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Equilibria and Kinetics of Nonionic Organic

Compound Sorption in Soil Organic Matter

presented by

Tyler Thomas Ames

has been accepted towards fulfillment
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M.S. CHE

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EQUILIBRIA AND KINETICS OF NONIONIC ORGANIC COMPOUND SORPTION
IN SOIL ORGANIC MATTER

By

Tyler Thomas Ames

A THESIS

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

MASTER OF SCIENCE

Department of Chemical Engineering

1994

ABSTRACT

EQUILIBRIA AND KINETICS OF NONIONIC ORGANIC COMPOUND SORPTION
IN SOIL ORGANIC MATTER

By

Tyler Thomas Ames

While it is known that soil organic matter (SOM) is responsible for nonionic
organic compound sorption in water-saturated soil, the equilibria and kinetics of this
process are not fully understood. Regarding equilibria, a method of predicting SOM-
water partition coefficients for nonionic organic compounds is successfully developed
from the UNIFAC type models of Gmehling et al. (1993) and Kontogeorgis et al. (1993).
Regarding kinetics, desorption is examined with a goal of developing a fundamental mass
transfer model. A gas purge apparatus is critically analyzed and used to collect kinetic
data while scanning electron microscopy and x-ray fluorescence spectroscopy are used to
examine soil morphology. A spherical geometry, Fickian diffusion model is used to
determine effective diffusion coefficients. Kinetic and morphological data indicate soil
organic matter may be appropriately modeled as organic-mineral aggregates or solid
particles. Steam explosion is used to modify soil morphology as demonstrated with

mercury porosimetry.

ACKNOWLEDGEMENTS

I would like to express my gratitude to Dr. Eric Grulke for guidance and support
throughout my Masters program. I also thank Dr. Dennis Miller for advising, as well as
Dr. R. Mark Worden for help with mathematical modeling.

I am grateful to Dr. Steven Boyd for use of the MSU gas purge apparatus as well
as his expertise in soil science. I thank Dr. Thomas Benzing for instruction with the gas
purge apparatus as well as many discussions. I also thank Michael Rich for his skill and
patience with scanning electron microscopy of soil samples.

I am perhaps most thankful for my loved ones who have provided encouragement
and understanding. I am forever grateful to my mother, my father, and Krista for sharing

my achievements as well as tribulations.

TABLE OF CONTENTS

List of Tables ............................................................................................................... vii
List of Figures ............................................................................................................. ix
Nomenclature .............................................................................................................. xi
Chapter 1: Literature Review and Research Objectives ................................................. 1
1.1 Introduction ............................................................................................... l
1.2 Significance of soil organic matter .............................................................. 2
1.3 Models for sorption kinetics ....................................................................... 4
1.4 Equilibrium of saturated soil organic matter systems ................................... 7
1.5 Group contribution activity coefficient models ............................................ 8
1.6 Research objectives .................................................................................... 12
Chapter 2: Solute phase equilibria in soil organic matter and water ............................... 14
2.1 Introduction ............................................................................................... 14
2.2 Materials and methods ................................................................................ 17
2.2.1 Literature data .......................................................................... 17
2.2.2 A modified ELBRO-FV model for prediction of equilibrium ...... 17
2.2.2.1 Model soil organic matter molecules ........................... 17
2.2.2.2 Model development .................................................... 19
2.3 Results and Discussion ............................................................................... 24
2.3.1 Preliminary Results .................................................................... 24
2.3.2 Partition Coefficients from the modified ELBRO-FV model ...... 28
2.3.3 Partition Coefficients using literature activity coefficients .......... 31
2.4 Conclusions ................................................................................................ 35

iv

Chapter 3: Desorption kinetics ..................................................................................... 37

3.1 Introduction ............................................................................................... 37
3.2 Materials and methods ................................................................................ 38
3.2.1 Gas purge system ...................................................................... 38

3.2.1.1 Apparatus ................................................................... 38

3.2.1.2 Experimental procedure .............................................. 40

3.2.1.2.1 Solute-soil equilibration ................................. 4O

3.2.1.2.2 Batch stripping of soil sample ......................... 41

3.2.1.2.3 Heated re-stripping ........................................ 42

3.2.2 Soil sample preparation ............................................................. 43

3.2.3 Model development and implementation .................................... 44

3.2.3.1 Head space ................................................................. 45

3.2.3.2 Gas phase ................................................................... 46

3.2.3.3 Liquid phase ............................................................... 46

3.2.3.4 Solid phase ................................................................. 47

3.2.3.5 Multiple solid phases .................................................. 49

3.2.3.6 IMSL solution of differential equations ....................... 50

3.2.3.7 Data analysis ............................................................... 52

3.3 Results and Discussion ............................................................................... 54
3.3.1 Apparatus analysis ..................................................................... 54

3.3.2 Soil sample analysis ................................................................... 57

3.3.2.1 Virgin and steam exploded Colwood A ....................... 57

3.3.2.2 Size fractions of organic enriched Colwood A ............. 59

3.3.2.3 Heated re-stripping analysis ........................................ 61

3.3.2.4 Total mass removed .................................................... 64

3.3.2.5 Parameter sensitivity ................................................... 65

3.3.2.5.] Mass transfer coefficient ................................ 65

3.3.2.5 .2 Effective diffusion coefficient ......................... 68

3.4 Conclusions ................................................................................................ 71

Chapter 4: Soil Morphology ........................................................................................ 73

4.1 Introduction ............................................................................................... 73

4.2 Materials and Methods ............................................................................... 73

4.2.1 Sample preparation ................................................................... 73

4.2.2 Scanning electron microscopy ................................................... 74

4.2.3 X-ray fluorescence spectroscopy ............................................... 74

4.2.4 Steam explosion ........................................................................ 75

4.2.5 Mercury porosimetry ................................................................. 76

4.3 Results and Discussion ............................................................................... 77

4.3.1 Scanning electron microscopy ................................................... 77

4.3.1.1 Particle morphology .................................................... 77

4.3.1.2 Steam explosion ......................................................... 78

4.3.1.3 Particle shearing during batch stripping ....................... 80

4.3.2 X-ray fluorescence spectroscopy ............................................... 83

4.3.3 Mercury porosimetry ................................................................. 84

4.4 Conclusions ................................................................................................ 85

Conclusions and Recommendations ............................................................................. 87

Appendix A: Sub-group representations for model SOM molecules ............................. 91

Appendix B: MathCad worksheets for activity coefficients calculations ........................ 93
Appendix C: FORTRAN code for batch stripping simulation ....................................... 104
Appendix D: Phase equilibrium calculations for batch stripping experiment .................. 110
List of References ........................................................................................................ 112

LIST OF TABLES

Table 1: SOM-water partition coefficients for propylbenzene, concentration basis ........ 15
Table 2: UNIFAC sub-group representation for Stevenson model. ............................... 20
Table 3: Summary of method for predicting activity coefficients ................................... 24
Table 4: List of solutes used in equilibrium calculations ................................................ 24

Table 5: Predicted and experimental octanol-water partition coefficients, mole
fraction basis. ..................................................................................................... 28

Table 6: Predicted and experimental SOM-water partition coefficients, mole
fraction basis. ..................................................................................................... 29

Table 7: UNIFAC vs. modified ELBRO-FV partition coefficient predictions for
Stevenson SOM model, mole fraction basis. ....................................................... 31

Table 8: Predicted vs. literature solute activity coefficients in water .............................. 32

Table 9: Predicted octanol-water partition coefficients using activity data, mole
fraction basis. ..................................................................................................... 33

Table 10: SOM-water partition coefficients for Bondi molar group volumes, mole
fraction basis. ..................................................................................................... 34

Table 11: SOM-water partition coefficients for Gmehling molar group volumes,

mole fraction basis. ............................................................................................. 34
Table 12: Organic carbon content of soil samples. ........................................................ 43
Table 13: Ideal vs. actual head space break-through times in seconds. .......................... 45
Table 14: Hypothetical morphologies of soil organic matter and respective material

balance equations. .............................................................................................. 48
Table 15: Batch stripping model parameters. ................................................................ 52
Table 16: Henry's law coefficients for alkylbenzenes ..................................................... 53

Table 17: Fitted model parameters for batch stripping experiment. ............................... 56
Table 18: Percent of total propylbenzene removed for batch stripping runs ................... 65

Table 19: Stevenson molecule sub-group representation for Bondi molar group
volumes. ............................................................................................................. 91

Table 20: Stevenson molecule sub-group representation for Gmehling molar group
volumes. ............................................................................................................. 92

Table 21: Aiken molecule sub-group representation for Bondi molar group
volumes. ............................................................................................................. 92

Table 22: Aiken molecule sub-group representation for Gmehling molar group
volumes .............................................................................................................. 92

LIST OF FIGURES

Figure l: Humic acid structure from Aiken et al. (1985) ............................................... 18
Figure 2: Humic acid structure of Stevenson (1982) ..................................................... 19

Figure 3: VLE vs. LLB parameter predictions of octanol-water partition
coefficients, mole fraction basis. ........................................................................ 25

Figure 4: VLE vs. LLE parameter predictions of SOM (Aiken model)-water
partition coefficients, mole fraction basis. .......................................................... 26

Figure 5: VLE vs. LLE parameter predictions of SOM (Stevenson model)-water
partition coefficients, mole fraction basis. .......................................................... 26

Figure 6: Stevenson vs. Aiken partition coefficient predictions using UNIFAC
(Sandler, 1989), concentration basis. ................................................................. 27

Figure 7: Gas purge system for batch stripping experiments. ........................................ 39

Figure 8: Stripping gas propylbenzene concentration vs. time for blank runs at 100
mUmin flow rate. .............................................................................................. 54

Figure 9: Stripping gas propylbenzene concentration vs. time for blank runs at 600
mUmin flow rate. .............................................................................................. 55

Figure 10: Stripping gas propylbenzene concentration vs. time for virgin and steam
exploded Colwood A at 100 lemin flow rate. ................................................. 58

Figure 11: Stripping gas propylbenzene concentration vs. time for three soil sample
particle sizes at 600 mL/min flow rate ................................................................ 60

Figure 12: Stripping gas propylbenzene concentration vs. time for initial stripping
at 600 mm flow rate, 25 degrees Celsius ...................................................... 62

Figure 13: Stripping gas propylbenzene concentration vs. time for heated re-
stripping at 600 mL/min flow rate, 75 degrees Celsius. ...................................... 63

Figure 14: Stripping curve dependence on mass-transfer coefficient for no-soil runs
at 100 mljmin flow rate. ................................................................................... 66

Figure 15: Stripping curve dependence on mass-transfer coefficient for no-soil runs

at 600 mL/min flow rate. ................................................................................... 67
Figure 16: Stripping curve dependence on effective diffusion coefficient (cmzlmin)

for 280 micron spheres at 100 mL/min flow rate and kLa=69 llhr. .................... 69
Figure 17: Stripping curve dependence on effective diffusion coefficient (cm2/min)

for 280 micron spheres at 600 mL/min flow rate and kLa=69 1/hr. .................... 70
Figure 18: Virgin and steam exploded Colwood samples at 35 X magnification. ........... 79

Figure 19: Pulverization effects of batch stripping on 250-500 micron Colwood
samples. ............................................................................................................ 82

Figure 20: Incremental pore volumes for virgin and steam exploded Colwood
samples. ............................................................................................................ 84

NOMENCLATURE

Abbreviations

CSTR continuous stirred tank reactor

NMR nuclear magnetic resonance

PFR plug flow reactor

SE steam explosion

SEM scanning electron microscopy

SOM soil organic matter

XRF x-ray fluorescence spectroscopy

Symbols

a gas-liquid interfacial area per volume of liquid (1/cm)
As total surface area of solid phase (cm2)

C 8 solute concentration in stripping bubbles (g/mL)

C an solute concentration in gas entering stripping flask (g/mL)
C h solute concentration in head space (g/mL)

C, solute concentration in liquid phase (g/mL)

C s solute concentration in solid phase (g/mL)

C 3'" solute mass fraction in soil

Dagg diffusion coefficient for soil aggregate

Ddisp head space dispersion coefficient (cmzlmin)

Def solid phase effective diffusion coefficient (cmzlrnin)
DSOM diffusion coefficient for soil organic matter

F stripping gas flow rate (mL/min)

H Henry's law coefficient (dimensionless)

K partition coefficient (dimensionless)

K d solute partition or dispersion coefficient for soil (dimensionless)
kL gas-liquid mass transfer coefficient (cm/min)

i~
r:

N ><..<:.,<.,<“H°>u_;u‘l‘r aw§ :5»

Greek letters

gas-liquid mass transfer coefficient (llmin)

mole fraction partition coefficient (dimensionless)
mass of soil (g)

total solute added to stripping flask (g)

pressure

radius of solid phase (cm)

spatial variable over radius of solid phase (cm)
spatial variable over radius of solid phase (dimensionless)
inner radius (cm)

outer radius (cm)

temperature

time (minutes)

pure component molar free volume

volume of stripping bubbles (mL)

volume of liquid phase (mL)

mole fraction

spatial variable over length of head space (cm)

solute activity coefficient

free volume contribution to activity coefficient
residual contribution to activity coefficient
free volume fraction

gas velocity in head space (cm/min)

CHAPTER 1: LITERATURE REVIEW AND RESEARCH OBJECTIVES

1.1 Introduction

Soil and groundwater contamination by man-made chemicals is a leading
environmental problem for government, industry and the public. An important class of
contaminants is nonionic organic compounds. This broad classification includes gasoline
constituents, industrial solvents, pesticides and herbicides. Many of these compounds are
susceptible to microbial degradation and therefore have been targeted for bioremediation.
Microbial degradation kinetics are well understood for many combinations of organisms
and pollutant compounds. However, solute mass transport and equilibria in a soil-water
system play an equally important role and are only partially understood. Successful
implementation of bioremediation methods depends on kinetic models which include mass
transport as well as microbial degradation. Such models will be able to accurately predict
solute concentrations and removal rates.

Pollutant interaction with the soil and water has two important aspects: equilibria
and kinetics. Pollutant degradation involves a series of steps including: desorption from
soil into the aqueous phase, transport in the aqueous phase to the microorganisms, and
microbial degradation. Thermodynamic equilibrium provides limiting case information as
to solute concentrations in the soil and aqueous phases. One such limiting case would
result if solute desorption from soil was rapid relative to degradation. Solute
concentrations would be governed by soil-water equilibria. In contrast, desorption and
degradation rates may be on the same order of magnitude. Solute concentrations would
be determined by the kinetics of both processes. In any case, soil concentrations are
required parameters for determining the level of contamination and if clean-up criteria

have been met. Aqueous phase concentrations are needed to determine if the substrate

concentration is sufficient for efficient microbial degradation, but not so high that toxicity
is a problem.

Both the equilibria and kinetics of nonionic organic compounds in soil-water
systems are addressed in this work. A group contribution activity coefficient model has
been adapted to predict solute partition coefficients between soil and water. Soil organic
matter, which is responsible for nonionic compound sorption in saturated systems,
constitutes the soil phase. Predictions are made for several compounds including
halogenated hydrocarbons and pesticides. The work demonstrates the feasibility of
modeling soil organic matter with a single molecule and predicting equilibria.

A fundamental mass transport model is needed for soil organic matter.
Development of a model in this work is approached from two angles: analysis of kinetic
data and physical probing of soil morphology. Insight to the morphology of soil organic
matter is gained by batch stripping propylbenzene from soil and analyzing the results with
a simple Fickian diffusion model. Microscopic and spectroscopic techniques are used to
visually examine soil samples. Conclusions about hypothesized soil models can be drawn

with kinetic and visual information.

1.2 Significance of soil organic matter

A great deal of research has been aimed at predicting the sorptive behavior of
organic pollutants in soil. One of the most significant concepts resulting from research is
the key role that soil organic matter plays in sorption of nonionic organic compounds.
Although soil is a complex and heterogeneous mixture, its components can be classified as
organic or mineral matter. Soil organic matter is the decomposition products of living
organisms, and is a complex and heterogeneous mixture. It has been reported as a
flexible, cross-linked, branched, amorphous (noncrystalline), polyelectrolytic polymeric
substance (Brusseau et al., 1991). Most important is the difference between organic and

mineral matter with respect to water. Organic matter is considered hydrophobic while

polar mineral surfaces are hydrophilic due to strong polar interactions with water (Chiou,
1990). The consequence of water's preference for mineral matter and the polymeric
nature of soil organic matter have been uncovered in numerous publications as described
hereafter.

The direct correlation between soil organic matter content and sorption capacity
has been established by many researchers (Brusseau and Rao, 1989; Chiou et al., 1979;
Chiou, 1990; Lambert et al., 1965; Lambert, 1968). The correlation agrees with the
physical concepts of the system. In a water saturated system, water will wet the mineral
surfaces due to polar interactions. The sorption of the organic solute to the mineral matter
is therefore extremely hindered. Sorption of the organic solute to hydrophobic soil
organic matter is not hindered. The sorption capacity of the soil organic matter is far
greater than that of the few sorption sites on hydrated mineral surfaces, thus the
correlation between sorption capacity and soil organic matter. Only polar groups on the
organic solute would be attracted to the mineral surfaces. The situation is quite different
for dry or nonsaturated soils. The organic solute can freely adsorb to the mineral surfaces.
In general, the sorptive capacity of the abundant mineral surfaces is much greater than of
the small amounts of soil organic matter. As a result, the sorption capacity of dry soils is
higher than that of saturated soils. Starting with saturated soil, water vapor was found to
sharply reduce sorption capacities by about two orders of magnitude (Chiou and Shoup,
1985). At about 90% relative humidity, sorption isotherms were comparable to that of an
aqueous system. The strong interaction between water and mineral surfaces is further
demonstrated by wetting dry soils to which an organic solute has been sorbed. The water
displaces the organic solute from the mineral surfaces (Chiou, 1990).

It should be noted that the preceding discussion on the role of sole organic matter
is only valid for nonionic organic compounds. A ionic compound will interact with
mineral surfaces in a manner similar to water. Additionally, experimental results may

deviate from the soil organic matter correlation with increasing polarity. As solute

polarity increases, the solute will adsorb in increasing quantity to the polar mineral
surfaces. While sorbate molecular structure has been found to have minimal impact on
sorption for nonionic, low polarity compounds, structure becomes more important for
complex molecules such as pesticides (Brusseau and Rao, 1991). While the soil organic
matter correlation still holds for complex or polar molecules, some variation from

nonpolar molecule behavior can be seen in equilibrium and rate studies.

1.3 Models for sorption kinetics

With a basic knowledge of the role of soil organic matter in sorption, the aspect of
sorption kinetics can be addressed. In the effort to predict and explain the nonequilibrium
sorption of organic compounds, many researchers have presented conceptual models for
rate limited desorption. Brusseau et a1. (1991) divided rate limiting processes into two
useful classifications: transport related processes and sorption related processes.
Transport related processes can be defined as physical non-equilibrium resulting from a
heterogeneous flow domain. Rate limitation by transport processes has been handled
either by including it in the models, or designing experiments in which it is insignificant.
An example of the prior is including film diffusion and/or axial and radial diffusion in a
model for a soil column experiment. An example of the latter is a well mixed batch
stripping experiment such as the gas purge apparatus found in the literature (Benzing,
1993; Brusseau et al., 1990).

Regardless of how transport limitations are handled, sorption related processes
have also been divided into two classes: chemical nonequilibrium and physical
nonequilibrium. Chemical nonequilibrium is assumed to be the result of a time-dependent
sorption reaction at the sorbent surface (Brusseau and Rao, 1989). Chemical
nonequilibrium would result from strong sorbate-sorbent interactions such as covalent or
ionic bonding, not expected with nonionic organic compounds and soil organic matter.

The literature is consistent that chemical nonequilibrium is probably not important for

nonionic organic compounds in an aqueous system (Brusseau and Rao, 1989; Brusseau et
al., 1991; Chiou, 1990). The body of literature which addresses physical nonequilibrium,
however, is large and appears to have developed in somewhat independent circles with no
consensus on the specific mechanism for sorption.

Chemical engineering models for diffusion in catalyst pellets have provided ideas
for modeling diffusion in soil. The catalyst pellets consist of crystalline catalyst particles
compressed together. The void space between the particles constitutes macropores, while
the crystalline particles themselves are porous, constituting micropores. Wakao and Smith
(1962) presented a theoretical model for predicting diffusion rates in catalyst pellets
dividing the pellet into a homogeneous macropore fraction and a homogenous micropore
fraction. A more conceptually correct model was proposed by Ruckenstein et al. (1971).
The model includes radial macropore diffusion into the pellet and radial micropore
diffusion into particles which is dependent on the radial position of the particle. A
diffusion model for activated carbon was presented by Peel et al. (1981). The model
accounted for film diffusion, macropore and micropore diffusion, with a first-order rate
expression for transfer between macropores and micropores. A comparison of diffusion
models was provided by Miller and Weber (1986) for soil systems. The study compared
three models: a first-order rate expression, a first-order rate expression with a parallel
instantaneous equilibrium term, and a dual resistance model with film diffusion and Fickian
particle diffusion in series. The dual resistance model was not only more conceptually
correct but provided the best fit to data.

