AIR STRIPPING WlTH ELECTROMAGNETIC-VIBRATION … · The electrornagnetic-vibration-enhanced air...

AIR STRIPPING WlTH ELECTROMAGNETIC-VIBRATION

ENHANCEMENT FOR CLEANING UP SOlLS CONTAMINATED BY

PETROLEUM PRODUCTS

A Thesis Submitted to the Faculty of Graduate Studies and Research

in Partial Fulfillment of the Requirements for the Degree of

Master of Applied Science in Environmental Systems Engineering

University of Regina

by Linsen Zhang

Regina, Saskatchewan September 2000

@ Copyright 2000: L.S. Zhang

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ABSTRACT

Soils in many petroleurn-related sites have been contaminated with petroleum

products due to various activities in energy industries. In order to help solve this problem,

a number of innovative in-situ remediation methods have been developed to reduce the

volume and toxicity of petroleum-based wastes. However, difficulties exist in sites with

impermeable soils, where the efficiencies of many technologies are significantly reduced.

In this study, electrornagnetic-vibration-enhanced air stripping method was proposed, and

many related factors, such as soi1 type and air injection pressure were systematically

investigated through bench-scde expenments.

A z4 full factorial design was implemented for the expenments that involved four

factors (NO levels for each). The main factors that had significant effects on the toluene

removal were identified. A response surface mode1 was then fomulated based on the

factorial analysis results, reflecting interrelationships between the system conditions and

the toluene adsorption rate.

Continuous air injection experiments were also conducted to examine the fate of

benzene, toluene. ethylbenzene and (m+p)-xylene (BTEX) in different enhancement tests.

The effects of soi1 type, soi1 moisture, contaminant type and adsorption duration were

investigated.

The electrornagnetic-vibration-enhanced air stripping experhents were conducted

to compare efficiencies of different enhancement tests in improving toluene removal. The

effects of soil type, contaminant concentration and au injection pressure on toluene

recovery were studied. The results include that

1. The electromagnetic-vibration-enhanced air stripping technology could

significantly increase toluene removal efficiency in a system of fine sand with

different clay contents (40%, 50%, 60% and 70%).

2. The clay content was negatively correlated with toluene recovery under limited air

injection pressure. The higher the clay content, the lower the toluene removal

efficiency.

3. The air injection pressures within a limited range was positively correlated with

the toluene removal effciency. However, when the air-injection pressure

exceeded soil overburden, fiacture phenomenon may occur, resulting in reduced

removal efficiencies.

4. The effect of adsorption at different gasoline concentrations on the removal

efficiency was insignificant.

5. The effect of BTEX adsorption duration on their removal efficiency within a

certain range was also insignificant.

ACKNOWLEDGEMENTS

First of all, 1 would like to express rny deep gratitude to Dr. Gordon Huang and Dr.

Amitabha Chakrna, my supervisors, for their excellent supervision, inestimable guidance

and kind encouragement, which contributed to the successful completion of this thesis. The

knowledge, working philosophy and methods I learned from them would be an invaluable

asset for my future career.

I would like to thank my committee members Dr. M. Dong, Peter, Gu and Stephen K.

O'leavy for their technical assistance. My appreciation also goes to the following people

who helped me make this thesis successful. Dr. Paitoon Tontiwachwuthikul provided me

space For setting up my expenmentd apparatus. Dr. Xiang Rong gave me constructive

comments and insightful advises. 1 would also like to thank Mr. Yunfeng Hu, Mr. Amr

Henni Mr. Jason Dunbar and Mr. Ryan Sinfield for their technical assistance.

1 am also grateful to the Faculty of Graduate Studies and the Faculty of Engineering

at the University of Regina for providing scholanhips during my graduate study.

Finally, I would like to express my special thanks to my wife, Caizhen Hu, and my

son, Qiao Zhang, for their love, support, understanding and emotional encouragement

throughout the coune of my graduate study.

iii

CONTENTS

ABSTRACT

ACKNOWLEDGEMENTS

LIST OF FIGURES

LIST OF TABLES

CHAPTER 1 INTRODUCTION

1.1 Background

1.2 S tatement of Problems

1 .3 Objectives

CHAPTER 2 LITERATURE REVIEW

2.1 The Role of Air in Soil Remediation

2.1.1 Soi1 Vapor Extraction

2.1.2 Air Sparging

2.1.3 Bioventing

2.2 Enhanced Soil Remediation Technologies

2.2.1 HydrauliJPneumatic Technologies

2.2.2 Thermal Enhancement

i

iii

viii

xi

2.3 Parameter Affecting Volatilization

2.3.1 Soil Texture

2.3.2 Soil Moisture

2.3.3 Air Injection PressurJFlow Rate

2.3.4 Vapor Pressure and Boiling Point

2.4 Electrornagnetic Vibration and Application

2.4.1 Electromagnetic Vibration Phenornena

2.4.2 Application of Electromagnetic Vibration

2.5 Analytical Methods

2.6 Methods for Experimental Design and Result Analysis

2.7 Literature Review Summary

CHAPTER 3 MATERIAL AND METHOD

3. t Matenai and Instnunent

3.1.1 Material Preparation

3.1.2 Instrument

3.2 Experimental Apparatus

3.2.1 Electromagnetic Vibration Generator

3.2.2 Experimental Set-up for Continuous Air Injection

3.2.3 Experimental Set-up for Electromagnetic

Vibration Enhanced Air Stripping

3.3 Experimental Method

3.3.1 Continuous Air ln, ection

3.3.2 Electromagnetic-Vibration-Enhanced Air Stripping

3.4 Sarnpling Methods

3.4.1 Continuous Air injection

3.4.2 Electromagnetic Vibration Enhanced Air SQ$ping


3.5.1 Toluene Vapor Analysis

3.5.2 Analysis of Toluene in SoiIs

3.5.3 Data Analysis

CHAPTER 4 STATISTICAL ANALYSIS

4.1 Induction

4 . 2 Randomization

4 . 3 Variables

4.4 Response Surface Mode1

CHAPTER 5 RESULT AND DISSCUSSION

5.1 Testing Conditions

5.2 Testing Results

5.3 Statistical Analysis

5.3. L R d t Interpretation

5.3.2 Sensitivity Analysis

5.3.3 Analysis of Variance

5.3.3.1 Process Development Data

5.3.3 -2 Diagnostic Exaxnination

5.4 Surnrnary

5.5 Continuous Air Injection System for BTEX Removal

5.5.1 Effect of Water Content on BTEX Removal

5 S.2 Effect of Clay Content on BTEX Removal

5.5.3 Effect of Adsorption Time on BTEX Removal

5.6 Effect of Soi1 Type on Toluene Removal

5.7 Effect of Air Injection Pressure on Toluene Removal

5.8 Effect of Electrornagnetic Vibration

CHAPTER 6 CONCLUSIONS

6.1 Summary

6.2 Research Achievements

6.3 Recommendations for Future Research

REFERENCES

vii

LIST OF FIGURES

Figure 3.1 Size Distribution of Sand Particles

Figure 3.2 Schematic of Experiment Set-up for Continuous Air injection Tests

Figure 3.3 Schematic of Experiment Set-up for Electromagnetic-Vibration

-Enhanced Air Stripping Tests

Figure 3.4 Standard Calibration Cuve for Toluene Vapor Analysis

Figure 3.5 Standard Calibration Curve for Toluene Analysis in Soils

Figure 5.1 Toluene Adsorption Rates Under Various Experimental Conditions

Figure 5.2 Cumulative Toluene Removal efficiency vs. Time (Run 1 )

Figure 5.3 Cumulative Toluene Removal eficiency vs. Time (Run 2)

Figure 5.4 Cumulative Toluene Removal eficiency vs. Time (Run 3)

Figure 5.5 Cumulative Toluene Removal efficiency vs. Time (Run 4)

Figure 5.6 Cumulative Toluene Removal efficiency vs. Time (Run 5 )



Figure 5.9 Cumulative Toluene Removal efficiency vs. T h e (Run 8)


Figure 5.1 1 Cumulative Toluene Removal efficiency vs. Time (Run 10)

Figure 5.1 2 Cumulative Toluene Removal effciency vs. Time (Run I 1 )

Figure 5.13 Cumulative Toluene Removai efficiency vs. Time (Ru. 12)

viii

Figure 5.14 Cumulative To luene Removal efficiency vs. Time (Ru. 1 3)

Figure 5.15 Cumulative Toluene Removal efficiency vs. T h e (Run 14)

Figure 5.16 Cumulative Toluene Removai efficiency vs. Time (Run 1 5)


Figure 5.1 8 Detailed Examination for Effects of x2, x3 and x4

Figure 5.19 Normal Plot of Process Effects on Probability Paper

Figure 5.20 Normal Plot of Residual Effects on Probability Paper

Figure 5.21 Benzene Removal Efficiency vs. Air Injection Duration

for Different Soil Water Contents (Run 17)

Figure 5.22 Toluene Removal Efficiency vs. Air Injection Duration


Figure 5.23 Ethylbenzene Removal Efficiency vs. Air Injection Duration


Figure 5.24 (m+p)Xylene Removal Efficiency vs. Air Injection Duration



for Di fferent Clay Contents (Run 2 1)


for Different Clay Contents (Run 22)

Figure 5.27 Ethylbenzene Removal Efficiency vs. Air injection Duration

for Different Clay Contents (Run 23)

ix

Figure 5.28 (rn+p)Xylene Removal Eficiency vs. Air injection Duration

for Different Water Contents (Run 24)


for Different Adsorption Durations (Ru 25)


for Different Adsorption Durations ('un 26)

Figure 5.3 1 Ethylbenzene Removal Efficiency vs. Air injection Duration

for Different Adsorption Durations (Run 27)

Figure 5.32 (m+p)Xylene Removai Efficiency vs. Air Injection Duration

for Different Adsorption Duration (Run 28)

Figure 5.33 Cumulative Toluene Removal efficiency vs. Air Injection Duration for

Different Clay Contents Without Electrornagnetic Vibration 109


Different Clay Contents With Electromagnetic Vibration 1 IO


Different Air Injection Pressures Contents Without Electromagnetic Vibration 1 13

Figure 5.36 Cumulative Toluene Removd eficiency vs. Air Injection Duration for

Different Air Injection Pressures With Electromagnetic Vibration 114

Figure 5.37 Toluene Removal emciency vs. Soi1 Clay Content I l 7

Figure 5.38 E ffect of EIectrornagnetic Vibration Duration on Cumulative

Toluene Removal efficiency

X

LIST OF TABLES

Table 3.1 BTEX Concentrations in Gasoline

Table 4.1 Full Factorial Design of Four Variables at Two Levels

Table 4.1 Design Variables

Table 5.1 Experimental Conditions for the Factond Design

Table 5.2 Analysis of Toluene Adsorption Rate

Tab te 5.3 Main Effects and Interactions

Table 5.4 Sensitivity Andysis

Table 5.5 Result of Robability Analysis

Table 5.6 Values of y, 9 , and y- j for Factorial Design

Table.5 7 Physical and Chernical Properties of BTEX

Table 5.8 Experimental Conditions for the Different Expenment Runs

Chapter L

Introduction

1.1 Background

Soi1 and groundwater contamination at sites of landfill operation, agricultural

practice, and various activities in chemical and energy industries is acquiring more and

more attention by the public and govemments. The contamination can lead to a variety

of impacts on and nsks to the communities and the polluten themselves.

The public has recognized the need to greatly reduce the volume and toxicity of

contaminants and to develop safe, effective and economic alternatives for their

disposa1 (Nicholas, 1987). A number of remediation technologies have been developed

to cleanup soils contaminated by petroleum hydrocarbons, as s h o w in a survey of 169

rernedial actions (Neely et al., 198 1). However, those techniques have showed limited

success when they were used to sites with complex soi1 and groundwater conditions

(Brown et al., 1986). Traditional remediation efforts at contaminated sites were

partiaily effective 54% of the time and completely successful only 16% of the time

(NeeIy et al., 198 1 ; Lee and Ward, 1985). Most of these treatrnent schemes were not

completely effective and did not oKer permanent solutions for containment or

remediation. Some methods might even create additional uncontrolled hazards.

Therefore, more efficient and economicai technologies are desued for improving the

remediation performance (Catallo and Portier, 1992).

