29.9

3ppm

v

0.11

g/m

3

32.9

7ppm

v

0.12

g/m

3

51.4

6ppm

v

0.18

g/m

3

63.5

2ppm

v

0.22

g/m

3

71.1

9ppm

v

0.25

g/m

3

94.0

5ppm

v

0.33

g/m

3

0th re

actio

n co

nsta

nt, g

/(m3 h)

9

13.3

2 10

.8

14.7

6 18

.72

32.7

6 42

.48

R2

0.95

45

0.88

41

0.92

56

0.71

69

0.73

78

0.90

37

0.78

54

1st re

actio

n co

nsta

nt, h

-1

222.

48

301.

68

236.

88

271.

8 29

4.84

39

5.64

49

8.96

R2

0.96

15

0.92

42

0.95

62

0.87

46

0.93

08

0.99

93

0.93

99

Inle

t con

cent

ratio

n

2-m

ethy

lbut

anal

22

.54p

pmv

0.08

g/m

3

27.4

0ppm

v

0.10

g/m

3

35.7

0ppm

v

0.13

g/m

3

42.0

1ppm

v

0.15

g/m

3

51.3

6ppm

v

0.18

g/m

3

62.0

6ppm

v

0.22

g/m

3

84.6

5ppm

v

0.30

g/m

3

0th re

actio

n co

nsta

nt, g

/(m3 h)

10

.08

12.2

4 10

.44

13.6

8 23

.04

17.6

4 24

.12

R2

0.91

08

0.93

45

0.79

53

0.90

73

0.83

74

0.66

81

0.64

4

1st re

actio

n co

nsta

nt, h

-1

263.

52

281.

52

249.

48

270

398.

88

292.

68

338.

04

R2

0.92

47

0.98

24

0.89

06

0.96

41

0.95

2 0.

8627

0.

8464

90

Tab

le 4

.1. C

ontin

ued

Inle

t con

cent

ratio

n

Hex

anal

11

.09p

pmv

0.05

g/m

3

25.2

3ppm

v

0.10

g/m

3

34.3

9ppm

v

0.14

g/m

3

49.2

4ppm

v

0.20

g/m

3

56.2

2ppm

v

0.23

g/m

3

73.3

4ppm

v

0.30

g/m

3

82.6

0ppm

v

0.34

g/m

3

0th re

actio

n co

nsta

nt, g

/(m3 h)

8.

28

13.3

2 14

.76

16.5

6 18

.72

25.2

43

.56

R2

0.99

84

0.89

39

0.84

53

0.67

69

0.63

76

0.70

16

0.76

63

1st re

actio

n co

nsta

nt, h

-1

308.

16

356.

04

297

354.

24

373.

68

395.

28

579.

96

R2

1 0.

99

0.84

7 0.

9178

0.

8713

0.

9743

0.

9564

Inle

t con

cent

ratio

n

Met

hane

thio

l 11

.46p

pmv

0.02

3g/m

3

12.4

6ppm

v

0.02

4g/m

3

13.1

4ppm

v

0.02

6g/m

3

14.5

5ppm

v

0.02

9g/m

3

16.0

8ppm

v

0.03

2g/m

3

20.1

4ppm

v

0.04

g/m

3

30.5

9ppm

v

0.06

g/m

3

0th re

actio

n co

nsta

nt, g

/(m3 h)

1.

44

1.08

1.

08

1.44

2.

16

1.8

2.88

R2

0.77

77

0.95

0.

972

0.98

72

0.87

75

0.92

71

0.99

33

1st re

actio

n co

nsta

nt, h

-1

77.7

6 62

.64

56.5

2 64

.44

98.2

8 52

.2

58.6

8

R2

0.83

98

0.96

38

0.98

35

0.99

0.

