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Rapid Small Scale Column Testing for Evaluating Arsenic Adsorbents

Subject Area:High-Quality Water

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Prepared by:Bruce Thomson, Alicia Aragon, and Jeremy AndersonCivil Engineering, University of New Mexico, Albuquerque, NM 87131

Joe ChwirkaCH2M-Hill, 6001 Indian School NE, Albuquerque, NM 87110and

Patrick BradySandia National Laboratories, P.O. Box 5800, Albuquerque, NM 87185

Jointly sponsored by:Awwa Research Foundation6666 West Quincy Avenue, Denver, CO 80235-3098and

U.S. Environmental Protection AgencyWashington D.C.

Published by:


DISCLAIMER

This study was jointly funded by the Awwa Research Foundation (AwwaRF) and the U.S. Environmental Protection Agency under Cooperative Agreement No. CR826432-01. AwwaRF and USEPA assume no responsibility for the content of the research study reported in this publication or for the opinions or statements of fact express in the report. The mention of trade names for commercial products does not represent or imply the approval or

endorsement of AwwaRF or USEPA. This report is presented solely for informational purposes.

Copyright © 2005by Awwa Research Foundation

All Rights Reserved

Printed in the U.S.A.


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CONTENTS LIST OF TABLES........................................................................................................................ vii LIST OF FIGURES ....................................................................................................................... ix FOREWORD ............................................................................................................................... xiii ACKNOWLEDGMENTS ........................................................................................................... xiv EXECUTIVE SUMMARY ......................................................................................................... xvi CHAPTER 1: INTRODUCTION...................................................................................................1

Background..........................................................................................................................1 Adsorption Processes ...........................................................................................................1 Rapid Small Scale Column Tests.........................................................................................3 Project Objectives ................................................................................................................3 Organization of the Report...................................................................................................4

CHAPTER 2: THEORETICAL CONSIDERATIONS..................................................................5

Chemistry of Arsenic in Solution ........................................................................................5 Sources of Arsenic ...................................................................................................5 Arsenic Solution Chemistry.....................................................................................5 Arsenic Adsorption ..................................................................................................8

Adsorption Column Theory ...............................................................................................13 Homogenous Surface Diffusion Model (HSDM)..................................................16 Development of Scaling Relationships for Column Performance.........................20 Determination of the Surface Diffusion Coefficient..............................................22 Mass Transfer Zone Considerations ......................................................................25 Summary Comments..............................................................................................26

CHAPTER 3: RESEARCH METHODS......................................................................................27

Analytical Methods & Materials........................................................................................27 Selection of Media .................................................................................................27 Media Preparation..................................................................................................28 Test Solutions.........................................................................................................28 Media Characterization..........................................................................................29 Scanning Electron Microscopy ..............................................................................29 Surface Elemental Analysis of Spent Media .........................................................29 Measurements of Surface Areas and Pore Size Distributions ...............................29 pH of the Zero Point of Charge..............................................................................30 Bulk Density ..........................................................................................................30 Particle Density......................................................................................................30 GFH Mass Correction ............................................................................................31

Isotherm Experiments ........................................................................................................31


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Determination of Surface Diffusion Coefficients ..................................................32 Laboratory Column Experiments.......................................................................................36 Field Column Experiments ................................................................................................38

CHAPTER 4: RESULTS AND DISCUSSION �MEDIA CHARACTERISTICS & ADSORPTION PROERTIES........................................................................................................41

Media Characterization......................................................................................................41 Scanning Electron Microscopy ..............................................................................41 BET Analysis .........................................................................................................47 pH of the Zero Point of Charge..............................................................................50 Media Density & Correction for Moisture Content of GFH..................................52

Batch Isotherm Results ......................................................................................................53 Measurements of Surface Diffusion Coefficients..............................................................58

CHAPTER 5: RESULTS AND DISCUSSION � COLUMN EXPERIMENTS..........................67

Lab Testing with UNM Tap water.....................................................................................67 Field Column Experiments ................................................................................................74

Testing by the City of El Paso ...............................................................................74 Testing by the City of Albuquerque.......................................................................76

Mass Transfer Zone Considerations ..................................................................................80 CHAPTER 6: CONCLUSIONS ...................................................................................................83 APPENDIX A � GRAPHICAL ISOTHERM RESULTS .............................................................88 APPENDIX B � SEM ANALYSIS OF VIRGIN AND SPENT MEDIA.....................................99 REFERENCES ............................................................................................................................105 ABBREVIATIONS LIST AND DEFINITION OF TERMS ......................................................109


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LIST OF TABLES 1.1 Summary of technologies available for providing arsenic removal from ground water. ....2 2.1 Dissociation constants for arsenic acid (As(V)) and arsenous acid (As(III)). .....................5 2.2 Summary of different mathematical models used for fixed bed column adsorber design.16 2.3 Important dimensionless groupings from mathematical analysis of adsorptive transport.18 3.1. Summary of analytical methods used in project. ...............................................................27 3.2 Constant parameters used in the rate studies. ....................................................................34 3.3 Variable parameters used in the rate studies......................................................................35 3.4 Batch pore surface diffusion model input parameters. ......................................................36 3.5 Laboratory column experiment parameters .......................................................................37 3.6 Pilot column operating parameters for City of Albuquerque adsorption column tests......39 3.7 Water quality characteristics at the City of Albuquerque Pino Yards facility (analyses

provided by the City of Albuquerque). ..............................................................................40 4.1 Elements detected by EDX analysis of fresh and spent adsorption media. .......................42 4.2 Summary of BET surface analyses of adsorption media (results provided by PMI, Ithaca,

NY). ...................................................................................................................................49 4.3 Summary of pore diameters of adsorption media in Angstroms and microns (results

provided by PMI, Ithaca, NY). ..........................................................................................49 4.4 Summary of measurements of the pHzpc. ...........................................................................52 4.5 Summary of density measurements. ..................................................................................52 4.6 Freundlich parameters for batch adsorption studies using a three week equilibration

period. ................................................................................................................................54 4.7 Summary of measurements of surface diffusion coefficients (Ds). ...................................64 5.1 Performance characteristics of GFH laboratory columns..................................................72 5.2 Performance characteristics of SORB E-33 laboratory columns.......................................72


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5.3 Performance characteristics of SANS laboratory columns................................................72 5.4 Calculated diffusivity factors and corresponding EBCTsc.................................................73 5.5 Water quality at the City of Albuquerque Pino Yards facility...........................................77 5.6 Performance characteristics of GFH field columns. ..........................................................79 5.7 Performance characteristics of E-33 field columns. ..........................................................79 5.8 Performance characteristics of SANS field columns.........................................................80 5.9 Dimensionless parameters representing the height of the mass transfer zone...................81