1.2 Statement of Problems

Numerous sites exist where soil has been contaminated with petroleurn products

due to spills or leaking underground storage tanks. In order to help solve this problem,

a number of in-sihi remediation methods have been developed. One such method,

known as in-situ air stripping, injects air into subsurface soil below the lowest known

point of contamination. As clean air replaces the contaminant-saturated vapor that is

removed, the contaminant rmaining as a residuai liquid and dissolved in pore water

ail1 be volatilized in the k h air, seeking to re-establish equilibrium. Due to

vaporkation of contaminants, the vapor of mixed air and contaminants will begin to

nse through the soi1 matrix and migrate toward the surface.

Several laboratory experiments involving convective transport due to air flow

inside soil pores have been reported since 1980. Marley and Hoag (1984) used soi1

columns to measure the removai rate of gasoline from contaminated soil. They

reported higher than 99% rernoval o f gasoline initially present in a reasonably short

time. Rainwater et al. (1988) reported large-size soil column expenments to study the

volatilization mechanism in porous media and provided removal rate data of

hydrocarbon mixtures with preliminary modeling effort. They concluded that the

presence of the residual water in porous media significantly retarded the diffision of

the hydrocarbon vapor and slowed the removal process. Most of previous studies have

focused on movement and distribution of air through the aquifer in laboratory mode1

and in the field (Ji et al.. 1993, Johnson et al., 1999). Laboratory studies have been

performed only to investigate air Bow patterns and to evaluate the removal rates of

contaminants in both grave1 and sand (Semmer et al., 1996; Semmer and Reddy,

1998). However, the effects of system variables particularly the soil type, the regime of

air injection on the removal efficiency have not been systematicaily investigated.

Most of the previous enhanced-remediation methods were carried out by

pneumatic fracturing and thermal enhancement (EPA, 1994, Brown et al., 1986, Chao

and Ong, 1995, Ehlers et al., 1994, Johnson, 1998). With varied system pressure and

temperature, many subsuface media conditions will be changed. However, their

efficiencies are not satisfactory £tom both environmental and economic points of view.

More effective technologies are thus desired.

Electromagnetic vibration was used for desorption of CO and NO from metal

(Pt) surface (Schott and Racz, 2000). The technique cm help to prornote mass transfer

in reaction systems. However, there has been no application of this method to the field

of soil remediation.

Soi1 composition and contaminant distribution in many contaminated sites could

be complicated with various contents of sand and clay. The clay content is a critical

factor related to soil permeability, and hence contaminant removal. The traditional

enhancement techniques might not be directiy suitable for such complicated sites.

Consequently, development electromagnetic-vibration-enhanced air stripping

technology for enhanced soil remediation would be of value for bringing improved

environmental and economic efficiencies.

13 Objectives

The objectives of this research are:

1. To develop an electrornagnetic-vibration-enhanced air stripping technology

for cleaning up petroleum-contaminated sites where soil conditions are complex and

disadvantageous (e.g. impermeable).

2. To build up several scaled experimental systems for evaluating the

effectiveness of the proposed technology under various experimental conditions.

3. To develop a multivariate analysis approach for studying complicated

interrelationships among various factors that affect the removal efficiency.

Chapter 2

Literature Review

The problem related to contamination of groundwater and soil From leaking

underground storage tanks is recognized as a major environmental concem in the

world. Most of the leaked petroleum products will remain trapped in the groundwater

and soil, posing major threats to underground aquifers that provide drinking water for

the surrounding comrnunities. Consequently, a number of techniques for soil

remediation at petroleum exploration, production and processing sites have been

developed (Huang et al., 1999).

2.1 The Role of Air in Soi1 Remediation

Based on the technologies that have been developed and used in conventional

water and wastewater treatment and in rnining, oil, gas, and chemical process

industries, a number of processes and systems have been developed for in situ clean-up

of soil contarninated with petroleum hydrocarbons and other organic solvents.

These methods use physical, biological, themal, and chemical processes to

extract, degrade, detoxify and immobilize contaminants. Among thern, air plays an

important role in sorne soil remediation pmcesses.

2.1.1 Soi1 Vapor Extraction

One of the most popular physical methods using for soil remediation is soil

vapor extraction (SVE) for remediating volatile hydrocarbon contamination fkom the

soils. Air flow is induced through contaminated soil by applying a vacuum to vapor

extraction vents and creating a pressure gradient in the soil. As the soil vapor migrates

through the soi1 pores toward the extraction vents, VOCs are volatilized and

transported out of subsurface soil.

Several laboratory expenments involving convective transport due to air flow

inside soil pores have been reported since 1980. Before that time, research involving

soil gas movements was restncted to diffbsion transport. Laboratory research is often

perfomed with mal1 soil colurnns. Marley and Hoag (1984) and Baehr et al. (1989)

used soil columns to measure the removal rate of gasoline from contaminated soil.

They reported expenments on the removal rate of partially saturated gasoline in the

capillary fiinge above the water table by steady air flow. More than 99% removal of

gasoline initially present was observed in a reasonably short time. Aware, Inc. (1987)

conducted soil column experiments to evaluate the SVE process with various

contaminant conditions and soil types. They reported removal rates of 40 to 90% of the

initial amount of VOCs applied in less than 8 days of operation in the temperature

controlled environment. They concluded that there is the possibility of success in

contaminated soil cleaning with SVE process. Rainwater et al. (1988 a, b) reported

large-shed soil column experiments to study the volatilization rnechanism in porous

media and provided removal rate data of hydrocarbon mixtures with a preliminary

modeling effort. They concluded that the presence of the residual water in porous

media significantly retarded the diffision of the hydrocarbon vapor and slowed the

removal process. Brown et al. (1986; 1991) and Johnson et al. (1982) described a

closed monitored field scale expenment. Each of these studies provided evidence that

soil vapor extraction methods are effective for control of higher vapor pressure

components from contaminant residual saturation.

The performance of SVE system, based on the mass removal rate, the tirne

required to achieve cleanup goals, and the cost of cleanup have also been studied

(Ghuman, 1995). These performance parameten depend on physical and chemical

factors, such as the rate and pattern of air flow through the affected soil characteristic,

contaminant type and properties, and the degree of partitioning among the vapor,

liquid, dissolved, and adsorbed phases.

in examining the role of mass exchange between vapor and water phases, both

one-dimensional diffision and one-dimensional advection experhents were

conducted. Johnson et al. (1993) stated that the injected air always travels in small,

continuous and discrete air channels. Pankow et al. (1993) reported that in moderately

permeable soils (e-g., sandy soil) and at low air injection rates, stable channels of air

are formed. However, SVE effect is dependent on the pmperties of both the

contaminants and the soil (Boulding, 1995).

VOCs, such as BTEX can be removed fiom unsaturated (vadose zone) soils by

use of SVE (Ram, et al., 1993). High concentrations of garoline vapors become mobile

when air is injected into soil (Downey et al., 1995). in the remediation of gasoline-

contaminated soils, total effluent hydrocarbon concentration for an SVE system ranged

up to 1121 ppmv. corresponding to a hydrocarbon removal rate of about 1.68 Ib/h

(Felten et al., 1992). SVE alone could provide excellent removal of VOCs adsorbed to

unsatwated soils.

A DPE system was successfbl in remediating hydrocarbon-impacted clays and

contaminated groundwater at a former service station in northem California

(Dockstader, 1994). AAer 76 days of operation, the soil total petroleum hydrocarbon

(TPH) concentrations went from 2500 ppm to 100 ppm.

The most important property affecting removal rate of contaminant for DPE is

volatility. Hydrocarbon compounds with high volatility are more likely to be removed

by DPE than those with low volatility. The contaminants to be removed by DPE must

have relatively low water solubility and must be above the water table or, in the case of

light non-aqueous phase liquids (LNAPLs), floating on it and the soil moisture content

must be quite low (Wilson, 1995).

2.1.2 Air Sparging

Air sparging, also known as in situ air stripping and in situ volatilization. is a

process in which air is injected into the saturated zone below or within the areas of

contamination through a system of wells. As the injected air rises through the

formation, it may volatilize and biodegradation adsorbed VOCs in soils. Air sparging

is an innovative treatment technology that expands the remediation capabilities of SVE

to the saturated zone.

Most recently, system effects on VOCs removal fkom saturated soils and

groundwater using air sparging were presented (Reddy and Adams, 1997). Semrner

and Reddy (1998) investigated air flow patterns and the removal rates of toluene in

tests using fine grave1 and medium sand as representative soils in the laboratory. In

addition, Adams and Reddy (1997) studied the effect of grain size and distribution on

the removal of benzene using in-situ air sparging.

Rutherford and Johnson (1996) performed a laboratory study to determine how

process control changes affect oxygenation rates during air sparging. Elder and Benson

(1999) analyzed air channel formation, size, spacing, and tortuosity during air

sparging. It was found that no appreciable benefit was achieved through the use of

pulsed air injection when compared to continuous air injection.

Effect of flow rate changes and pulsing on the treaûnent of source zones by in

situ air sparging was studied and it was indicated that pulsing the air can improve the

long-term cumulative removal efficiency (Johnson et al.. 1999). Laboratory

expenments were conducted to observe flow patterns as a fùnction of porous media

size and air flow rate (Brooks and Mcginty, 1987) and to investigate the behavior of

dense non-aqueous phase Iiquids (DNAPLs) during air sparging (Adams and Reddy,

1997). The primary rnechanisrns for removing mass during air sparging are

volatilization and biodegradation (Hinchee, 1994).

Volatilkation is the dominant removal mechanisrn during the early stages of

sparging, and is dnven by a gradient in Gibbs fkee energy that develops between the

aqueous and gaseous phase contaminants (Weber, 1972). Biodegradation can be a

signi ficant m a s tram formation process during the later stages of sparging, but

removes much less mass than volatilization (Boenma et al., 1995).

Air sparging stimulates aerobic biodegradation by increasing the dissolved

oxygen concentration of the soil and groundwater (Johnson et al., 1993). For the

pulsed tests, Baker (1996) rneasured temporal changes in air saturation and water table

elevation. Baker found that pulsing did not change the location of air channels or the

size of the air plume. He concluded that air remains in the formation following shut

down, and that air-filled pores become preferential flow paths once sparging is

resumed. Thus, pulsing can cause mixing within the plume, but not necessarily re-

distribution of air.

Column tests to determine mass transfer rate during air sparging were conducted

by Semmer et al. (1996) and Chao and Ong (1995). The results indicated that greater

m a s removal was obtained in the coarse sand at low rates, mass removal increased

with injection rate for soil, and contaminants with higher Henry's law constant were

removed at faster rates.

However, applicability of air sparging is limited to cases involving low

groundwater table and loose sand formation. It is not recornrnended for low hydraulic

conductivity soils where it is difficult to monitor or control treatment progress and

completeness.

2.1 -3 Bioventing

Soi1 venting rnay be either passive (with no energy input) or active (Lyman et al.,

1990). Passive venting consists of perforated pipes sunk into the contaminated area and

active venting uses vacuum to the subsurface to volatilize and remove contaminant.

Contaminants exist in the vapor, liquid, and/or dissolved phase in the unsaturated zone

(Lyman et al., 1990). Contaminants in the vapor phase or volatilized contaminants may

be removed by this method.

Bioventing is an in situ remediation technology that uses indigenous

microorganisms to biodegrade organic constituents adsorbed to soils in the unsaturated

zone. Soils in the capillary ninge and the saturated zone are not affected. In bioventing,

the activities of the indigenous bactena are enhanced by introducing air flow into the

unsaturated zone (using extraction or injection wells) and, if necessary. by adding

nutrients.

When extraction wells are used for bioventing, the process is similar to soi1 vapor

extraction (SVE). However, while SVE removes constituents primady through

volatilization, bioventing systems promote biodegradation of constituents and minimize

volatilization (generally by using lower air 8ow rates than those for SVE). In practice,

some degree of volatilization and biodegradation occurs when either SVE or bioventing

is used.

In contrast to SVE, bioventing is not constrained by contaminant volatility and is

therefore applicable to contaminants with moderate to low volatility (Hinchee, 1994).

Moreover, bioventing is also weil suited for conditions where SVE's application rnay

lead to lengthy processes with low removal efficiencies. Cost analyses suggest that

bioventing can be more cost-effective than SVE, since treatrnent of the off-gas is

required for SVE (Crocetti et al., 1993; Reisinger et al., 1994).