942

0.92

12

0.99

9

91

Tab

le 4

.2. E

stim

ated

firs

t and

zer

o or

der

kine

tics o

f ald

ehyd

e an

d m

etha

neth

iol d

egra

datio

n in

a sy

nthe

tic m

ediu

m p

acke

d be

d bi

ofilt

er w

ith 6

L/m

in fl

ow r

ate

Com

poun

d In

let c

once

ntra

tion

(ppm

v)

k (z

ero

orde

r)

(g m

-3h-1

) R

2 k

(fir

st o

rder

)

(h-1

) R

2

Met

hane

thio

l 16

.35 ±

5.28

1.

44 ±

0.4

6 0.

92

56.9

2 ±

13.3

6 0.

94

3-m

ethy

lbut

anal

57

.89 ±

33.4

4 19

.78 ±

10.9

4 0.

82

295.

18 ±

83.

70

0.94

2-m

ethy

lbut

anal

66

.85 ±

42.7

9 21

.62 ±

12.2

7 0.

80

300.

82 ±

73.

01

0.94

hexa

nal

45.7

5 ±

29.4

3 21

.35 ±

12.8

1 0.

84

405.

34 ±

103

.08

0.95

Tab

le 4

.3. F

irst

ord

er r

eact

ion

rate

con

stan

ts o

f ald

ehyd

e be

fore

and

aft

er m

etha

neth

iol a

dditi

on

cond

ition

s he

xana

l 2-

met

hylb

utan

al

3-m

ethy

lbut

anal

4.7

L/m

in fl

ow ra

te

0.10

0 ±

0.00

6 1/

s 0.

054 ±

0.01

9 1/

s 0.

047 ±

0.02

1 1/

s

6 L/

min

flow

rate

with

MT

addi

tion

0.11

3 ±

0.02

9 1/

s 0.

084 ±

0.02

0 1/

s 0.

082 ±

0.02

2 1/

s

M

T: m

etha

neth

iol

92

CHAPTER 5

EXTERNAL MASS TRANSFER EFFECTS ON KINETICS OF DEGRADATION IN A

BIOFILTER

93

ABSTRACT

External mass transfer in a bench-scale, synthetic medium based biofilter was

investigated. The reactor had been continuously operated for 15 months and an aldehyde mixture

containing hexanal, 2-methylbutanal, and 3-methylbutanal was degraded in the biofilter.An

external mass transfer model was developed based on several fundamental assumptions.

Comparing the predicted data generated from the model with the experimental data indicated that

the reactor transitioned from reaction limited condition to an external mass transfer limited

condition. The Mears’ criteria was also calculated and gave a similar conclusion. A higher flow

rate was applied and an increase of the first order reaction rate of the aldehyde were observed

confirming the biofilter was apparently mass transfer limited at the lower flow rates.

Key Words: biofiltration, aldehyde, mass transfer, mears

94

INTRODUCTION

In a biofilter system, the overall degradation rate depends on the mass transfer rate and

the rate of biodegradation. The system tends to be mass transfer limited when (1) the

biodegradation rate increases (e.g., at higher biomass concentrations); (2) the mass transfer

coefficient kc decreases; and (3) the driving force for mass transfer decreases (e.g., at lower gas

concentrations). Therefore, in a biofilter where mass transfer is the rate limiting step, we can

increase mass transfer coefficient kc or increase inlet gas concentration to increase mass transfer

rate, and thus increase the overall reaction rate.

In heterogeneous systems, the overall reaction process involves diffusion, adsorption, and

surface reaction. Two types of diffusion resistances are external resistance, which indicates

diffusion of the reactants or products between the bulk fluid and the external surface of the

catalyst, and internal resistance, which indicates diffusion of reactants or products from the

external pellet surface to the interior of the pellet (Fogler, 2006).

This chapter will focus on the external mass transfer analysis and effect on a biofilter

using a porous spherical shaped synthetic matrix. An external mass transfer limitation model was

developed and used to predict concentration profile along a packed-bed reactor and compared to

the actual concentration, from which we can determine whether the process was limited by

external mass transfer or by the overall biodegradation rate. In addition, the Mears’ criteria was

also calculated to determine if external mass transfer was rate limiting.