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LIST OF FIGURES 2.1 Speciation of As(V) in aqueous solution. ............................................................................6 2.2 Speciation of As(III) in aqueous solution. ...........................................................................7 2.3 pe-pH diagram for arsenic. Total arsenic concentration = 10-6 M, total sulfur

concentration = 10-4 M.........................................................................................................8 2.4 Schematic illustration of the surface structure of As(V) adsorbed onto Goethite (α-

FeOOH)................................................................................................................................9 2.5 Proposed mechanism for As(V) adsorption onto a goethite surface with electron

movement indicated by arrows. (Prepared by Brian McAuley, 2004) .............................10 2.6 Fractional distribution of surface functional groups on goethite (α-FeOOH) as a function

of pH. .................................................................................................................................12 2.7 Conceptual model of transport processes associated with adsorption onto a porous

material. .............................................................................................................................14 3.1 Mixing apparatus for batch experiments............................................................................32 3.2 Diagram of apparatus used in the differential column test. ...............................................33 3.3 Photographs of the differential column batch reactor system............................................34 3.4 Photographs of the laboratory columns for the three different media sizes used in this

study. The three systems are (clockwise from upper left) 28x48 mesh media, 100x200 mesh media, and 48x100 mesh media. ..............................................................................38

4.1 Scanning electron micrographs of activated alumina for (left to right) 28 x 48 mesh, 48 x

100 mesh, and 100 x 200 mesh: (a) 100x magnification (scale bar = 500 um), (b) 500x magnification (scale bar = 100 um), (c) 2500x magnification (scale bar = 20 um). .........42

4.2 Scanning electron micrographs of granular ferric hydroxide for (left to right) 28 x 48

mesh, 48 x 100 mesh, and 100 x 200 mesh: (a) 100x magnification (scale bar = 500 um), (b) 500x magnification (scale bar = 100 um), (c) 2500x magnification (scale bar = 20 um). ....................................................................................................................................43

4.3 Scanning electron micrographs of SORB-33 for (left to right) 28 x 48 mesh, 48 x 100

mesh, and 100 x 200 mesh: (a) 100x magnification (scale bar = 500 um), (b) 500x magnification (scale bar = 100 um), (c) 2500x magnification (scale bar = 20 um). .........44


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4.4 Scanning electron micrographs of SANS for (left to right) 28 x 48 mesh, 48 x 100 mesh, and 100 x 200 mesh: (a) 100x magnification (scale bar = 500 um), (b) 500x magnification (scale bar = 100 um), (c) 2500x magnification (scale bar = 20 um)..................................45

4.5 Transmission electron micrograph image of SANS media................................................46 4.6 Pore size distribution as a plot of pore volume vs. pore diameter. ....................................47 4.7 Pore size distribution as a plot of surface area vs. pore diameter. .....................................47 4.8 Cumulative pore surface area vs. pore diameter. ...............................................................48 4.9 Pore volume plotted as a range of pore diameters. ............................................................48 4.10 Change in pH vs. initial pH using salt addition method for AA 48x100...........................50 4.11 Change in pH vs. initial pH using salt addition method for GFH 48x100.........................51 4.12 Change in pH vs. initial pH using salt addition method for E-33 48x100.........................51 4.13 Change in pH vs. initial pH using salt addition method for SANS 48x100. .....................52 4.14 Adsorption capacity for GFH in equilibrium with 100 ug/L As(V) as a function of pH

(calculated from Freundlich isotherm constants)...............................................................55 4.15 Adsorption capacity for E-33 media in equilibrium with 100 ug/L As(V) as a function of

pH (calculated from Freundlich isotherm constants).........................................................55 4.16 Adsorption capacity for SANS media in equilibrium with 100 ug/L As(V) as a function

of pH (calculated from Freundlich isotherm constants). ...................................................56 4.17 Comparison of adsorption capacity for media size 28 x 48 in equilibrium with 100 ug/L

As(V) as a function of pH (calculated with Freundlich constants)....................................57 4.18 Comparison of adsorption capacity for media size 48 x 100 in equilibrium with 100 ug/L

As(V) as a function of pH (calculated with Freundlich constants)....................................57 4.19 Comparison of adsorption capacity for media size 100 x 200 in equilibrium with 100

ug/L As(V) as a function of pH (calculated with Freundlich constants). ..........................58 4.20 Batch rate data and BPSDM prediction for SANS, 28x48 mesh.......................................59 4.21 Batch rate data and BPSDM prediction for SANS, 48x100 mesh.....................................60 4.22 Batch rate data and BPSDM prediction for SANS, 100x200 mesh...................................60


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4.23 Batch rate data and BPSDM prediction for E-33, 28x48 mesh. ........................................61 4.24 Batch rate data and BPSDM prediction for E-33, 48 x 100 mesh. ....................................61 4.25 Batch rate data and BPSDM prediction for E-33, 100 x 200 mesh. ..................................62 4.26 Batch rate data and BPSDM prediction for GFH, 28x48 mesh.........................................62 4.27 Batch rate data and BPSDM prediction for GFH, 48 x 100 mesh.....................................63 4.28 Batch rate data and BPSDM prediction for GFH, 100 x 200 mesh. ..................................63 4.29 Dependence of the surface diffusion coefficient (Ds) on particle diameter (error bars

represent the standard deviation of the determinations). ...................................................65 5.1 Laboratory column breakthrough curves for activated alumina, As(V) Co = 100 ug/L, pH

7..........................................................................................................................................68 5.2 Laboratory column breakthrough curves for GFH (raw data), As(V) Co = 100 ug/L, pH

7..........................................................................................................................................69 5.3 Laboratory column breakthrough curves for GFH (erroneous data removed for 48x100

mesh column), As(V) Co = 100 ug/L, pH 7. ....................................................................69 5.4 Laboratory column breakthrough curves for E-33 (raw data), As(V) Co = 100 ug/L, pH

7..........................................................................................................................................70 5.5 Laboratory column breakthrough curves for E-33 (erroneous data removed for 48x100

mesh column), As(V) Co = 100 ug/L, pH 7. ....................................................................70 5.6 Laboratory column breakthrough curves for SANS (raw data), As(V) Co = 100 ug/L, pH

7..........................................................................................................................................71 5.7 Laboratory column breakthrough curves for SANS (erroneous data removed for 48x100

mesh column), As(V) Co = 100 ug/L, pH 7. ....................................................................71 5.8 Arsenic breakthrough curves for El Paso adsorption columns. One column treated water

at ambient pH, the other two treated water adjusted to pH 6.8 using CO2(g). ....................75 5.9 Silica monitoring results for El Paso adsorption columns. One column treated water at

ambient pH, the other two treated water adjusted to pH 6.8 using CO2(g). ........................76 5.10 Field column breakthrough curves for GFH, City of Albuquerque Pino Yards tap water.78 5.11 Field column breakthrough curves for SORB E-33, City of Albuquerque Pino Yards tap

water...................................................................................................................................78