The concepts of the previously mentioned diffusion models were applied to a soil
system by Crittenden et al. ( 1986). The model included film diffusion, macropore
diffusion, surface diffusion (micropore diffusion), and axial/radial dispersion as the study
was conducted in soil columns. The study noted that all of the rate-limiting mechanisms
are important and cannot be completely ignored. In a follow-up paper, experimental

results for the model were presented (Crittenden et al., 1986). The authors noted mixed

satisfaction with the predictive capabilities of the model. It should be pointed out that
despite the model's conceptually sound basis, the role of organic matter versus mineral
matter in the soil was not considered.

A physical diffusion model which considers the role of soil organic matter was
presented by Wu and Gschwend (1986). The sorption is described by radial diffusion into
the soil aggregate, modified by a retardation factor reflecting microscale partitioning of the
solute between the pore fluid and soil organic matter. The organic matter could be
interpreted as coating on pore walls or as organic particles in an aggregate of both organic
and mineral matter. Adjustment of the one fitting parameter, the effective diffusion
coefficient, provided good fit to experimental data. A similar model was presented by
Ball and Roberts (1991b) where hindered pore diffusion due to restrictive pore sizes was
considered. Models of this type are typically referred to as retarded intraparticle diffusion.

While first-order rate-expression models have been used to approximate physical
diffusion, they are also somewhat synonymous with intraorganic matter diffusion. The
intraorganic matter diffusion model comes from the visualization of soil organic matter as
a polymeric partition medium. The conceptual model parallels the observation that the
majority of the solute sorbs (or desorbs) very quickly while the remainder sorbs (or
desorbs) over a long period of time (Robinson et al., 1990). The soil organic matter is
then visualized as having a fraction where sorption (or desorption) of the solute is
instantaneous and the remainder is rate-limited. The model can be thought of as having
three well mixed boxes or phases. The first box is the aqueous phase. The second box is
the fraction of the soil organic matter which is in equilibrium with the aqueous phase. The
third box is the remainder of the soil organic matter. Movement of the solute between the

second and third box is described by a first-order rate expression.

1.4 Equilibrium of saturated soil organic matter systems

In the effort to predict the behavior of organic compounds in soil, a great number
of researchers have determined and analyzed equilibrium conditions for organic pollutants
in soil. Studying equilibrium behavior not only gives sorption capacities but gives insight
into the mechanism of sorption. A study of equilibrium conditions typically results in a
sorption isotherm. The isotherm is graphical representation of amount solute sorbed
versus the amount of solute in solution or solute activity. Data points are taken at several
solution concentrations to produce a curve.

The shape of a sorption isotherm gives information about the system being studied.
The sorption of nonionic organic compounds onto saturated soil (sorption onto soil
organic matter) has been found to have linear isotherms (Chiou et al., 1979; Chiou, 1990;
Karickhoff et al., 1979; Robinson et al., 1990). This is in contrast to the five general
classes of Brunauer sorption isotherms. A Henry's law constant can then be used to
predict the amounts of solute in the soil and aqueous phases at equilibrium. Linear
isotherms are considered supporting evidence for the role played by soil organic matter.
The polymeric organic matter is viewed as a partition medium as opposed to a
conventional solid sorbent which would fit a Brunauer isotherm classification.
Additionally, studies have shown small heats of sorption and the absence of competition
between solutes supporting the argument of a partition medium (Chiou, 1990). Although
the concept of soil organic matter as a polymeric partition medium is fairly well accepted,
absolute linearity of the isotherms has been questioned. Kyle (1981) commented that
linearity of the isotherms had not been clearly established relative to scatter in the data.
Isotherms have been reported as nonlinear at higher solute concentrations (Ball and
Roberts, 1991a; Voice et al., 1983; Weber et al., 1983).

As previously mentioned, Henry's law has been used to represent linear isotherm
data. However, other models have been used in the literature. Nonlinear isotherms have

been fit with success by the Langmuir and Freundlich models (Ball and Roberts, 1991a).

The Flory-Huggins model has also been applied (Chiou et al., 1983). The use of the
Flory-Huggins model makes sense as it was derived for polymer systems. The model was
used by Chiou with assumptions for low solute concentration including a polymer volume
fraction of unity, giving a linear isotherm.

A final consideration in regards to equilibrium is that of time. Equilibrium of an
organic solute in a soil-water system can take a long time to reach. In a study of toluene
adsorbing to soil concentrations appeared steady after 45 days (Robinson et al., 1990).
However, analysis after 5 months showed sorption into the soil had not reached
equilibrium as previously thought. Ball and Roberts (1991a) reported that contact times
of tens to hundreds of days were required to reach equilibrium in the system which gave
nonlinear isotherms. Steinberg et a1. (1987) reported half-equilibration times of 2-3
decades. Generally, the papers with linear isotherms reported equilibration times less than
three days.

1.5 Group contribution activity coefficient models

Phase equilibrium for gases and liquids has been predicted with great success using
UNIFAC, a group contribution method for determining activity coefficients. UNIFAC is a
powerful predictive tool, as only the structural formula of the mixture components is
required. A given compound is represented by a relatively small number of functional
groups. Activity coefficients are calculated with only molar areas and volumes of these
groups as well as interaction parameters for the groups, which have been fit with existing
equilibrium data. While UNIFAC was developed for vapor-liquid equilibrium correlations,
it has been expanded to liquid-liquid and polymer-solvent equilibria. It is reasonable then
to apply UNIFAC to organic solute-water—soil organic matter equilibrium if soil organic
matter is viewed as a polymeric substance. The following review provides background for

this application.

Oishi and Prausnitz (1978) modified the UNIFAC model for application to solvent
activities in polymer solutions. The modification accounted for free volume effects. The
UNIFAC model does not account for changes in free volume due to mixing, as
appropriate for ordinary liquid mixtures. Free volume effects are significant for polymer-
solvent solutions, however, as polymer molecules are much more tightly packed than
solvent molecules (Oishi and Prausnitz, 1978). These effects were accounted for by
adding a free volume contribution term to the combinatorial and residual terms. The free
volume term requires the external degrees of freedom of the solvent molecule and a
proportionality factor which were picked to give good results. Predicted activities were
good for the nonpolar systems tested.

With increased interest in applying UNIFAC to liquid-liquid equilibria (LLE)
systems, Magnussen et al. (1981) developed a parameter table for liquid systems. A new
parameter table was needed as quantitative predictions of multicomponent LLE from
binary data only or LLE compositions using vapor-liquid equilibrium (VLE) parameters
were not possible (Magnussen et al., 1981). The LLE parameters were moderately
successful, but likely suffered from a lack of LLE data for parameter calculation. With the
strength of a group contribution method depending on the equilibrium data available,
several VLE parameter table updates were made for UNIFAC (Gmehling et al., 1982;
Macedo et al., 1983; Tiegs et al., 1987; Hansen et al., 1991). Updates included modified
interaction parameters based on increasingly available equilibria data as well as new main
groups.

An alternative approach is to focus on parameter sets for specific classes of
systems. Hooper et al. (1988) developed temperature dependent interaction parameters
for water-organic liquid systems. Agreement with experimental data was very good, but
the trade-off is limited application due to a small number of main groups. Similar

approaches were taken by Cooling et a1. (1992) for water-unsaturated chlorinated

10

hydrocarbons, and by Abed et al. (1992) for water-sucrose-glucose and water-sucrose-
fructose systems.

Efforts were also directed toward improvement of the UNIFAC model itself.
Larsen et al. (1987) made two modifications. The group interaction parameters were
made temperature dependent, and the combinatorial term was modified. The Staverman-
Guggenheim combinatorial expression, used in UNIFAC, was found to lead to negative
values for combinatorial excess entropy and thus dropped for the simpler Flory-Huggins
combinatorial expression. These changes along with rearrangement of some groups and
new parameter fitting made for improved predictions over UNIFAC.

Changes to the UNIFAC-FV model were made by Iwai and Arai (1989) for
application to hydrocarbon vapors in molten polymers. The combinatorial and residual
terms were that of Oishi and Prausnitz (1978) while the free volume term was modified.
In previous papers, the Bondi (1968) method was used to calculate specific hard-core
volumes. The authors state that the dead-space of large, branched molecules is difficult to
estimate by this method. Instead they estimated hard-core volumes from specific volumes
at 0 K, estimated by the method of Biltz (1934). The external degrees of freedom were
determined from heat of vaporization data of the pure solute. This method seems to have
more of a conceptual basis than the approach of Oishi and Prausnitz (1978). Despite use
of original UNIFAC parameters (Fredenslund et al., 1977), the results were quite good
even at elevated temperatures (130 - 175 degrees Celsius). When the predictions were in
error, the activities were usually underestimated.

Doong and Ho (1991) addressed the sorption of organic vapors in semicrystalline
polyethylene. Only amorphous domains are thought to be accessible, but the presence of
crystalline domains appears to affect sorption. The authors present a theoretical model
which includes free energy contributions from combinatorial entropy, free volume,
interactional-enthalpy (from UNIFAC), and elastic factors. The model contains one

adjustable parameter, characteristic of a given polymer and independent of concentration

11

and temperature. The elastic term in the equation accounts for the crystalline domains
affecting the tension or tightness (constraining) of the amorphous chains.

A final modification of the UNIFAC model for solvent-polymer systems, leading to
a simpler result, is that of Kontogeorgis et al. (1993). Their ELBRO-FV activity
coefficient model contains a residual UNIFAC term and a free volume term given by Elbro
et al. (1990). The free volume term is derived from a generalized van der Waals partition
function and accounts for both combinatorial and free volume contributions. The
ELBRO-FV model offers: (i) a single expression for combinatorial-free volume effects, (ii)
no adjustable parameters as used by Oishi and Prausnitz (1978), and (iii) linear
temperature dependent interaction parameters (Kontogeorgis et al., 1993). Evaluation
with a large variety of polymer solutions showed good experimental agreement and
compared favorably with UNIFAC-FV (Oishi and Prausnitz, 1978) predictions.

Gmehling et al. (1993) presented a modified UNIFAC model, which can be viewed
as the current update for VLE and LLE applications. The combinatorial term was
empirically modified to deal with compounds very different in size, and an extensive data
base of VLE, LLE, molar excess enthalpy, and infinite dilution activity coefficient data
was used to fit interaction parameters. The temperature dependence of the interaction
parameters was also modified from linear to a second-order polynomial. In previous
UNIFAC models, the molar group volume and area parameters were obtained from van
der Waals group volumes and areas as given by Bondi (1968). Gmehling et al. (1993) fit
the parameters using the extensive database. The combination of these factors makes this

the group contribution model of choice for VLE or LLE equilibria.

12

1.6 Research objectives

1. To demonstrate the feasibility of using a group contribution activity coefficient
method for predicting pollutant partition coefficients between soil organic

matter and water.

A UNIFAC type method will be developed for predicting solute activity
coefficients in soil organic matter and water. Soil organic matter will be modeled using
molecules proposed for humic acid in the literature. Corrections developed for polymer
solution systems will be applied. Success of predictions compared to literature data will

be quantified.

2. To mathematically model and determine the limitations of the gas purge
apparatus as a tool for developing a fundamental mass transfer model for soil

organic matter.

The batch stripping experiment (gas purge apparatus) must be accurately modeled
before soil analysis can be done. Existence of rate limited gas-liquid mass transfer needs
to be determined and quantified. Limitations of the experiment are to be explored
including ability to discern between particles of different size or diffusion coefficient and

complete a mass balance.

3. To develop a mass transport model for solute desorption from soil organic

matter based on morphological information.

Desorption data will be obtained with a batch stripping experiment. As a first

approach, a spherical geometry Fickian diffusion model will be tested for ability to predict

l3

desorption curves. Effective diffusion coefficients will be estimated. While this model is
similar to that of Wu and Gschwend (1986) as well as Ball and Roberts (1991b), efforts
will be made to understand soil organic matter morphology as needed for a good mass
transport model. Soil organic matter general morphology is hypothesized as solids,
coatings, or aggregates. Microscopy and spectroscopy will be used to visualize
morphology. Desorption data will be used to deduce sample characteristics using virgin,

organic enriched and sieved, as well as steam exploded soil samples.

CHAPTER 2: SOLUTE PHASE EQUILIBRIA IN SOIL ORGANIC MATTER
AND WATER

2.1 Introduction

The equilibrium concentrations of a nonionic organic solute partitioned between
soil organic matter and water is the first of two system aspects which must be addressed
when considering a bioremediation process. The thermodynamic equilibrium provides
limiting case information as to solute concentrations in the soil and aqueous phases. The
soil concentration is a required parameter for determining the level of contamination and if
clean-up criteria have been met. Aqueous phase concentrations are needed to determine if
the substrate concentration is sufficient for efficient microbial degradation, but not so high
that substrate toxicity is a problem.

The distribution of a solute between two phases, such as soil and water, can be
conveniently described by a partition coefficient. The partition coefficient is typically the
ratio of solute mole fractions or concentrations in the two phases. As described later, the
partition coefficient is a function of solute activity coefficients in each phase. In typical
environmental systems, solute concentrations are low and the activity coefficients in both
phases are essentially constant with respect to concentration and equal to infinite dilution
activity coefficients. This conveniently allows the use of a single partition coefficient
which does not depend on solute concentration. While infinite dilution systems are
evaluated in this work, the theory is applicable to non-dilute systems as well.

As previously outlined in the literature review, soil organic matter is the specific
soil component responsible for nonionic compound sorption in aqueous systems.
Experimental data does exist for partition of organic solutes between soil organic matter

and water, but it is limited. Additionally, the heterogeneous nature of soil organic matter

14

15

leads to varied partition data depending on the source of the soil sample. Table 1

illustrates propylbenzene partition coefficient differences between soil samples.

Table l: SOM-water partition coefficients for propylbenzene, concentration basis.

 

 

Soil KQM
Capac A 158
Colwood A, whole soil 209
Colwood A, 53-125 micron, SOM enriched fraction 295
Colwood A, 250-500 micron, SOM enriched fraction 331

 

(Source: Benzing, 1993)

 

The sparse and varied nature of partition data provides motivation for development
of a method of predicting equilibrium. Such a method would allow for equilibrium
predictions where data was not available. Equilibrium could be predicted for engineered
compounds in the developmental stage, such as new pesticides. Ideally, the method would
also correctly predict equilibrium differences due to variations in chemistry of soil organic
matter. Available information such elemental analysis and percent carbon aromaticity
could correlate predictions. A group contribution method such as UNIFAC is a prime
candidate for meeting the above criteria. Only limited information is required to predict
activity coefficients such as structural formula and molar volume. Differences in elemental
content and aromaticity are also accounted for by UNIFAC.

This work develops a method for predicting partition coefficients for nonionic
organic solutes in soil organic matter and water. In general, the partition coefficient of an

organic solute between two phases can be described by Equation 1 from Sandler (1989).

K _ concentration of solute in phase I

 

. . (1)
concentratron of solute in phase II

16

For a solute which is completely dissolved between two immiscible phases, equilibrium is

described by Equation 2.

x{r{(T,P,x1’)=xr”v1”(T,P.xr”) (2)

where (x) is the mole fraction of the solute, gamma is the activity coefficient of the solute,
(T) is temperature and (P) is pressure. Equation 2 can be rearranged to the very useful

form of Equation 3.

I II II
_x_1__ _’Yr (T,P,x1 )
x1 _Kx— 711(T,P,xlll (3)

 

The distribution or partition coefficient for the solute mole fractions is equal to the
reciprocal of the ratio of the solute activity coefficients in the two phases.

Octanol-water partition coefficients were calculated in addition to soil organic
matter-water partition coefficients. Solute equilibrium between SOM and water has been
correlated to equilibrium between octanol and water in a linear fashion (Chiou, 1990;
Chiou et al., 1983; Karickhoff et al., 1979). As octanol is a pure component organic
phase, group contribution method predictions of partition equilibrium are expected to be
good. The quality of octanol-water predictions qualitatively represents the most which
could be expected of SOM-water predictions. In other words, if octanol-water
predictions averaged 20% error then SOM-water predictions would not be reasonably
expected to have only 10 or 15% error. The quality of SOM-water predictions can be

measured against that of octanol-water predictions.

17

2.2 Materials and methods

2.2.1 Literature data

Experimental data from the literature was collected in order to develop and test a
group contribution activity coefficient model for soil organic matter-water systems. Data
collected includes infinite dilution activity coefficients, octanol-water partition coefficients,
and soil organic matter-water partition coefficients. Partition coefficient data was taken
from Chiou et al. (1983), Briggs (1981), and Benzing (1993). This data was compared to
predicted partition coefficients obtained from the ratio of predicted solute activity
coefficients in each phase. While this is a comparison of the desired quantity, the
predicted value depends on two variables (the two activity coefficients). Two given
models may yield similar partition coefficients, but very different activity coefficients. It is
valuable to have a direct measure of a model's predictive capability of the activity
coefficient for at least one of the two phases. Infinite dilution activity coefficient data was
collected in order to make this comparison. Data was taken from Hwang et al. (1992b)
and Cooling et al. (1992).

2.2.2 A modified ELBRO-FV model for prediction of equilibrium

2.2.2.1 Model soil organic matter molecules

The predictive value of UNIFAC and similar group contribution methods lies in
the fact that relatively little knowledge is required about components in the system to be
modeled. In the case of UNIFAC models, only the structural formula is required in order
to specify the number of each sub-group in the compound. The ELBRO-FV model (and
other polymer-solvent models) also requires the pure component molar volume for each

component. This information is readily available for water, octanol, and many solutes.

18

Soil organic matter, however, is far from a pure component, and assumptions must be
made in order to provide a structural formula and molar volume.
Two structural models were chosen for the calculations. The first is a tetramer

structure proposed for humic acid from Aiken et al. (1985) shown in Figure 1.

HOCH 0” 0 KHZOH fOOH
H H

 

OH

 

COOH COOH n

 

 

 

Figure 1: Humic acid structure from Aiken et al. (1985).

The Aiken model is based on a variety of information including elemental analysis, titration
data, and NMR analysis as well as the assumption of a lignin pre-cursor backbone. A
value of n=l was used for the repeating hydrocarbon chain. The second model is a
hypothetical humic structure of Stevenson (1982) which is shown in Figure 2.

One additional assumption is required to obtain a pure component molar volume.
A density of 1.1 g/mL was arbitrarily chosen. This is comparable to the value assumed by
Chiou et al. (1983) of 1.2 g/mL. The molar volume is given by the molecular weight
(from the structural model) divided by the density. The molar volume can also be
determined experimentally by measuring the specific gravity of a soil organic matter

sample.

19

 

 

HC = O
I (Sugar)
(HC - OHM
foou HE: = o
| 0- HO OH
0 COOH
—HO
H 0 COOH
R- CH
I (Peptide)
C = O
I
NH
1

 

 

Figure 2: Humic acid structure of Stevenson (1982).

2.2.2.2 Model development

As a preliminary examination, the BASIC program for UNIFAC of Sandler (1989)
was used to calculate solute activity coefficients in the three phases: water, octanol, and
soil organic matter. The Sandler UNIFAC program was chosen as it provided quick
preliminary calculations. Both the Stevenson and Aiken models were used for soil organic
matter. Benzene, toluene, ethylbenzene, and propylbenzene were used as solutes.
Additionally, a second analysis was done with the parameter set for liquid-liquid
equilibrium of Magnussen (1981). The LLE parameter set was chosen as the SOM-water
system is solid-liquid system. A solid-liquid system would seemingly be better modeled as
a liquid—liquid system opposed to a vapor-liquid system. The main group parameters (van
der Waals areas and volumes for combinatorial term calculations) as well as interaction
parameters (for residual term calculations) were changed to those of Magnussen (1981).

Inputs to the BASIC program were the UNIFAC sub-group representations for
each component. As an example, the Stevenson model representation used with the

Sandler (1989) UNIFAC is shown in Table 2.

20

Table 2: UNIFAC sub-group representation for Stevenson model.

 

 

macaw Menu
CH2 1
CH 3
ACH l 5
AC 22
OH 4
ACOH 1 3
CH2CO 2
CHO 1
HCOO 5
CH-O 6
CH2NH l
ACNH2 l
CHNH l

 

 

It should be noted that exact representations of the model soil organic matter molecules
were not possible with most sub-group sets, as required sub-groups were not available.
An example is quinone which was represented by an aromatic carbon and a hydroxyl. The
result is representations which vary slightly depending on the set of groups and sub-groups
used. Also, as the SOM molecules are quite large, multiple representations are possible
with a given set of sub-groups. Sub-group representations for both SOM molecules
applicable to the final modified ELBRO-FV method of this work are listed in Appendix A.

Solute activity coefficients as a function of solute concentration were determined
by the BASIC program. A greatly simplifying assumption was that solvent phases (SOM,
water or octanol) were pure. In other words, any limited mutual solubilities were ignored.
In reality, the water phase would have a small amount of octanol while the octanol phase
would have a small amount of water. The mutual solubilities, if accounted for, would
depend on solute concentrations. This assumption was checked for octanol-water
partitioning. Predicted partition coefficients decreased by less than 3% when octanol-
water solubility was accounted for. Additionally, solvent phase mixing is considered

negligible for SOM-water as soil organic matter is a cross-linked, polymeric substance.

21

A polynomial curve fitting program (Polymath) was used to represent the activity
coefficients as a function of concentration with fifth-order polynomials. Microsoft Excel
was used to iteratively solve for solute concentrations in each phase, e.g. Stevenson SOM
and water, and thus the partition coefficient. As mentioned in the introduction, the
analysis thus far is applicable to non-dilute as well as dilute systems as activity coefficients
are a function of concentration. However, as concentrations of interest were low, the
solute activity coefficients were found to be independent of solute concentration and equal
to the infinite dilution activity coefficient. Therefore, infinite dilution activity coefficients
were calculated in future work using a solute mole fraction of 0.00001.