MASS TRANSFER MODEL

In a reactor system where the reactants diffuse from the bulk fluid to the external surface

of a catalyst, flow past a single catalyst pellet was first considered. The hydrodynamic boundary

95

layer is usually defined as the distance from a solid object to where the fluid velocity is 99% of

the bulk velocity U0. The mass transfer layer thickness, δ, is defined as the distance from a solid

object to where the concentration of the diffusing species reaches 99% of the bulk concentration.

When modeling diffusive transport taking place in fluid-solid phase system, a typically

approach is to treat the fluid layer next to the solid boundary as a stagnant film of thickness δ

(Figure 5.1). Two assumptions were made in the development of our model:

(1) Assume that all the resistance to mass transfer is within this stagnant film;

(2) Assume that the properties (i.e., concentration, temperature) of the fluid at the outer

edge of the film are identical to those of the bulk fluid.

In our packed bed reactor (Figure 5.2), when the reaction is completely mass transfer

limited, a fast reaction rate compared to a slow mass transfer rate results in a low surface

concentration so that it can be neglected with respect to the bulk concentration. At steady-state,

we solve the mole balance differential equation with the following boundary conditions, which

are CAs=0 and at z = 0, CA=CA0, and get the following equation:

)exp(0

zU

akCC c

A

A −= (1)

where, ck is mass-transfer coefficient (m/s), a is external surface area of catalyst per volume of

catalytic bed (m2/m3), U is superficial gas velocity through the bed (m/s), Z is the position of the

reactor (m), CA0 is the initial concentration at Z=0, CA is the concentration at positon Z.

In equation (1), a and ck are defined as below:

a = 6(1-εb)/dp for packed beds (2)

ck = p

AB

dDSc )Re6.02( 3/12/1+ =

p

AB

AB

p

dD

Dv

vUd

))()(6.02( 3/12/1+ (3)

96

ABD = 2/13/13/1

2/39

]11[)(

10*3.4MBMAVVp

T

BA

++

−

(4)

In our system, εb is porosity of the bed, dp is particle diameter (m) of the synthetic matrix,

v is kinematic viscosity of air (m2/s), ABD is gas phase diffusivity of aldehyde in air (m2/s), AV

and BV are molar volume of aldehyde and air respectively (m3/kgmol), AM and BM are

molecular weight of aldehyde and air (kg/kmol), T is temperature (K), P is pressure (atm).

RESULTS AND DISCUSSION

In the biofilter system, the reaction rate was determined by the rate of external mass

transfer and the rate of microbial degradation. In order to determine which is the rate limiting

step, we need to study a model in which the reaction is completely external mass-transfer limited

and compare the predicted concentrations with the actual concentrations. In our model, we

propose there is a stagnant gas layer surrounding the matrix particle in which the aldehyde

diffuses from the bulk gas to the surface of the matrix. We assume that all the resistance to mass

transfer is found within this hypothetical stagnant film. With this model, mass transfer

coefficient was calculated based on the approximation of bed properties (Table 5.1) and the

predicted concentration was compared to the actual concentration.

Figure 5.3 showed the model fitting of 3-methylbutanal after one month and four months

of operation respectively. After one month of operation, the actual concentration profile was

above the profile predicted by external mass transfer limited model. This indicated that,

compared with the external mass transfer limited model, the actual overall reaction rate was

lower, which means the overall reaction was limited by reaction or microbial degradation. While

after four months operation, the actual concentration profile fell below the external mass transfer

97

model, which indicated that the overall reaction rate was now limited by external mass transfer.

2-methylbutanal had the same trend as 3-methylbutanal - the overall reaction went from reaction

limited to external mass transfer limited (Figure 5.4). While for hexanal, the overall reaction was

external mass transfer limited all the time (Figure 5.5).