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5.12 Field column breakthrough curves for SANS, City of Albuquerque Pino Yards tap water...................................................................................................................................79

A.1 Linear regression of three-week isotherm data for SORB E-33, 28x48, pH 5 � pH 9. .....89 A.2 Linear regression of three-week isotherm data for SORB E-33, 48x100, pH 5 - pH 9. ....90 A.3 Linear regression of three-week isotherm data for SORB E-33, 100x200, pH 5 - pH 9. ..91 A.4 Linear regression of three-week isotherm data for GFH, 28x48, pH 5 � pH 9. ................92 A.5 Linear regression of three-week isotherm data for GFH, 48x100, pH 5 � pH 9. ..............93 A.6 Linear regression of three-week isotherm data for GFH, 100x200, pH 5 � pH 9. ............94 A.7 Linear regression of three-week isotherm data for SANS, 28x48, pH 5 � pH 9. ..............95 A.8 Linear regression of three-week isotherm data for SANS, 48x100, pH 5 � pH 9. ............96 A.9 Linear regression of three-week isotherm data for SANS, 100x200, pH 5 � pH 9. ..........97 B.1 Scanning electron micrographs for (left to right) virgin and spent AA 28x48 mesh at

magnifications of: (1) 20x (scale bare = 500 um), (b) 1000 x (scale bar = 10 um).........100 B.2 Scanning electron micrographs for (left to right) virgin and spent GFH 28x48 mesh at

magnifications of: (a) 20x (scale bar = 500 µm), (b) 100x (scale bar = 100µm), (c) 1000x (scale bar = 10µm), (d) 10,000x (1µm). ..........................................................................101

B.3 Scanning electron micrographs for (left to right) virgin and spent SORB E-33 28x48

mesh at magnifications of: (a) 20x (scale bar = 500 µm), (b) 100x (scale bar = 100µm), (c) 1000x (scale bar = 10µm), (d) 10,000x (1µm)...........................................................102

B.4 Scanning electron micrographs for (left to right) virgin and spent SANS 28x48 mesh at

magnifications of: (a) 20x (scale bar = 500 µm), (b) 100x (scale bar = 100µm), (c) 1000x (scale bar = 10µm), (d) 10,000x (1µm). ..........................................................................103


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FOREWORD The Awwa Research Foundation is a nonprofit corporation that is dedicated to the implementation of a research effort to help utilities respond to regulatory requirements and traditional high-priority concerns of the industry. The research agenda is developed through a process of consultation with subscribers and drinking water professionals. Under the umbrella of a Strategic Research Plan, the Research Advisory Council prioritizes the suggested projects based upon current and future needs, applicability, and past work; the recommendations are forwarded to the Board of Trustees for final selection. The foundation also sponsors research projects through the unsolicited proposal process; the Collaborative Research, Research Applications, and Tailored Collaboration programs; and various joint research efforts with organizations such as the U.S. Environmental Protection Agency, the U.S. Bureau of Reclamation, and the Association of California Water Agencies. This publication is a result of one of these sponsored studies, and it is hoped that its findings will be applied in communities throughout the world. The following report serves not only as a means of communicating the results of the water industry�s centralized research program but also as a tool to enlist the further support of the nonmember utilities and individuals. Projects are managed closely from their inception to the final report by the foundation�s staff and large cadre of volunteers who willingly contribute their time and expertise. The foundation serves a planning and management function and awards contracts to other institutions such as water utilities, universities, and engineering firms. The funding for this research effort comes primarily from the Subscription Program, through which water utilities subscribe to the research program and make an annual payment proportionate to the volume of water they deliver and consultants and manufacturers subscribe based on their annual billings. The program offers a cost-effective and fair method for funding research in the public interest. A broad spectrum of water supply issues is addressed by the foundation�s research agenda: resources, treatment and operations, distribution and storage, water quality and analysis, toxicology, economics, and management. The ultimate purpose of the coordinated effort is to assist water suppliers to provide the highest possible quality of water economically and reliably. The true benefits are realized when the results are implemented at the utility level. The foundation�s trustees are pleased to offer this publication as a contribution toward that end. Walter J. Bishop James F. Manwaring, P.E. Chair, Board of Trustees Executive Director Awwa Research Foundation Awwa Research Foundation


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ACKNOWLEDGMENTS

The authors of this report are indebted to the following water utilities and individuals for their cooperation and participation in this project: El Paso Water Utility �Ed Archuleta, John Balliew, David Brosman City of Albuquerque � Barbara Gastian, William Lindberg, John Stomp Jim Krumhansl of Sandia National Laboratories provided extensive assistance with geochemical analyses of the media and interpretation of the results. David Teter, Nadim Khandaker and Hank Westrich of Sandia National Laboratories provded the SANS adsorption media. David Hand of Michigan Technological University provided the Batch Pore Surface Diffusion Model for use in determining internal surface diffusion coefficients. Special appreciation is extended to Jennifer Warner, the AwwaRF Project Officer, and the Project Advisory Committee, David Hand, Harish Arora, and Thomas Jefferson, for their guidance and assistance on the project.


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EXECUTIVE SUMMARY Implementation of the new, more stringent drinking water standard for arsenic (As) will have an extraordinary impact on public water systems that rely upon ground water as their source of supply. These water systems generally provide no treatment other than chlorination, hence these utilities must install treatment processes to remove As from otherwise high quality water. The most promising technologies for selective As removal from small systems are adsorption processes. Adsorbents that have been proposed include a variety of metal oxide (MeOx) media such as activated alumina and ferric oxides, and recently titanium and zirconium oxides. Depending upon the characteristics of the solution, and especially its pH, these media are capable of treating from perhaps 20,000 to in excess of 100,000 bed volumes (BVs) of water prior to exhaustion. A major problem in evaluating potential adsorption media is that laboratory or field scale pilot testing of these media require very long runs and therefore are very costly. For example, a small lab scale column (2.5 cm diameter, 10 cm long, with an empty bed volume of 200 mL) operated for 10,000 bed volumes at a five minute empty bed contact time (EBCT) requires a continuous run length of 35 days and will use 2,000 L of water. The logistics associated with a study of this duration make it difficult to determine the effects of solution characteristics such as pH on system performance, evaluate the effects of different design criteria on system performance, and to make direct comparisons of alternative media. RESEARCH OBJECTIVES The objective of this project was to develop a Rapid Small Scale Column Testing (RSSCT) procedure similar to that used for adsorption of organic constituents by granular activated carbon (GAC). The RSSCT process enables determination of the long-term performance of arsenic adsorbents by measuring the performance of laboratory columns using small diameter media operated at reduced EBCTs. The media that were investigated included activated alumina, ferric oxyhydroxides, and a proprietary media developed by Sandia National Laboratories. The emphasis of this project was to investigate the theoretical aspects of the RSSCT process and development of testing procedures. The work included an investigation of the surface and pore structure of selected media and geochemical characterization of the media to determine surface area, and pH of the zero point of charge (pHzpc). Equilibrium and kinetic adsorption studies were conducted to characterize the performance of media of different sizes. Subsequently, column studies were conducted using three different size media (28x48 mesh, 48x100 mesh, and 100x 200 mesh) to validate the RSSCT process. RESEARCH APPROACH The project began with a review of the literature on arsenic adsorption by MeOx and on the theory behind the RSSCT process. This was of value because there are significant differences between adsorption of organics by GAC and adsorption of inorganic arsenic by MeOx. For iron and aluminum oxyhydroxy surfaces arsenic is initially attracted to the surface by coulombic forces between negative arsenate species (H2AsO4