After preliminary calculations were reviewed, an improved method of calculation
was developed based on literature for polymer-solvent systems. A simple approach would
be to choose one of the literature models for all calculations. However, combining
elements of different models may lead to better predictions. For example, the residual
contribution term to the activity coefficient is common to all models. As described in the
literature review, the interaction parameters used to calculate this term are updated over
time as databases expand or have been fit for specific classes of compounds. Activity
coefficient predictions may be improved by using an interaction parameter set other than
that which was published with a given model.

The ELBRO-FV model of Kontogeorgis et al. (1993) was chosen as a basis for
calculation of solute activity coefficients in the soil organic matter phase. Strengths of the
model include a simplified derivation of a single free volume and combinatorial
contribution term as well as proven predictive capabilities. ELBRO-FV was evaluated
with polymer solution data from atherrnal to strongly polar systems. Predicted infinite
dilution activity coefficients were in very good agreement with experimental data, and

predictions compared favorably with UNIFAC-EV (Kontogeorgis et al., 1993).

22

In the ELBRO-FV model (Kontogeorgis et al., 1993) the predicted activity
coefficient (7) is the sum of the free volume term and the residual term as given by

Equation 4 for a given component (i ).
1nv.=1nr§”+lnr?‘s (4)

The free volume term is given by Equation 5 from Elbro et al. (1993). The free volume

fraction is given by Equation 6.

fv fv
1nyfV=1n¢x4+1—¢x4 (5)
if =—x"V"" (6)
l 2)”;va
J'

(x) is the component mole fraction, (j) ranges from one to the number of components,
and (Va) is the component molar free volume. The molar free volume is given by the
difference between the pure component molar volume and the van der Waals volume as
calculated by the method of Bondi (1968). However, Gmehling et al. (1993) published a
set of fitted molar volumes in place of the traditional van der Waals molar volumes.
Therefore, the free volume term could be calculated with group molar volumes taken

from:

(i) van der Waals molar volume parameters based on the method of Bondi (1968)
(ii) fitted/optimized molar volumes of Gmehling et al. (1993)
Both sets of values were used in partition calculations in order to determine if one set was
more successful in predictions than the other.
The residual term of the ELBRO-FV model is given by UNIFAC from

Fredenslund et al. ( 1977). It is logical to replace the outdated 1977 interaction parameter

23

set used to calculate this term with a more appropriate one. As seen in the literature
review many sets are available, some with broad application while are some specific.
Three options were identified:

(i) the most recent database parameters of Gmehling et al. (1993)

(ii) the LLB database parameters of Magnussen et al. ( 1981)

(iii) the water-organic liquid database parameters of Hooper et al. (1988)

The Gmehling et al. (1993) set was ultimately chosen as the database used to fit
parameters is the largest and most recent. The LLE parameters of Magnussen et al.
(1981) seemed a logical choice as the solute-SOM-water system is solid-liquid-liquid
being modeled as a liquid-liquid-liquid system. However, the preliminary results showed
that even the outdated VLE parameter set used by Sandler (1989) had significantly greater
success with the solute-SOM-water system than the Magnussen et al. (1981) set. While
the specific focus of the Hooper et al. (1988) parameter set was appropriate, it was
rejected as it lacked too many groups required for modeling of soil organic matter.

A frnal model adjustment was made based on the following preliminary study.
Activity coefficients were calculated on MathCad worksheets for benzene, toluene, and
ethylbenzene in octanol, water, and the Stevenson SOM phase. The ELBRO-FV free
volume contribution was apparently not appropriate for the aqueous phase. Predicted
activity coefficients were two to three orders of magnitude too low. Residual term
contributions alone were close to experimental values, but free volume term contributions
were negative. The rectify this problem, the modified ELBRO-FV model was used only
for SOM phase predictions while the modified UNIFAC model of Gmehling et al. (1993)
was used for water and octanol phase predictions. While it is surprising that the ELBRO-
FV model does not handle an aqueous phase well, it should be noted that predictions with
an aqueous phase were not evaluated by Kontogeorgis et al. (1993). Table 3 summarizes

the final modified ELBRO-FV model used for calculating partition coefficients.

24

Table 3: Summary of method for predicting activity coefficients.

 

 

Erase Method
Solute in soil organic matter Modified ELBRO-FV (Kontogeorgis et al., 1993)
- Molar group volumes by :
(a) Method of Bondi (1968)

(b) Fitted Gmehling et al. (1993)
- Interaction parameters of Gmehling et al. (1993)

 

Solute in water Modified UNIFAC (Gmehling et al., 1993)
Solute in octanol Modified UNIFACLGmehlingeitalq 1993)

 

The completed model was used to predict partition coefficients for a wide variety
of pollutant compounds. A complete list is given in Table 4. Effort was made to test a set
of compounds which was diverse as possible, while constrained by the limited literature
SOM partition data. The group includes several important pollutants as well as pesticides.
Calculations were performed on MathCad worksheets. Worksheets for benzene
calculations are included in Appendix B. Predicted activity coefficients and partition

coefficients were compared to experimental values using Microsoft Excel.

Table 4: List of solutes used in equilibrium calculations.

 

 

1,2,4-trichlorobenzene ethylbenzene

2-chloro biphenyl hexachlorobenzene

4-bromo nitrobenzene m-toluidine

aniline methyl tert-butyl ether
benzene n-propyl-N-phenylcarbamate
captan naphthalene

carbaryl nitrobenzene

chlorobenzene phenol
ethyl-N-phenylcarbamate

 

 

2.3 Results and Discussion

2.3.1 Preliminary Results
Preliminary work was done in order to determine the feasibility of applying a

group contribution approach such as UNIFAC to soil organic matter-water systems. The

25

partition coefficients predicted with UNIFAC (Sandler, 1989) were in reasonably good
agreement with experimental data. Additionally, the VLE parameter set of Sandler (1989)
was superior to the LLB set of Magnussen (1981). The results for the octanol-water

system are shown in Figure 3. Results for the Aiken and Stevenson model molecules are

shown in Figure 4 and Figure 5 respectively.

 

 

 

 

 
 
   
   

 

 

 

 

6.00E+04 ._
I Predicted, Sandler 1989
(VLE)
5.oor3+04 +
C . agnussen
El Predi ted M 198101.15)
E 4.00E+04 *- . Experimental data (toluene,
8 ethylbenzene 8!. propylbenzene are
u averaged values)
.3 3.oor~:+04 J-
§ Data from Chiou et al. (1983), Briggs (1981). and Benzing (1993)
E 2.oor3+04 .-
>4
1.00E+04 ‘-
0.00E+00 a

 

 

Benzene Toluene Ethylbenzene Propylbenzene

 

 

Figure 3: VLE vs. LLE parameter predictions of octanol-water partition coefficients,
mole fraction basis.

26

 

 

 

 

 

 

 

 

 

 

 

 

 

6903+“ " Data from Chiou et a1. (1983).Bri§g(1981). and Benzing (1993)
I Predicted. Sandler 1989
50054-04 “r (VLE)
E 4005 04 Cl Predicted, Magnussen 1981
8 . + (LLB)
E 3-..... ~~ ' firemazrzizzz...
2
g are averaged values)
E 2.oora+04 .
.‘2
LOOE+O4 *
0.00E+00 ~

 

 

Benzene Toluene Ethylbenzene Propylbenzene

 

 

 

Figure 4: VLE vs. LLE parameter predictions of SOM (Aiken model)-water partition
coefficients, mole fraction basis.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

   

"203*“ Data from Chiou et al. (1983). Briggs (1981), and Berm'ng (r993)
I‘OOEIOS I Predicted. Sandler1989 (VLE)
E 8905,04 .. [:1 Predicted, Magnussen 1981(LLE)
- I Experimental data (toluene,
E 6.008+04 “r ethylbenzene & propylbenzene are
g averaged values)
E 4.00r3+04 L
M
20084-04 «-
00031.00 I .
Benzene Toluene Ethylbenzene Propylbenzene

 

 

 

Figure 5: VLE vs. LLE parameter predictions of SOM (Stevenson model)-water partition
coefficients, mole fraction basis.

Octanol-water partition coefficients are included as a reference for SOM-water results.
UNIFAC predictions for octanol-water systems were expected to be in good agreement

with experimental data and they are. Figure 3 shows that the VLE parameter set is better

27

than the LLB parameter set. This was not expected as the experimental data is for LLE
equilibrium. The VLE set superiority is likely due to the larger number of data sets used
in parameter fitting and their diversity. The trend is even more significant in SOM-water
predictions. The results imply that parameter sets based on the most diverse and
numerous data sets should be used when possible.

A second important result of preliminary work is the difference in predictions made
with the Stevenson and Aiken model SOM molecules. As seen in Figure 4 and Figure 5,
predictions made with the Stevenson model are much closer to experimental values than

the Aiken model predictions. Another illustration of the results is provided in Figure 6.

 

 

 

 

 

 

 

 

 

7.(X)E+02 -- ——
I Chiou. I983
6.(X)E+02 <- [:1 Benzing. 1993 (Capac A)
I Benzing. 1993(Colwood A)
A 5.oor~:+o2 1-
.2 I Benzing. 1993 (Colwood A 53-125
g micron)
,8 4.00ra+02 --
g [:1 Benzing. r993 (Colwood A 250-500
3 micron
g 3.ooa+02 4-
.9. [II Predicted, Aiken model
E
>2 2.(X)E+02 -_ E Predicted. Stevenson model
1.00E+02 -~
0.(X)E+(X) - '
Benzene Toluene Ethylbenzene Propylbenzene

 

 

 

Figure 6: Stevenson vs. Aiken partition coefficient predictions using UNIFAC (Sandler,
1989), concentration basis.

Individual experimental values are shown to illustrate the variation in the data depending
on the soil. The results indicate that the Stevenson molecule may be more representative

of actual soil organic matter. The primary difference between the two molecules is

28

aromaticity, as seen in Figure 1 and Figure 2, suggesting that aromaticity may be an

important factor in SOM chemistry related to equilibrium.

2.3.2 Partition Coefficients from the modified ELBRO-FV model

The modified ELBRO-FV method developed in this work was used to predict
partition coefficients for a variety of solutes. Accuracy varied greatly with solute. As a
reference, octanol-water partition coefficients are shown in Table 5 along with the ratio of

predicted to experimental values.

Table 5: Predicted and experimental octanol-water partition coefficients, mole fraction

 

 

basis.

MTBE 113 N/A N/A
Benzene 1,185 1,178 1.01
Ethylbenzene 12,420 12,336 1.01
Propylbenzene 39,173 41,800 0.937
Naphthalene 46,399 20,006 2.32
1,2,4-Trichlorobenzene 5,459 91,446 0.0597
Hexachlorobenzene 25,844 2,405,280 0.0107
2-Chloro-biphenyl 15,278,592 302,807 50.5
Phenol 72 252 0.286
Chlorobenzene 1,969 6,042 0.326
Nitrobenzene 59 618 0.0955
4-Br-Nitrobenzene 914 3,477 0.263
Aniline 46 69 0.667
m-Toluidine 159 230 0.691
Carbaryl 23,045 1 .825 12.6
Captan 31 3,028 0.0102
Ethyl-N-phenylcarbamate 2,269 1,5 89 1 .43
n-Propyl-N-phenylcarbamate 7,279 5,5 10 l .32
(Experimental partition coefficients from Chiou et al. (1983) and Briggs (1981))

 

 

Some trends are apparent in the results of Table 5. The predictions for the alkylbenzenes
are very good. This is expected for these simple, non-polar compounds. The error for

naphthalene, however, indicates that some effect of two aromatic rings is not accounted

29

for. Halogenated solutes, in particular chlorinated compounds, are a problem.
Chlorobenzene, 1,2,4-trichlorobenzene, and hexachlorobenzene show a trend of increasing
error with increasing number of chlorine atoms. Polarity itself does not seem to be a
problem as predictions are good for aniline and m—toluidine. Good results are also
achieved for the phenylcarbarnate pesticides which are large molecules.

The results are similar for predicted SOM-water partition coefficients.
Calculations were made using the Stevenson SOM model, and molar group volumes of
both Bondi (1968) and Gmehling (1993). The results, experimental data and predicted-
experimental ratios are shown in Table 6. Averaged values were used when multiple

experimental data was available.

Table 6: Predicted and experimental SOM-water partition coefficients, mole fraction

 

basis.

MTBE 166 124 N/A N/A N/A
Benzene 1,984 1,222 1,570 1.26 0.778
Ethylbenzene 6,077 3,816 8,242 0.737 0.463
Propylbenzene 10,737 6,911 18,830 0.570 0.367
Naphthalene 58,305 9,006 20,702 2.82 0.435
1,2,4-Trichlorobenzene 13,733 6,866 43,253 0.318 0.159
Hexachlorobenzene 258,445 129,222 1,534,660 0.168 0.0842
2-Chloro-biphenyl 9,420,336 6,453,188 146,600 64.3 44.0
Phenol 344 225 2,606 0.132 0.0863i
Chlorobenzene 353 242 4,131 0.0855 0.0586
Nitrobenzene 874 451 4,325 0.202 0.104
4-Br-Nitrobenzene 44,667 22,488 13,062 3 .42 1 .72
Aniline 813 521 1,276 0.637 0.408
m-Toluidine 1,558 982 2,218 0.702 0.443
Carbaryl 22,955 15,427 5,200 4.41 2.97
Captan 5,254 5,1 12 9,909 0.530 0.516
Ethyl-N—phenylcarbamate 17,901 12,483 3,281 5.56 3.80
n-Propyl-N-phenylcarbamate 30,478 21 ,423 5,702 5.35 3.76
(Experimental partition coefficients from Chiou et al. (1983) and Briggs (1981))

 

 

 

30

Trends seen with octanol-water predictions are seen in the SOM-water predictions. The
simple alkylbenzenes are again the most accurate predictions. Halogenated compounds
are a problem, but relative errors are lower than the octanol-water case for 1,2,4-
trichlorobenzene, hexachlorobenzene, and nitrobenzene (Bondi values). The carbaryl and
captan predictions are better, but the phenylcarbamate predictions are worse. Of
particular interest is the comparison predictions made with the Bondi versus Gmehling
group molar volume parameters. Neither approach has a clear advantage over the other.
The Bondi values are generally better for the alkylbenzenes and chlorinated compounds as
well as aniline and toluidine. The Gmehling values are somewhat better for the four
pesticides. Molar group volume parameters may be best selected based on the solute(s) of
interest.

Despite a high variation in relative error, the modified ELBRO-FV predictions are
an improvement over the UNIFAC (Sandler, 1989) predictions. Kontogeorgis et al.
(1993) states that the original UNIFAC model generally leads to an underestimation of
solvent activities as free volume differences are neglected. This would result in a
overestimation of the SOM-water partition coefficient which is seen in the preliminary
results presented in this work. Table 7 provides a comparison of UNIFAC (Sandler,
1989) predictions to the modified ELBRO-FV predictions using the Bondi molar group
volumes. Table 7 includes all of the solutes for which predictions have been made by both
methods (excepting MTBE as no experimental data was available). The modified
ELBRO-FV predictions compare favorably excepting propylbenzene. Additionally, the
error in the modified ELBRO-FV predictions is fairly uniform, while the UNIFAC error is

unpredictable as exemplified by hexachlorobenzene.

31

Table 7: UNIFAC vs. modified ELBRO-FV partition coefficient predictions for
Stevenson SOM model, mole fraction basis.

 

 

 

QMAQ (mime) flllilEAC.) 0111mm
Benzene 3,900 1,980 1,570 2.48 1.26
Ethylbenzene 12,400 6,080 8,240 l .50 0.738
Propylbenzene 20,900 10,700 18,800 1 .1 1 0.569
Hexachlorobenzene 1,730,000,000 258,000 1,530,000 1 , 1 30 0. 169
(Experimental partition coefficients from Chiou et al. (1983) and Briggi(l981))

 

2.3.3 Partition Coefficients using literature activity coefficients

The accuracy of predictions of SOM-water partition coefficients varies with the
solutes examined. As described previously, the partition coefficient is a function of two
variables, the solute activity coefficients in each phase. Large errors in a partition
coefficient could be due to either activity coefficient or both. Comparison of predicted to
experimental activity coefficients allows the source of error in predicted partition
coefficients to be determined. Water phase activity coefficients from the literature were
found for some of the solutes examined and are shown in Table 8 along with predicted
values. The predicted-experimental ratios also vary greatly with solute. More importantly
however, the errors in the activity coefficient predictions strongly parallel the partition
coefficient errors. This suggests that error in partition coefficient predictions is primarily
due to error in water phase activity coefficient predictions. If the water phase predictions
are erroneous, then experimental values (when available) should allow for improved

partition coefficient predictions.

32

Table 8: Predicted vs. literature solute activity coefficients in water.

 

 

MTBE 167 82. 1 2.03
Benzene 2,320 2,430 0.955
Ethylbenzene 28,700 36,900 0.778
Naphthalene 172,000 63,000 173
2—Chloro biphenyl 88,600,000 1,550,000 57.2
Chlorobenzene 3,316 13,900 0.239
Nitrobenzene 461 3,540 0.130
Aniline 137 118 1.16

(Literature activity coefficients from meget al. (1992))

 

SOM-water partition coefficients can in fact be improved by using literature water
phase activity coefficients in place of predicted ones. While this approach is no longer
purely predictive as it requires experimental data, it is still of utility. While no data are
currently available for solute activity coefficients in soil organic matter, activity coefficient
data are available for water. Partition coefficients can be predicted therefore using: (i)
SOM phase activity coefficients predicted with the modified ELBRO-FV model, and (ii)
experimental water phase activity coefficients. Water phase infinite dilution activity
coefficients are taken from Hwang et al. (1992). The benefit of using experimental
activity coefficients is shown in Table 9 for octanol-water partition predictions as a
reference.

SOM-water partition predictions are also improved with the use of experimental
water phase activity coefficients. The results are shown for Bondi molar group volume
predictions in Table 10, and for Gmehling molar group volumes in Table 11. Results are
shown for all solutes where activity coefficients in water were available. Clearly, solutes

which have the poorest partition predictions gain the most from use of activity coefficient

 

33

Table 9: Predicted octanol-water partition coefficients using activity data, mole fraction

 

 

basis.

MTBE 113 N/A N/A 55 N/A
Benzene 1,185 1,178 1.01 1,240 1.05
Ethylbenzene 12,420 12,336 1.01 15,974 1.29
Naphthalene 46,399 20,006 2.32 16,995 0.849
2-Chloro-biphenyl 15,278,592 302,807 50.5 267,380 0.883
Chlorobenzene 1 .969 6,042 0.3 26 8,254 1.37
Nitrobenzene 59 618 0.0955 449 0.727
Aniline 46 69 0.667 39 0.565
(Experimental partition coefficients from Chiou et al. (1983)and Briggs (1981))

 

 

data. The results for naphthalene and 2-chloro-biphenyl in particular demonstrate that
large errors in partition coefficients are due to errors in the water phase predictions. All
three of the halogenated solutes show significant improvement. The difficulty with
predictions of halogenated compounds also apparently lies in the water phase not the
organic phase. Unfortunately, predictions for some compounds can actually be
compromised by using experimental activity coefficients. In these cases, errors in water
phase activity coefficient predictions were off-set by errors in the SOM phase activity
coefficient predictions. The result is better partition coefficients before any adjustment.
Benzene and ethylbenzene predictions are very good as a large number of systems with
these compounds were probably available for parameter fitting. Any adjustments to the
already near perfect predictions results in decreased accuracy. Aniline shows what can be
expected of an intermediate case where the original prediction was moderately good. The
accuracy is reduced, but only by a small amount. The benefit of using experimental

activity coefficients is more uniform and predictable error. The predictions can be used as

 

34

an engineering estimate when data is not available. The trade-off is decreased accuracy

for some solutes.

Table 10: SOM-water partition coefficients for Bondi molar group volumes, mole fraction

 

basis.

'v' 1 'v' 1
Benzene 1,984 1,570 1.26 2,075 1.32
Ethylbenzene 6,077 8,242 0.737 7,816 0.948
Naphthalene 58,305 20,702 2.82 21,356 1.03
2-Chloro-biphenyl 9,420,336 146,600 64.3 164,859 1.12
Chlorobenzene 353 4,131 0.0855 1,478 0.358
Nitrobenzene 874 4,325 0.202 6,705 1 .55
Aniline 813 1,276 0.637 698 0.547
(Experimental partition coefficients from Chiou et al. (1983) and Briggs (1981))

 

 

 

Table 11: SOM-water partition coefficients for Gmehling molar group volumes, mole
fraction basis.