By managing air flow rate, it should be possible to increase the rate of

degradation to 85% (Miller, 1990; Miller et al., 1991). Bioventing is becoming

increasingly popular in soil remediation for removing VOCs and semi-volatile organic

compounds (SVOCs) fiom the vadose zone (Crocetti et al., 1993). while supplying

oxygen to the soil to increase the bioremediation process. However, like SVET it is

ineffective when the porosity and transmissivity are low, such as in silt and clay soils

(Burke and Rhodes. 1995). There may be little capital investment initially for this

procedure, but it cm be expensive to monitor the site for regulatory compliance.

2.2 Enhanced Soi1 Remediation Technologies

Enhancement technologies should be considered when contarninants or soil

characteristics limit the effectiveness of soi1 remediation operation, or when

contarninants are present in saturated soil. By creating Fractures in the subsurface, it is

possible to enhance permeability to improve the flow of carrier fluids for contaminant

removal or deliver nutrients or reactive agents (Kidd, 1996). The following section will

describe some soil remediation enhancement methods.

2.2.1 HydrauliJPneumatic Fracturing Enhancement

There are two types of fracturing: hydraulic (water-based) and pneumatic

fracturing (air-based). The main difference is in the penetrating fluids. in soil

remediation, The pneumatic hcturing is more popularly applied due to its low

operation cost and no chernicd contaminants.

Pneurnatic hcturing involves injecting air into low pemeability soils to create

fracture, and thus increasing the permeability of the soil. A pilot study of the integrated

pneumatic hchuing systern demonstrated enhanced removal of BTEX fiom a

gasoline-contaminated, low pemeability soil formation. Fractunng improved

subsurface permeability by over 3 6 times and established extra charnels (Venkatrarnan

et ai., 1995).

Pneumatic flacturing test was perfonned at Air Forced Base, Oklahoma City,

resulted in significantly hproved formation permeability by enhancing secondary

permeability and promoting removal of excess soi1 moisture fiom the unsaturated

zone. Post-fracture air flow was 500 to 1700 times after the treatment (Anderson et al.,

1995).

2-32 Themal Enhancement

Themal enhancement for soil remediation involves transferring heat to the

subsurface to increase the vapor pressure of VOCs or SVOCs or to increase air

permeability in the subsurface formation by drying it out. Thermal enhancement

technologies are normally used with hot air, hot water and stearn injection, radio

fiequency heating, electrical resistance heating.

Steam, hot air and hot water injection rely on contact between the injected fluids

and the contaminant for the transfer of heat to and recovery of the contaminant. Steam

injection will displace mobile contarninants in nont of the steam as well as vaporize

volatile residud contaminants, and therefore can recover volatile contaminants in both

the liquids and vapor phase. Past applications of s t e m injection technologies have

focused primarily on moving and vaponzing free petroleum product in the subsurface

toward extraction wells for removal. Tests have show that 99.5% of the

petrochemicals polluting soil and groundwater were removed by steam injection

(Baum, 1988).

Hot air injection has been used to increase the vapor pressure of VOCs and

SVOCs in the vadose zone, thus decreasing remediation tirne and increasing

contaminant removal. Normally, hot air injection is used to recover contaminants only

in the vapor phases. Hot water injection generally recovers contaminants only in the

liquid phase. Electrical energy has been applied to the soil in the low fiequency range

used for electricai power as well as in the radio frequency range, which have pnmarily

focused on increasing mass removal rates of contaminants in low penneability soil

(U.S.EPA, 1994). With radio frequency heating, soil is heated to high temperatures,

thereby desorbing most organic contaminants (Edesterin et al., 1994). When the

temperature is 150°C, 95% to 99% of VOCs and 90% of the SVOCs can be rernoved

(Johns and Nyer, 1996).

However, with increasing temperature, soil properties, such as microbial

populations, will be changed. Operation cost of thermal process is higher than other

processes.

2.3 Parameters Affecting Volatilhation

During physical operation, for volatilization from soil to occur, organic

compounds must move through a complex structure of solid particles and void spaces

io soi1 sudace (Bell et al., 1987). Affecting volatilization of organics in a soi1 matrix

include (1) contaminant physical properties, such as Henry's law constant. vapor

pressure, and contaminant solubility in soil organic matrix (Ehrenfeld el al., 1986). (2)

Soil physical properties, such as soil texture, water content and soil temperature; (3)

Enhanced process, such as air injection pressure or air flow rate and related to

transfemng energy into subsurface. Among them, the most important factors affecting

soil volatilization are soil texture, soil moisture and contaminant properties and air

injection pressure under limited environmental temperature ranges.

2.3.1 Soil Texture

The texture of a soil refers to the proportions of various particles size groups in

the soi1 rnass, typically caNed sana silt and clay (Devitt et al., 1987). Clay soils have

high voiumetric water content at saturation than medium-textured or coarse soils. As

the clay content increases, the water-holding capacity and the exchange capacity

increase, while the air-filled porosity and the rate of vapor dimision decrease. A high

clay content acts as a retarding layer to the vertical flux of VOCs. Clayey soils tend to

have a more unifom pore size distribution than do corner soils (Hillel, 1971), whereas

the coane soils tend to have large mean size, which will transfer fluids fmer under

saturated and unsaturated conditions.

Soil type affects the tirne of transit of a contaminant, as well as the potential for

biodegradation. It also influences the mobility of microorganisms ihrough the

subsurface. Bactena generally do not move farther in the fine-textured soil, but they can

travel much larger distances in coarse-textured or fractured materials (Romero, 1970).

Soil type has been found to have a strong effect on the rate of contaminant

removal fiom soils (de Percin, 1991; Lighty et al., 1988). Adsorption ont0 glas beads

and silica sands does not appear to fonn tight bonds; the adsorption is readily revenible

even at low temperatures. Experiments performed by Lighty et al. (1988) showed that

essential al1 of the xylene adsorbed by silica sands was removed rapidly. However, for

reactive media, the desorption process is much slower, which may be caused by

strongly adsorbed monolayer on the particle surface (Lighty et al., 1988; Tognotti et al.,

1991) or slow diffision from maIl inner pores to the surface of the particle (Keyes and

Silcox, 1994).

Soil composition influences infiltration rate and pemeability, water holding

capacity, and adsorption capacity for various waste components (Homick, 1983). Clay

soils have a greater capacity for physicochemical attenuation of contarninants than

coarse sands or fissured rock (Pye and PatrÎck, 1983). A predominance of clay and d t

particles in h e texhired soils can result in very srna11 pore sizes, with a slow infiltration

rate (Hornick, 1983). Coarse soils of sand and grave1 have large intercomecting pores

and allow rapid water and air rnovement.

A continuous air phase is established when it occupies approximately 80% of the

available pore spaces (Frendlund and Rahardio, 1994). The available pore volume or

porosity is dependent on grain size distribution of the soi1 matrix. As well, the porosity

can be an important controlling factor on permeability of the soi1 matrix. Clayey soils in

general have higher porosity but lower penneability than that of sandy soils.

Soil penneability is one of the most important variables for making possible the

delivery of air to contaminated regions. Diffision in low penneability soils plays an

important role in natural replacement of oxygen. However, clayey soils tend to retain

higher rnoisture content, which also inhibits air and contaminant vapor difision.

2.3.2 Soil Moisture

Soil moisture content provides an indication of VOCs removal efficiency and

possibly soil VOCs residuals (Mcdevitt et al., 1987). Soil moisture is important in

deterrnining the extent of adsorption of neutral, non-polar molecules like most VOCs

onto soil surface (Poe, 1998). VOCs are strongly adsorbed to soils at low moisture

contents. They are displaced fiom their adsorption sites as soi1 moisture increases, as a

result of competition for adsorption sites on the polar mineral surface fkom polar water

molecules. It is also shown that it will take longer to cleanup soils with high moisture

content than similar soils with lower moisture content. Dry air might be injected into

soils to reduce soil moisture content and produce tensiometric, or dry, barrier to

contain liquid-phase transport (Thomson et al., 1996). If large amounts of water were

withdrawn fiom the soil with the gaseous steam and contarninants during SVE, the

pneumatic soil permeability could change, the temperature could &op d o m to 10°C

(Garcia-Hemo et al., 1994). Thus, with decreasing temperature, the vapor pressure of

contaminant would be decreased as well as removal efficiency decreases.

2.3.3 Air Injection Pressure or Flow Rate

Air injection pressures are govmed by the static water head above the injection

point, the required air entry pressure of the saturated soils, and the air injection flow

rate. The lowest effective air injection pressure will correspond to the pressure required

to maintain a minimum continuous air flow through the saturated zone. Higher

pressures will produce higher air injection flow rates, and due to the random

distribution of air entry pressure in the soil, will likely produce additional air channels.

Ji et al. (1993) showed air travels through the saturated zone air channels that are

continuous and stable when air flow is maintained. Furthemore, when air flow rate

increases, existing air channels will be enlarged and few new air channels form.

The higher air injection pressures required in fine-grained soils cm cause the

formation of significant subsurface gas pockets. A gas pocket is essentially an

unsaturated volume that expands fiom the air injection point during the injection

process until pressure within the pocket is SuffiCient to overcome the vertical air entry

pressure of the overlying soils. Too high an air injection pressure may create fractures

in the injection point, which will result in a loss of system efficiency or in some case

rnay actually improve channel distribution.

Chao and Ong (1995) reported that greater mass removal was obtained in the

corne sand at low flow rates. Mass removai increased with injection rate for both

soils. However, as the flow rate was increased to approximately 50 Umin, the

difference in mass removal rate between coarse and fine soil became negligible. Chao

and Ong (1995) also reported that chernicals with higher Henry's law constants are

removed faster.

Air flow rates that are typically used in the field are in the range of 3 to 20 cubic

feet per minute. Pulsing of air flow into injection point is considered to provide a better

distribution of air flow channels and ground water mixing over the project duration.

2.3.4 Vapor Pressure and Boiling Point

Vapor pressure is a measurement of the equilibrium between the liquid and

vapor phases of a pure compound (Eckenfelder et al., 1993). Thus, at spill sites,

organic compounds with high vapor pressures would be expected to be present to some

degree in the vapor phase of soil pores. Highly volatile fuels, such as gasoline,

evaporate relatively rapidly, even in subsoil, forming an envelope of hydrocarbon

vapors around the core of the spill.

The major contaminant property affecting volatilization is its vapor pressure in

the soil air space (Shs, 1985). The vapor pressure of soil organic compound is the

most important factor at low water content (presumably due to the vapor-phase

dimision), while with greater water content, aqueous-phase diffision becomes most

important (Ehlers et al., 1969a & b). The vapor pressure of an organic compound in the

soil increases to an equilibrium vaiue that corresponds to its vapor pressure (Bell et al.,

1987). This is due to result of increasing concentrations of the cornpound in the soil

until there is saturation of adsorption sites on the soil minera1 and organic fraction

surfaces. Vapor pressure increases with increasing temperature. Several inches below

topsoil, the vapor pressure can drop below saturation, because of higher gas mixing

and exchange rates. The presence of electmlytes (often concentrated near the soil

surface from evaporation) cm also lower the vapor pressure.

The contaminants with the lowest boiling points also generally have lower heat

of vaponzation; thus these contaminants are relatively easy to volatilize. Normally, the

lower the boiling point, the higher the vapor pressure. Compounds with higher boiling

points have lower vapor pressure at ambient temperature and higher heat of

vaponzation; thus more energy is required to convert them to the gaseous phase.

Laboratory experiments have show that vaponzation of even highly volatile

compounds can cause a measurable decrease in the temperature of the system

(Lingineni and Dhir, 1992).

Reducing the vapor pressure in the soi1 pores will have a significant efFect on the

adsorption of organic vapors (Chiou and Shoup 1995). The minerai fraction of a dry or

slightly hydrated soil is a powerful adsorbent for organic vapors at lower vapor

pressure. However, some other properties of contaminant, such as Henry's Iaw

constant, water solubility, molecular weight, boiling point and viscosity also play an

important role during soi1 remediation.

2.4 Electromagnetic Wave Vibration and Application

Electromagnetic waves can be characterized by their wavelength, frequency, or

energy. These three parameters are interrelated. The fiequency is measured in cycles

per second, or hertz (Hz). The shorter the wavelength, the higher the frequency.

2.4.1 Electromagnetic Wave Phenornena

The electromagnetic radiation i ncludes radio waves, microwaves, in frared

radiation, ultraviolet rays, X-rays, and gamma rays. The only difference between them

is their wavelength, which is directly related to the amount of energy the waves carry.

The shorter the wavelength of the radiation, the higher the energy.