To verify the above conclusion, Mears’ Criterion for external diffusion was calculated.

This dimensionless parameter uses the measured rate of reaction to determine if mass transfer

from the bulk gas phase to the catalyst surface can be neglected. Mears proposed that when

15.0<−

Ac

pbA

Ckndr ρ

(5)

where -rA = reaction rate, kmol/kg/s

n = reaction order

dp = catalyst particle radius, m

ρb = bulk density of catalyst bed, kg/m3

CA = bulk concentration, kmol/m3

kc = mass transfer coefficient, m/s

external mass transfer effects can be neglected. When equation (5) is satisfied, no concentration

gradients exist between the bulk gas and external surface of the catalyst pellet.

Concentration profiles at four months operation were used and first order reaction was

assumed, the reaction rate was calculated from the first order reaction rate, inlet concentration,

and the bulk density. Mears’ criteria were determined and were 1.27 for 3-methylbutanal, 1.48

for 2-methylbutanal, and 1.73 for hexanal. They were all larger than 0.15 which led to the same

conclusion that external mass transfer can not be neglected.

To learn the effect of flow rate on conversion, we need to determine the correlation for

the mass transfer coefficient for the particular geometry and flow field. According to Thoenes

98

and Kramers (1958), when 0.25 < εb < 0.5, 40 < Re < 4000, and 1 < Sc < 4000, kc is directly

proportional to the square root of the velocity and inversely proportional to the square root of the

particle diameter:

2/1

2/1

pc d

Uk ∝ (6)

For external mass transfer-limited reactions in packed beds, the rate of reaction in the bed

can be expressed as:

AcA aCkr =− (7)

From equation (2),

pd

a 1∝ (8)

Combine equation (6), (7) and (8), we can get

2/3

2/1

pA d

Ur ∝− (9)

Therefore, in external mass-transfer limited system, increasing flow rate should increase

reaction rate as well when other medium properties hold constant. So a higher flow rate was

tested (6 L/min vs. 4.7 L/min) and the calculated reaction rate constants were shown in table 5.2.

Although the data at higher flow rate before methanethiol addition was limited by experimental

conditions, the results indicated that after the flow rate was increased from 4.7 L/min to 6 L/min,

the first order reaction rate constants for 2-methylbutanal and 3-methylbutanal increased,

however there was no effect on hexanal. Then methanethiol was added into the system with

aldehydes at 6 L/min flow rate and the first order reaction rate constants for all the aldehydes

were higher than those at 4.7 L/min flow rate (Table 5.2). As mentioned in chapter 4,

99

methanethiol might go through an abiotic reaction, which should not affect aldehyde

biodegradation. However, literature has mentioned that aldehydes may react with methanethiol.

Therefore, it was not clear whether the increase in reaction rate constant was due to methanethiol

addition or due to the increase in flow rate.

CONCLUSIONS

An external mass transfer model was fit to the kinetic data which indicated that the

overall reaction rate for hexanal, 2-methylbutanal, and 3-methylbutanal were all limited by

external mass transfer. Mears’ criteria were also calculated and gave the same conclusion. Few

data were obtained at steady state, yet indicating an increase in the reaction rate constant at

higher flow rate. After methanethiol was introduced into the reactor and operated one month at

higher flow rates, data indicated an increase in the reaction rate for all the three aldehydes.

100

REFERENCES

D.Thoenes, Jr., and H. Kramers. 1958. Chem. Eng. Sci. 8, 271

Fogler, H.S. 2006. Elements of chemical reaction engineering, 4th Ed. Pretice Hall PTR.