 

 

 

. . l . . I

Benzene 1,222 1,570 0.778 1,278 0.814
Ethylbenzene 3,816 8,242 0.463 4,908 0.595
Naphthalene 9,006 20,702 0.435 3,299 0.159
2-Chloro—biphenyl 6,453,188 146,600 44.0 1 12,933 0.770
Chlorobenzene 242 4,131 0.05 86 1,013 0.245
Nitrobenzene 451 4,3 25 0.104 3,457 0.799
Aniline 521 1,276 0.408 447 0.350
(Experimentzflartition coefficients from Chiou et al. (1983) and @3098 1))

 

 

35

The results are similar to that of octanol-water partition results. 2-Chloro-biphenyl once
again show great improvement from previously poor results. Naphthalene improved an
amazing 182 to 3 percent error for Bondi predictions, although accuracy decreased for
Gmehling predictions. All three halogenated solutes show significant improvement in both
the Bondi and Gmehling cases. Notable is an error decrease of 90 to 20 percent for
nitrobenzene with Gmehling parameters. These results again indicate that large errors in
partition predictions are due to water phase error. While the benzene prediction decreases
in accuracy for the Bondi case, it improves slightly for the Gmehling case. Ethylbenzene
improves for both cases, from 26 to 5 percent for the Bondi case. The benzene and
ethylbenzene results compare very favorably to octanol-water results where both solute
predictions were worse with the use of activity coefficient data. Aniline again decreases in
accuracy by a small amount. In summary, the use of water phase activity coefficient data
significantly improves SOM-water partition coefficient predictions by removing large
errors when present, and improving some of the moderate predictions. Apparently only

minimal decrease in accuracy results for some solutes with good predictions.

2.4 Conclusions

The results presented here are significant as the feasibility of using a group
contribution activity coefficient method for predicting soil organic matter-water partition
coefficients is demonstrated. A modified version of ELBRO-FV (Kontogeorgis et al.,
1993) is used to predict equilibria. The method used here is suitable for SOM as: (i)
ELBRO-FV was developed for polymer solutions were molecules vary greatly in size, and
(ii) the interaction parameters have been updated with the most recent, broad-based set
available. Predictions are varied in accuracy when compared to literature data, but
compare favorable to UNIFAC for the limited number of compounds tested. In general,

the predictive capabilities are good when considering the heterogeneous nature of soil.

36

Large errors in partition coefficient predictions were a result of poor activity
coefficient predictions for the water phase, as opposed to soil organic matter. The use of
experimental water phase activity coefficients greatly improved equilibria predictions
where results were originally poor. Halogenated compounds in particular were a problem,
but predictions benefited greatly from using experimental water phase activity
coefficients.

The Stevenson (1982) humic acid model molecule yielded better results than the
Aiken (1985) molecule when compared to literature data. The results indicate that the
Stevenson molecule may be more representative of soil organic matter. The primary
difference is higher aromaticity in the Stevenson molecule. Further work should include
the attempt to correlate SOM chemistry with differences in partition data. Elemental
analysis and percent carbon aromaticity could be used to modify the Stevenson molecule
and represent a variety of SOM samples. Modified ELBRO-FV predictions could then be
compared to experimental partition data for the samples. Successful correlation would
demonstrate new understanding of soil organic matter as well as a new predictive method
for equilibria. Equally important however is improvement in water phase calculations.
More data sets which include water are probably needed in the equilibrium data base used

to fit interaction parameters.

CHAPTER 3: DESORPTION KINETICS

3.1 Introduction

The kinetics of nonionic organic solute desorption from water saturated soil is the
second of two issues (thermodynamic equilibrium being the first) which must be addressed
when considering a bioremediation process. Understanding of the system kinetics allows
predictions of solute concentrations as well removal rates for batch or continuous
processes. Soil concentrations as a function of time are needed to determine processing
times and equipment design which will meet clean-up criteria. Aqueous phase
concentrations are needed to determine whether the substrate concentration is sufficient
for degradation, but not so high that substrate toxicity is a problem.

A rigorous mass transport model is needed to describe solute desorption from soil
into an aqueous phase. As summarized in the literature review, many models have been
proposed for desorption kinetics. At best, researchers have modeled the soil as spherical
particles and determined an effective diffusion coefficient with a average sieve tray
diameter. The limited approaches have resulted in disagreement about sorption
mechanism, effective diffusion coefficients, and equilibration times. A rigorous model
requires knowledge of the soil morphology. In particular, the morphology of soil organic
matter is needed. The location with respect to mineral matter, shape, and pore structure
of soil organic matter serve as inputs to a good model.

The understanding of soil morphology can be converged upon from two angles.
First, techniques such as scanning electron microscopy, x-ray fluorescence spectroscopy,
and mercury porosimetry can be used to physically probe the soil. This approach parallels
the characterization of a catalyst, allowing visualization of structure. Second, desorption
kinetics can help elucidate morphology with the use of structured soil models. By

hypothesizing several physical models for the soil morphology, the model predictions can

37

38

be compared to experimental results. Some models will predict the data while others will
not. The two approaches are used in an attempt to converge on one or more
morphologies which allows for rigorous desorption predictions.

A batch stripping experiment, commonly known in the literature as a gas purge
system, is used to obtain kinetic data. While the apparatus has been used previously for
kinetic data (Benzing, 1993), the system is re-evaluated as a tool for probing soil
morphology. Issues addressed include: (i) the need for a mass transfer coefficient due to
rate limited gas-liquid mass transfer, and (ii) the limits of the experiment in regards to
discerning between soil particles of different sizes or effective diffusion coefficients. Once
the experimental system is properly modeled, soil samples can be evaluated. Kinetic data
for soil samples of various sieve tray sizes, as well as ground samples can the be used to
evaluated hypothesized soil models. Steam explosion, as a pre-remediation treatment to
increase desorption rates, is evaluated. The applicability of an spherical geometry, Fickian

diffusion model is examined by estimating effective diffusion coefficients.

3.2 Materials and methods

3.2.1 Gas purge system

A gas purge system is used to conduct batch stripping experiments.
Propylbenzene was chosen as a solute for the studies. Propylbenzene represents a wide
class of nonionic organic compounds and has a strong preference for soil organic matter
over water as well as a large Henry's law coefficient. These properties allow use of

smaller soil sample sizes and make for shorter stripping times respectively.

3.2.1.1 Apparatus
The gas purge apparatus used in this work was the MSU Gas Purge System of the
MSU Department of Crop and Soil Science. This system is described in detail by Benzing

39

(1993), and only a brief description will be included here. The experimental system is
depicted in Figure 7.

T Effluent
Rotarneter
Bubble éfiow valve 3

 

 

    
 
 

flowmeter

Photoionization

 

 

 

 

 

 

 

 

Four-way
valve
181 .
(recirculatc) /
\
\
\

 

 

 

 

 

 

 

 

 

 

Figure 7: Gas purge system for batch stripping experiments.

The stripping gas (air) flow is as follows when operated in a open system mode.
Air is drawn in through a activated charcoal filter by the air pump. Coarse flow rate
control is made with a needle valve at the pump exit. The air is routed to either the
stripping flask or a bypass loop. Air enters the flask near the bottom via a cylindrical
sparger, just above a magnetic stir bar. The air passes through the liquid phase (stripping
the solute) and leaves the flask. The air is then split into a stream which passes through
the photoionization detector and a detector bypass stream. A needle valve on each stream
is used to adjust the flow rate through the detector. The detector stream flow rate is
measured with a rotameter at the detector outlet. Finally, the air flows through a bubble

flow meter before exiting the system to a fume vent. The bubble flow meter is used to

40

measure the total air flow rate. The system can also be run in a recirculation mode where

the loop is closed between the detector and the air pump.

3.2.1.2 Experimental procedure

3.2.1.2.] Solute-soil equilibration

The experimental procedures, with some modification, were developed by Benzing
(1993) but are included here for clarity. The first of two procedures for the gas purge
experiments is sample preparation. Sample preparation consists of adding a soil sample,
solute, and liquid phase to the stripping flask, followed by equilibration of the system. 250
mL round bottom flasks with a side arm were used for the stripping flasks. The side arms
were sealed with a silicon-teflon septa. The dry flask with magnetic stir bar was weighed,
followed by addition of the soil sample and re-weighing. Soil sample sizes ranged from
1.0 to 2.5 grams depending on the type of sample. The liquid phase in the experiments
consisted of Millipore water with 0.01 N calcium chloride and 0.5% by weight mercuric
chloride. Calcium chloride is added to mimic groundwater while mercuric chloride
prevents biodegradation of the solute. The stripping flask was then sealed with a glass
stopper and wrapped with parafilm at the flask-stopper junction.

Propylbenzene was added to the stripping flask in the form of propylbenzene
saturated aqueous solution. The use of a aqueous solution insured a pure solute phase did
not form in the stripping flask. Millipore water and a layer of propylbenzene were
equilibrated in 1 L amber glass bottles. A 10 mL gas-tight syringe was used to withdraw
10 mL of the aqueous phase. The syringe contents were injected through the side arm
septum into the flask liquid. The septum and septum cap-side arm junction were wrapped
with several layers of parafrlm. The flask was then placed on a rotary shaker for 2 to 5

days in order to achieve system equilibrium. Typically, two flasks with soil samples would

41

be prepared at time as well as a third control flask. The control flask preparation was

identical to the sample flasks excepting the addition of soil to the flask.

3.2.1.2.2 Batch stripping of soil sample

The second phase of experimentation is batch stripping of the sample. Typically,
three flasks were stripped on a day, each flask referred to as a run. At the beginning of the
day, the air pump was powered up and allowed to run for about one hour in order to reach
a steady temperature. During this time fresh anhydrous CaSO4 (Drierite) and granular
activated carbon were loaded in their respective tubes at the inlet of the gas purge system.
During set-up, air was drawn through the Drierite and routed through the flask by-pass
loop. The photoionization detector temperature was regulated by a Hewlett Packard 5890
Gas Chromatograph. The cartridge heater and ceramic temperature probe were connected
to a proportional control circuit for an unused injector port. After one hour, the
photoionization detector lamp was turned on at a power setting of 8. A blank stripping
flask was prepared consisting of 200 mL Millipore water and connected in the flask loop
of the gas purge system. The air flow was then switched from Drierite to carbon and from
by-pass to stripping flask. At this time the system flow rates were set. The coarse setting
valve was used to adjust the total flow to roughly 100 mljmin or set at full open for 600
mIJmin runs. The detector and detector by-pass needle valves were used to adjust the
detector flow rate between 45 and 60 mm which is the linear range of the detector
(Benzing, 1993). Baseline readings were taken on the recorder for ranges of l V, 100
mV, and 10 mV. Flow was switched back to the flask by-pass and the blank flask was
disconnected. The gas purge system was switched to recirculating mode. A sample or
control flask was then prepared by quickly replacing the glass stopper with the two-way
purging stopper assembly. The flask was connected and flow was directed through the
flask. After an initial signal spike, a steady signal was reached in 2 to 5 minutes. At this

time the system was switched back to open mode. Initial temperature and total flow rate

42

measurements were taken and repeated during the experiment. Temperatures were 23 to
25 degrees Celsius excepting the heated re-stripping experiment. The recorder chart
speed was set initially at 3 cm/min as the signal changes rapidly. The chart speed was
slowed through the experiment as signal slope decreased to conserve paper. The initial
recorder range was 1 V. The 100 and then 10 mV settings were selected once the signal
was on chart for the respective setting.

A run was stOpped once the signal was deemed constant. The flow was switched
back to the flask by-pass, and the flask was disconnected. The blank flask was
reconnected and baseline readings were taken again. Once finished, the blank flask was
disconnected and the system was dried for 30 minutes. The inlet flow was switched to
Drierite and routed through the flask by-pass loop. A laboratory air line was attached to
the stripping flask loop. Once drying was completed the system was ready for the next

run, or to be powered down for the day.

3.2.1.2.3 Heated re-stripping

Experiments were conducted to determine if all the propylbenzene was removed
from the soil (250-500 micron enriched Colwood A) during a stripping experiment. A
purge experiment was conducted in the manner described above with a total flow rate of
600 mUmin. When the signal was nearly level the system was switched to flask by-pass
and recirculation mode. A water bath was placed around the flask. The bath was heated
with a heating plate and stirred frequently with a spatula. To avoid soil sample
pulverization, flask stirring was done for 15 seconds every 5 minutes during the heating
period. Once the bath had reached 75 degrees Celsius, the system was switched from
recirculate to open followed by switching from flask by-pass to stripping flask. The run

was continued until the signal again appeared constant.

43

3.2.2 Soil sample preparation

Due to the heterogeneity of soil, a balance must be struck between a
morphologically uniform, easy to model sample, versus one which is representative of
nature. Having identified the importance of soil organic matter, a high organic carbon soil
was chosen for study, and modified in order increase organic carbon content with minimal
alteration to the natural morphology.

Colwood A horizon was chosen for its organic carbon content of 5.36% (Benzing,
1993) and availability from the Michigan State University Department of Crop and Soil
Science. Organic fractionated samples, prepared in the following manner (Benzing, 1993)
were also obtained from the Department of Crop and Soil Science.

Air-dried Colwood A horizon (200 g) was added to a scalable 1 L polypropylene
bottle. The bottle was filled with 5% sodium hexametaphosphate solution (Millipore
MilliQ water), sealed and placed on a rotary shaker for 24 hours. After shaking, the
mixture was transferred to a continuous flow separation system. A density separation was
made the liquid vortex in the column. The lightest material passed through the column
into a series of sieves. Sieve tray sizes were 500, 250, 125, and 53 micron mesh diameter.
The size fractions were centrifuged to remove excess solution, freeze dried, and stored in
polypropylene bottles. The organic carbon content for the samples was determined by

combustion (Benzing, 1993) and is listed in Table 12.

Table 12: Organic carbon content of soil samples.

 

 

Sexual: thanisz cerium 1221. measured
Colwood A horizon 5.19 21:0.13

53-125 micron 23.30:t0.57

125-250 micron 24.80i 0.97

250-500 micron 26.09i 0.95

(Source: Benzing, 1993)

 

 

44

In order to evaluate an appropriate scale for a diffusion path length, crushed
samples were also prepared. Approximately 2 grams of the 250-500 micron size fraction
was crushed to a powder with a mortar and pestle. The sample was ground for
approximately 10 minutes.

Steam exploded samples were prepared in order to effect the sample morphology
in a way detectable by gas-purge analysis and/or scanning electron microscopy. In brief,
soil slurries were heated in a closed vessel, elevating the pressure, followed by a rapid
pressure drop to ambient conditions. The instantaneous vaporization of water in pores is
intended to disrupt soil organic matter. A full description of sample preparation is

included in Chapter 4.

3.2.3 Model development and implementation

A mathematical model was developed for the gas purge experiment in order to
interpret kinetic data. Model criteria include the ability to: (i) discern gas-liquid phase
mass transfer from solid phase (soil) mass transfer, and (ii) account for multiple solid
phase (soil) morphologies such as size, shape, and soil organic matter distribution. The
prior is accomplished by including rate limited gas-liquid mass transfer. The latter is
achieved by choice of several solid phase mass balances which if needed, can be
implemented simultaneously. The model consists of a mass balance for each of the three
phases, with the following assumptions:

(a) Both the gas (sparging bubbles) and liquid phases are well mixed.

(b) Mass transfer resistances between the liquid and solid phases is negligible.

(c) Gas-liquid equilibrium obeys Henry's law.

((1) The liquid phase volume is constant as verified by Benzing (1993).
The model is given by Equation 7 through Equation 10 and is described in the following

sections.

45

3.2.3.1 Head space
The head space is the volume of gas between the liquid phase and the stainless

steel tubing leading to the photoionization detector. As the head space is irregularily
shaped (varying cross-sectional area), the total volume and height were used to calculate
an average cross-sectional area. The volumetric gas flow rate divided by the cross-
sectional area gives the linear gas flow rate (Vh) which is required for the model described

in Equation 7.

new, idea; a,
at p dz dz
Equation 7 represents a plug flow reactor with axial dispersion. (Ch) is the head space
concentration as a function of space and time, (Ddisp) is a diffusion coefficient, and (z) is
the length of the head space. This representation was chosen as experimental behavior
was indicative of neither a CSTR or PFR. As stripping experiments were started at
equilibrium, the detector signal remained constant after stripping was started due to plug
flow behavior of the tubing to the detector and the head space. Ideal versus actual

breakthrough times are compared in Table 13.

Table 13: Ideal vs. actual head space break-through times in seconds.

 

 

W IdeaLEER Elysium W
100 49 30 6
600 8 5 1

 

 

The ideal CSTR times are non-zero as they represent the breakthrough times of the tubing
to the detector modeled as a PFR. The PFR with axial dispersion model was chosen as:

(i) the break-through times are close to that of a PFR, and (ii) a laminar flow model

46

requires two spacial variables which is not accommodated by an IMSL routine.
Additionally, Fogler (1986) notes that the PFR with dispersion model can compensate not
only for problems caused by axial mixing, but also for those caused by radial mixing and
nonflat velocity profiles. As the tubing residence times were on the order of experimental
error in strip chart measurements, a separate equation for the length of tubing to the

detector was not included in the model.

3.2.3.2 Gas phase
The gas phase is the volume of gas which is in contact with the liquid phase, i.e.
the sparging bubbles. The mass balance consists of convective terms and mass-transfer

with the liquid phase as given by Equation 8.

dC
T’svg=F(Cg,in_Cg)—kLa(Cg_H'Cl)VI (8)

(C 8) is the concentration in the gas bubbles as a function of time, (V3) is the gas volume,

(F) is the gas volumetric flow rate, (C ) is the inlet concentration, ( k La) is the gas-

gin
liquid mass transfer coefficient, (H) is a Henry's law coefficient, (C1) is the liquid phase

concentration, and (V,) is the liquid phase volume.

3.2.3.3 Liquid phase
The liquid phase mass balance consists of mass-transfer with the gas phase as well
as the solid phase as given by Equation 9. The solid phase term is the diffusion flux in or

out of the soil at the solid surface multiplied by total area over which the flux occurs.

5
dt

 

8C5 -A (9)

w=k.a(C.—H-C.)V.—D.- a, .
r=R

e

47

(Def) is the soil effective diffusion coefficient, (R) is the soil particle radius, and (As) is

the total surface area of the soil particles.

3.2.3.4 Solid phase

The solid phase mass balance consists of Fickian diffusion inside soil particles. As
previously indicated the mass balance is adjustable. Several morphologies have been
hypothesized and are listed in Table 14. The use of different structured soil models allows

for testing of hypothesized morphologies.

48

Table 14: Hypothetical morphologies of soil organic matter and respective material

 

 

 

balance equations.
Visualization of SOM Description Equation
Solid Particles
Diffusion into a solid sphere ac 82C 2 3C

of SOM

__—_D _+_
at SOM 8r2 rdr

 

Diffusion into a solid cylinder
of SOM

at 5"” Brz ran 322

 

 

 

Diffusion into spherical
coating of SOM, or
approximate with an infinite
plane

same as sphere above with annular
boundary conditions, or for a plane:

ac 32c

at _ so” 8x2

 

Diffusion into cylindrical
coating of SOM, or
approximate with an infinite
plane

same as cylinder above with annular
boundary conditions, or for a plane:

ac 32c

at — SOM 8x2

 

 

 

 

 

 

 

 

 

Diffusion into non-mineral a C 32 C a C
space of spherical aggregate # -_- a __208_8 _2_ 088
coupled with diffusion into at 33 a 1' 1' a r
solid spheres of SOM 2

a C a C 2 a C

SOM

at ar' 2 r' 81"
Diffusion into continuous 2
sphere of SOM disrupted by a—C- = Def §_C + 329
"void" spaces of mineral at a 1‘2 r a 1‘

matter, SOM viewed as
"mortar in concrete"

 

with diffusion coefficient reflecting "void"
spaces of mineral matter

 

 

49

3.2.3.5 Multiple solid phases
In order to accommodate multiple soil particle sizes with a single spacial variable
the spacial variable must be made dimensionless. In the present work, however, only the

simple case of diffusion into a sphere was considered as given by Equation 10.

ac, 32C, 28c,
= efi. +— (10)

 

 

 

Br Br2 r 8r

(Def) is an effective diffusion coefficient which is dependent on the morphology of the
spherical particle. The spacial variable (radial position) was nondimensionalized with the

inner (R,) and outer (R0) particle radii with Equation 1 1.

 

(11)

Annular geometries such as coatings can be accommodated by using both an outer and an
inner radius for the nondimensionalization. The substitution yields the following two

equations for the liquid and solid phase mass balances.

 

 

 

 

3- D -V
iC—‘l/lszng—H-CJVg- efir s a?" (12)
dt (R.—R.-)R. er I...
3C5 = Defl' 82C: +38CS (13)
8: (R0 — R02 872 7 a;

The dimensionless spacial variable now ranges from zero at the center of the sphere to
unity at the outer surface. Additional soil particle types are handled by (i) adding
additional equations of the form of Equation 13 for each particle, and (ii) adding additional

50

soil flux terms onto Equation 12. Thus for each solid phase particle type the outer radius,
inner radius, effective diffusion coefficient, and total volume is specified. While not
developed further here, the model could be expanded to include not only spheres but
various combinations of morphologies listed in Table 14. This provides flexibility for

modeling morphologies as visualized by techniques such as scanning electron microscopy.

3.2.3.6 IMSL solution of differential equations

The base-case model (for only one soil particle type) consists of two ordinary
differential equations and two partial differential equations. More advanced models may
include multiple differential equations for multiple soil particles. The IMSL v. 10.0
routine MOLCH was chosen to solve the model equations. The routine solves a system of
partial differential equations of the form ut = f (x, t, u, ux, “xx) using the method of lines.
The solution is represented with cubic Hennite polynomials. A FORTRAN program was
written to utilize MOLCH and output numerical results. The program is listed in
Appendix C. Text output file results were imported into Microsoft Excel spreadsheets for
interpretation and graphic representation.

The solution of the two ordinary differential equations and two second-order
partial differential equation requires four initial conditions and four boundary conditions.