Radio waves are used to transmit radio and television signals. Radio waves have

wavelengths that range from less than a centimeter to tens or even hundreds of meters.

Microwave wavelengths range kom approximately one millimeter (the thickness

of a pencil lead) to thirty centimeters (about twelve inches). Ln a microwave oven, the

radio waves gmerated are tuned to fiequacies that cm be absorbed by the food. The

food absorbs the energy and gets warmer.

Infiared is the region of the electromagnetic spectnim that extends fiom the

visible region to about one millimeter (in wavelength). m a r e d waves include thermal

radiation.

Ultraviolet radiation has a range of wavelengths from 400 billionths of a meter to

about 10 billionths of a meter. Sunlight contains ultraviolet waves. Most of these are

blocked by ozone in the Earth's upper atmosphere.

X-rays are high energy waves which have great penetrating power and are used

extensively in medical applications and in inspecting welds. The wavelength range is

from about ten billionths of a meter to about 10 trillionths of a meter.

Gamma rays have wavelengths of less than about ten trillionths of a meter. They

are more penetrating than X-rays. Gamma rays are generated by radioactive atoms and

in nuclear explosions, and are used in many medicai applications.

An electromagnetic wave consists of very small packets of energy called

photons. The energy in each packet or photon is directly proportional to the frequency

of the wave: The higher the frequency, the larger the amount of energy in each photon.

2.4.2.Application of Electromagnetic Vibration

By application of recent advances in the generation of ultra-fast laser pulses,

researchers have now obtained direct measuements of the vibrational lifetime of

adsorbates on metal d a c e s (Budde et al., 1993). In its experiments, an adsorbate

vibration is resonantly excited by a strong ultrafast laser pulse at the appropriate

Uifrared kquency. Budde et ai. (1993) desorbed NO molecules fiom a Pd (1 11) surface

by laser pulses of 400s duration. The application of electromagnetic wave vibration is

growing continuously in the field of surface science. Tabulina et al. (1993) studied the

influence of static and aiternahg magnetic fields and of vibrating agitation of the

nickel-plating process in hydrophosphite solutions and showed that vibrating agitation

has a positive effect on the nickel-plating process within a narrow fiequency range, and

decreases the rate of Ni-P plating. The desorption processes of NO and CO from Pt

(1 11) and Pt (001) surface were described by a mode1 of substrate-mediated excitation

proposed by Gadzuk et ai. (1990). Montroll (1950) showed that vibrational specific heat

had an additional term proportional to the surface area of the crystal. The application of

electromagnetic wave vibration in soi1 remediation has not been reported.


The USEPA has developed a series of matrix-specific methods for VOC analysis

(e.g. EPA Methods 602, 624, and 8240). Although there are minor differences among

the methods, al1 employ dynamic headspace or purge and trap technique to separation

for gas chromatograph andysis was normally accomplished through a non-polar

packed column. Photo ionization detector (PD) and flame ionization detector (FID)

were often used for detecting BTEX and other volatile compounds.

An alternative solvent-ke extraction procedure emplo ying solid phase micro-

extraction (SPME) of organic compounds frorn aqueous sarnples was reported. This

new extraction rnethod integrattes samplhg, extraction, concentration, and sarnple

introduction Uito a single step (Rong, 1996). There is no simple and direct method to

determine BTEX concenbations in the soi1 samples contarninated by gasoline, because

of low boiling points and very volatile nature of BTEX.

When attempts were made to use experimentaliy developed equilibnurn models

to predict environmental VOC concentrations in a vapor and the buik soi1 matrix,

discrepancies of more than one order of magnitude have resulted between theoretical

and measured values (Smith et al., 1990). Similarly, most studies dealing solely with

environmental samples have failed to demonstrate significant correlations between

VOC concentrations in soil vapor and those in collected bulk or discrete soil samples

(Sextro, 1996). The major problem is the use of inadequate sampling procedures. For

example. current soil sampling and handling methods used for VOC characterization

are likely to underestimate their concentrations because of losses fkom volatilkation

between the time of collection and the time of sample analysis (Hewitt, 1995).

2.6 Methods for Experimental Design and Result Analysis

Fractionai factonal design is arnong the most cornmonly used methods for

designing experiments. Many successful applications of this method in the quest of

industrial quality and productivity are recent testimony to its importance. A key

question in selecting such desigos is how to develop a good criterion for it. It has been a

standard practice to choose a fiactional factorial design with maximum resolution. Since

desips with the same resolution are not equally go04 a more refined criterion cailed

minimum aberration was introduced by Fnes and Hunter (1980). When the

expenrnenter has little knowledge about the relative sizes of the factorial effects, the

minimum aberration criterion selects designs with good overall properties.

When designing sequential NO-level Fractional factorial experiments, there is a

wide choice of designs îhat could be used at each stage. In some designs, one of the

facton is fixed at a particular level after the first set of experiments is completed. This

may allow important effects to be estimated in fewer runs than would the standard

sequences of designs. The extensions to fixing more than one factor and to factors with

more than two levels were discussed by Gihour and Mead (1 996).

Luna et al. (1996) applied a factorial design to determine the stability of

methylmercury (MMHg) standard solutions in water. To detemine the effects of some

variables on the stability of MMHg standard solutions, a 2' factorial design was used

with a first-order model. It was concluded chat the factorial design was a tool that

allowed one to find, in a rapid and efficient way, the individual variables and

corresponding interactions îhat may influence the long-terni stability of MMHg

solutions.

A study of colurnn leaching to remove iron fiom quartz sands was conducted

using a complete factorial design (Ubaldini et al., 1996). The facton assumed to affect

dissolution of iron, such as temperature, oxalic acid concentration, pH, and flow rate.

were studied with a î4 full factorial design in order to assess the main effects and the

interactions among the facton.

The best material for use in analysis of dry atrnospheric mercury deposits was

determincd with a 252 hctional factonai design (Tong, 1998). Four materials and five

factors (two levels for each factor) were considered. By comparing results of the

fractional factorial design with a complete factorial design, it was shown that the

hctional approach was effective in reflecting interactions arnong different factors in

the study system. The successfbl experimental design for bioremediation (Wu, 2000) in

which two z6" fiactionai factorial designs were implemented for the experiments was

involved six factors (two levels for each). The main factors that have significant effects

on the bioremediation rate were identified. A response surface mode1 was then

formulated based on the factorial analysis results, reflecting interrelationships between

the system conditions and the biodegradation rates.

2.7 Literahire Review Summary

Most previous enhanced-remediation methods were carried out at relatively high

air flow rates (higher injection pressure for fiactunng) for the purpose of increasing

recovery rate. There have been not previous studies using electromagnetic vibration as

an enhanced process for soil remediation. The electromagnetic vibration was mainly

used for desorping NO and CO fiom metal surface. No application of electromagnetic

vibration to soil rernediation was reported. Factoriai design has been widely used in

expetllnental design and multivariate analysis. It can be extended to study of

remediation-process design and analysis.

Chapter 3

Materials and Methodology

Laboratory experiments in packed column were conducted to evduate both the

rernoval of BTEX by continuous air injection and the fate of toluene throughout the process

of electromagnetic-vibration-enhanced air stripping.

3.1 Material and Instrument

3.1.1 Material Preparation

(1) Soi1

To investigate the effect of the composition of soi1 on the efficiency of continuous air

injection and electromagnetic-vibration-enhanced air stripping system, a series of soils

were used by mixing fine sands with different content of clay (40%, 50%, 60% and 70%).

(2) Sand

Based on the manual of test sieving methods (USA Standard Sieve Series

Specification - E 11, E161, and E 323, 1998), the commercial sand was screened with a

#50 U.S.A. standard sieve. The particles passing through the sieve had diameters below

0.30 mm. Figure 3.1 shows the size distribution of the sand with 62% wt of the sand being

in the range of 0.3 to 0.25 mm in diameter and the rest being less than 0.25 mm in diameter.

(3) Clay

The clay (nom the Canadian Clay Products Inc.) was used in the experiments and had

the following physical properties:

Specific gravity: 2.5 @cm3;

pH value: 9

Particle diameter: < 0.20 mm

(4) Contaminant

Reagent grade benzene, toluene, ethylbenzene and (m+p)-xylene were used as the

expenmental contaminants for al1 continuous air injection tests.

Gasoline was used as the contaminant for those experiments involving

electromagnetic vibration. It was purchased from a commercial gas station (ESSO). The

gasoline had a dynamic viscosity of 1.71 Centipoise (cp) and a density of 0.73 @mL. The

concentrations of benzene, toluene, ethylbenzene, and (m+p)-xylene (BTEX) in gasoline

were analyzed using a gas chrornatograph (GC) and the results of the analyses are presented

in Table 3.1.

( 5 ) Reagent

Reagent grade methano1 was used to extract BTEX from the soil.

Table.3.1 BTEX Concentrations in Gasoline

Compound Concentration (wt) Concentration (wt)@

Benzene

Toluene

Ethylbenzene

(m+p)-Xylene

O-X ylene

Source: The State o f California, Leaking Underground Fuel Tank Field Manual, Academic Press, Orlando, FL, 1987.

A micro-processor controlled pump ( ~ a s t e r t l e x ~ ~ , mode1 7305-40), a soap film fîow

rate meter (Optiflow 650, Humonics Inc, U.S.A.), a DMA-4500 density meter, a balance

(Boulder Co., U. S. A. Max = 500 g and d = 0.001 g), and a Varian GC (CP-3800) were

used in this study.

3.2 Experimental Apparatus

There are a nurnber of routes one cm follow to gain a better understanding of how

process changes impact the performance of the in situ continuous air injection and

electromagnetic-vibration-enhanced air stripping. Field-scale studies, physical mode1

experiments, statistical analysis and numerical simulations were among the options. Each

had its advantages and limitations. In this research, scaled physical-modeling studies were

conducted, because:

( 1) Field-scale studies involved in Iengthy process and associated high costs, so that

the range of conditions that can be practically studied is limited;

(2) Proven numerical simulators have yet to be developed; and

(3) Relative to actual field studies, physical models can be more easily rnonitored and

characterized, and the expenments are of much shorter duration (Johnson et al., 1999).

3.2.1 Electmrnagnetic Vibration Generator

Two electromechanical reiays were comected together to generate electromagnetic

vibration waves. The electrornagnetic vibration facility had a direct current resistance of

284 ohm (a), electric current of 0.159 arnpere (A) in electric circuit as measured by a

heavy duty digital multi-meter (Model: HDI ISB, Wavetek Corp, USA) and a vibration

frequency of 60 Hz.

3.2.2 Expenmental Set-up for Conhuous Air Injection Pmcess

The schematic of the experimental set-up for continuous air injection is shown in

Figure 3.2. The holder consisted of two Teflon caps and a glass core holder with inside

dimensions of 5 cm in length and 1 cm in diameter. The soi1 was packed in a glas column.

The column was placed in a heat exchanger that was connected to a water bath for keeping

a stable temperature. The injection line consisted of a steel tube, needle valves, quick

connects (fiom Regina Valve & Fitting Ltd.), and a micro-processor controlled pump

( ~ a s t e r f l e x ~ , mode1 7305-40). The pump was used to supply stable air (4 mumin) and

calibrated with a soap film flow rate meter (Optiflow 650, Humonics Inc., U.S.A.). A

balance (Boulder Co., U. S. A, Max = 500 g and d = 0.001 g) was used to measure weight

difference. The outlet of the column was c o ~ e c t e d with another glass column, which was

packed with active carbon for adsorbing contaminant and purifjmg air.

3.2.3 Experimental Set-up for Electromagnetic-Vibration-Enhanced Air Stripping

The schematic of expenmental set-up for electromagnetic-vibration-enhanced air

sûipping system is shown in Figure 3.3. In this experimentai set-up, a stainless steel

cylindncal core holder column was used. The inside dimensions of the core holder were 45

cm in length and 5.5 cm in diameter. The injection line consisted of an au compressor, an

air desiccator, an air pressure gauge, a flow meter, a needle valve and a stainless steel tube

which was comected to a Bange cap. An air distributor was attached to the inlet flange. A

filter screen (#IO0 U.S.A. standard stainless mesh attached with a filter cloth) was placed in

the inlet of the column and a Teflon O-ring was put between the air distributor and the filter

screen.