101

Figure 5.1. Concentration profile in stagnant film model

Figure 5.2. Diffusion across stagnant film surrounding catalyst pellet

δ

CA0

CAS

CAs CA

Boundary Layer

δ

102

Length, m

0.0 0.1 0.2 0.3 0.4 0.5 0.6

Con

cent

ratio

n, g

/m3

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

length, m

0.0 0.1 0.2 0.3 0.4 0.5 0.6

conc

entr

atio

n, g

/m3

0

1

2

3

4

5

Figure 5.3. External mass transfer limitation model for 3-MethylButanal after 1 month (A) and 4 months (B) operation, the actual concentration ( ) and the concentration predicted

by the external mass transfer limiting model ( )

A

B

103

Length, m

0.0 0.1 0.2 0.3 0.4 0.5 0.6

Con

cent

ratio

n, g

/m3

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

length, m

0.0 0.1 0.2 0.3 0.4 0.5 0.6

conc

entr

atio

n, g

/m3

0

2

4

6

8

10

12

14

16

18

Figure 5.4. External mass transfer limitation model for 2-MethylButanal after 1 month (A) and 4 months (B) operation, the actual concentration ( ) and the concentration predicted

by the external mass transfer limiting model ( )

A

B

104

Length, m

0.0 0.1 0.2 0.3 0.4 0.5 0.6

Con

cent

ratio

n, g

/m3

0.00

0.02

0.04

0.06

0.08

0.10

0.12

length, m

0.0 0.1 0.2 0.3 0.4 0.5 0.6

conc

entr

atio

n, g

/m3

0

20

40

60

80

100

120

140

160

180

Figure 5.5. External mass transfer limitation model for Hexanal after 1 month (A) and 4 months (B) operation, the actual concentration ( ) and the concentration predicted by the

external mass transfer limiting model ( )

A

B

105

Table 5.1. Mass transfer model parameters nomenclature and values (values presented were from assumption or calculation)

parameter name unit value

a external surface area of catalyst

per volume of catalytic bed m2/m3 180

εb porosity of the bed 0.4

dp particle diameter m 0.02

v kinematic viscosity of air m2/s 1.55E-5

T temperature K 296.15

P pressure atm 1

AV Molar volume of hexanal m3/kgmol 0.1406

BV Molar volume of air m3/kgmol 24.3

AM molecular weight of hexanal kg/kmol 100.16

BM molecular weight of air kg/kmol 28.9

ABD gas phase diffusivity of hexanal in air m2/s 1.35E-6

ck mass-transfer coefficient m/s 4.64E-4

106

Table 5.2. First order reaction rate constant comparison at two flow rate for aldehyde biodegradation

hexanal 2-methylbutanal 3-methylbutanal

4.7 L/min flow rate 0.100 ± 0.006 1/s 0.054 ± 0.019 1/s 0.047 ± 0.021

6 L/min flow rate

without MT1 addition 0.0599 0.063 0.062

6 L/min flow rate

with MT addition 0.113 ± 0.029 0.084 ± 0.020 0.082 ± 0.022

1 MT: methanethiol

107

CHAPTER 6

CONCLUSIONS

A bench-scale, synthetic media based biofilter treating a mixture of hexanal, 2-

methylbutanal, and 3-methylbutanal with or without methanethiol was investigated. Media

characterization suggested high surface area of the synthetic matrix comparing to the compost.

Adsorption capacity experiment was carried out using a model VOC, 3-methylbutanal. A higher

Freundlich constants of the synthetic media comparing to the compost ( FK was 0.037 for the

compost and 1.3 for the synthetic media; n was 0.91 for the compost and 1.31 for the synthetic

media) was obtained by fitting part of the experimental data using Freundlich equation. The

Langmuir isotherm was also used to fit the entire data set for the synthetic media and the

maximum adsorption capacity was 0.06 mol/kg.

Residence time distribution (RTD) analysis via a tracer study was performed at the

beginning and after 6, 11, and 15 months, respectively. The flow pattern and the Peclet number

indicated plug flow without channeling in the synthetic media and lack of compaction in the

reactor. Pressure drop along the reactor was 0.32 inch water (159 Pa/m) with 0.01 m/s linear

velocity. The pH value of the media, which was around 9, did not change after one year of

operation.