- and HAsO42-) and positively

charged surface sites. With time, inner sphere complexes involving covalent bonds between the


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metal atoms and As form. This mechanism explains the importance of solution pH and the relatively long time to achieve equilibrium conditions. The theory behind the RSSCT process is based on the concept that the performance of adsorption columns is determined primarily by mass transfer considerations, including external and internal processes. Frequently the internal mass transport is the rate limiting process. This can be reduced by using smaller diameter particles which allows operation of the columns at smaller EBCT values. The scaling ratio between EBCTs in the large columns and the small columns depends upon the radius of the particles and the internal pore surface diffusion coefficient. Four adsorption media were selected for testing: an iron coated activated alumina (Alcan FS50 media), granular ferric hydroxide (US Filter GFH), a granular ferric oxide (Severn Trent E-33) and a proprietary media developed by Sandia National Laboratories (SANS). The experimental work included the following:

• Measurement of the equilibrium adsorptive properties of the media as a function of pH and particle size.

• Measurements of the surface characteristics of the media including pH of the zero point of charge, the specific surface area and pore size distribution.

• Measurement of the internal pore surface diffusion coefficients using differential column batch reactors.

• Extensive column studies using media of all three sizes, both in the field and in the laboratory. One set of columns was operated for nearly 120,000 bed volumes without reaching breakthrough.

• Geochemical analysis of the spent media to detect changes resulting from column operation.

CONCLUSIONS This project found the following:

• The adsorption properties of the four media tested were strongly dependent on pH with the highest capacities for all occurring below pH 7.5. Time to reach equilibrium was generally greater than two weeks.

• The media have high internal surface areas that ranged from 113 m2/g to 274 m2/g. The average pore diameters ranged from 3.4 nm to 15.7 nm.

• The pH of the zero point of charge ranged from 6.4 for activated alumina to 7.7 for E-33. • The internal surface diffusion coefficient for E-33 was found to depend on particle size,

whereas it was not dependent on size for the other media. • The number of bed volumes of water treated to breakthrough depended most strongly on

pH. • A linear scaling relationship between EBCT and particle radius produced good similarity

between the breakthrough curves for lab scale and field scale adsorption columns. • Adsorption columns containing SANS and E-33 media operated at pH 6.8 by the City of

El Paso treated nearly 120,000 bed volumes of water over a period of 15 months. Breakthrough was achieved for the E-33 media, but the SANS media never did reach breakthrough.


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The most significant conclusion of this work is that the RSSCT process appears to be valid for the scaling between laboratory columns containing small diameter adsorbent media and and field scale columns containing large diameter media. However, the results also suggest that the nature of the scaling relationship likely depends on the system. In particular, the surface diffusion coefficient for one media was found to be proportional to particle size, whereas it was independent of particle size for the others. Thus, proper application of the scaling relation should be preceded by determination of the dependence of the diffusion coefficient on particle size. In spite of this uncertainty, the RSSCT process has considerable value in pilot testing of processes where the objective is to make a relative comparison of the affect of different variables on process performance.


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1

CHAPTER 1 INTRODUCTION

BACKGROUND In 2001 the USEPA established a new maximum contaminant level (MCL) for arsenic (As) in drinking water of 10 µg/L to replace the interim value of 50 µg/L. Information developed by EPA and others (Focazio et al., 1999) estimated that this new standard would affect about 4,000 public water supply systems in the U.S. This analysis also showed that the new standard for As in drinking water will have a disproportionately large impact on systems which rely upon ground water for water supply. This is especially significant because these systems generally provide no water treatment beyond simple chlorination, and therefore do not have the infrastructure nor the expertise to operate complicated water treatment facilities. The USEPA (2000) identified six technologies as best available technologies (BAT) for As removal: ion exchange (IX), activated alumina (AA) adsorption, reverse osmosis, modified coagulation and filtration, modified lime softening and electrodialysis reversal. In addition, point-of-use (POU) technologies which are based on some of the same principles may be appropriate for some public water systems. Table 1.1 lists these technologies and summarizes the relative merits of each. Chwirka et al. (2001) described a design study conducted for the City of Albuquerque which considered three treatment technologies for a large (2.3 Mgal/d) well: ion exchange, activated alumina adsorption, and iron coagulation and microfiltration (C/MF). This study found that C/MF was the least expensive process, principally because of the relatively poor adsorptive capacity of activated alumina. Subsequent developments of improved As adsorption media and the high capital cost and complexity of the C/MF process have resulted in improved performance of the adsorption process so that it is an increasingly attractive treatment alternative, especially for small communities. ADSORPTION PROCESSES The principal advantages of As adsorption over other technologies are: 1) it provides selective removal of As with little or no impact on other water quality parameters, and 2) the process is generally very simple. A major disadvantage of early adsorption schemes using activated alumina is that they required pH adjustment of the influent solution and also required regeneration of the media using caustic soda (NaOH). These two requirements added complexity and cost, and resulted in generation of large amounts of potentially hazardous brine that required disposal (Chwirka et al., 2001). In the past couple of years several new types of media have been developed that offer significant advantages over activated alumina. Most of these media are similar to activated alumina in that they are based on metal oxide minerals, however, they have much greater sorption capacities and they do not require pH adjustment prior to treatment. Furthermore, the capacity of these new media is sufficiently large that regeneration of the media is not practiced; spent media is simply disposed of directly to a landfill.


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Table 1.1 Summary of technologies available for providing arsenic removal from ground water.

Process Advantages Disadvantages IX • Well established technology

(essentially off-the-shelf) • SO4

2- is removed preferential to As(V) • Short column runs (500-1,000 BVs) • Very large salt use • Regenerant is potential hazardous waste • Sophisticated O&M requirements.

Fe Precipitation & Filtration

• Selective As removal • Waste is not hazardous • Lower cost

• Limited experience • Complicated process • Sophisticated O&M requirements.