The outlet of the column was welded with a stainless steel tube to prevent any VOC

leakage to the aünosphere. The outlet portion included a needle valve, an air pressure

gauge, a flow meter, a heat exchanger c o ~ e c t e d with water bath, a sarnphg point and a

cleaning system packed with activated carbon for adsorbing contaminant vapor and

purifying outflow air.

After the flange (together with the air distributor), Teflon O-ring and filter screen

were rernoved, a filter screen (#IO0 U.S.A. standard stainless mesh attached with a filter

cloth) was firstly placed inside the outlet of the column. Secondly, the test soi1 mixed with

water and gasoline was carefully placed into the column; and a second filter screen (#IO0

U.S.A. standard stainless mesh), a Teflon O-ring and a flange were placed at the inlet of the

c o i m . The flange was tightened up with a torque wrench. The electromagnetic generator

was fixed on a cylindricai core holder.

3 3 Experimental Method

3.3.1 Continuous Au injection

Tests of continuous air injection to remove benzene, toluene, ethylbenzene, (m+p)-

xylene in soils were conducted with different moisture contents, soil types and adsorbing

durations. The following experimental procedures were adopted:

(1) Weigh the empty glas column and the two Teflon caps by a balance;

(2) Mix sand, water and clay in the column according to the required proportions and

then weigh the loaded glass column together with two Teflon caps on the balance. This

weight was denoted as wo:

(3) Use a 50-mL gas tight syringe to add benzene, toluene, ethylbenzene, and (m+p)-

xylene separately into different core holders till the inside soil having required proportions.

Then measure the total weight of glass column, the two TeBon caps and the BTEX

compounds (Wo);

(4) Place the colurnn on a continuous air stripping set up, connect it with an air line.

and use the balance to weigh the loaded column (W,) at different time stages.

(5) Calculate cumulative removal efficiency (RE) using the following equation:

w here:

W, - W. is weight of contaminants leR in the column, mg.

Wo - wo is the initial contaminant weight, mg.

3 -3 -2 Electromagnetic-Vibration-Enhanced Air Stripping

To study the effects of electromagnetic-vibration-enhanced air injection on removal

of toluene in columns with different soil types, gasoline contents and air injection

pressures, the following experirnental procedures were employed:

(1) Ln the outlet of the column, a stainless steel mesh filter with a filter cloth was

included to prevent the soil from escaping and to ensure a uni form axial flow condition.

(2) The contaminated soils were packed in a cylindrical core holder according to

ASTM-D558 standard procedure (ASTM, 1986). The soils were carefully placed into the

column to avoid any segregation, density variation, or channeling within the column. The

column had a soil packed section being 37 cm long in the middle, and two additional

sections at the inlet and outlet ends. The section at the inlet end was filled with sand for

better air distribution, while that at the outlet end was a stainless mesh filter with filter

cloth.

(3) M e r finishing soi1 packing, sand @article size = 0.3 - 0.43 mm), the stainless

steel mesh filter, the Teflon O-ring and the flange including the air distribution groove were

put on the inlet of the column for injecting air homogeneously and for ensunng a unifom

axial flow condition.

(4) The colurnn for electromagnetic-vibration-enhanced air stripping was sealed for 8

hours to ensure that the contaminants were adequately adsorbed by soil.

(5) Switch on air supply and power, adjust air pressure and flow rate, take gas

sampies (for being analyzed by GC) nom the sampling point, and record the initial time.

(6) Mer finishing the experimenf use a sampler to get soil samples in the middle and

the two ends of the column to fil1 into 22ml headspace bottles for sample analysis.

(7) The cumulative removal efficiency (RE) was calculated using the following

equation:

RE = - y xKN% w , + R

where:

W, is the weight of effluent contaminant, mg.

R is the weight of residuai contaminant in soil, mg.

3.4 Sampling Method

3.4.1 Continuous Air injection

(1) Sample Preparation

In the glas column, the clay and sand were weighed precisely. Distilled water was

added into the sand to get different moisture contents, and then the sand and clay were

mixed at required ratios to f o m different soil types. The soi1 was weighed and imrnediately

"contaminated" with a BTEX compound. Then the column was seaied shacked by hand for

homogeneous contaminant distribution in the soil.

(2) Sample Preservation

The column containing contaminated soil was sealed and placed in the continuous air

stripping system for different durations (with sufficient adsorption time). During the

experiment, the leakage should be tested using leakage detector in al1 the connection parts

for tubes and valves. Blank experiment (with non-contaminated soil) was camied out at the

same time to subtract weight of water evaporated from the systern.

(3) Frequency of Sample Analysis

The variations of in column weights were measured every 4 hours.

3.4.2 Electrornagnetic-Vibration-Enhanced Air Stripping

( 1) Sample Preparation

In the sealed container, the clay and sand were weighed precisely. Distilled water was

added into the sand, which was then mixed with the clay at required proportions to get

different soil types and soi1 moishue contents. The soil was weighed and irnmediately

"contaminated" with gasoline of different weights. The soil was then agitated in the sealed

container (for exactly 5 minutes), and packed uito a stainless steel column.

(2) Effluent Sample Analysis

Samples of effluent air from the column were taken directly by a gas-tight

chromatograph syringe and injected into the Varian 3800 GC equipped with a FID for

analyzing toluene concentrations.

(3) Soit Sample Anaiysis

After nnishing experiment, soil samples were taken fiom middle and the two ends of

column, and then put into gas-tight bottles for analyzing toluene concentrations in soi1

vapor and soil rnatrix.

(4) Sample Preservation

The column containing contarninated soils was sealed and placed in the

electromagnetic-vibration-enhanced air stripping system for 8 hours to ensure sufficient

adsorption time. A leakage detector was used to test leakage.

(5) Test Duration

In the first hour of the tests, samples were collected every 15 minutes. After that,

samples were collected with time intervals of 30 minutes.

3.5 Aoalytical Method

3 S. 1 Toluene Vapor Analysis

A Varian mode1 CP-3800 GC equipped with a flame ionization detector (FID). a

photo ionization detector (PD), and a Chrompack WCOT fusedsilica 0.53 mm x 30 m

capillary column were used for analyzing contaminant contents in al1 gaseous vapon. The

GC was controlled by a computer system. The initial oven temperature was programmed at

6S°C. From 6S°C, the oven temperature was increased to 135°C at a rate of 10°C per

minute. The oven temperature was held at 135 O C for 5 minutes and temperatures of the

injector port and the detector were maintained at 250 OC and 200 OC during that time,

respectively. Helium, at an initial flow of 5.5 mL per minute, served as the carrier gas.

Hydrogen and air were used for detector flame with both of their pressures being set at 60

psi and the total mn time being 13 minutes.

Toluene concentrations within air samples from the electrornagnetic-vibration-

enhanced air stripping system was measured. A multi-p0ir.t calibration curve had been

prepared for detemining the arnounts of toluene in the sarnples. A standard caiibration

curve was obtained by taking the integrated area for each standard and linearly regressing

the values. The standard curve is shown in Figure 3.4.

3.5.2 Analysis of Toluene in Soils

Soi1 samples were taken from the column and injected into a 22-mL Varian

headspace bottle. First, the soil vapor was anaiped according to the method discussed in

section 3.4.1, and then the vapor was removed from the bottle through a 20-mL gas-tight

syringe. Methanol was used to extract soil and the contaminant concentration in methanol

was analyzed. Thus, the total of the above two concentration values was the actual toluene

concentration in soil. The GC was also used for analyzing contaminant concentration in

liquid.

The initial GC oven temperature was programmed at 6S°C. From 6S°C, the oven

temperature was increased to 135'C at a rate of 10°C per minute. The oven temperature was

held at 135 O C for 8.5 minutes, and temperatures of the injector port and the detector were

maintained at 250 OC and 200 OC during that tirne, respectively. Helium served as the

carrier gas with a flow rate of 5.5 W m i n , hydrogen and air were used for detector flarne

with both pressures set at 60 psi, and reagent grade methylene chlonde was used as the

solvent. The total run time was 15 minutes. A standard caiibration curve was obtained by

taking the integrated area for each standard and linearly regressing the values. The standard

c w e is shom in Figure 3.5.

3.5.3 Data Analysis

Graphs of BTEX removal efficiency versus time were constnicted based on data

obtained from the experiments. A 2' Full factorial design was used to determine whether or

not electromagnetic-vibration influenced toluene removal efficiency. The details are

discussed in Chapter 5.

O 50 100 150 200 250 300 350 400 450

Toluene Concentration (ppm)

Figure 3.4 Standard Calibration Curve for Toluene Vapor Analysis

1 OOOOOO

500000

O

O 50 100 150 200 250 300 350 400 450 500

Toluene Concentration (ppm)

Figure 3.5 Standard Calibration Curve for Toiuene Analysis in SoiIs

Chapter 4

Method of Statistical Analysis

The application of statistical methods to the electromagnetic-vibration-enhanced air

stripping studies included two aspects. The experiments were designed based on the

principle of factonal analysis. Then, based on the qerimental results, a modi fied response

surface model was developed to identify factors that had strong influences on the toluene

removal efficiency.

4.1 Introduction

Many expenmental variables are believed to have influences on the experimental

result, and it is desirable to know how these variables exert their influences. Interactive

effects are common when the reaction temperature is varîed. together with other factors that

may influence the kinetics of a reaction. To cl&@ such effects, it will be necessary to

determine the direct influence of the experimental variables as well as their interactive

eflects. For this, two-level factonal design is an appropriate tool.

Many experiments involve studies of the effects of two or more factors. It can be

shown that, in general, factorial designs are most efficient for this type of studies. A

factorial design is m a t to investigate possible combinations of the levels of the factors in

each complete trial or replication of the expenment.

The idea of a factorial design is to arrange the experiments in such a way that the

variation in response obtained with different settings from experimental factors can be

traced back to the variations of the factors. By proper arrangement of the factor settings, it

will be possible to determine the influence of the variation of each factor on the response in

the presence of simultaneous variations of al1 the other facton. Al1 these effects can be

detedned independently of each other. This means that the estimated value of any effect

does not depend on the estimated value of any other effects. These designs are of

importance for a number of reasons:

(1) They require relatively few mns per factor studied; and although they are unable

to explore fully a wide region in the factor space, they can indicate major trends and so

detemine a promising direction for furthet expenmentation.

(2) When a more local exploration is needed, they cm be suitably augmented to forni

composite designs.

(3) The interpretation of the observations produced by the designs can proceed

largely by using common sense and elementary arithmetic.

To allow the experimental erron to be anaiyzed by known statistical probability

distributions, such as normal distribution, t distribution and f distribution. the following

assumptions as to the experimental erron are made:

(1) The experimental errors should be independent, i.e. the disturbances leading to the

errors should occur independently of each other between expenmental runs.

(2) The variance of the experimental errors, 02, should be constant in the

experirnental domain.

However, a system can be influenced by disturbances which are not random and may

produce systematic errors. For example. as experimental skill of a researcher is improved

over time, the experimental erron will be gradually reduced. There is always a risk that the

experirnental result may be influenced by non-random time-dependent erroa.

Randomization of sequence of test nins protects against unknown or unrneasured sources of

possible bias. Randomization also helps validate the assumptions needed to apply certain

statistical techniques. This means that the order of executing the experimental runs should

be randomized; and the order of ana1yzing samples should also be randomized. These

precautions wili break time-dependent phenornena and transfomi systematic erron into

random errors.

4.3 Variables

In this study, four variables with two Ievels were considered. They were air injection

pressure, clay content in the soil, gasoline concentration and presence or absence of

electromagnetic vibration. For each experimental group, a full 2" factorial design would

need 16 experiments. Table 4.1 shows the design matrix of a two-level full factorial design.

Table 4.2 lists the design variables. Let +1 and -1 represents a higher and a lower level,

respectively. The four variables were denoted as follows:

s = clay content (+ 1, - 1 );

x2 = air pressure (+ 1, - 1 );

x3 = gasoline concentration (+ 1, - I );

.rd = electromagnetic vibration frequency (+ 1, -1 ).

4.4 Response Surface Mode1

Factorial designs estimate not only main effects of individual variables but also their

interactions. The measurement of interaction effect of AxB is defined as a half of the

difference of the effects of A when they are determined with factor B on its higher and

lower levels, respectively:

I AB = $, - A , )

w here:

AB+ is the effect of factor A with B at its higher level;

AB- is the effect of factor A with B at its lower level;

AB is interaction effect A and B.