The boundary conditions for the soil phase are given by Equation 14 and Equation 15.

 

BC}. =0 (14) C,|__1=Kd-C, (15)
at“ 7:0 ’-

 

Equation 14 describes spherical symmetry. Equation 15 describes equilibrium between the
liquid phase and the outer surface of the solid phase using the solute partition coefficient
(Kd). The boundary conditions for the head space are given by Equation 16 and Equation
17.

51

8C
C,,|Z=o = C8 — Dd,” °_az O (16) a—z" L
z: z:

= 0 (17)
These are the Danckwerts boundary conditions (Danckwerts, 1953). The first boundary
condition describes the step change in concentration due to diffusion (dispersion) at the
entrance of the vessel.

The initial conditions are specifications for initial concentrations in the gas, liquid,

and solid phase. The initial conditions are calculated by Equation 18 through Equation 20.

C,goV,g+C,-V,+C;"-M,=mr (18)

Equation 18 is a total mass balance where (m?) is the known amount of solute injected
into the system. The subscript (,g) represent 'total gas' including sparging bubbles, head
space, tubing to the detector, and recycle tubing. (C ;" ) is a mass fraction and (M s) is the
mass of soil. Equation 19 and Equation 20 are equilibrium relationships where (H) is a
Henry's law coefficient and (K d) is the propylbenzene soil-water partition coefficient.
The initial head space and gas phase concentrations are simply (Cm, ). Calculations for
initial concentrations are included in Appendix D.

The model parameters used for initial condition calculations and batch stripping

predictions are listed in Table 15. Parameters which are dependent on gas flow rate are

listed with the value for each flow rate.

52

Table 15: Batch stripping model parameters.

 

 

 

Parameter Educ

Head space volume 72 cm3

Head space length 12 cm

Head space cross-sectional area 6.0 cm2

Head space dispersion coefficient (fitted) cm2/min

Stripping gas flow rate 97.2 or 599 cm3/min

Gas phase volume 1 or 5 mL

Gas-liquid mass transfer coefficient (fitted) 1/min

Henry's law coefficient 0.387 (g per mL / g per mL)
Liquid phase volume 210 mL

 

The values for the stripping gas flow rates are average values for experimental runs. The
Henry's law coefficient for propylbenzene was calculated using the Modified UNIFAC
model of Gmehling et al. (1993). A value of 0.29 to 0.30 (depending on temperature) was
calculated from solubility and vapor pressure data. However, experiments show
propylbenzene is removed from the water phase significantly faster than is theoretically
possible for Henry's law-equilibrium stripping using these values. Additionally, the trend
in Henry's law coefficients for alkylbenzenes shown in Table 16 indicates a value of 0.30 is
erroneous. The value predicted with Modified UNIFAC (Gmehling et al., 1993) fits the

increasing trend for alkylbenzenes shown in Table 16.

3.2.3.7 Data analysis

Each experimental run yielded data in the form of a continuous detector response
versus time. Discrete response values versus time were manually recorded from the strip
charts and entered into a Microsoft Excel spread sheet as raw data. Baseline detector

responses before and after the run were also entered and averaged. Subtracting the

53

average baseline and correcting for recorder range setting (1 V, 100 mV, 10 mV) yielded
a detector response curve. The data could then be analyzed in a differential

(concentration) or integral (mass removed from system) manner.

Table 16: Henry's law coefficients for alkylbenzenes.

 

 

 

mm W
benzene 0.2208

toluene 0.3018

ethylbenzene 0.3460

propylbenzene N/A

isopropylbenzene 0.5889

(Source: HwanEt al., 1992b)

 

Examining the experimental data on a differential or concentration basis is useful
for comparison to model predictions. Additionally, only the initial condition assumptions
are involved excepting that of detector response linearity. Concentration data was either
examined directly as arbitrary concentrations versus time or normalized to the initial gas
phase concentration calculated for the experimental run.

Integrating the concentration data yields the solute mass removed from the system.
Of interest is the percent of total mass removed versus time curve. This curve can be
generated in one of two ways. First, the integral data curve is normalized with the total
area under the detector response curve. The normalization assumes that all of the solute
has been removed from the system at the end of the experiment. Second, the differential
data which has been normalized to initial concentrations is integrated yielding
(mass)(time)/(volume). Multiplying by the average gas flow rate gives the total mass
removed at a given time. Dividing by the total mass of solute added to the system yields

percent of total mass removed.

54

3.3 Results and Discussion

3.3.1 Apparatus analysis

The batch stripping apparatus itself must be correctly modeled before any soil
analysis can be done. The two fitable parameters, (kLa) and (Ddisp), were determined and
the model was verified as able to represent experimental data. The analysis was done for

both 100 mUmin and 600 mljmin flow rates as stripping characteristics are quite

 

 

 

 

 

 

0.0000006 ~—

different.
0.0000014 --
0.0000012 -*
0 A. blank
0 000001 — U B. blank
A . C, blank
3 0.0000008 ~~ . 0. blank
g Model
0

0.0000004 ~—

0.0000002 —~

 

 

 

Time (minutes)

 

 

 

Figure 8: Stripping gas propylbenzene concentration vs. time for blank runs at 100
mUmin flow rate.

55

Calculated initial concentrations for the head space (measured variable) were used
to normalize experimental data. The results are desorption curves in the form of
concentration versus time. The curves for 100 mIJmin and 600 mIJmin flow rate are
shown in Figure 8 and Figure 9 respectively. Good model fits were obtained at both flow
rates although the 600 mL/min flow rate data is more sporadic than the 100 mUmin data.
Mass transfer coefficients and head space dispersion coefficients were fit by a trial and
error method. The model fitted model parameters are listed in Table 17. The values
found to be optimal are listed with a range. Values outside the range are unable to

provide a good model fit regardless of the value of the other parameter.

 

0.0000014 ~—

0.0000012 j

 

0.000001 ‘-

 

9 M. blank

. N, blank
0.0000008 ‘—

5 0, blank

0.0000006 MM“

 

 

 

Concentration (glmL)

0.0000004

0.0000002

 

 

 

Time (minutes)

 

 

 

Figure 9: Stripping gas propylbenzene concentration vs. time for blank runs at 600
mL/min flow rate.

56

Table 17: Fitted model parameters for batch stripping experiment.

 

 

Parameter 1001011111111 Wu
kLa (mix) 69 i- 6 210 i 30
Ddisp (cmz/min) 0.033 i 0.033 0.50 i 0.33

 

 

The values determined for the mass-transfer coefficient are indicative of rate
limited mass transport. As seen in the desorption curves, the stripping gas concentration
drops rapidly as the gas phase (bubbles) are rapidly depleted of propylbenzene by
convection, while mass transfer from the liquid phase is slower. This finding contrasts the
assumption of liquid-gas equilibrium made by Benzing (1993) for the 100 mI/min flow
rate. If the gas and liquid were in equilibrium a more shallow curve would result, as
discussed further in the section on parameter sensitivity. The 600 mIJmin flow rate (kLa)
is roughly three times greater than the 100 mUmin flow rate (kLa). However, the 600
mlJmin gas volume (bubbles) is estimated at five times greater than the 100 mUmin
volume. As (a) is defined as the surface area per volume of liquid, it should be 2.9 times
greater for the 600 mIJmin case assuming a constant bubble radius. This indicates that
(k[) is roughly the same at both flow rates. Comparison of the mass transfer coefficient
values to literature values should be made with some caution. The fitted values are
dependent on the values used for the gas phase volume which were estimated from the
very small rise in liquid level during sparging. Dispersion in the PFR results not only in an
earlier breakthrough time, but also in a smoothing out of the rate limited portion of the
desorption curve. The dispersion coefficient magnitude of effect is much less than the
mass transfer coefficient. The 600 mUmin dispersion coefficient is significantly greater
than for 100 mL/min. This is interpreted as a result of increased head space turbulent

mixing at the higher flow rate.

57

3.3.2 Soil sample analysis

Knowing the values of model parameters related to the experimental apparatus, the
desorption of soil samples can be analyzed. The assumption is made that parameters
determined without soil remain constant for experiments with soil. Differences between
desorption curves of soil and no-soil samples as well as different soil samples can then be
examined and interpreted with a chosen model for the soil phase. In this work, the soil
phase was modeled as spheres in which Fickian diffusion occurs, dependent on a single

effective diffusion coefficient.

3.3.2.1 Virgin and steam exploded Colwood A

Both virgin (untreated) and steam exploded samples were batch stripped at the 100
mL/min flow rate. The objectives were to: (1) determine if the desorption kinetics had
been effected by steam explosion, and (ii) determine if the simple soil model could
describe the desorption curves of the samples. The results are shown in Figure 10. For
clarity, the steam exploded runs are shown with dark data points, while virgin runs are
white. A difference is seen in the virgin versus steam exploded runs indicating steam
explosion has effected the sorption/desorption kinetics. The virgin Colwood runs are fit
very well using an average particle radius of 280 microns and an effective diffusivity of 1

E-6 cm2/min (1.67 E-8 cmzlsec).

58

 

 

0.0000012 ‘—
0'00000' I D A 1: Colwood
0 A2: Colwood
0.0000008 “r- . Bl: SE Colwood

A C1: Colwood

‘ or: a 1 d
0.0000006 —— S C° w°°

Model

 

 

 

Concentration (g/mL)
0

0.0000004

0.0000002

 

 

 

Time (minutes)

 

 

 

Figure 10: Stripping gas propylbenzene concentration vs. time for virgin and steam
exploded Colwood A at 100 mL/min flow rate.

No model curve is included for the steam exploded runs as a reasonable fit could
not be obtained. It was hypothesized that the addition of soil, in particular the small
particles of a steam exploded sample, may have effected the gas-liquid mass transfer
coefficient. Mass transfer coefficients are known to change upon addition of solids
(Atkinson and Mavituna, 1983). Supporting this, the steam exploded desorption curves
are indicative of decreased mass transfer as the initial concentration drop is steeper than
that of virgin runs. Potential mechanisms may include solids coating of bubble interfaces
and viscosity effects. Despite this, the steam exploded runs could not be fit even by

lowering the gas-liquid mass transfer coefficient. While the mass transfer coefficient is

59

probably effected by the addition of soil, the effects of steam explosion cannot be modeled
by the simple spherical, Fickian diffusion soil model.

3.3.2.2 Size fractions of organic enriched Colwood A

Samples of different particle size were batch stripped at the 600 mIJmin flow rate.
Two sieve fractions (53-125 and 250-500 micron) of organic enriched Colwood A as well
as a ground 250-500 micron sample were examined. Similar to the previous section, the
objectives were to: (1) determine the effect of particle size (if any) on desorption curves,
and (ii) determine if the simple soil model could describe the desorption curves. The
results are shown in Figure 11. The graph indicates that the particle size has an effect on
the desorption kinetics. Apparently, rate limiting diffusion is occurring over a diffusion
path length on the order of magnitude of the particle radius. In particular, the ground
250—500 micron sample has higher gas phase concentration than the 250-500 micron past
1.5 minutes. The ground sample is expected to contribute propylbenzene to the gas phase
at a higher rate. At early times however, the results are the opposite. The model fits
therefore are not as good as with the whole Colwood samples. In each case, the model
curve is fit somewhat poorly at early times to get a good fit in the 2 to 10 minute range. It
should be noted that the data points do not form smooth curves, making curve fitting over
the entire time-scale difficult. Variation among replicate 600 mI/min no-soil runs was
also higher than with the 100 mL/min no-soil runs. The apparent correlation between
desorption curves and radius should be viewed with caution as variation in no-soil runs is
on the order of magnitude with variation in the soil runs. Additional experiments are

required to verify the differences in desorption curves are not merely experimental error.

 

0.0000009
0.0000008 E

 

A L2: 53-125 micron

 

0.0000007 —~
0 L3: 250-500 micron
0.0000006 —- 0 M2: ground 250-500
A micron
g 0.0000005 ~~ Model. 53-125 micron

 

----- Model, 250-500 micron
0.0000004 ~1-

— ' — Model, ground 250-500
micron

 

 

 

0.0000003 —-

0.0000002 ——

0.0000001 ——

 

 

 

Time (minutes)

 

 

 

Figure 11: Stripping gas propylbenzene concentration vs. time for three soil sample
particle sizes at 600 mL/min flow rate.

At this point, speculation can be made about soil organic matter morphology.
Several model morphologies were listed in Table 14 for SOM. Assuming the diffusion
path length is the particle radius, then SOM would be expected mainly in the form of solid
particles or aggregates. Solid SOM particles would obviously be expected to have
different desorption rates for different particle sizes. Aggregate desorption rates would
also depend on aggregate radii as long as the aggregate or pore diffusion was significantly
rate limiting compared to intraorganic matter diffusion. Diffusion rates for SOM coating
morphologies would be expected to show very little or no dependence on particle size, as

the diffusion path length would be the coating thickness.

61

Effective diffusion coefficients were fit to the nearest half-order of magnitude in
order to obtain model curves. Values for 53-125 and 250—500 micron were 5 E6 and l
E-5 cm2/min respectively using average radii of 89 and 375 microns. An average radius
for the ground sample was not determined, but an estimated value of 50 microns yielded a
diffusion coefficient of 1 E-6 cmZ/min. The similarity of fitted diffusion coefficients for
53-125 and 250-500 micron samples is again indicative of a diffusion path-length on the

order of magnitude of the particle radius.

3.3.2.3 Heated re-stripping analysis

The entirety of the solute may not be removed from the soil as commonly assumed
in gas purge experiments. The heated re-stripping described in the materials and methods
section was used to determine if all of the propylbenzene had been removed from the soil
for the 600 mI/min experiments. The results of the initial stripping are shown in Figure

12, while the re-stripping portion is shown in Figure 13.

62

 

800

700

600

500

400

Detector Response

300

200

100

 

 

 

 

 

 

+ 01: No Soil

—°'— 03: 250 micron
Colewood

 

 

 

 

 

8

4)
41
0

10 12 l4 16 18 20
Time (minutes)

 

 

Figure 12: Stripping gas propylbenzene concentration vs. time for initial stripping at 600

mL/min flow rate, 25 degrees Celsius.

The results demonstrate that all the propylbenzene may not be removed from the

soil. As seen in Figure 12, the initial stripping was continued until the detection limit was

reached, and past that point to 30 minutes. Despite this, upon heating the system and

resuming stripping, additional propylbenzene was removed from the system. Surprisingly

however, additional propylbenzene was also removed from the no-soil sample. The data

indicates that more solute was removed from the soil sample as would be expected.

63

 

300 7—

250

 

+ 01: No 8011

+ 03: 250 micron
Colewood

 

 

 

100

50

 

 

 

0 0.5 l 1.5 2 2.5 3

Time (minutes)

 

 

 

Figure 13: Stripping gas propylbenzene concentration vs. time for heated re-stripping at
600 mL/min flow rate, 75 degrees Celsius.

The additional solute removal in both samples is most likely related to the
detection limit of the experiment. The detection limit of the experiment is not zero.
Therefore, once the detection limit is reached, any solute which is removed is not detected
and not accounted for. The results above could be explained in two ways. The first
relates to the rate limited gas-liquid mass transfer. Once the stripping flow is stopped and
the system is closed, gas-liquid equilibrium can be achieved, resulting in a re-saturating of
the gas phase. When stripping is resumed, the gas phase concentration is detectable,
although quickly drops back under the detection limit. The second explanation relates to
the heating of the system. Heating of the closed system should drive the solute into the

gas phase. The result would again be a detectable gas phase concentration which quickly

64

drops below the detection limit upon resuming stripping. Both of these mechanisms are
probably responsible for the additional solute removal in both samples. However, the data

also indicates that more solute was removed from the system containing soil.

3.3.2.4 Total mass removed

While the detection limit issue does not pose a problem for modeling based on
concentration data, it is a problem when detennining percent of total mass removed. Gas
purge data is typically integrated and expressed in the form of percent of total mass
removed versus time. Without initial and final concentration measurements, all the solute
is assumed to be removed from the system once the detection limit is reached. This would
obviously be in error if the detection limit was significantly above zero. Further
complicating matters, the detector signal did not usually return to baselines for the most
sensitive (10 mV) setting which were measured before and after the run. Thus a steady
signal just above the baseline could be a detectable concentration or noise. These issues
make the batch stripping experiment incapable of closing a mass balance on the system
without initial and final concentration measurements by a method such as gas
chromatography or high performance liquid chromatography. Percent of total mass
removed values were calculated by integrating normalized concentrations curves and
multiplying by average flow rates. The degree of variation is illustrated in Table 18. The
results would seemingly indicate that roughly all the solute is removed from soil samples
during the stripping experiment. However, as mentioned above the proper time to stop
the experiment is not known. Continuing concentration readings for long times may
integrate signal noise into the mass removed. This may have contributed to the erroneous
results of runs L and 0. Additionally, all the results may still be overestimations of the
mass removed as concentration curves were normalized to calculated, not measured

values.

65

Table 18: Percent of total propylbenzene removed for batch stripping runs.

 

 

MW % m v Soflmples % v
A 83 A1 90
B 94 A2 98
C 97 B1 110
L 190 C1 91
M 100 G1 100
N 120 L2 110
O 150 L3 96
M2 100
O3 92

 

 

3.3.2.5 Parameter sensitivity

Model sensitivity to adjustable parameters is important not only for understanding
model predictions, but also for interpretation of data. Understanding the effect of varying
a parameter may give insight to the nature of a physical mechanism, or demonstrate the
limits of application of an experiment. The gas-liquid mass transport coefficient (kLa) and
the soil effective diffusion coefficient (Daft) are examined in terms of model sensitivity and

physical significance.

3.3.2.5.1 Mass transfer coefficient

The gas-liquid mass transfer coefficient describes the rate of solute transfer from
the liquid to the gas phase. At infinite values of (kLa), the gas and liquid phases will be at
equilibrium as described by the Henry's law coefficient. At finite values, mass transfer to
the gas phase will be rate-limited, and the gas phase concentration will be lower than that

of the equilibrium case. At very low values mass transfer may be so poor that it limits the

66

overall rate of solute removal from the system instead of the mechanism of interest, soil
desorption. The batch stripping model was used to: (i) evaluate how close the
experiments were to gas-liquid equilibrium, and (ii) compare experimental values to that
which may be expected from a system designed for good mass transfer. A value of 1000
1/hr was used as an obtainable value for a standard design stirred bubble ferrnenter
(Atkinson and Mavituna, 1983). The results for the 100 mUmin and 600 mlJmin cases

are shown in Figure 14 and Figure 15 respectively.

 

 

 

 

 

0.0000014 ——
0.0000012 — ‘ A'bl‘m"
D B,blank

0.000001 4 0 C, blank
A 0 G, blank
g 0.0000008 —- Model, kLa=69.0
g """ Model, kLa=1000
g 0.0000006 4— — - - Model,kLa infinite

 

0.0000004 #—

0.0000002 re

 

 

 

Time (minutes)

 

 

 

Figure 14: Stripping curve dependence on mass-transfer coefficient for no-soil runs at
100 mL/min flow rate.

The batch stripping experiments, as mentioned earlier in the results section, are not

at equilibrium. The above curves show that the experimental curves are far from infinite

67

mass-transfer coefficient curves. Additionally, the curves for a (kLa) of 1000 1/hr indicate
that proper experimental design would allow for essentially equilibrium operation at 100
mUmin and near equilibrium operation at 600 mIJrnin. Experimental design
modifications should include: a cylindrical vessel, baffles, a bottom sparger, and a motor
driven impeller. Such a stripping vessel may allow for the use of a simplified model which
does not include a gas-liquid mass transfer coefficient, at the 100 mUmin flow rate. The
potential benefits of an improved stripping vessel include: simpler modeling, shorter
experiment times, and improved ability to discern different soil diffusion coefficients or

particle sizes. The later is discussed in the following section on diffusion coefficient

 

 

sensitivity.
0.0000014
0.0000012 -
'1 0 M,blank
0.000001 -
° N, blank
A 0, blank

0.0000008 ar-
Model, kLa=210

Concentration (glmL)

 

 

 

 

 

0.0000006 4- l ‘1 ----- Model, kLa=1000

\ — ' — Model, kLa infinite
0.0000004 ——

O\ A
0.0000002 -- a
. a A
0 i 1 i iiiii . """""" r . - ———»—-‘—
0 r 2 3 4 5 6 7 8 9 10

Time (minutes)

 

 

 

Figure 15: Stripping curve dependence on mass-transfer coefficient for no-soil runs at
600 mL/rnin flow rate.

68

3.3.2.5.2 Effective diffusion coefficient

The effective diffusion coefficient describes the rate of solute transfer within the
soil particle as modeled. Ultimately, it describes the rate of solute transfer from the soil to
the liquid for the model chosen in this work. Since it is the only variable parameter for the
soil, its value can be determined within reason from the batch stripping curves. This
method is limited however to a range of diffusion coefficient values for a given particle
radius. As the diffusion coefficients become very small, the rate of solute transfer from the
soil to the liquid becomes insignificant. Therefore, the curves approach that of the no-soil
case. This is a problem of time-scale, as the time-scale of the experiment is much shorter
than that of the diffusion process. A second limitation occurs as the diffusion coefficients
become large. Mass transfer may be limited at the gas-liquid interface. The time-scale of
the diffusion would be faster than that of the experiment. For a particle radius of 280
microns and an average enriched Colwood partition coefficient, several stripping curves
were generated as a function of effective diffusion coefficient. The range of curves for
which good separation is obtained are shown in Figure 16 and Figure 17 for the 100
mIJmin and 600 mL/min cases respectively.