Membrane Processes

• Removes other constituents in addition to As

• Complicated technology • Expensive • Wastes 5%-20% of water

Adsorption with Regeneration (conventional media)

• Selective As removal • Long column runs (5,000-10,000 BVs)

• Limited experience • May require pH adjustment • Requires handling acids & bases • Regenerant is potential hazardous waste • Sophisticated O&M reqts.

Adsorption with no Regeneration (new media)

• Selective As removal • New media offer promise of very long

column runs (to 100,000 BVs) • Reduced waste management costs • Easy O&M • No hazardous chemicals or residuals

• Almost no U.S. pilot scale experience • May require pH adjustment • Dependent on media costs

RO based POU systems

• Inexpensive • Good removal of As(V), F, & other

contaminants when maintained

• Intensive & invasive O&M procedures • Won�t achieve 100% compliance • Wastes much water • Poor success rate to date

There are five factors that will determine whether a candidate adsorption media is

suitable for application in an As treatment process:

• The ability to achieve selective As removal in a variety of water qualities (pH, total dissolved solids (TDS), and concentrations of competing species (silica, fluoride, etc.))

• Whether there are pretreatment requirements, especially pH adjustment • The capacity of the media which in turn determines the amount of water that can be

treated prior to column exhaustion • Cost • Management of the spent media: method of regeneration, potential generation of

hazardous wastes, or one time media use followed by direct disposal.

A practical problem when evaluating these media is that due to their high capacity, extremely long run lengths are required to test the media to exhaustion. Associated with this is the need for very large volumes of water. Work with AA and iron oxide coated sand has shown that columns with five minute EBCTs operated at pH 6.0 can treat up to 10,000 empty bed volumes (BV) of Albuquerque ground water prior to exhaustion (Warder, 1999; Clifford, 1999). Thus, a small lab scale column (2.5 cm diameter, 10 cm long, with an empty bed volume of 50 mL) operated for 10,000 BVs at a five minute EBCT requires a continuous run length of 35 days


3

and will use 500 L of water. Testing a media with a capacity to treat 100,000 BVs as claimed by some manufacturers of new adsorbents, would take nearly a year. These logistics make it difficult, time consuming and costly to evaluate candidate media. RAPID SMALL SCALE COLUMN TESTS Similar problems of very long run lengths were encountered in evaluating the performance of granular activated carbon (GAC) adsorption processes. These problems have been substantially resolved by development of the Rapid Small-Scale Column Test (RSSCT) procedure (Crittenden et al., 1987a, 1991). This method is based on application of adsorption theory to develop scaling relationships that allow correlation of lab scale column results operated at accelerated flow rates to full-scale column performance. There are three principal factors that affect the performance of an adsorbent: 1) selectivity, 2) capacity, and 3) adsorption kinetics. The RSSCT concept is based upon a theoretical analysis of the adsorption processes that govern performance including solution and surface mass transport and adsorption kinetics. Adsorption theory has been used to develop scaling laws to relate the performance of GAC in prototype columns to that in very small laboratory columns using small diameter media. Mass transfer models have been used to determine dimensionless parameters that establish similitude between the small and large-scale columns. For GAC columns, the RSSCT procedure can reduce the time required for testing by factors ranging from 1/4 to 1/10 (Crittenden, et al., 1991). Due to the significant time savings, various adsorbents can be quickly compared while varying particle size, water quality parameters, hydraulic loadings, and empty bed contact times (EBCT). The performance of small columns containing small adsorbent media can then be scaled up to predict performance and aid in the design of a full-scale treatment operation. The objective of this project was to determine whether the RSSCT procedure can be applied to As adsorption onto metal oxy-hydroxide media. The RSSCT process for arsenic removal uses laboratory testing procedures similar to those developed for GAC adsorption. In considering application of the RSSCT process for arsenic removal, it is important to note that there are significant differences between adsorption of organics by GAC and adsorption of arsenic by metal oxides. The most important differences are the nature of the bonds and the internal pore structure of the adsorbents. In contrast to the hydrophobic bonds between organic solutes and GAC, arsenic adsorption onto metal oxide surfaces is dominated by electrostatic attraction, at least initially. Also, metal oxide surfaces have much less internal porosity than GAC which results in less surface area and different types of mass transfer. PROJECT OBJECTIVES The objective of this project was to develop an RSSCT process that will enable prediction of the long-term performance of arsenic adsorbents by measuring the performance of small columns using small diameter media operated under reduced EBCTs. Media under investigation included activated alumina, ferric oxyhydroxides, and a proprietary media developed by Sandia National Laboratory. This project focused on examining the theoretical aspects of RSSCT testing and the development of testing procedures. The work included an investigation of the surface and pore structure of selected media and geochemical characterization of the media to determine surface area, and pHzpc. Equilibrium and kinetic adsorption studies were conducted to


4

characterize the performance of media of different sizes. Subsequently, column studies were conducted using three different size media (28 x 48 mesh, 48 x 100 mesh, and 100 x 200 mesh) to validate the RSSCT process. ORGANIZATION OF THE REPORT This report is organized into five chapters. Chapter 1 presented an overview of the technologies associated with As treatment, briefly summarized the principal considerations associated with adsorption processes, and stated the objectives of this study. Chapter 2 provides a discussion of the theoretical concepts underlying the RSSCT process. This includes a discussion of the chemistry of As adsorption and of the mathematical models that have been developed to describe adsorption onto GAC. Chapter 3 describes the experimental program that was conducted during this project. These include both laboratory and field experiments conducted by participating utilities. Chapter 4 discusses and interprets the results of the research on the media characteristics and the equilibrium adsorption properties of the different media. The data is analyzed in the context of the RSSCT theory. Chapter 5 presents the laboratory and field column studies. Chapter 6 presents a summary of the project and provides conclusions. Appendices are included which provide supporting information including adsorption isotherm plots, a description of a method leading to an approximate solution of the homogeneous surface diffusion model (HSDM), and results of adsorption column testing conducted by the City of El Paso.


5

CHAPTER 2 THEORETICAL CONSIDERATIONS

Before considering the performance of As adsorption columns it is beneficial to gain an understanding of the chemistry of arsenic in solution and the nature of adsorption reactions. Emphasis is placed on the similarities and differences between As adsorption by metal oxide surfaces and adsorption of organics by GAC. A review of the theories that have been developed to model the performance of adsorption columns is presented. Finally, a review of the theory and development of the RSSCT procedure is given. CHEMISTRY OF ARSENIC IN SOLUTION Sources of Arsenic Arsenic occurs in water supplies as a result of both natural and anthropogenic sources. Mineral weathering is a major source of naturally occurring As in ground and surface waters. Arsenic originates from As-containing minerals such as sulfides and some volcanic glasses, and is released into solution by redox processes and changes in solution pH. Arsenic is also a by-product of smelting, refining, and mining operations (Fowler, 1977). It was historically used in agriculture as a pesticide and feed additive. Arsenic was also used extensively as a wood preservative in copper-chromate-arsenic (CCA) treated wood. Agricultural use of arsenic was discontinued prior to 1980 and the wood treatment industry voluntarily discontinued the use of CCA as a preservative in December 2003. Although it has been widely used in the past, the cause of elevated arsenic levels in nearly all public water supplies is due to natural sources.