Table 4.1 Full Factorial Design of Four Variables at Two Levels

Test No. x, (Clay) x2 (Air pressure) x3 (Gasoline) x4 (Vibration )

1 -1 -1 -1 -1

2 +1 -1 -1 -1

3 -1 + I -1 -1

4 +1 + l -1 - 1

5 -1 - 1 +1 - 1

6 + I -1 +l - 1

7 -1 +1 + 1 - 1

8 +1 + 1 +1 -1

9 -1 -1 -1 +1

IO +l -1 -1 + I

I l -1 + I -1 - 1

12 +1 +1 -1 -1

13 -1 -1 + 1 T 1

14 +l -1 + 1 +1

15 -1 +1 + l + i

16 + I +1 + l - 1

The significance of effects may be judged by (a) an estimate of variance obtained

from higher order interactions; and (b) by plotting effects on normal probability paper. The

presence of an interaction effect AB means that the influence of changing factor A will

depend on the setting of factor B. This cm be analyzed by comparing the effects of A under

different levels of B. The main effects of a variable should be individually interpreted only

if there is no evidence kat the variable interacts with other variables. When there is

evidence of one or more such interaction effects, the interacting variables should be

considered joint1 y.

A response surface model (RSM) is a collection of mathematical and statistical

techniques used for analyzing problems in which several independent variables influence a

dependent response. The response (y) is assumed to be a random variable.

Because the fomi of the relationship beh~een the response and the independent

variables is unknown, the first step in the response surface analysis is to identify a suitabie

approximation for the functional relationship between response y and the set of independent

variables. Usually, a fint-order model is employed, if the response c m be well-modeled as

a linear huiction of the independent variables:

where:

bo = average effect;

bi = main effect;

e = a random error component.

However, when there are evidences of one or more interaction effects, the interacting

variables should be considered. In this case, the response surface model is:

where:

bo = average effect;

bi = main effect;

bq = two-factor interaction;

bok = three-factor interaction.

Thus the model cm be written in a rnatrix form as follows:

y = X p + e

where Po = bo, pi = Kt b,, fi, = !4 bu, and fijk = Kbyk, and the columns in X correspond to the

variables. The modeling rnatnx is obtained h m the factorial design, by adding a column of

ones (corresponds to Bo) and columns of cross products (interaction among the variables).

Chapter 5

Results and Discussion

This section describes the experimental results, the response model, and the analysis

of variance for the experimental results. The relationships among toluene adsorption rates.

soi! types and air injection pressures were analyzed, such that the effectiveness of differeni

soi1 remediation methods were then evaluated.

5.1 Testing Conditions

The experimental conditions given in Table 5.1 were determined based on the

principle of z4 full factorial design as discussed in Chapter 4. Two injected air pressure

options were considered (Le. 15 psi air injection pressure for soi1 with 50% clay contents

and 5% wt gasoline; and 22 psi air injection for soi1 with 40% clay contents and 3% wt

gasoline) .

The two fiequency levels for the electromagnetic vibration system were 60 Hz and O

Hz, corresponding to presence and absence of electromagnetic vibration.

5.2 Testhg Results

Table 5.1 Experimental Conditions for the Factorial Design

Run ClayISand Vibration Gasoline Soi1 Water Air Injection Air Flow No. (wt%) Frequency Weighl Weight Weight Pressure Rate

(Hz) (B) (6) (g) (psi) (mumin)

The experimental results under various conditions are shown in Figures 5.1 to 5.17.

The removal efficiencies (RE) were calculated as follows:

where:

Ci = concentration of contaminant (mgk),

F = air flow rate (Umin),

t = time duration (min),

Cz = average residual concentration of contaminant (mg/g),

W = weight of soi1 (g).

Toluene adsorption rate (%) = 100 - Toluene removal efficiency

5.3 Statistical Analysis

5.3.1 Result Interpretation

An analysis of the toluene adsorption rate under various system conditions is shown

in Table 5.2.

There are two quicker methods for calculating the effects; one is a table of contrast

coefticients and the other is Yaks's Algorithm (Box et al., 1978). A table of contrast

coefficients is used for analyzing observations after they have been rearranged in what is

called order.

O 1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7

Test Number

Figure 5.1 ToIuene Adsorption Rates Under Various Experirnental Conditions

O 60 120 180 240 300 360 420 480 540 600

Time (min)

Figure 5.2 Cumulative Toluene Rernoval Efficiency vs. T i e (Run 1)

O 60 120 180 240 300 360 420 480 540 600

Time (min)

Figure 5.3 Cumulative Toluene Removal Efficiency vs. Time

O 60 120 180 240 300 360 420 480 540 600

Tirne (min)

Figure 5.4 Cumulative Toluene RemovaI Efficiency vs. Tirne (Run 3)

O 60 120 180 240 300 360 420 480 540 600

Time (min)

Figure 5.5 Cumulative ToIuene Removal Efficiency vs. Tirne

O 60 120 180 240 300 360 420 480 540 600

Time (min)

Figure 5.6 Cumulative Toluene Removal Efficiency vs. Time (Run 5 )

O 60 120 180 240 300 360 420 480 540 600

Time (min)

Figure 5.7 Cumulative Toluene Removal Efficiency vs. T h e

O 60 120 180 240 300 360 420 480 540 600

Time (min)

Figure 5.8 Cumulative Toluene Removal Efficicncy vs. Time

O 60 120 180 240 300 360 420 480 540 600

Tirne (min)

Figure 5.9 Cumulative Toluene Removal Efficiency vs. T h e (Run 8)

O 60 120 180 240 300 360 420 480 540 600

Time (min)

Figure 5. IO Cumulative Toluene Removal Eficiency vs. Time (Run 9 )

O 60 120 180 240 300 360 420 480 540 600

Time (min)

Figure 5.1 1 Cumulative Totuene Removal Efflciency vs. Time (Run 10)

O 60 120 180 240 300 360 420 480 540 600

Time (min)

Figure S. 12 CumuIative Toluene Removal Efficiency vs. Time (Run 11)

O 60 120 180 240 300 360 420 480 540 600

Time (min)

Figure 5.13 Cumulative Toluene Removal Efficiency vs. Time (Run 12)

O 60 120 180 240 300 360 420 480 540 600

Time (min)

Figure 5-14 Cumulative Toluene Removal Efflciency vs. T h e (Run 13 )

O 60 120 180 240 300 360 420 480 540 600

Time (min)

Figure 5 . t 5 Cumulative Toluene Removai Efficiency vs. Time (Run 14)

O 60 120 180 240 300 360 420 480 540 600

Time (min)

Figure 5.16 CumuIative Toluene Removal Efficiency vs. Tirne (Run 15)

O 60 t20 180 240 300 360 420 480 540 600

Time (min)

Figure 5.17 CumuIative Toluene RemovaI Efkiency vs. The (Run 16)

The calculations performed to obtain the various effects cari be charactenzed by the

table of signs as shown in Table 5.2. In this table, a z4 factorial design was in a standard

order, when the k t column of the design matrix consisted of successive minus and plus

signs , the second column of successive pairs of minus and plus signs, the third column of

four minus signs followed by four plus signs and the last column of eight minus signs

followed by eight plus signs.

Table 5.2 shows the influence of the variables. Columns 1 to 4 (main effects) were

from a matrix of a 2" full factonal design. Based on columns 1 to 4, the signs for the two or

more variable interactions can be obtained in columns 12 to 1234 (interactive effects). The

remaining effects were identified by locating the plus and minus signs in the design rnatiix.

Based on the toluene adsorption rate (TAR), Table 5.2 also showed the calculated effects in

the bottom row, which were obtained as follows:

1 6, = -[27.95 + 33.44+ 16.89 + 2 1.89 + 29.77 + 36.22 + 20.78 + 24.56 + 21.72 +

16 29.21+8.91+15.72+18.96+ 26.45+10.11+16.01]

(5.6) = 22.41

where bi defines the effect of variable x,, b, represents the effect of interaction between i

and j, and bo is the average of toluene adsorption rate.

Multiplying the colurnn labels of the complete variable matrix by the generators gave

the confounding pattern. The identification of each effect including the confounding

patterns is summarized in Table 5.3.

A positive effect means that an increase in the variable would increase the toluene

adsorption rate (%); and a negative effect means that an increase in the variable would

decrease the toluene adsorption rate. If the absolute value of an effect is much bigger than

the others, it means that this variable (or the interaction between variables) has the

significant influence on the toluene adsorption rate. In Table 5.3, among the confounded

effects, b2 and b4 (main effect) were negative with their absolute values being much bigger

than the others, indicating that an increase in air injection pressure or electromagnetic

vibration would decrease toluene adsorption rate significantly.

The main effects of xl and x j were positive, with xi having the highest value and x,

the lowest value, which meant that xi rnight play the most significant role (while x, had the

least effects) on toluene adsorption rate among al1 the main effects. The interactive effect of

xi* was negative with its absolute value being much smailer than those of x2 and u, which

indicated that the interaction between xi and x2 had lower effect on the toluene adsorption

rate. More detaited discussions are provided as follows:

Table 5.3 Main Effects and interactions

Identity of Effects E ffec t

Average

1

2

3

4

23

34

12

14

234

123

24

1234

13

1 24

134

( 1) A change in the ratio of clay to sand Erom 40% to 50% (corresponding to -1 and

+l in Table 4.1) would increase toluene adsorption rate significantly, leading to a higher

effect value as shown in Table 5.2.

(2) A change in the air injection pressure fiom 15 psi to 22 psi (corresponding to -1

and +l in Table 4.1) would reduce toluene adsorption rate by almost 6%.

(3) A change in the gasoline concentration in the soi1 From 3% to 5% (corresponding

to -1 and +1 in Table 4.1) would slightly increase toluene adsorption rate.

(4) A change in the electromagnetic vibration fiequency from O Hz to 60 Hz

(corresponding to -1 and +1 in Table 4.1) would lead to reduction of toluene adsorption

rate.

(5) For the interactive effects, a change both air injections pressure and gasoline

concentration (corresponding to both air injections pressure and gasoline concentration a<

their lower level or higher level), would result in increasing toluene adsorption rate.

(6) A change both gasoline concentration and electromagnetic vibration frequency

(conesponding to both gasoline concentration and electromagnetic vibration frequency at

their lower level or higher level), would result in decreasing toluene adsorption rate.

5.3.2 Sensitivity Analysis

For the interactive effects, x z ~ and XN are rnost significant. To examine whether the

high effect levels are from the interactions, sensitivity analysis of relations between x2 and

x3 and between x3 and are desired. The detailed procedures is as follows (as shown in

Figure 5.1 8):

(1) For ~ 2 3 , the experimental domain is marked as a square in the plane spanned by x2

and xs Figure 5.18). The average response, 22.4, is given at the ongin;

(2) The corner in the lower left quadrant corresponded to the setting for x l equal to - 1

and x~ equal to - 1. There were four expenments corresponding to this setting (which reflect

variations of x2 and x~ ) with the toluene adsorption rates being 27.95, 33.44, 21.72 and

29.21 ( average = 28.08). This value was written in the lower left comer. The same

procedure was then used for other combinations of variable settings, i.e. (x2. x3) = (- 1, + 1 ).

(+ 1 ,-1) and (+l, +1), with the average toluene adsorption rates being 29.6. 15.85 and 17.87

respectively. Similarly, variable settings for (xJ, a) = (- 1, - 1). (- 1. + l ), (+ 1 ,-1 ) and (+ 1,

+ 1 ) were calculated.

Table 5.4 shows the sensitivity of response y (toluene adsorption rate) the variations

of variable xz, x3 and a.

It is indicated that the response y shows the lowest sensitivity to x~ When x i and x~

are at their lower leveis, the differences of y at lower and upper bounds of x i are 1.52 and

2.79, respectively. When xz and Q are at their upper levels, the differences are 2.02 and

- 1 .O 1, respectively. This meant that the toluene adsorption rate wouid not signi ficantiy with

the variation of x3. in cornparison, response y shows the highest sensitivity to x2 and Q. For

the effeîts of variable ~ 2 x 3 and x94, impacts from x2 and a need to be considered. This

could be malized through M e r analysis of variance

Figure 5.18 Detailed Examination For Effects of xz, x, and a

Table 5.4 Sensitivity Analysis

Variable Level Sensitivity (Ay)

X2 x~ at lower level

x3 at upper level

X 3 xz at lower level

x2 at upper level

at lower level

x4 at upper level

XJ ~3 at lower level

x3 at upper level

5.3.3 Analysis of Variance

5.3.3.1 Pmcess Development Data

Two problems arise in the assessrnent of effects from unreplicated factorials: (1)

occasionally real and meaningful high-order interactions occur; and (2) it is necessary to

allow for selection. The assessrnent of effects using a reference distribution scaled by an

error estimated fiom higher order interactions does not confiont the first of these problems.