Only aldehydes were present in the reactor during earlier stage of the experiment. Simple

first-order and zero-order kinetic models both equally fit the experimental data, yet analysis of

the measured rate constants versus fractional conversion suggested an overall first order model

was more appropriate. Kinetic analysis indicated that hexanal had a significantly higher reaction

rate (k1st order = 0.0998 ± 0.0059 1/s; 18-28 ppmv) compared to the branched aldehydes (k1st

108

order = 0.0505±0.0188 1/s; 21-46 ppmv). After 3 months of operation, all three compounds

reached 100% removal (50 sec residence time, 18-46 ppmv inlet). Media samples withdrawn

from the biofilter were analyzed by SEM. Microbial growth was observed, suggesting removal

of the aldehydes could be attributed to biodegradation.

An external mass transfer model was used to fit the kinetic data. The results indicated that

the overall reaction rate for hexanal, 2-methylbutanal, and 3-methylbutanal were all limited by

external mass transfer. Mears’ criteria were also calculated and the same conclusion was

obtained. Therefore, a higher flow rate was applied and an increase of the first order reaction rate

of the aldehyde was observed confirming the biofilter was apparently mass transfer limited at the

lower flow rates.

After the biofilter had been continuously operated utilizing a mixture of hexanal, 2-

methylbutanal, and 3-methylbutanal for 15 months and approached 92-100% removal

efficiencies for all compounds, methanethiol was subsequently introduced into the reactor.

Simple first-order and zero-order kinetic models were both fit the experimental data, and the

correlation coefficients suggested an overall first order model was more appropriate. Kinetic

analysis indicated that hexanal had a significantly higher reaction rate (k1st order = 0.113 ±

0.029 1/s; 11-82 ppmv) compared to the branched aldehydes (k1st order = 0.083 ± 0.021 1/s, 20-

94 ppmv). Methanethiol had a very low degradation rate (k1st order = 0.016 ± 0.004 1/s, 11-30

ppmv). An unknown substance (identified as dimethyl disulfide with GC/MS analysis) was

formed with an increasing concentration along the reactor as methanethiol decreased. Further

analysis on the mechanism from both biotic and abiotic points of view indicated that the

formation of DMDS from methanethiol might be due to a catalytic oxidation of methanethiol by

the synthetic matrix.

109

APPENDIX

For aldehyde degradation analysis, standard curves for 3-methylbutanal, 2-methylbutanal,

and hexanal were performed on GC/FID. Known amount of liquid compound was mixed with

known volume of air. The obtained concentrated gas solution was then diluted in series to

generate lower concentration. At least five standard concentrations were generated and analyzed

on GC at least three times. Plot of mean peak area versus concentration gives fit of the curve.

The regression equation indicates the relation between peak area and concentration. The standard

curves obtained were used for further analysis and were periodically confirmed throughout the

course of study.

3-MB standard curve

y = 27.439x + 46.588

R2 = 0.9985

0

200

400

600

800

1000

1200

1400

1600

1800

0 10 20 30 40 50 60

concentration, ppmv

PA

110

After methanethiol was added into the biofilter system, GC/MS portable unit was utilized

to perform the analyses of aldehyde and methanethiol. Standard concentrations for the aldehydes

were same as with GC/FID. Concentrated methanethiol was generated by mixing known amount

of high concentration of methanethiol from the cylinder and was diluted to obtain lower

concentrations. The samples were analyzed using GC/MS. The respective peak areas of each

compound and the internal standard were measured, and the peak area ratio (peak area of the

compound over peak area of internal standard). Standard curves were obtained by plotting the

concentration against the corresponding peak area ratios.

Methanethiol Standard Curve

y = 0.0204x - 0.0462R2 = 0.9875

0

0.2

0.4

0.6

0.8

1

1.2

0 10 20 30 40 50 60

Concentration, ppmv

Rat

io