69

 

9.(X)E-07 T
8.1XiE-07 j
7.(X)E-07 +
A 6.111807 ~—
g 50113-07 «-
4.(X)E-07 ~—
3.(X)E-07 *-
2.(X)E-07 ‘-

1.(X)E-07 fir

 

 

 

 

0.00E+00 1r § i I——_____ - __
0.(X)E+OO 1.00E+01 2.00E+01 3.(X)E+01 4.0013+01 SIDE-+01 60154-01
Time (minutes)

 

 

 

Figure 16: Stripping curve dependence on effective diffusion coefficient (cm2/min) for
280 micron spheres at 100 mL/min flow rate and kLa=69 1/hr.

As expected, the stripping curves become indistinguishable at small and large
values of the diffusion coefficient. Comparing the two flow rates, the difference in
experimental time-scale can be seen. The shorter time-scale of the 600 mljmin case
distinguishes high-end diffusion coefficients better than the 100 mL/case. These results
indicate that the stripping gas flow rate can be used to adapt the batch stripping system to

the desired time-scale of diffusion.

70

 

9005-07 1
8005-07 -«
7005-07 4

A 6005-07 ~-

g 5005-07 «-
4005-07 --
3005-07 -—
200507 1“ 1.00 5-07

”DE-07 “r

 

 

 

 

 

 

 

0.(X)E+00 i i i 1* i i i 1

Time (minutes)

 

 

 

Figure 17: Stripping curve dependence on effective diffusion coefficient (cm2/min) for
280 micron spheres at 600 mljmin flow rate and kLa=69 l/hr.

The results also show that the batch stripping system, at either flow rate, is
applicable for fitting (Deff) for the soil samples being studied with some caution. The
fitted effective diffusion coefficients were in the range of 1 E-S to 1 E-6 cm2/min. The
curve separation in this range is good at both flow rates. It should be noted however, that
the important parameter is actually the effective diffusion coefficient divided by the square
of the particle radius. Thus, if the maximum discernible diffusion coefficient was 1 E4
cmz/rnin for 280 micron particles, the maximum discernible diffusion coefficient for 53
micron particles would be 3.6 E-6 cm2/min. Depending on the diffusion coefficient, the
batch stripping experiment may not be appropriate for small particle samples such a
ground or steam exploded soil even at high gas flow rates. The maximum (Deep/R2)

appears to be roughly 0.06 llmin for 100 mlJmin and 0.6 llmin for 600 mlJmin.

71

Assuming an effective diffusion coefficient of 1 E-5 cm2/min, the minimum particle size
would be approximately 130 and 40 microns for 100 mUmin and 600 mI/min
respectively. In other words, no difference in the desorption curve would be expected for
smaller particles. This implies the 600 mlJmin flow rate would be sufficient for the 53-
125 micron size fraction. The 53 and 125 micron sieve trays are estimated as good
bottom limits for the 600 and 100 mUmin flow rates respectively. Furthermore, if whole
soil is to be used, the particle size distribution should be determined by use of sieve trays.
A final consideration related to diffusion coefficient sensitivity is that of the benefit
in increasing the gas-liquid mass transfer coefficient. A small improvement in ability to fit
effective diffusion coefficients may be gained with an improved batch stripping vessel.
Regardless of the effective diffusion coefficient, the initial gas-liquid rate limited drop in
gas concentration is the same. If the mass transfer coefficient (kLa) is increased, then
diffusion coefficient (Deg) effects are evident sooner for a (kLa) of 1000 l/hr. A larger
time range (and data points) would then be available for fitting of the diffusion coefficient.
This may be of value if a mathematical fitting routine was employed. It was expected that
the gas-liquid mass transfer coefficient might be overall rate limiting for large diffusion
coefficients. However, increasing the (kLa) only produced a small difference in separation

is seen in the 20 to 30 minute range.

3.4 Conclusions

The gas purge apparatus is a useful system for obtaining batch desorption data.
Important aspects of the system have been examined in this work. The gas and liquid
phases are not in equilibrium, and proper modeling requires the use of a mass transfer
coefficient (kLa). With improved vessel design, (kLa) can be increased, possibly
approaching gas-liquid equilibrium for the slower 100 mlJmin gas flow rate. The mass

transfer coefficient may change upon the addition of soil to the liquid phase.

72

Batch stripping results indicate that desorption rates are dependent on particle size.
Additional experiments are needed to confirm this preliminary result. Dependence on
particle size supports a hypothesis of SOM as solid chunks or aggregates, but not
coatings. The use of a single effective diffusion coefficient was able to fit whole Colwood
A desorption curves well, and enriched Colwood sieve fractions within reason.

The batch stripping experiment has a limit of resolution with respect to desorption
rate. For a given effective diffusion coefficient, all particles smaller than a critical size will
yield the same desorption curve, rate-limited by the experimental apparatus. Only sieved
samples should be used as whole soil contains fines. The 53 and 125 micron sieve trays
are estimated minimum size limits for the 600 and 100 mUmin gas flow rates respectively.

Batch stripping utility is currently limited by the uncertainty in: (i) pre-stripping
solute equilibria between soil and water, and (ii) removal of the entirety of the solute from
the soil during stripping. This information can only be provided by measurement of solute
concentrations in the various phases before and after stripping. Initial concentration
measurements would verify the attainment of equilibria and provide model initial
conditions in place of calculated values. Final concentrations would close the system mass
balance.

Steam explosion also effects desorption characteristics, although results are not fit
with the spherical geometry, Fickian diffusion model. Differences in desorption curves of
steam exploded and virgin samples may be to a change in soil organic matter morphology
or mass transfer effects. The utility of steam explosion as a pre-remediation treatment can

only be determined once any existing mass transfer effects are sorted out.

CHAPTER 4: SOIL MORPHOLOGY

4.1 Introduction

Knowledge of morphology is required for development of a fundamental mass
transport model for soil. Several such models were hypothesized in Chapter 3. Along
with kinetic data, physical probing of soil samples is used to understand soil morphology.
Visualization of the soil system is achieved with scanning electron microscopy. The effort
to distinguish between organic and mineral matter is made with x-ray fluorescence
spectroscopy. Finally, steam explosion of soil is identified as a tool for altering
morphology. Comparison of changes in measurable quantities such as pore size and

desorption kinetics may yield insight to soil organic matter morphology.

4.2 Materials and Methods

4.2.1 Sample preparation

Whole Colwood A horizon soil as well as the 53-125, 125-250, and 250-500
micron size fractions of organic matter enriched Colwood A were used in morphological
studies. These samples were prepared as described in Chapter 3. Cryogenic grinding was
also used to prepare samples. The 125-250 micron size fraction of organic matter
enriched Colwood A was placed in a scalable 500 mL polypropylene bottle. Six half-inch
diameter steel ball bearings were also placed in the bottle. Liquid nitrogen was added until
the liquid nitrogen no longer boiled when added to the bottle. The bottle was sealed and
shaken by hand in both axial and circular motions for about five minutes. The sample was
then removed and stored in a sealed polypropylene vial.

Scanning electron microscopy was also used to determine the amount of particle
size reduction occurring in batch stripping experiments. Particle shearing due to stirring

was suspected. Samples which had been used in batch stripping experiments as described

73

74

in Chapter 3 were compared to a fresh sample. After batch stripping was completed, the
solids were allowed to settle in the stripping flask for a few days. The supernatant liquid
was then withdrawn using a pipette. The remaining wet cake was frozen in an acetone-dry

ice bath and freeze dried.

4.2.2 Scanning electron microscopy

Soil samples were mounted on microscope stubs with the aid of an adhesive.
Press-on glue tabs were used to apply adhesive to the stubs. Soil samples were placed and _
even distributed on the stubs with a metal spatula. The stubs were turned on their side and
tapped on the lab bench in order to achieve a thin coating of soil sample. The loaded stubs
were inserted into a mounting stage. The complete assembly was coated with gold for
approximately five minutes in a Polaron SEM Coating System at 25 milliamps and 2.5 kV.
The long coating time provided a thick gold coating required to minimize sample charging.
Soil samples are non-conductive and will build a charge without the conduction to the stub
provided by the gold coating. Charged samples will occasionally discharge resulting in
lines on SEM photographs. Scanning electron microscopy (SEM) was performed with a
JEOL JSM-T330. A beam energy of 10,000 eV was used for all samples. Magnifications
of 35 to 15,000 X were used.

4.2.3 X-ray fluorescence spectroscopy

X-ray fluorescence spectroscopy (XRF) was used to obtain elemental dot maps
which corresponded to scanning electron microscope photographs. Bombardment with an
electron beam results in the ejection of a X-ray photon which carries elemental
information. By rastering over a sample frame, elemental dot maps can be obtained for a
SEM picture of the same sample frame.

The 53-125 micron size fraction of organic matter enriched Colwood A was used

for the XRF studies. The sample was prepared for coating as described for SEM. A

75

carbon coating was applied to the sample for conductivity. SEM photographs and XRF
dot maps were done with a with a beam energy of 20,000 or 15,000 eV using a integrated
SEM/XRF system. The SEM was a JEOL ISM-35C and the XRF was a Tracor Northern
TN2000. Elemental dot maps examined included silicon, calcium, and aluminum. The
desired elemental dot map is carbon, as it would be most useful in finding soil organic
matter. Carbon dot maps were not possible however due to the composition of the x-ray

collection window.

4.2.4 Steam explosion

Steam exploded samples were prepared in order to effect the sample morphology
in a way detectable by batch stripping analysis and scanning electron microscopy. In brief,
soil slurries were heated in a closed vessel, elevating the pressure, followed by a rapid
pressure drop to ambient conditions.

The apparatus consists of a 25 mL stainless steel pressure vessel with an o-ring
sealed lid. The lid was fit with (i) a thermocouple probe, and (ii) a 600 psia pressure
gauge. The pressure gauge was connected to the vessel by 30 cm of stainless steel tubing
to prevent damage from heating. The vessel was fitted with a 90 degree ball valve,
allowing for quick pressure release. Heat was applied with a fiberglass coated heating
tape. A second thermocouple probe was wrapped in the heating tape. A digital
thermocouple thermometer was used to measure internal and external (heating tape)
temperatures. A voltage controller was used to manually regulate the heating tape
temperature.

The pressure vessel was loaded with 8 to 10 grams of whole Colwood A soil and
enough water to completely fill the vessel (18-20 mL). The lid was sealed and the vessel
was mounted in a wood stand which was placed in a vacuum hood. The heating tape was
wrapped around the vessel and the external thermocouple. Once electrical connections

were made, the voltage to the heating tape was set to give a rapid increase in the external

76

temperature to approximately 200 degrees Celsius in under five minutes. The voltage was
then decreased to give a slow increase to an external temperature of 225-250 degrees
Celsius. The external temperature was kept in this range for the duration of the
experiment by frequent manual adjustments. The vessel pressure was monitored, and
upon reaching 220 psia the heating was continued for five more minutes. At the end of
five minutes the internal temperature was recorded (186-189 degrees Celsius). The ball
valve was then opened resulting in a 2-3 second discharge into a 2 L Nalgene beaker. The
processed slurry was distributed into 25 mL test tubes and centrifuged until all solid had
apparently settled. The supernatant fluid was decanted and the samples were frozen in a
acetone-dry ice bath. The samples were immediately freeze dried and stored in
polypropylene vials. Samples were freeze dried as a precaution against capillary forces
found in normal drying. Pores which were expanded during steam explosion could be
shrunk or collapsed during air drying. Preliminary samples were both air dried and freeze

dried after freezer freezing, resulting in caking which required break-up with a spatula.

4.2.5 Mercury porosimetry

Mercury porosimetry was performed in order to determine if steam explosion
altered the pore structure of soil samples. Both virgin and steam exploded samples (as
described previously) were tested. The instrument used was a Micromeritics Pore Sizer
9310. Both low and high pressure testing was performed covering a range from
approximately 1 to 30,000 psia. Low pressure testing (1 to 25 psia) was performed
manually, while high pressure testing (25 to 30,000 psia) was computer (a PC) controlled.
All data was automatically recorded by the PC. A 5 cc powder penetrometer was used.
Operating procedures are described in detail in the Micromeritics Pore Sizer manual.

Results in the form of cumulative intrusion volume and incremental pore volume
per gram were calculated by the Pore Sizer 9310 software. Capillary calculations assumed

round pores, allowing pore diameters to be determined by Equation 21.

77
1
D=—F-4-r-cos(x) (21)

The pore diameter is (D), (P) is the applied pressure, (r) is the surface tension, and (x)
is the contact angle. Values of 484 dynes/cm for surface tension and 130 degrees for
contact angle were used. The change in penetrometer mercury volume and pore diameter

versus pressure yields an incremental pore volume as well as cumulative intrusion volume.
4.3 Results and Discussion
4.3.1 Scanning electron microscopy

4.3.1.1 Particle morphology

The organic matter enriched size fractions of Colwood A were examined in order
to visualize the solid phase in the kinetic model. Four size fractions were initially
examined: 53-125, 125-250, 250-500, and > 500 microns. Samples were examined at
100 and 500 times magnification. A notable characteristic of the smallest size fraction is
the two distinct particle types. Most particles appear to be either aggregates of smaller
sub-particles or solid particles of a single material. This trend is seen in the 125-250
micron fraction, but with less distinction. In the 250-500 micron sample, partially decayed
plant material becomes apparent as evidenced by tubular structures in the particles. The
evidence of plant matter is quite clear in the largest size fraction. Woody material in this
size fraction could be seen with the naked eye.

Preliminary results allowed for speculation on the nature of organic matter in the
soil. The aggregate particles seen in the smaller size fractions were hypothesized as a
mixture of organic and mineral matter. Organic matter was obviously also found in the

form of the partially degraded plant matter, particularly in the larger size fraction samples.

78

In terms of solid phase modeling, both aggregates and solid chunks shown in Table 14 of
Chapter 3 would be appropriate. Aggregate particles could also be modeled with a single
effective diffusion coefficient as done in Chapter 3.

Further investigation of the aggregate particles was done to determine the internal
morphology. Cryogenically ground 125-250 micron fraction was examined at 1,500,
5,000, and 15,000 times magnification. Cryogenic grinding is done in order to fracture
particles into smaller pieces, thereby exposing the inner morphology. Several aggregate
particles as small as 10 microns were seen indicating particle fracture had been achieved.
The small aggregate particles appeared identical to the large aggregates, indicating a
uniform morphology throughout the particles. The results indicate a homogeneous
organic-mineral aggregate model may be applicable. Modeling parameters such as pore
sizes and sub-particle diameters can be estimated from a 5,000 X photograph. Pores

appear to be around 1 micron wide while sub-particles may range from 1 to 10 microns.

4.3.1.2 Steam explosion

Steam explosion of soil samples was investigated for two purposes. Steam
explosion would be a valuable soil remediation processing step if desorption rates of
organic compounds from soil could be increased. This might occur if water vaporizing to
steam expanded or ruptured pores in the soil organic matter. Steam explosion was also
examined as tool for manipulating soil morphologies. If the soil or soil organic matter
could be manipulated in a known way, resulting changes in desorption kinetics could
provide new information about the sorption mechanism. It is in this second capacity that
steam exploded samples were examined with scanning electron microscopy.

SEM pictures demonstrated that steam explosion changed the soil morphology.
Steam explosion was performed as described in Chapter 3 on whole Colwood A soil.
Fresh Colwood A was wetted and freeze dried in a manner identical to steam exploded

samples as a control. The results at 35 times magnification are shown in Figure 18.

 

(b) Steam exploded Colwood.

Figure 18: Virgin and steam exploded Colwood samples at 35 X magnification.

80

At 35 times magnification (as well as 100 X), a size reduction of medium to large particles
is apparent. The explosion or break-up of 50 to 500 micron particles are termed here as
macro-scale effects. Micro-scale effects would be disruption of particles 10 microns or
less, expansion of pore systems in aggregates, or expansion of micro-pores in soil organic
matter (if viewed as a cross-linked polymer). However, efforts to find these types of
effects were not successful. SEM images at 500 and 2,000 times magnification appeared
the same for both samples. This is not interpreted as proof against micro-scale effects, as
they are not expected to be readily apparent like large particle break-up. The search for
micro-scale effects suffers primarily from not knowing what is soil organic matter and
what is mineral matter.

These results have implications for steam explosion as a investigative tool for
desorption kinetics. Assuming that steam explosion only causes macro-scale effects, a
change in desorption kinetics for steam exploded samples would only be expected if the
characteristic diffusion path length was the particle radius. If the path length was the
radius of 1 to 10 micron sub-particles in an aggregate particle, then the macro-scale

morphology changes would have little or no effect on sorption.

4.3.1.3 Particle shearing during batch stripping

SEM qualitatively showed that particle shearing was a problem at long stripping
times. Two sets of comparisons were made. Fresh 53-125 micron enriched Colwood A
was compared to the same soil which had been stripped for 195 minutes. Fresh 250-500
micron enriched Colwood A was compared to the same soil which had been stripped for
(i) 55 minutes, and (ii) 81 minutes. The 53-125 micron sample stripped for 195 minutes
had a significant amount of particles with a diameter smaller than the 53 micron cut-off.
Particle size reduction is severe at this long stripping time.

Similar results are found with the 250-500 micron samples. Figure 19 compares a

fresh sample with a sample stripped for 55 minutes. Some particle pulverization is

81

apparent at 55 minutes of stripping. Fewer large particles as well as more fines are present
in the stripped sample. The fines were likely generated from shearing of larger particles.
Further particle pulverization is apparent at 80 minutes. Most of the large particles seen in
the previous samples are not present. While a few particles in the 250-500 micron range
remain, most of the soil appears to be particles of 100 microns or less. Comparing the
fresh sample to the 80 minute sample, the particle size reduction is extreme.

Particle size reduction during sample stripping has important implications for
kinetic data. The characteristic diffusion path-length has many possibilities including the
particle radius, the thickness of particle coatings, or the radius of small 'sub-particles'
which comprise an aggregate particle. If the diffusion path length were that of the particle
radius then shearing would change the path length over the course of an experiment.
Results would be worthless. The qualitative information gathered here provides only an
estimate of appropriate experimental consideration. Very long stripping times (195
minutes) are clearly not acceptable. Results from 250-500 micron samples are more
ambiguous do to heterogeneous nature of the sample, but stripping times of less than 30
minutes are probably needed. This information provides motivation to use faster gas flow
rates, such as 600 mljmin in order to prevent particle pulverization. Achievement of
higher gas-liquid mass transfer coefficients (kLa) may also decrease stripping times as

discussed in Chapter 3.

82

 

(a) Fresh sample.

 

(b) 55 minutes stripping time.

Figure 19: Pulverization effects of batch stripping on 250-500 micron Colwood samples.

83

4.3.2 X-ray fluorescence spectroscopy

A viewing field of several particles was examined at 200 times magnification. The
particles were fairly uniform ranging in size from roughly 5 to 100 microns (major axis).
Exceptions were some ribbon shaped objects thought to be plant or animal matter. The
silicon dot map was similarly uniform giving a general outline of the particles. However,
the dot density (concentration) was not uniform for all particles. Some particles of 10
microns or less had a particularly high concentration of silicon and were presumably
mineral matter.

A particle of lower silicon content was magnified by 1000 times. The particle
appeared as a homogenous mixture. The silicon dot map highlighted a few particles of
roughly 2 to 10 microns in diameter on the surface of the particle. A calcium dot map was
completely uniform, probably due to the soluble calcium in groundwater. Silicon and
aluminum dot maps taken at 3900 times magnification were similarly uniform, showing no
correlation with visible differences in the particle.

Another particle of interest was examined at 3600 times magnification. The
particle appeared to be fairly homogeneous mixture with generally with soft edges.
Dispersed in the continuous phase were some very smooth particles appearing to have
sharp edges. A dot map showed some areas as higher silicon content, in particular the
smooth, sharp edged particles. These results may suggest homogenous mixture of mineral
and organic matter.

Similar results were found in another viewing field at 1300 times magnification.
Several soft edged, dark particles which appeared to be a single material (opposed to an
aggregate) were dispersed in a lighter colored, semi-continuous phase. The dark particles
had a particularly high concentration of silicon. The picture again suggests a mixture of
mineral and organic matter. An appropriate visualization model may be that of concrete.
The mineral matter may be visualized like the stones or gravel, while the organic matter is

represented by the cement, binding the gravel together.

84

Finally, a particle quite different in nature was found while at 2400 times
magnification. The particle was approximately 40 by 20 microns, had soft edges, and
appeared to be comprised of a single material. A few smaller particles of 1 to 5 microns
were attached to the surface. Silicon, as well as heavier elements, were of negligible

concentration suggesting a solid chunk of organic matter, or at least a thick coating.

4.3.3 Mercury porosimetry
Steam explosion was found to effect the pore structure of Colwood A soil
samples. Changes in pore structure were examined by mercury porosimetry. Results

comparing a virgin and steam exploded sample are shown in Figure 20.

 

 

 

 

 

 

25
20
g I Virgin Colewood A
U
E 15 1:1 Steam Exploded Colewood A
6
>
10

 

 

 

nnMnflIlilililJlflflilnn Il.III.,.,_..,....,-.. .1. . ,. . .11,

1
rrrrrrrrrrrrrrrrrr

 

Pore Diameter (micrms)

 

 

 

Figure 20: Incremental pore volumes for virgin and steam exploded Colwood samples.