However, Plotting the effects on a normal probability paper oRen provides an effective way

of overcoming both of the di fficulties (Daniel, 1959).

If the variables do not have any influence on the response, the response surface is

completely flat and the estimated effects in such cases would be nothing but different

average surnmations of experimental erron. Because ail the erperiments were executed in a

random order to avoid systematic enon, the set of estimated pararneten, [b,, b2. . . .. b,,. . . 1,

would be random sarnples, with appraxirnately normal distribution. Hence, those estimated

effects that fa11 on the straight line of the nomal distribution could reasonably be assurned

to be nothing but various summations of error terms. They do not represent any significant

effects of the correspondhg variables. On the other hand, effects derived From the straight

line in such a way that they are either too large to fit (upper right) or too small to fit (lower

lett) cm be assurned to represent real effects.

The significance of the estimated effects can be assessed from a normal probability

plot through the following steps (Box, 1978):

(1) Arrange the estimated effects in ascending order (not including the average bo).

(2) Count the number (m) of estimated effects. In this case, rn = 1 5.

(3) Calculate the probability as follows:

1 f: =100*(i--)lm, V i

2 (5-7)

For illustration, refer to the results fiom the process development experiment

considered before. Suppose that these data had occurred simply as the result of random

(roughly normal) variation about a fixed mean, and the changes in levels of the variables

had had no real effect at al1 on the percent conversion. Then the m = 15 eflects (main

effects and interactions), representing 15 contrasts between pairs of average containing 15

observations each, would have been roughly normal and been distributed about zero (as

shown in Figure 5.19). It happened that for these data the estimated main effect of factor 1

represents the fint 1/15 = 6.7% of the cumulative distribution and should therefore be

ploned against the value 1/2/15 = 3.3% (as shown in Table 5.5). An error distribution line

can be drawn by pivoting a straight line with effects of magnitudes close to zero.

Obviously, 10 of the estimated effects in Figure 5.19 fit reasonably well on the straight line.

Those corresponding to XI, .Y?, x4 and xg~ do not fit the line. Therefore, it can be concluded

that these effects are not easily explained as chance occurrences.

5.3.3.2 Diagnostic Examination

Normal plotting of' residuals provides a diagnostic check for any tentatively

entertained model. The plot in Figure 5.19 suggests that al1 effects. with the exception of

the average (22.41 ), XI (6.05), xz (-1 1.1 l), x4 (-8-05}, and ~ 3 x 4 (-1.90), can be explained by

noise. If this is tme, the estirnateci percent conversion for the process development data is

given at the venices of the design by:

6.05 -11.11 - 8.05 - 1 -90 y = 22.41 + (-)x, + (- 1x2 + (- ).Y, + (-)x3x4 2 2 2 2

where x,, xz , XI and xg4 take the value of -1 or + 1 according to their signs in Table 5 2.

Notice that the coefficients that appear in the equations are halves of the calculated effects.

This is so because a change from x = -1 to x = +1 is a change of two units along the x-ans.

The values of y, 9 , and y - j are shown in Table 5.6. The model was then checked by

plotting these residuals on a normal probability paper as shown in Figure 5.20. Unlike the

original plot of the effects, al1 the points ftom this residual plot lie close to a line.

According to the above analysis, x, (soi1 clay content), x2 (air injection pressure) and .r,

(electromagnetic vibration), and interaction . (interaction between gasoline

concentration and electrornagnetic vibration) are used to produce a response surface model.

It confïrmed that the conjecture effects other than xi, .n, x4, and . r ~ x ~ were readily erplained

by random noise. This residual check is valuable provided that the number of effects

eliminated (four in this case) is fairly small compared to m.

Thus the model is set up as follows:

Y = 22.41 + 3 . 0 3 ~ ~ - 5 . 5 6 ~ ~ - 4 . 0 3 ~ - 0 . 9 5 ~ ~ Q + e

Table 5.5 Result of Probability Analysis

Order No (i) Effects Identib of Effects

- 15 - 10 - 5 O 5 10

Effect

Figure 5.19 Normal Plot of Process Effects on Probability Paper

5.4 Summary

From the above analysis, the following surnmary cm be drawn for the experimental

studies.

xi: The clay content played a significant role to the toluene adsorption rate. The

positive effect meant that the toluene adsorption rate would increase as the clay content

increased. Thus lower clay content will lead to lower toluene adsorption rate.

XZ: The air injection pressure had a significant effect on toluene adsorption. A

higher level of air injection pressure would result in a lower toluene adsorption rate. and

thus a higher level of toluene removal eficiency.

XJ: The gasoline concentration is not critical under the experimental conditions. It

has little influence on toluene adsorption rate. However, the tendency is that higher

gasoline concentration is related to lower toluene adsorption.

Y : The effect of electromagnetic vibration on toluene adsorption rate is significant.

The higher the electromagnetic vibration frequency, the lower the toluene adsorption rate.

Table 5.6 Values of y, j , and y-? for z4 Factorial Design

Order No (i) Y

Effect

Figure 5.20 Normal Plot of Residual Effects on Probability Paper

5.5 Conthuous Air Injection System for BTEX Removal

A number of laboratory experiments were conducted on loose soi1 sarnples to

determine removal efficiencies under different experimental conditions. The experiments

were conducted to also examine the effects of clay content, water content and adsorption

t h e on the removal efficiency (for benzene, toluene, ethyl-benzene and (m+p)-xylene)

under the condition of continuous air injection. The results of these expenments served as

ba i s for designing the electromagnetic-vibration-enhanced air stripping system.

The soils were purposely contaminated by BTEX and then decontaminated with

continuous air injection. For each experiment the removal efficiency was expressed on a

cumulative basis. Table 5.7 contains some of the important characteristics of the BTEX

compounds.

5.5.1 Effect of Water Content on BTEX Removal

To study the effect of soil water content on removal efficiency, reagent-grade

benzene, toluene. ethyl-benzene and (m+p)-xylene (m-xylene : p-xylene = 1 : 1 ), were used

separately as contaminants. The BTEX removal efficiency (RE) was calculated as Follows:

Total Mass of Conta minants Removal RE(%) = x 100% Initial Mass of Contaminants in Soi1

The experimental results are shown in Figures 5.21 to 5.24. The column was initially

packed with coarse-grained soit (clay/sand = 112, total soil weight 30 g). The soil water

content was 3%, 4.5%. 6% (wt), and contaminants (BTEX) were 10% W. The pump

injection rate was fixed at 4 mL/min, the experimental temperature was 2S°C, and the

atrnosphenc pressure was 95 kPa.

It can be seen from Figures 5.21 through 5.24 that, as soil water contents increased,

the benzene, toluene, ethyl-benzene and (m+p)-xylene removal efficiencies decreased

slightly. Specifically, the removal efficiency decreased from 95.1% to 90.7% for benzene,

from 40.7 to 32.7% for toluene, from 16.8% to 14.2% For ethylbenzene, and from 14.1 % to

12.7% for (m+p)-xylene within 12 houn.

For a fixed air injection rate and the same soil weight, as the water content increased.

the soil pemeability decreased, leading to reduce air flow rate in the soil. This reduced air

flow rate would result in reducing energy input as well as reduced desorption of

contaminant from the surface of soi1 particles.

Volatilization is the main mechanism of contaminant removal during air injection.

The primary mechanism for mass transfer is volatilization, which occun when air passes

through the contaminated zones and VOCs evaporate into the air channels (Johnson, 1993).

It will take longer to clean soils with high moisture content than similar soils with

Iower moisture (Zwick et al., 1995). Dry air might be injected into the soil to reduce soil

moisture content and produce a tensiometric, or dry. barrîer to contain liquid-phase

transport (Thomson et al., 1996). If large amounts of water are withdrawn From soil with

the gaseous s t e m and contarninants, the pneumatic soil pemeability could change. and

temperature could drop down to 10°C (Garcia-heruzo et al., 1994). This will decrease

system energy and will result in di fficulty in BTEX evaporation.

+ 3% water

+ 4.5% water + 6% water

O 5 IO 20 25 3 0 3 5 40 45 50 Air Injection Duration (Iir)

Figure 5.22 'I'oluene Rernoval Efficiency vs. Air Injection Duration for Different Soi1 Water Contents (Run 18)

O 5 i O 15 20 25 30 35 40 45 50 Air Injection Duration (hr)

Figure 5.24 (m+p) Xylene Removol Efficiency vs. Air Injection Duration for Differen~ Soil Water Contents (Run 20)

However, when soil moisture is increased during bioremediation of contaminant, the

microbial activity can be significantly enhanced.

In addition, the lower the boiling point and Henry's Law Constant of a contaminant,

the higher the removal efficiency (Ji et al., 1993).

5.5.2 Effect of Clay Content on BTEX Removal

Three types of soils were used to study the effect of clay content on contaminant

rernoval efficiency. Benzene, toluene, ethyl-benzene and (m+p)-xylene (m-xylene: p-

xylene = l : 1) were used as contaminants.

The colurnn was initially packed with soils that had different clay contents (claykand

= 30%- 50% and 70% wt). The total soil weight was 30 g, and the water content was 3%

wt, and the BTEX concentration in soil was 10% wt. The air injection rate was fixed at 1

&min. The experimental temperature was 2S°C, and the atmospheric pressure was 95

kPa,

The experirnental results shown in Figures 5.25 through 5.28 indicate that, as clay

contents increased, BTEX removal efficiencies decreased. In detail, as the clay content

increased fiom 30% to 70% wt, the BTEX removal efficiency decreased from 99% to

85.6% for benzene, fiorn 54.1% to 37.9% for toluene, h m 16.8% to 14.1% for

ethylbenzene, and From 2 1.9% to 13.1% for (rn+p)-xylene with 12 hours of air injection.

o q q c. 0 0 0

Under the same air injection rate, as the clay content increased, the soi1 penneability

decreased, resulting in decreased air flow rate in the soil pore. This reduced air flow rate

would then lead to reduced energy input as well as reduced desorption of contaminant from

soil surface.

Clay in soils acts as a retarding layer to the vertical and horizontal flux of VOCs. The

stead-state vapor diffusion of benzene in soil under isothermal conditions and extremely

slow water flow is directly related to the soil's air-filled porosity (Goring, 1962). Clay soils

tend to have a more uniform pore size distribution than do coarser soils. The coane soils

tend to have larger mean pore sizes, which will transfer fluids faster under saturated

conditions and vapors faster under unsaturated conditions (Hillel, 197 1).

Clay particle had larger surface area per unit weight than that of sand. A material with

higher surface area had higher adsorption capability. It becarne more difficulty to desorb

contaminant frorn a system with higher surface energy. Thus contaminants in soils with a

high clay content are more difficult to be desorbed.

5.5.3 Effect of Adsorption Time on BTEX Removal

To study the effect of adsorbing time on removal eficiency, reagent-grade benzene,

toluene, ethylbenzene and (m+p)-xylene (m-xy1ene:p-xylene = 1:I) were used as

contaminants. The column was initially packed with clay/sand = 50%, total soil weight=30

g, and soil water content = 3% wt. The purnp injection rate was fixed at 4 W m i n , the

experimental temperature was 25 OC, and the atmosphenc pressure was 94.6 kPa.

-+ 12 hrs + 24 hrs

-O- 36 hrs

O 5 10 15 20 35 30 35 40 45 50

Air Injcciion Lhruiion (hr)

Figure 5.30 Tolueiie Removal Efficiency vs. Air injection Duration for Different Adsorption Durations (Run 26)

After the soil was contaminateci and sealed in column for 12, 24 and 36 hr, air

injection started. The experimental results are s h o w in Figures 5.29 through 5.32. It is

indicated that BTFX adsorption duration had slight effects on the removal efficiency. The

ultimate removal efficiency varies from 99.5% to 96.9% for benzene, from 98.9% to 96.8%

for toluene, fiom 53.1% to 51.7% for ethylberuene, and fiom 46.3% to 43.2% for (m+p)-

xy lene.

Adsorption cm be both physical and chemical in nature. However, under normal

environmental conditions and ambient temperanires, the predominant process is physical

adsorption (Valsaraj and Thibodeaux, 1987). This is due to the lact that chemical

adsorption requires actual chernical bonding between the adsorbate and the adsorbent,

which is an energy-intensive process only occumng at high temperatures.