Arsenic Solution Chemistry Arsenic is stable in water in two oxidation states: arsenate (As(V)) and arsenite (As(III)). While both are weak acids, they have quite different dissociation constants which significantly affects their solution chemistry. These are summarized in Table 2.1.

Table 2.1. Dissociation constants for arsenic acid (As(V)) and arsenous acid (As(III)).

No. Reaction log K As(V) Species 1 H3AsO4 = H2AsO4

- + H+ -2.25 2 H2AsO4

- = HAsO42-+ H+ -6.75

3 HAsO42- = AsO4

3- + H+ -11.60 As(III) Species 4 H3AsO3 = H2AsO3

- + H+ -9.22 5 H2AsO3

- = HAsO32- + H+ -12.11

6 HAsO32- = AsO3

3- + H+ -13.41 Reduction of As(V) and As(III 7 H3AsO4 + 2e- + 2H+ = H3AsO3 + H2O -19.444 Source: Data from Hering et al., 1996


6

The acid-base chemistry of As at each oxidation state can be illustrated by plotting the ionization fractions of each species. The ionization fraction is defined as the fraction of the total amount of the compound present as the individual species. The speciation of As(V) is presented in Figure 2.1 and the speciation of As(III) is shown in Figure 2.2. These diagrams show that whereas the dominant forms of As(V) are all ionic down to pH 2.25 (the pK of reaction 1), the dominant form of As(III) present in solution is non-ionic H3AsO3 up to pH 9.22 (the pK of reaction 4). This difference is important because ionic constituents are more readily removed by most treatment processes including ion exchange, membrane processes such as reverse osmosis, and precipitation processes. Adsorption of As onto inorganic metal oxides is also strongly affected by the ionic nature of As(V) because such surfaces have positive or negative charges depending on the solution characteristics.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 2 4 6 8 10 12 14

pH

Frac

tion

H3AsO4 H2AsO4- HAsO4

2- AsO43-

Figure 2.1. Speciation of As(V) in aqueous solution.


7

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 2 4 6 8 10 12 14pH

Frac

tion

H3AsO3 H2AsO3-

HAsO32-

AsO33-

Figure 2.2. Speciation of As(III) in aqueous solution.

The redox and acid-base chemistry of As can be conveniently summarized by a pe-pH diagram (Stumm and Morgan, 1996). The pe-pH diagram for As is presented in Figure 2.3. It shows that As(V) is thermodynamically stable under oxidizing conditions while As(III) is stable under reducing conditions. Under strongly reducing conditions, which occur in the presence of hydrogen sulfide, inorganic arsenic-sulfide minerals are found to be stable.


8

0

5

10

15

20

-5

-10

-15

pe

0 2 4 6 8 10 12 14pH

0

H AsO3 4

H AsO2 4-

HAsO42-

AsO43-

H AsO3 3

HAsO32-

AsO33-

As S2 3(s)

.5

1.0

-.5

Eh (volts)

H AsO2 3-

Figure 2.3. pe-pH diagram for arsenic. Total arsenic concentration = 10-6 M, total sulfur concentration = 10-4 M. Arsenic Adsorption The nature of the bonds between As species and metal oxide surfaces has been the subject of much investigation. Investigators have primarily used Fourier Transform Infrared (FTIR) spectroscopy, X-ray Adsorption Near Edge Structure (XANES) and Extended X-ray Absorption Fine Structure (EXAFS) to characterize these bonds (Sun and Doner, 1996; Suarez et al. 1999;, Lumsdon et al., 1984; Harrison and Berkheiser, 1982; Fendorf et al. 1997). It appears that there are three different types of bonds formed between arsenate and a metal hydroxide


9

surface. Fendorf et al. (1997) proposed the following structures for adsorption of As(V) onto a goethite (α-FeOOH) surface (Figure 2.4). At low adsorption densities the monodentate complex is formed consisting of a linear bond Fe-O-As. Additional bonds form between the arsenate and hydroxide surface with time and with increasing As coverage of the surface. It is believed that the bidentate-binuclear complexes are the most important bonds, but they have the lowest surface coverage.

Fe

Fe

Fe

Fe Fe

Fe

Fe Fe

O

O O

O

O O

O

As

O O

O

Monodentate Complex(As-Fe = 3.59 A)

As

O O

O O

As

O O

Bidentate Complex (Binuclear)(As-Fe = 3.24 A)

Bidentate Complex (Mononuclear)(As-Fe = 2.85 A)

Source: Reprinted with permission from Fendorf et al., 1997. Arsenate and Chromate Retention Mechanisms on Goethite. 1. Surface Structure. Envi. Sci. Tech. Copyright 1997 American Chemical Society.

Figure 2.4. Schematic illustration of the surface structure of As(V) adsorbed onto Goethite ( α-FeOOH).

The transition from monodentate to bidentate bonds occurs over a period ranging from tens of minutes to hours and depends on pH, temperature, and As concentration (Grossl et al., 1997). The proposed mechanism of the binuclear bidentate complex is shown in Figure 2.5.


10

Source: McAuley, 2004 Figure 2.5. Proposed mechanism for As(V) adsorption onto a goethite surface with electron movement indicated by arrows. (Prepared by Brian McAuley, 2004) The structure of As(III) adsorbed onto goethite (α-FeOOH) was investigated by Manning et al. (1998) by a combination of X-ray absorption spectroscopy and XANES and found to also form bidentate bonds on the Fe surface that are similar to that shown in Figure 2.4 for As(V). The analysis suggested that the As(III)-α-FeOOH surface complex is stable with respect to heterogeneous oxidation, which may explain the prevalence of As(III) in some environments. Consideration of adsorption processes begins with a discussion of electrostatic interactions between the solute (As) and the adsorbent (the solid phase). While As will form covalent bonds with some surfaces, including metal hydroxides, initial interactions are principally due to electrostatic attraction. Reference to Figure 2.1 and Figure 2.2 show that under neutral pH conditions As(V) is negatively charged as either H2AsO4

- or HasO42-, while

As(III) is present as non-ionic H3AsO3. This is one of the most important distinguishing characteristics between the chemistry of the two oxidation states because it means that As(V) species are more amenable to adsorption processes when electrostatic attraction between negative As compounds and positive surfaces are present. Electrostatic attraction requires that the solute and the adsorbent have an opposite charge, In IX resins, a positive surface charge is provided by a quaternary amine functional group