The most significant effects of steam explosion are: (i) a large increase in pore volume for

roughly 3 to 6 microns diameter pores, (ii) approximately a doubling of pore volume for

85

12 to 112 microns diameter pores, and (iii) a near complete loss of pore volume for 0.8 to
1.7 microns diameter pores.

The intended effect of steam explosion is to expand or rupture pores by rapid
vaporization of water. The results above would seem to agree with this process. Overall
pore volume per gram is increased. Unclear however, is the mechanism of change as well
as how much of the pore volume is actually that of soil organic matter. Porosimetry of a
sample in which the SOM was removed thermally may elucidate organic versus mineral
contributions.

Some speculation can be made on steam explosion mechanism. Steam explosion
may simply enlarge pores. A good case for this mechanism is made by the loss of pore
volume for 0.8 to 1.7 micron diameter pores, and the large gain in the 3 to 6 microns
diameter range. The small pores would of course be lost upon expansion, showing up as
larger pores in the exploded sample. Additionally, an increase in pore volume would be
expected if the number of pores remained constant. This is certainly seen. A similar
although less dramatic increase is seen in roughly the 10 to 100 micron range. New pores
appear in the 140 to 180 micron range. Considering the large size, this may represent the
break-up of large particles.

An alternative explanation for the loss of pore volume in the 0.8 to 1.7 micron
range is pore collapse. The problem with this explanation is two-fold. First, some signs of
the collapsed pores may be expected at very small diameters. The increase in volume at
0.006 microns may represent this, or merely experimental error. Second, pore collapse

would not explain the large net increase in pore volume.

4.4 Conclusions
Insight to the nature of soil organic matter was gained through scanning electron
microscopy and x-ray fluorescence spectroscopy. In general, the morphology appeared to

be either aggregates of a mixture of organic and mineral or solid organic matter.

86

Aggregates are envisioned as concrete with stone and cement representing the mineral and
organic matter respectively. An aggregate model including intraorganic matter diffusion
as well as pore diffusion (between sub-particles of aggregate) would seem appropriate. A
simplified approach is the single effective diffusion coefficient model used in Chapter 3 of
this work. Solid organic matter would be modeled similarly, but the diffusion would be
that of the organic material. The results are limited as the identification of organic versus
mineral matter requires additional probing techniques. Such techniques may include x-ray
fluorescence spectroscopy for carbon maps or examination of samples in which the
organic matter has been thermally removed.

Steam explosion can be used as a tool to change the soil morphology. The pore
structure is effected with an increase in pore volume as shown by mercury porosimetry.
The mechanism of change is not understood but is hypothesized as expansion of pores.

Particle pulverization due to stirring during batch stripping is significant at longer
stripping times. If diffusion path lengths are that of particle radii, pulverization will
invalidate any kinetic results. Experiment times must be kept short (probably less than 30
minutes). The batch stripping vessel should be re-designed to keep particle shearing

forces to a minimum.

87

CONCLUSIONS AND RECOMMENDATIONS

The research objectives of this work concern both the equilibria and kinetics of
nonionic organic compound sorption to water saturated soil systems. The results are
significant as accurate prediction of both equilibrium concentrations as well as desorption
kinetics is an integral part of designing successful bioremediation processes.
Achievements of this work include: (i) presentation of a method for predicting solute-soil-
water equilibrium, (ii) critical evaluation of a batch stripping method for collecting
desorption data, and (iii) preliminary development of a mass transport model for soil
organic matter based on morphological information.

A group contribution activity coefficient method has been developed from the
ELBRO—FV model of Kontogeorgis et al. (1993) and the Modified UNIFAC model of
Gmehling et al. (1993). The method has been tested by predicting SOM-water partition
coefficients for a limited number of nonionic organic compounds with good results. Large
errors were found to be a result of water phase activity coefficient predictions. Marked
improvements in predictions therefore were made with the use experimental water phase
activity coefficients. UNIFAC predictions were significantly different for Stevenson
(1982) and Aiken (1985) model molecules. The Stevenson predictions were closer to
literature values indicating the higher aromaticity of the Stevenson molecule may be more
representative of actual soil organic matter.

The difference in Stevenson and Aiken predictions as well differences in literature
values dependent on soil source, indicates that SOM chemistry may be correlated with
partition coefficients. A logical continuation of the current work would be to develop new
model SOM molecules which are representative of samples which vary in chemistry.
Information from samples such as elemental analysis and percent aromaticity could be
used to modify the Stevenson or Aiken models. Hopefully, the partition coefficient
predictions made with the method presented in this work would agree with differences

seen in experimental values for various samples. Significance of such efforts would

88

include a better understanding of the implications of SOM chemistry as well as a more
accurate, site-specific method for predicting equilibrium pollutant concentrations in soil
and water.

The gas purge apparatus of Benzing (1993) was analyzed for collection of batch
desorption data. The experiment was found to be rate-limited and was therefore modeled
with a gas-liquid mass transfer coefficient. Head space non-idealities were accounted for
by a PFR model with dispersion. Accurate modeling of the system was a required
precursor to soil desorption studies. The spherical geometry, Fickian diffusion model for
soil samples allowed for elucidation of experimental limitations. Appropriate minimum
sieve sizes were estimated at 125 and 53 microns for 100 and 600 mUrnin gas flow rates
respectively. Smaller particles sizes are not expected to yield different desorption curves
as the experiment itself will be rate limiting. The experiment is not capable of closing a
mass balance without measuring solute concentrations. Future work should minimally
include gas or liquid chromatography detennination of solute concentrations in the
aqueous phase before and after stripping. The gas-liquid mass transfer could be improved
with better vessel design, leading to simpler modeling and shorter stripping times. Short
stripping times are important as scanning electron microscopy showed significant particle
pulverization due to stirring.

The groundwork for a morphological based, mass transport model for soil organic
matter has been laid down. Two approaches were taken. First, kinetic data was collected
using the gas purge apparatus and analyzed with a mathematical model. A spherical
geometry, Fickian diffusion model was able to produce reasonable fits of the data by
adjusting the effective diffusion coefficient. Desorption kinetics were found to be
dependent on particle size, indicating the particle radius may be an appropriate diffusion
path length for the model. Second, scanning electron microscopy and x-ray fluorescence
spectroscopy were used to view the soil morphology. Results indicate soil organic matter

may be found in organic-mineral aggregates or solid chunks. Either of these morphologies

89

could accommodate a diffusion path length dependent on particle size as found in
desorption studies.

The simple effective diffusion coefficient model can be used for both aggregates
and solid chunks. The effective diffusion coefficient may be a function, however, of
whether the particle is an aggregate or solid, porosity, tortuosity, and the pure SOM
diffusion coefficient. Future work should continue to elucidate the soil morphology with
regard to the above aspects. More kinetic data should be collected using soil samples of
the narrowest size distributions possible (close sieve tray sizes). Sample pulverization due
to stirring must be monitored and minimized. X-ray fluorescence spectroscopy for carbon
dot maps should be attempted to discern organic matter from mineral matter. Samples in
which organic matter has been thermally removed should be used for kinetic studies and
examined by SEM as well as mercury porosimetry. These approaches may also help in
discerning mineral matter (and its properties) from organic matter.

Steam explosion was shown to effect soil morphology, apparently expanding
pores. Kinetic implications are unclear however as desorption curves could not be fit by
the spherical geometry, Fickian diffusion model. Further work is warranted using sieved
samples so that particles sizes are known. Gas-liquid mass transfer may be altered by
addition of steam exploded soil (small particles) to the aqueous phase. These effects, if
any, must also be understood if they are rate limiting.

The work presented here has application in design of bioremediation processes.
The solute-soil-water system studied is typical of remediation process where microbial
degradation is employed to remove a pollutant compound. The modified ELBRO-FV
method provides a engineering estimate for partition coefficients of solute for which no
data is available. The gas purge apparatus has been accurately modeled and provides an
effective method for collecting desorption data. The development of a fundamental mass
transport model for soil organic matter systems has been started, and further work

recommended. Such a model, based on soil morphology, would allow for rigorous

90

prediction of pollutant removal rates and aqueous concentrations which are both critical

for process design.

APPENDIX A

Appendix A: Sub-group representations for model SOM molecules

Slightly different sub-group representations were required to accommodate the use
of molar group volumes by the method of Bondi (1968) and of Gmehling eta]. (1993).
Bondi molar group volumes are taken from the UNIFAC parameter set of Gmehling et al.
(1982), and therefore can only be represented with sub-groups included in that parameter

set for free volume term calculations. Free volume term calculations for Gmehling molar

 

group volumes of course use the Gmehling et al. (1993) sub-group set. Sub-group
representations for both molecules are listed in Table 19 through Table 22.

Table 19: Stevenson molecule sub-group representation for Bondi molar group volumes.

 

 

 

CH2 1
CH 3
ACH l 5
AC 22
OH 4
ACOH l3
CH2CO 2
CHO l
HCOO 5
CH—O 6
CH2NH 1
CHNH l
ACNH2 l

 

91

 

92

Table 20: Stevenson molecule sub-group representation for Gmehling molar group
volumes.

 

 

CH2 1

CH 3

ACH l 5
AC 22
OH (secondary) 4
ACOH 1
CH2CO 2
CH0 1
HCOO 5
CH-O 6
CH2NH 1
CHNH 1
ACNH2 1

 

 

Table 21: Aiken molecule sub—group representation for Bondi molar group volumes.

 

 

ACOH
CH2CO
HCOO
CH-O

>
O
ww—AQMCF‘QWN

 

 

Table 22: Aiken molecule sub-group representation for Gmehling molar group volumes.

 

 

AC

0H (PrimaIY)
OH (secondary)
ACOH

CH2CO

HCOO

CH-O

~w—‘bWUJLIIO—‘QMN

 

 

APPENDIX B

Appendix B: MathCad worksheets for activity coefficients calculations

Three MathCad worksheets are included to illustrate the calculation of activity

coefficients for benzene. Activity coefficients are calculated for each of the three phases:

octanol, water and soil organic matter (Stevenson model molecule).

93

 

94

Prediction of Solute Activity Coefficient In Octanol by Gmehling
(1993) UNIFAC Model

Tyler T. Ames

Department of Chemical Engineering
Michigan State University

East Lansing, MI 48823

Number of Molecules Temperature, K Set mole fractions for Benzene = 1

molecs 1= 2 component molecules Octanol = 2
T 3= 298 ,_ C : Z :

13:1..molecs x1.-0. 1

j1=1.. molecs xzi=0.99999

Calculation of Combinatorial Contribution to Activity Coefficient uslng:

 

 

nsgfit :: 85 Set number subgroups in
fitted R parameter file

k 1= 1.. nsgfit
Rk :: READ(r_f1tdta) Read in fitted R subgroup parameters from file
Qk 2= Wq_all93dra) Read in fitted Q subgroup parameters from file
vl k1: READ(benzeneall ) Read in molecule 1 subgroup frequencies
V2.1. :: READ( octanolall ) Read in molecule 2 subgroup frequencies
ri :va'Rk

k
qi Izzvm'Qk 3

k r. (1.) 4 q

._ 1 . ._ 1 _ 1

Vi .— V. .— F -

7 Ci 3: exp (lny Ci)

lny Ci ‘Y C

 

 

- 0.22 0.803
- 1.392-10' “ 1

 

 

 

 

 

 

 

 

95

Calculation of Residual Contribution to Activity Coefficient by original UNIFAC residual term:

nsgall t: 85

k := l..nsgall m := 1.. nsgall

gk 2: REAIX g_all93dta)
ngall 1:45
o 2:1..530 p :=1..8

a p I: READ( ip_alldta)

agl (30,1'a0 2) =30 I
ag2(‘o l'ao,2) :Za‘”
ag3(ao,1'ao.2) 3:80 5

ekzzszJ‘xI

 

l

' {-(alm‘kwLaZm
T .ZCX
m,k

k,i':§:G,miT,mk

lny R. I=q.-( (1- 1n(L
1

Y R :: exp(lnY R)»

l l

lnY R
I

 

0.893

424310’ll

 

 

 

 

-T+ fink-'12)]

 

 

2.442

 

 

 

 

Set number of subgroups for
Gmehling ALL (1993) parameters

Read in group designation for each
subgroup

Read in interaction parameter matrix from
Gmehling et al (1993)

Map interaction parameter matrix
into separate temperature dependent
interaction parameters for groups

Map group interaction parameters
into sub-group interaction
parameters

Equations from Smith and Van Ness (1987)

 

 

 

96

Addition of Combinatorial and Residual Contributions
lilyi :: 1117 C. + In R
l l
yi 1= exp(lnyi)
7i

1.96
1

 

 

 

 

 

97

Prediction of Solute Activity Coefficient in Water by Gmehling

(1 993) UNIFAC Model

Tyler T. Ames

Department of Chemical Engineering

Michigan State University
East Lansing, MI 48823

Number of Molecules
molecs := 2

T :2298

i t: 1.. molecs
j 2:1..molecs

Temperature, K

Set mole fractions for Benzene = 1
component molecules Water = 2

x] := 0.00001

x2 := 0.99999

Calculation of Combinatorial Contribution to Activity Coefficient using:

nsgfit :: 85
k z: 1.. nsgfit

Rll :: REAR r_fitdta )
Qk :: REAR q_all93dta)

vl k :: REARbenzcneall)

v2 k := REAR waterall)

ri :sziJflzk

 

 

r c. =expm(r c.)
I 1

kW Ci Y Ci

 

 

0.287 1.332
3.50410' “ 1

 

 

 

 

 

 

 

 

 

Set number subgroups in
fitted R parameter file

Read in fitted R subgroup parameters from file

Read in fitted Q subgroup parameters from file
Read in molecule 1 subgroup frequencies

Read in molecule 2 subgroup frequencies

 

 

 

 

98

Calculation of Residual Contribution to Activity Coefficient by original UNIFAC residual term:

nsgall I: 85

k1=1..nsgall m2=1..nsgall

gk 2: REAR g_all93dta)

Set number of subgroups for
Gmehling ALL (1993) parameters

Read in group designation for each

ngall ;= 45 subgroup

o:=1..53o pi=l..8

a p 3= REAR ip_alldta)

0.

ag] (30.1 ao.2) 33210.3 agl(a
ag2(ao'l “o 2) —a0 4 ag2<

383(3’ ’ ,2) 32210.5 ag3(ao.2’ao,l) I=ao'8

Read in interaction parameter matrix from
Gmehling et al (1993)

I: a . . .
02%,1) 0.6 Map interaction parameter matrix

.2 a into separate temperature dependent
a0.2%.1) °'7 interaction parameters for groups

alm .k '2 ag1(Sm'8k) Map group interaction parameters
into sub-group interaction

a2 I=a 2
“‘~" g (gm'gk) parameters

qi I=Zvi k-Qk Equations from Smith and Van Ness (1987)

 

 

 

 

7 Ri 1: exp(lrw R')

'"YR YR.

“64 1.744103
2.49910‘9 1

 

 

 

 

 

 

 

 

 

 

99

Addition of Combinatorial and Residual Contributions
ln'yi := lny C. + lny R
l l
yi I= exp(lnyi)
71

2.323103
1

 

 

 

 

 

100

Prediction of Solute Activity Coefficient in Polymeric Phase Using
an ELBRO-FV Model w/ Gmehling (1993) Parameters

Tyler T. Ames

Department of Chemical Engineering
Michigan State University

East Lansing, MI 48823

Number of Molecules Temperature. K Set mole fractions for Benzene = 1
molecs I: 2 component molecules Stevenson 1 = 2
.,_ T ‘2298 x :=o.00001
1 .- 1.. molecs 1
j 1= 1.. molecs x2 1:0.99999

Vl := READ ( benzcncmv ) v2 ;: READ (som_slmv ) Read in pure component molar

v1 ___ 89.13 v2 = l.413°103 volumes of molecules, cmAS/mole

Calculation of Free-Volume/Combinatorial Contribution to Activity Coefficient using:
(1) van der Waals surface area parameters based on the method of Bondi (1968)
(2) van der Waals surface area parameters fitted/optimized by Gmehling et al (1993)

Bondi Parameters" . (taken from Gmehling et al. 1982)

 

 

 

 

 

 

 

nsgvdw := 76 Set number subgroups in
vdW R parameter file
k 7= 1.. nsgvdw
Rk :2 READ (r_vdwdta ) Read in vdw R subgroup parameters from file
v1 1: ;= READ (benzcncvdw ) Read in molecule 1 subgroup frequencies
v2 k := READ ( som_slvdw ) Read in molecule 2 subgroup frequencies
v w. := vi k-Rk -15.l7 Calculate van derWaals volume
‘ k for each molecule from group
contributions of subgroup
V “’1 = 4835‘) V “'2 = 775-905 van derWaals volumes (Rs)
V f. L: V1 — V w.
' ' Calculate free volume for each
v f1 = 40.771 v f2 = 637.095 molecule
4’
4’ ,: x‘ f1 ¢ Calculate free volume fraction
fvi . Z (x ,v f) f" 1 for each molecule
j .
,- ’ 6.4- 10‘7
1 1
¢ fv, 9 fv. Calculate free volume/combinatorial
in N := In ' + l— ' contribution to activity coefficients
' "1 "1 for each molecule

 

f=cx In
vai p( vai) ln'vai ‘va

 

- 1.813 0.163
4.38140“ 1

 

 

 

 

 

 

 

Gmehling et al Parameters...
nsgfit 2= 85

kk i= 1.. nsgfit

RRkk 2: READ (r_fitdta )

W| n :2 READ (benzeneall )

W2.“ I=READ(som_slall )

vv wi :2 vvi'kk-RRkk -15.l7
kk

=34.251 W W

= 907.50]
W1 2

VV

W.

W f 32V.—W
i ' I

W f =54.879 vv f =505.499
l 2

 

 

 

 

 

 

 

 

 

 

’S'V" f.
44 fv. r= '
1 1086-10“6
‘ l
99 fv. W fv.
lnyyfv'rz 1n '+1— '
' ’S "1
W fvi :=exp(ln'yy fvi
"W fvi W fv.
~ 1.813 0.265

 

 

 

-4.38l-10'" 1

 

 

 

 

 

101

Set number subgroups in
fitted R parameter file

Read in fitted R subgroup parameters from file
Read in molecule 1 subgroup frequencies

Read in molecule 2 subgroup frequencies

Calculate van der Waals volume
for each molecule from group
contributions of subgroup

fitted volumes (Rs)

Calculate free volume for each
molecule

Calculate free volume fraction
for each molecule

Calculate free volume/combinatorial
contribution to activity coefficients
for each molecule

102

Calculation of Residual Contribution to Activity Coefficient by original UNIFAC residual term
with temperature dependent interaction parameters from the most current data base of
Gmehling et al (1993).

nsgall 2:85 Set number of subgroups for

Gmehling ALL (1993) parameters
It 2=1..nsga11 m :=1..nsga11

Read in subgroup surface area parameters

Q]: ‘= READ (‘1—“1'93‘1‘3 ) for Gmehling ALL (1993)

gk 2: READ (g_all93d1a ) Read in group designation for each
subgroup
ngall ::45
o 1= 1.. 530
p T: 1 .. 8
._ . Read in interaction parameter matrix from
amp "RE”D (‘p-and‘“ ) Gmehling etal (1993)

agl (10.1.1103) 1:a0.3 agl (flog-80‘ 1) 1 0.6 Map interaction parameter matrix
. into separate temperature dependent
0.2"o.1) 0'7 interaction parameters for groups

ll
93

332 (a a 1: 80,4 ag2 (a

0.1' 0.2

t.)

ag3(ao'i.a°.2) :Zao,5 ag3 (30.2.3 1) :: 0.8

0.

al m .k z: “31 (gm .3.) Map group interaction parameters
32 .2 332 into sub-group interaction
”'1‘ I (11m 'gk) parameters
a3m .k 1: 333 (gm .3.)
V1.1: 3: W1 . k
qi i=2vm-Qk Equations from Smith and Van Ness (1987)
k
Gk ' 5: V1 k'Qk

1' n
9k 2:20m'x1
1

2
- (a1m 1+ a2m ‘k-T + a3m ‘k-T )]

 

1m.k.=cxp

 

._ ._ ,_ qi
511.1 '_ZGm.i.Tm.k “k"zsm'xi L1"
1 2‘11”":

 

 

 

 

m
1
1 -- -1 1 L Zea-5"" G -1 5""
“VRi“qi( — “(1))“ k ‘ k.i n
k "I: “k
:: 1
7R1 cxp(n‘Y R1) 1‘17Ri VRi
1.971 7.179

 

 

9209-10"12 1

 

 

 

 

 

 

103

Addition of Free-Volume and Combinatorial Contributions

 

 

 

 

For Free-Volume by Bondi vdW Volumes For Free-Volume by Gmehling vdw Volumes
lnyBizzlrryt-vii-lnyR lrryGI=lrryy fv'i-lrryR
1 1 l I
7 Bi 1: exp(lny B.) 7 G 2: exp(1n'y G.)
l l I
1117 B. y B. 1W a Y o
l l 1 1
0.158 1.171 0.642 1.901

 

 

 

 

-34610'“ 1 -305210'” 1

 

 

 

 

 

 

 

 

 

 

 

 

APPENDIX C

Appendix C: FORTRAN code for batch stripping simulation

OOOOOOOOOOOOOOO

BATCH STRIPPING SIMULATION

THIS PROGRAM SIMULATES A BATCH STRIPPING EXPERIMENT.