For the same clay particle size and moisture of soil, BTEX adsorption rate varied

slightly when the adsorption duration was changed From 12 to 36 hr. However, too long

adsorption duration may lead to natural degradation of BTEX.

5.6. Effect of Soi1 Type on Toluene Removal

From the discussion in the previous section, the clay content plays an important role

in toluene removal. To investigate the effect of clay content in more detail, additional tests

were conducted under clay content of 60% and 70% wt, with and without electromagnetic -

vibration-enhancement. The detailed testing conditions of the four experirnents are s h o w

in Table 5.8. The air injection pressure was fixed at 22 psi for ail these experiments.

From Figures 5.33 and 5.34, it is indicated that under without electromagnetic

vibration, the toluene removal eficiency significantly decreased as the clay contents

increased fiom 40% to 70% respectively. Thus, the higher the clay content, the lower the

toluene removal e fficiency.

In detailed, without electromagnetic vibration. the toluene removal efficiency (after

LO tu), decreased from 79.61% to 58.65% (Figure 5.33) as the clay content increased from

40% to 70%. With electromagnetic vibration, the eficiency (after 10 hr) decreased fiom

89.85% to 69.97% (Figure 5.34) as the clay contents increased. The following are detailed

explanations of these facts:

(1) The main rnechanism of mass removal in air stripping is volatilization of the

VOCs, which occurs when air passes through the contaminated zones and results in VOCs

evaporation in the air channel. The injected air in the saturated porous media usually moved

in continuous air channels, with VOCs volatizing across the air-soi1 interface of channel

into the air phase. Soil physical properties (i.e. penneability, and surface areas) have

significant impacts on the remediation eficiency. The higher clay content results in less

and smaller air channels, and thus Iower removal efficiencies.

(2) Adsorption is a surface phenornenon, as such the extent OF adsorption is

proportional to specific surface area. Specific surface area is defined as that portion of the

total surface area that is available for adsorption. Desorption can occur between vapor

phase and soi1 surface. The equilibrium particle partial pressure of a volatile contaminant in

+- 40% Clay (R7) -+ 50% Clay (R8) 4 60% Clay (R29) + 70% Clay (R30)

O 60 120 180 240 300 360 420 480 540 600

Air Injection Duration (min)

Figure 5.33 Cumulative Toluene Removal Efficiency vs. Air Injection Duration for Different Clay Contents Without Electromagnetic Vibration

+ 40% Clay (R15)

+- 50% Clay (RI 6)

+ 60% Clay (R31) -x- 70% Clay (R32)

O 60 120 180 240 300 360 420 480 540 600 Air Injection Duration (min)

Figure 5.34 Cumulative Toluene Removal EfEciency vs. Air Injection Duration for Different CIay Contents With Efectromagnetic Vibration

the pore space and mass transfer boom the aqueous liquid or adsorbed phase will depend on

the properties of the contaminant and the soil environment in which it resides.

The more finely divided and the more porous the solid, the greater the arnount of

adsorption accomplished per unit weight of the solid adsorbent. Since clay has higher

surface area than sand, with clay content increasing, the surface area of particles increases.

Thus the media adsorption ability increased, resulting in decreased removal eficiency.

(3) With increasing clay content, the permeability of soil and the air flow rate will

decrease, which will result in less air channels. Thus less air will be held up in the channels

during the air injection period. This will also result in lower removal rate. Lower

permeability clay layers in the soil may restrict the horizon movement of contarninants and

thus allow them to accumulate on the top of the layer.

(4) Normally, during the desorption process, the system will need energy to overcome

bonding energy between two adsorpted componments. As clay content increases, the air

flow rate will decrease, leading to loss of input energy and more dificulty in recovenng

toluene fiom soil.

5.7 Effect of Air Injection Pressure on Toluene Removal

Based on the discussion of the previous section, the effect of air injection pressure on

toluene recovery is also an important factor. To investigate the pressure effect, two

additional tests were conducted with injection pressure settings of 26 and 41 psi without

electromagnetic vibration. For electromagnetic-vibration-euhanced process, one injection

pressure setting of 34 psi was used. The column was re-packed to ensure that air injection

provided sufficient clean inside column. The testing conditions for these experiments are

given in Table 5.8, and the experimental results are shown in Figures 5.35 and 5.36.

The air injection pressure within the tested range had a signifiant effect on toluene

removal efficiency (Figures 5.35 and 5.36). The toluene removal efficiency increased with

the increased air injection pressure. Without the enhancement of electromagnetic vibration,

the toluene removal efficiency increased fiom 66.56% to 75.35% as the air injection

pressure increased from 15 psi to 26 psi. However, as the air injection pressure increased to

41 psi (without electmmagnetic-vibration-enhancement), the toluene removal efficiency

decreased dramatically to 59.14%. Sirnilarly. in the tests with electromagnetic-vibration -

enhancement, the toluene removal emciency also decreased dramatically to 78.75% when

the pressure was raised to 4 psi. Detailed explanations for this phenornenon are provided as

follows:

(1) The higher air injection pressure would result in a higher air flow rate and a

correspondingly increase of energy input, resulting in more effective cleaning.

(2) nie higher air injection pressure will lead to increased system permeability, such

that more air cm be held in the soi1 channels and more toluene can be volatilized, leading to

increased the recovery rate. The higher injection pressure will also pmduce a higher air

flow rate, leading to additional air channels. In fine-grained soils, the higher injection

pressures can result in the formation of subsurface gas pockets, which will continue to

expand hl1 a steady-state condition of air inlet flow is achieved.

4 15 psi (Run 2) + 22 psi (Run 4)

-+ 26 psi (Run 33)

* 41 psi (Run 34)

O 60 120 180 240 300 360 420 480 540 600 Air Injection Duration (min)

Figure 5.35 Cumulative Toluene Removal Effîciency vs. Air Injection Duration for Different Air Injection Pressures without EIectromagnetic Vibration

-t- 15 psi (Run 9) -+- 22 psi (Run 11) -x- 34 psi (Run 35)

O 60 120 180 240 300 360 420 480 540 600


Figure 5.36 Cumulative Toluene Removal Efficiency vs. Air injection Duration for Different Air injection Pressures with Electromagnetic Vibration

(3) Too high injection pressure may create big channels or fractures in the soil. Most

of air will then quickly flow through these fractures, resulting in poor contact and poor

sweeping for the majority of soil. This irreversible Fracturing process could result in a

decrease in toluene recovery rate. Therefore, when using air stripping process in the field,

air pressure needs to be optimized for avoiding fracturing. According to Marley ( 199 1 ), 3 to

5 psi per foot of pressure difference may result in overburden of soil. However, this value

may change with soil types.

5.8 Effect of Electromagnetic Vibration

Figure 5.37 compares the toluene recovery rates with and without electromagnetic

vibration for different soil types. In order to investigate the effect of operational time (of

vibration) on toluene rernoval, comparative experiments were also performed (as shown in

Figure 5.38). the result of Figures 5.37 and 5.38 show that higher toluene removal

efficiencies were achieved with electromagnetic vibration than without it. in average, the

recovery rate with electrornagnetic vibration is 10% higher than that without

electromagnetic vibration. These results demonstrate that electromagnetic vibration

contnbuted significantly to the increase of toluene removal efficiency. More explanations

for this phenomenon are provided as follows:

(1) For desorption of toluene from soil, energy is needed to overcome interfacial

bonding on surface of soil particles. The electromagnetic vibration process increases the

system energy. resulting in easier desorption of toluene fiorn soil.

(2) The electromagnetic vibration will result in more turbulence in the air inflow.

Turbulent 80w compared with eddy fiow will allow air to have more contact area with the

contarninants and thus will increase removal efficiency.

Since the electromagnetic vibration generates a magnetic field, the mixture of air,

water and contaminant vapor would cut magnetic lines of force and produce curent. When

electric current flew fiom soil surface in the same direction as the air flow, the soils were

rnagnetized thus transporting the toluene molecules more quickly to the outlet of the

column.

(3) The electromagnetic vibration would promote movement of soii partic les. The

vibration direction was perpendicular to the air-flow direction. thus increasing air-

contaminant contact area and multing in increased evaporation of contaminants.

(4) Due to electrornagnetic vibration, the air flow became pulsed. The pulsed flow

would likely result in vibration of soil particles, which would produce heat and reduce bond

force between contaminant and soil surface, thus increasing contaminant removal

efficiency .

+ Electromagnetic-Vibration-Enhand Ait Sîripping

+Normal Ar Stripping

30 40 50 60 70 80

Clay Content (%)

Figure 5-37 ToIuene Removal Efficiency vs. Soi1 Clay Content

-x- O min (Run 3) +- 300 min (Run 36) -t- 600 min (Run 1 1 )

O 60 120 180 240 300 360 420 480 540 600


Figure 5.38 Effect of Elecrromagnctic Vibration Duration on Cumulative Tofuene Removal Effkiency

Chapter 6

Conclusions

The developed electromagnetic-vibration-enhanced air stripping technology is an

innovative rneans for remediating petroleum-contaminated soils. I t provides more rapid and

efficient removal of contaminants in soils. Although the initial capital cost for this process

is higher than those of othen, the accelerated removal rate and increased removal efficiency

c m eventually cesult reduced overall system cost.

Bench-scale experiments were conducted to examine (1) the removal of BTEX in soi1

under different experimental conditions using continuous' stable air injection; and (2) the

removal of toluene from soi1 under a variety of system conditions. A 2' factonal design was

used for the expenments with four factors being considered (two levels for each). The main

factors that have significant effects on toluene removal were identified. A response surface

mode1 was formulated based on the factorial analysis results, reflecting interrelationships

between the system conditions and the toluene removal efficiency. The following

conclusions can be drawn based on results of this study:

(1) Using continuous stable air injection can reduce BTEX concentration in soil. The

removal efficiencies are influenced significantly by soil water content and soil type, while

the BTEX adsorption duration has less influence on the efficiency.

(2) The electromagnetic-vibration-enhanced air stripping technology can effectively

remove toluene fiom fine-sand systems with 40%. SON, 60% and 70% clay content. This

method could result in increased removal rate for toluene and decreased operational time.

(3) Clay content, air injection pressure, and electromagnetic vibration have significant

effects on toluene removal efficiency.

(4) The effect of clay content (in fine sand) on toluene removal is significant at low

air injection pressures. The removal efficiency generally decreases with the increase of clay

content.

(5) Higher air injection pressure generally results in higher toluene remova

eficiency. However, when air injection pressure exceeds soil overburden pressure, the "soi

pocket" will form resulting in the lower removal eficiency. Also, the effect of air injection

pressure on toluene removal is insignificant in pure fine-sand systems with 40%, 50%, 60%

and 70% clay content.

(6) The developed electromagnetic-vibration device was made up of low-cost

commercial products. The entire system of electromagnetic-vibration-enhanced air

stripping is easy to set up and operate.

6.2 Research Achievernents

The following are the main achievements of this study fkom the environmental

engineering application perspective:

(1) Electromagnetic-vibration-enhanced air stripping is an innovative method that

wiil result in more rapid and more efficient removal of contaminants.

(2) This study provides a systematic analysis on facton that affect the performance of

the electromagnetic-vibration-enhanced air stripping system. Especially, impacts of soil

type and air injection pressure under a variety ofsysiem conditions were examined.

(3) Methods of factonal design and response-surface modeling were used for

comprehensively designing experiments and analyzing complicated interactions among a

nurnber of system facton and their impacts on the remediation performance.

6.3 Recomrnendations for Future Research

In this study, four factors were considered. In fact, the system is very complicated

and many additional factors and interactions may potentially exist. For example,

temperattue, soi1 moisture, pH and soil metal content may also affect the stripping process.

It is thus desired that more factors be considered in the friture research efforts. Besides,

examination of the systemts performance under vibration frequency other than 60 Hz would

be potentially helpfùl for further increasing system eficiency.

For each Factor, only two levels were considered. In reality, the response to each

factor's effect may not necessarily be linear, such that consideration of more than two

levels could be potentially beneficial.

in the absence of validated predictive models, quantitative results fiom physical

modeling studies cannot be easily extrapolated to any real-field setting. Thus, before

extrapolating physical modeling results to real-field applications, hrther pilot-scale

modeling and experimental studies are needed (Johnson et al., 1999).

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