11

(-N+[CH3]3) (Clifford, 1999). The most common materials associated with As adsorption are inorganic minerals in large part because most organic surfaces are either neutral or negatively charged as a result of dissociation of carboxylate functional groups as shown where S represents the solid phase: S-COOH = S-COO- + H+

Inorganic surfaces can develop a surface charge through a number of mechanisms including (Stumm and Morgan, 1996): isomorphous replacement, acid base reactions at the surface, and adsorption of a hydrophobic species. For metal hydroxide/oxide surfaces the acid-base reactions at the surface are frequently the most important, and result in the surface charge being dependent on the solution pH. For goethite (α-FeOOH) these reactions can be represented as where S is the mineral surface: SOH° + H+ = SOH2

+ log K1= 7.29 SOH° = SO- + H+ log K2 = -8.93 These equations can be used to calculate the fractional surface charge distribution as a function of pH which is shown in Figure 2.6 for goethite. This plot shows that the surface of the ferric oxide is positively charged up to about pH 8, and is negatively charged above this value. pH 8 is the point at which the positive surface charges exactly balance the negative charges and is known as the pH of zero point of charge (pHzpc). Below the pHzpc the surface will be positively charged, while at greater pH values the surface will be negatively charged. Clearly, the pHzpc will have a strong influence on As adsorption. It is important to remember that the major ion composition of the solution, especially the anionic constituents, will also affect surface charge characteristics (Stumm and Morgan, 1996).


12

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 2 4 6 8 10 12 14

pH

Frac

tion

FeOH2+

FeOHo

FeO-

Figure 2.6. Fractional distribution of surface functional groups on goethite (α-FeOOH) as a function of pH.

This discussion suggests that As adsorption onto a metal oxide surface can be described as a two step process. The first step consists of approach of anionic As(V) molecules to the positively charged surface and bonding due to electrostatic attraction. Because it is not site specific, it occurs rapidly, probably in time periods of seconds to minutes. The electrostatic bonds are slowly replaced by much stronger covalent bonds associated with shared electron density. Because of their electronic structure, these are specific for As(V) molecules, which explains why other multivalent anionic species such as SO4

2- do not strongly interfere with adsorption. Quantitative studies on the rate of bond formation have not been conducted, however Fendorf et al. (1997) describe time frames of tens of minutes to hours. Conversion of bonding from electrostatic to covalent might explain why some media recover some of their adsorptive capacity upon standing for a period of several hours, as it is possible that the density of covalent bond sites is greater than locations of positive surface charge. The physical properties of metal oxide adsorption media that influence adsorption include particle size and density, surface area, and pore characteristics. The typical particle size used in water treatment is 28 x 48 mesh. This size has low head losses, high arsenic capacity, low arsenic leakage, and longer run lengths than larger particle sizes (Clifford, 1999). The particle size should not be too small, however, that it impedes flow through a column configuration. Channeling and wall effects can be avoided by having a column diameter to particle diameter ratio greater than 50 (Crittenden, Berrigan and Hand., 1986). Finally, pore characteristics such as diameter and volume can affect adsorption efficiency by affecting internal pore transport.


13

Properties of both the bulk solution and adsorbate, whether physical or chemical in nature, can also affect arsenic adsorption onto metal oxides. These include solution pH, the oxidation state of the adsorbate, temperature, initial adsorbate concentration, and the presence of competing ions. It is important to keep in mind that adsorption of arsenic onto metal oxy-hydroxide surfaces is very different from adsorption of organics onto GAC. The forces responsible for physical adsorption on GAC include electrostatic attraction, hydrogen bonding, and weaker van der Waals forces (Snoeyink and Summers, 1999). In particular, the adsorption of a non-polar adsorbate onto activated carbon from water is an example of these forces. The non-polar adsorbate �prefers� the non-polar carbon surface to the polar water, whereas a polar adsorbate would be subject to hydrogen bonding to the water. Physical adsorption is an exothermic process and therefore, although the rate of adsorption increases at higher temperatures, the extent of reaction is decreased. ADSORPTION COLUMN THEORY The underlying theory of the RSSCT process is based on the external and internal mass transport processes and the rate of bond formation that is associated with adsorption. Adsorption of contaminants onto a porous media is a sequential process consisting of the following steps (Snoeyink and Summers, 1999):

1. Bulk solution transport to the boundary layer of water surrounding the adsorbent particle

2. External mass transfer to the surface of the adsorbent through the boundary layer film surrounding the particle

3. Internal transport of the solute through the pores within the adsorbent via a combination of pore diffusion and surface diffusion mechanisms

4. Adsorption of the solute to the surface

A schematic of the mass transfer processes involved in adsorption is shown in Figure 2.7.


14

AsO4

Bulk Fluid

cb

BoundaryLayer

csq

cp

SurfaceDiffusion

PoreDiffusion

Co

nce

ntra

tion

Distance from Center of Particle

FilmDiffusion

Local equilibrium betweensorbed & fluid phases

0 Adapted from Crittenden et al, 1987

Figure 2.7. Conceptual model of transport processes associated with adsorption onto a porous material. Understanding these mass transfer mechanisms aids in designing an efficient adsorption system. Bulk transport and adsorption are rarely the rate limiting steps in the adsorption processes (Prasad, 1994), and thus are not kinetically important in terms of design considerations. The transport mechanisms of concern, therefore, are external film diffusion and intraparticle diffusion. Intraparticle diffusion is likely the rate-limiting step in media with very small internal pore diameters. In surface diffusion, adsorbed molecules within the pore that have enough energy to leave the site they are presently occupying migrate along the surface of the pore. The molecules migrate from site to site as they are replaced by neighboring weakly adsorbed molecules. When an adjacent adsorption site is available, the adsorbate attaches itself to the new adsorption site on the pore�s surface. In pore diffusion, molecules diffuse farther into the pores before attaching themselves to available adsorption sites inside the particle. The Biot number (Bi) represents the ratio of the liquid phase mass transfer rate to the intraparticle mass transfer rate. Prasad (1994) noted that computing the Bi will determine whether internal or external diffusive transport is rate controlling. Hand, Crittenden and Thacker (1984) note that as Bi becomes large (> 30), the intraparticle mass transfer rate becomes the rate