THE EXPERIMENT CONSISTS OF FOUR PHASES: (1) A SOLID PHASE

(SOIL OR SOIL ORGANIC MATTER) WHICH HAS A SPHERICAL GEOMETRY
IN WHICH FICKIAN DIFFUSION OF A SOLUTE OCCURS, (2) A LIQUID
PHASE (WATER) WHICH IS PERFECTLY MIXED IN WHICH THE SOLID
PHASE RESIDES, (3) A GAS PHASE (AIR) WHICH IS USED TO STRIP

THE SOLUTE OUT OF THE LIQUID PHASE, AND (4) THE STRIPPING FLASK
HEAD SPACE. A FILM TYPE MASS TRANSFER RESISTANCE IS INCLUDED
BETWEEN THE LIQUID AND THE GAS PHASE.

THE MODEL IS COMPROSED OF TWO PDE(S) (DIFFUSION OF THE SOLUTE
IN THE SOIL, AND HEAD SPACE) AND TWO ODE(S) (SOLUTE MASS BALANCES
IN THE LIQUID AND GAS PHASES).

PROGRAM STRIP

EXTERNAL FCNUT, FCNBC

INTEGER LDY,NPDES,NX,I,J,IDO,NSPM

PARAMETER (NSPM=1, NPDES=4+NSPM, LDY=NPDES)
CHARACTER’1‘70 TITLE,DESCRIP

REAL A0(5),BO(5),GO(5),A1(5),B1(5),G1(5),CSO,CLO,CGO,

+ F,VH,VG,VL,VS(NSPM),KP,H,D,KLA,DELX,XBREAK(22),Y(NPDES,22),
+ GRADCS(NSPM),HINIT,T,DELT,DELTI,TFINAL,UT1CH,UTCL,

+ UTCG,CGF,CGIN,RO(NSPM),RI(NSPM),

+ AT,LT,DHS,AH,LH,UTH l ,UTTl ,UTI‘O

COMMON A0,B0,GO,A1,B 1 ,G1 ,F,VH,VG,VL,VS,KP,H,D,KLA,GRADCS,

+ UTCH,UTCL,UTCG,CGIN,RO,RI,
+ AT,LT,DHS,AH,LH,UTH1 ,U'ITl ,UTTO

C>>>> Open files and gather initial, boundary and model parameters

14

OPEN (UNIT=69,STATUS='UNKNOWN‘,FILE='bound.data')
DO 14 I=1,NPDES
READ (69,*) A0(I),BO(I),G0(I)
READ (69,*) A1(I),B1(I),G1(I)
CONTINUE
CLOSE (UNIT =69)

OPEN (UNIT=69,STATUS='UNKNOWN',FILE='init.data')

READ (69,*) CTO,CHO,CGO,CLO,CSO
CLOSE (UNIT=69)

104

105

OPEN (UNIT=69,STATUS='UNKNOWN',FILE='param.data')
READ (69,86) TITLE
READ (69,86) DESCRIP
READ (69,*) TOL
READ (69,*) TFINAL,DELTI,NX
READ (69,*) F
READ (69,*) AT,LT
READ (69,*) AH,LH,DHS
READ (69,*) VG
READ (69,*) VL
READ (69,*) KP.H,D,KLA
CLOSE (UNIT =69)
86 FORMAT (A70)

OPEN (UNIT=69,STATUS='UNKNOWN‘,FILE='soil.data')
DO 17 I=1,NSPM
READ (69,*) RO(I),RI(I),VS(I)
17 CONTINUE
CLOSE (UNIT=69)

C>>>> Set initial conditions, spatial break points
DELX=(1.0-0.0)/(NX-1)

DO 13 I=1,NX
XBREAKO)=(DELX*I)—DELX
13 CONTINUE

DO 15 J=l,NX
Y(1,I)=CTO
Y(2,J)=CHO
Y(3,J)=CGO
Y(4,J)=CLO
Y(5,J)=CSO

15 CONTINUE

CGF=H*Y(4, 1)

CGIN=0.0
CL=CLO
DO 19 I=1,NSPM
GRADCS(I)=0.0
19 CONTINUE
HINTT=0.0
T=0.0
TEND=0.0
IDO=1

C>>>> Write header information to file
OPEN (UNIT=69, STATUS='UNKNOWN',FILE=TITLE)

WRITE (69,92) TEND,Y(2,NX)

106

C>>>> Set end-time, calc/print rel. error, calc gas film conc.,
C and write concentrations to file

16 CONTINUE
DELT=DELTI

IF (TEND.LT.6.0) THEN
DELT=0.1
END IF

IF (TEND.LT.0.5) THEN
DELT=0.01
END IF

TEND=TEND+DELT
PRINT 84,TEND

84 FORMAT('Time = ',F12.6)

CALL MOLCH (IDO.ECNUT,FCNBC,NPDES,T,TEND,NX,XBREAK,
+ TOL,HINIT,Y,LDY)

RERRSL=(ABS(Y(4,1)*KP-Y(5,NX)))/((Y(4,1)*KP+Y(5,NX))/2)* 100
RERRHG=(ABS(Y(2, l )-Y(3,l 1)))/((Y(2, l )+Y(3,1 1))/2)* 100
PRINT 91,RERRHG
PRINT 85,RERRSL
85 FORMAT('% Error in partition constraint = ', F106)
91 FORMAT('% Error in head space b.c. = ', F10.6)

CGF=H*Y(4,1)
CL=Y(4,1)
IF (IDO.EQ.3) GO TO 18

WRITE (69,92) TEND,Y(2,NX)
92 FORMAT (T1,E15.5,',',T18,E15.5)

87 FORMAT(17E15.7)

88 FORMAT(l)

89 FORMAT(T‘3,’Time',T18,'Head Space',T‘33,'Gas Phase',T48,
+‘Gas Film',T63,'Liquid Phase',T78,'Radius >>>',T89,11E15.5)

IF (TEND.GE.T'FINAL) IDO=3
GO TO 16
18 CONTINUE

CLOSE(UNIT=69)
END

107

C>>>> Subroutines required by IMSL MOLCH

SUBROUTINE FCNUT (NPDES,X,T,U,UX,UXX,UT)

INTEGER NPDES

REAL X,T,U(5),UX(5),UXX(5),UT(5),A(1),

+ A0(5),B0(5),GO(5),A](5),B1(5),G1(5)

REAL F,VH,VG,VL,VS(I),KP,H,D,KLA,GRADCS(1),UTCH,UTCL,

+ UTCG,CGIN,RO(1),RI(1),

+ AT,LT,DHS,AH,LH,UTH1,U'IT1,U’I'I‘0

COMMON A0,BO,G0,A1,B 1 ,G1 ,F,VH,VG,VL,VS,KP,H,D,KLA,GRADCS ,
+ UTCH,UTCL,UT‘CG,CGIN,RO,RI,

+ AT,LT,DHS,AH,LH,UTH1,UTI‘1,UTI‘0

C>>>> Tubing between flask and detector: PFR

C UT( 1)=-F/AT/LT*UX( 1 )
UT( 1)=0

C>>>> Head space of flask: PFR with dispersion
UT(2)=DHS/LH**2*UXX(2)-F/AHILH*UX(2)

C>>>> Gas phase (purging bubbles): well mixed
UT(3)=FNG*(CGIN-U(3))-KLA*(U(3)-H*U(4))*VUVG

C>>>> Total area for each soil particle model/classification

A(1)=3.0*D/VL*VS(l)/RO(1)/(RO(1)-RI(1))
C Add additional A's here as needed

C>>>> Liquid phase: well mixed
C Add additional terms to end of equation as needed for
C additional soil models

UT(4)=KLA*(U(3)—H*U(4))—A(l)*GRADCS(1)
C ( No soil case liquid equation )
C UT(4)=KLA*(U(3)-H*U(4))
C ( No soil case liquid equation )

C>>>> Soil phase

UT(5)=D/(RO(1)-RI(1 ))* *2*(UXX(5)+2/X*UX(5))
C UT(S)=0
C Add additional equations here for soil models as needed

UTCI;UT(4)
UTCG=UT(3)

11

C

108

IF (X .GE. (1 .0—0.1*DELX)) GRADCS(1)=UX(5)
IF (X .GE. (1 .0-0. 1 *DELX)) UTH1=UT(2)

Add additional gradient assignments as needed

RETURN
END

SUBROUTINE FCNBC (NPDES,X,T,ALPHA,BETA,GAMP)

INTEGER NPDES

REAL X,T,ALPHA(S),BETA(5),GAMP(5),

+ A0(5),B0(5),GO(5),A1(5),B1(5),G1(5),

+ F,VH,VG,VL,VS(1),KP,H,D,KLA,GRADCS(1),UTCH,UTCL,

+ UTCG,CGIN,RO(1),RI(1),

+ AT,LT,DHS,AH,LH,UTH1,UTI‘1,UTI‘0

COMMON A0,B0,GO,A1,B l,G1,F,VH,VG,VL,VS ,KP,H,D,KLA,GRADCS ,
+ UT‘CH,UTCL,UTCG,CGIN,RO,RI,

+ AT,LT,DHS,AH,LH,UTH1,UTT‘1,UTI10

IF (X .EQ. 0.0) THEN

DO 11 I=1,NPDES
ALPHA(I)=A0(I)
BETA(I)=B0(I)
GAMP(I)=G0(I)
CONTINUE
BETA(2)=-DHS/F*AH
GAMP(4)=UTCL
GAMP(3)=UTCG
GAMP(2)=UTCG
GAMP(I)=UTH1

ELSE

DO 12 I=1,NPDES
ALPHA(I)=A l (I)
BETA(I)=B 1(1)
GAMP(I)=G1(I)

12 CONTINUE

GAMP(5)=UTCL*KP
GAMP(4)=UTCL
GAMP(3)=UTCG

ENDIF

RETURN
END

109
BOUNDDATA (Boundary conditions input file)

0.0, 0.0, 0.0
0.0, 0.0, 0.0
1.0, 0.0, 0.0
0.0, 1.0, 0.0
1.0, 0.0, 0.0
1.0, 0.0, 0.0
1.0, 0.0, 0.0
1.0, 0.0, 0.0
0.0, 1.0, 0.0
1.0, 0.0, 0.0

APPENDIX D

For: No Soil

Henry's Law Coeff. (mass/vol):
Partition Coeff. (mass/mass):

Gas Volume (mL): 112
Liquid Volume (mL): 210
Soil Mass (g): 0
Solute Mass (g): 8.40E-04

Liquid Cone. (g/mL): 3.316E-06

Gas Cone. (g/mL): 1.283E-06
Soil Cone. (g/g): 0.00E-1-00
F or: Colwood (53-125 micron)

Henry's Law Coeff. (mass/VOL):
Partition Coeff. (mass/mass):

Gas Volume (mL): 112
Liquid Volume (mL): 210
Soil Mass (g): 1
Solute Mass (g): 8.4OE-04

Liquid Cone. (g/mL): 2.274E-06
Gas Cone. (g/mL): 8.802E-07

Soil Cone. (g/g): 2.64E-04

0.387

0.387

116

111

For: Colwood A (whole)

Henry's Law Coeff. (mass/vol):
Partition Coeff. (mass/mass):

Gas Volume (mL): 112
Liquid Volume (mL): 210
Soil Mass (g): 1

Solute Mass (g): 8.40E-04

Liquid Cone. (g/mL): 3.089E-06
Gas Cone. (g/mL): 1.195E-06

Soil Cone. (g/g): 5.75E-05

For: Colwood (250-500 micron)

Henry's Law Coeff. (mass/VOL):
Partition Coeff. (mass/mass):

Gas Volume (mL): 1 12
Liquid Volume (mL): 210
Soil Mass (g): 1
Solute Mass (g): 8.40E-04

Liquid Cone. (g/mL): 2.103E-06
Gas Cane. (g/mL): 8.14E-07

Soil Cone. (g/g): 3.07E-04

0.387
18.6

0.387

146

LIST OF REFERENCES

LIST OF REFERENCES

Abed, Y., Gabas, N., Delia, ML. and Bounahmidi, T. (1992) Measurement of liquid-solid
phase equilibrium in ternary systems of water-sucrose-glucose and water-sucrose-
fructose, and predictions with UNIFAC, Fluid Phase Equilibria 73, 175.

Aiken, G.R. et al.. (1985) Humic Substances in Soil Sediment and Water, Wiley, New
York.

Atkinson, B. and Mavituna, F. (1983) Biochemical Engineering and Biotechnology
Handbook, Macmillan Publishers Ltd, England.

Ball, W.P. and Roberts, P.V. ( 1991a) Long-term sorption of halogenated organic
chemicals by aquifer material. 1. Equilibrium, Environ. Sci. Technol. 25, 1223.

Ball, W.P. and Roberts, P.V. ( 1991b) Long-term sorption of halogenated organic
chemicals by aquifer material. 2. Intraparticle diffusion, Environ. Sci. Technol. 25,
1237.

Benzing, TR. (1993) The desorption kinetics of nonionic organic compounds from
hexadecylttimethylammonium-modifled soils and clays, Ph.D. Dissertation, Michigan
State University, East Lansing.

Biltz, W. (1934) Rauchemie der festen stoffe, 1. Voss, Leipzig.

Bondi, A. (1968) Physical Properties of Molecular Crystals, Liquids and Glasses, Wiley,
New York.

Briggs, G.G. (1981) J. Agric. Food Chem. 29, 1050.

Brusseau, ML. and Rao, P.S.C. (1989) Sorption nonideality during organic contaminant
transport in porous media, Critical Rev. in Environ. Control 19, 33.

Brusseau, ML. and Rao, P.S.C. (1991) Influence of sorbate structure on nonequilibrium
sorption of organic compounds, Environ. Sci. Technol. 25, 1501.

Brusseau, M.L., Jessup, RE. and Rao, P.S.C. (1990) Sorption kinetics of organic
chemicals: evaluation of gas-purge and miscible-displacement techniques, Environ.
Sci. Technol. 24, 727.

112

113

Brusseau, M.L., Jessup, RE. and Rao, P.S.C. (1991) Nonequilibrium sorption of organic
chemicals: elucidation of rate-limiting processes, Environ. Sci. Technol. 25, 134.

Chiou, CT. (1990) Roles of organic matter, minerals, and moisture in sorption of
nonionic compounds and pesticides by soil, Humic Substances in Soil and Crop
Sciences; Selected Readings, American Society of Agronomy and Soil Science Society
of America.

Chiou, CT. and Shoup, TD. (1985) Soil sorption of organic vapors and effects of
humidity on sorptive mechanism and capacity, Environ. Sci. Technol. 19, 1196.

Chiou, C.T., Peters, L]. and Freed, V.H. (1979) A physical concept of soil-water
equilibria for nonionic organic compounds, Science 206, 831.

Chiou, C.T., Porter, RE. and Schmedding, D.W. (1983) Partition equilibria of nonionic
compounds between soil organic matter and water, Environ. Sci. Technol. 17, 227.

Cooling, M.R., Khalfaoui, B. and Newsham, D.M.T. (1992) Phase equilibria in very dilute
mixtures of water and unsaturated chlorinated hydrocarbons and of water and
benzene, Fluid Phase Equilibria 81, 217.

Crittenden, J .C., Hutzler, NJ. and Geyer, DC. (1986) Transport of organic compounds
with saturated groundwater flow: Model development and parameter sensitivity,
Water Resources Research 22, 271.

Danckwerts, P.V. (1953) Continuous flow systems: distribution of residence times, Chem.
Eng. Sci. 2, l.

Doong, SJ. and Ho, W.S.W. (1991) Sorption of organic vapors in polyethylene, Ind. Eng.
Chem. Res. 30, 1351.

Elbro, H.S., Fredenslund, Aa. and Rasmussen, P. (1990) A new simple equation for the
prediction of solvent activities in polymer solutions, Macromolecules 23, 4707.

Fogler, HS. (1986) Elements of Chemical Reaction Engineering, Prentice-Hall,
Englewood Cliffs, New Jersey.

Fredenslund, Aa., Gmehling, J. and Rasmussen, P. (1977) Vapor-liquid equilibria using
UNIFAC, Elsevier Scientific, New York.

Gmehling, J., Li, J. and Schiller, M. (1993) A modified unifac model. 2. Present
parameter matrix and results for different thermodynamic properties, Ind. Eng. Chem.
Res. 32, 178.

Gmehling, J ., Rasmussen, P. and Fredenslund, Aa. (1982) Vapor-liquid equilibria by
UNIFAC group contribution. Revision and extension. 2, Ind. Eng. Chem. Process
Des. Dev. 21, 118.

 

114

Hansen, H..,K Rasmussen, P., Fredenslund, Aa., Schiller, M. and Gmehling, J. (1991)
Vapor-liquid equilibria by UNIFAC group contribution. 5. Revision and extension,
Ind. Eng. Chem. Res. 30, 2352.

Hooper, H.H., Michel, S. and Prausnitz, J.M. (1988) Correlation of liquid-liquid equilibria
for some water-organic liquid systems in the region 20-250 degrees celsius, Ind. Eng.
Chem. Res. 27, 2182.

Hwang, Y., Keller, G.E. H and Olson, J.D. (1992a) Steam stripping for removal of
organic pollutants from water. 1. Shipping effectiveness and stripper design, Ind.
Eng. Chem. Res. 31, 1753.

Hwang, Y., Keller, G.E. H and Olson, J.D. (1992b) Steam stripping for removal of
organic pollutants from water. 2. Vapor-Liquid Equilibrium Data, Ind. Eng. Chem.
Res. 31, 1759.

Iwai, Y. and Arai, Y. (1989) Measurement and prediction of solubilities of hydrocarbon
vapors in molten polymers, J. Chem. Eng. Japan 22, 155.

Karickhoff, S.W., Brown, BS. and Scott, T.A. (1979) Sorption of hydrophobic pollutants
on natural sediments, Water Research 13, 241.

Kontogeorgis, G.M., Fredenslund, A. and Tassios, DP. (1993) Simple activity coefficient
model for the prediction of solvent activities in polymer solutions, Ind. Eng. Chem.
Res. 32, 362.

Kyle, BC. (1981) Soil-water equilibria for nonionic organic compounds, Science 213,
683.

Lambert, SM. (1968) Omega, a useful index of sorption equilibria, J. Agr. Food Chem.
16, 340.

Lambert, S.M., Porter, RE. and Schieferstein, RH. (1965) Movement and sorption of
chemicals applied to the soil, Weeds 13, 185.

Larsen, B.L., Rasmussen, P. and Fredenslund, Aa. (1987) A modified UNIFAC group-
contribution model for prediction of phase equilibria and heats of mixing, Ind. Eng.
Chem. Res. 26, 2274.

Macedo, E.A., Weidlich, U., Gmehling, J. and Rasmussen, P. (1983) Vapor-liquid
equilibria by UNIFAC group contribution. Revision and extension. 3, Ind. Eng.
Chem. Process Des. Dev. 22, 676.

Magnussen, T., Rasmussen, P. and Fredenslund, Aa. (1981) UNIFAC parameter table for
prediction of liquid-liquid equilibria, Ind. Eng. Chem. Process Des. Dev. 20, 331.

115

Miller, CT. and Weber, W.J. Jr. (1986) Sorption of hydrophobic organic pollutants in
saturated soil systems, Journal of Contaminant Hydrology 1, 243.

Oishi, T. and Prausnitz, J .M. (1978) Estimation of solvent activities in polymer solutions
using a group-contribution method, Ind. Eng. Chem. Process Des. Dev. 17, 333.

Peel, R.G., Benedek, A. and Crowe, CM. (1981) A branched pore kinetic model for
activated carbon adsorption, AIChE Journal 27 , 26.

Robinson, K.G., Farmer, W.S. and Novak, LT. (1990) Availability of sorbed toluene in
soils for biodegradation by acclimated bacteria, Wat. Res. 24, 345.

Ruckenstein, E., Vaidyanathan, AS. and Youngquist, GR. (1971) Sorption by solids with
bidisperse pore structures, Chemical Engineering Science 26, 1305.

Rutherford, D.W., Chiou, CT. and Kile, DE. (1992) Influence of soil organic matter
composition on the partition of organic compounds, Environ. Sci. Technol. 26, 336.

Sandler, SJ. (1989) Chemical and Engineering Thermodynamics, John Wiley and Sons,
New York.

Steinberg, S.M., Pignatello, 1.1. and Sawhney, BL. (1987) Persistence of 1,2-
dibromoethane in soils: Entrapment in intraparticle micropores. Environ. Sci. Technol.
21, 1201.

Stevenson, E]. (1982) Humus Chemistry: Genesis, Composition, Reactions, Wiley, New
York.

Tiegs, D., Gmehling, J ., Rasmussen, P. and Fredenslund, Aa. (1987) Vapor-liquid
equilibria by UNIFAC group contribution. 4. Revision and extension, Ind. Eng.
Chem. Res. 26, 159.

Wakao, N. and Smith, J .M. (1962) Diffusion in catalyst pellets, Chemical Engineering
Science 17, 825.

Weber, W.J. Jr., Voice, T.C., Pirbazari, M., Hunt, GE. and Ulanoff, D.M. (1983)
Sorption of hydrophobic compounds by sediments, soils and suspended solids - II,
Wat. Res. 10, 1443.

Wu, Shian-chee and Gschwend, RM. (1986) Sorption kinetics of hydrophobic organic
compounds to natural sediments and soils, Environ. Sci. T echnol. 20, 717.