15

limiting step in the adsorption process. When Bi < 0.5 liquid phase mass transfer is the rate limiting step. Although the actual adsorption step is generally not a rate-limiting step in the adsorption process, the mechanisms of adsorption are important in the design of an efficient system. Arsenic adsorption mechanisms may include molecule-surface interaction, electrostatic interaction (i.e., ion exchange, coulombic attraction) (Gupta and Chen, 1978), ligand exchange, surface complexation (Prasad, 1994), covalent bonding, and Van der Waals forces (Korte and Fernando, 1991). In the case of arsenic, it is likely that the initial adsorption is a result of electrostatic attraction between anionic As(V) and positively charged metal oxide surfaces such as Al2O3 and FeOOH. With time, covalent bonds will form (Fendorf, et.al., 1997). Parameters that affect the efficiency of an adsorption process include media characteristics, solution characteristics, and design parameters. Media characteristics of concern are the particle size, surface area, surface chemistry, and pore size distribution. Solution characteristics include adsorbate concentration, pH, redox conditions, temperature, dissolved organic and inorganic constituents, and microbial activity. Design parameters that affect adsorption efficiency include contact time, surface loading, and design flow. Other factors affecting the adsorption process include regeneration practice, regenerant disposal, and spent media disposal. In GAC, the rate of adsorption is commonly more rapid than the transport steps, so the slowest transport process (steps 1 through 3) become rate limiting. The rate limiting step is usually the internal mass transport, which is controlled by either the internal pore diffusive transport or internal surface diffusion. The RSSCT procedure for modeling the performance of GAC columns is based on the mathematical models of adsorption theories developed primarily by Crittenden and co-workers (1978a, 1978b, 1986, 1987a, 1987b). A number of mathematical models of fixed bed column adsorption processes have been proposed. These are summarized in Table 2.2. Two of the most important differences in these models are how they handle intraparticle mass transport, and whether they include axial dispersion along the column length. The most general model, the dispersed flow, pore surface diffusion model (DFPSDM), includes both pore diffusion and surface diffusion, as well as axial dispersion. However, Crittenden, Luft and Hand. (1987b) note that under many conditions some of these transport mechanisms are not important and simpler models can predict column performance and provide useful insights on the adsorption process.


16

Table 2.2 Summary of different mathematical models used for fixed bed column adsorber design.

Name Abbrev. Processes Included Reference1

Equilibrium Column Model

ECM 1. Equilibrium for multiple components 2. Doesn�t include mass transfer processes

Crittenden et al. (1987c)

Homogeneous Surface Diffusion Model

HSDM 1. Advective flow 2. Liquid-phase mass transfer resistance 3. Local adsorption equilibrium at exterior

surface 4. Surface diffusion

Crittenden & Weber (1978a)

Pore & Surface Diffusion Model

PSDM 1. Advective flow 2. Liquid-phase mass transfer resistance 3. Local adsorption equilibrium at exterior

surface 4. Surface diffusion 5. Pore diffusion

Crittenden, Luft, & Hand (1987b)

Dispersed Flow Homogeneous Surface Diffusion Model

DFHSDM 1. Advective flow 2. Dispersive axial transport 3. Liquid-phase mass transfer resistance 4. Local adsorption equilibrium at exterior

surface 5. Surface diffusion 6. Competitive adsorption of multiple solutes

Crittenden, Berrigan & Hand (1986)

Dispersed Flow Pore Surface Diffusion Model

DFPSDM 1. Advective flow 2. Dispersive axial transport 3. Liquid-phase mass transfer resistance 4. Local adsorption equilibrium at exterior

surface 5. Surface diffusion 6. Pore diffusion 7. Competitive adsorption of multiple solutes

Crittenden, Berrigan & Hand (1986)

1The cited reference gives a description of the model, but is not necessarily the original paper. Homogenous Surface Diffusion Model (HSDM) One of the most important parameters underlying the scaling laws of the RSSCT is the internal surface diffusion constant (Ds), and particularly, whether it depends on the radius of the adsorbent particles. This constant is determined by measuring the rate of adsorption in columns containing a very short bed of adsorbent which approximates a differential element in a long column. These are referred to as differential column experiments. Interpretation of the results of the differential column experiments was done by comparing them to predictions using the HSDM. The HSDM is similar to the DFPSDM except that it ignores axial dispersion. Because the bed length is very short, this is an appropriate simplification for modeling differential column elements. The HSDM model was first described by Crittenden and Weber (1978a) and has been subsequently used to model fixed-bed GAC adsorption dynamics for a variety of systems (Crittenden and Weber, 1978b; Crittenden et al., 1980; Weber and Lieu, 1980; Weber and Pirbazari, 1982; Thacker et al., 1984; Summers and Roberts 1984). The assumptions that are incorporated in the model include: (1) plug flow conditions in the bed, (2) constant hydraulic loading, (3) surface diffusion is the predominant intraparticle mass transfer mechanism and is not


17

dependent on concentration, (4) the local liquid-phase mass transfer rate can be described by the linear driving force approximation, (5) the adsorbent is in a fixed position in the column (i.e., no mixing due to back washing), and (6) the adsorption equilibrium is described by the Freundlich isotherm. The HSDM model was summarized by Hand, Crittenden and Thacker (1984) who presented a simplified user-oriented method for design of adsorption system. The model consists of two simultaneous partial differential equations and the Freundlich isotherm. The first equation is a liquid-phase mass balance:

- ∂C(Z,T)

∂Z = 1

(Dg+1) ∂C(Z,T)

∂T +3 St[C(Z,T) � Cs(Z,T)] (Equation 1)

Equation (1) has the following initial condition:

C

T(Dg+1) < Z ≤ 1 T <

1(Dg+1) = 0 (Equation 2)

The influent boundary condition is: C(Z=0, T≥0) = 1 (Equation 3) A mass balance on the solute within the particle is:

∂q(R,z,T)

∂T =

1+

1Dg+1

EdR2

∂∂R

R2

∂q(R,z,T) ∂R (Equation 4)

The initial condition is: q(0 ≤ R ≤ 1, 0 ≤ Z ≤ 1, T = 0) = 0 (Equation 5) The boundary conditions are:

∂

∂R q(R = 0, 0 ≤ Z ≤ 1, T≥ 0) = 0 (Equation 6)

∂

∂R q(R = 1,Z,T) = Bi[C(Z,T) � Cs(Z,T)] (Equation 7)

Equilibrium adsorption, modeled by the dimensionless Freundlich equation, is used to establish the mathematical connection between the two mass balances: q(R=1,Z,T) = [Cs(Z,T)]1/n (Equation 8) The variables in equations (3) � (10) are defined in the Abbreviations List and Definitions of Terms sections at the end of the report , and the dimensionless groups are summarized in Table 2.3.


18

Table 2.3 Important dimensionless groupings from mathematical analysis of adsorptive transport.

Parameter Name Formula Bi Biot number kfR(1-ε)

DsDgεφ = liquid-phase mass transferintraparticle mass transfer

C Dimensionless Concentration c/co Dgp,i Pore solute distribution

parameter εP(1-ε)

ε = Volume within adsorbent

Porosity

Dgs,i Surface solute distribution parameter

ρaqei(1-ε)εcoi

= Mass sorbed

Mass in solution

Ed Solid diffusion modulus DsDgτR2 =

StBi

Edp,i Pore diffusion modulus DsiR2

τ Dgsi

Eds,i Surface diffusion modulus DpiR2

τ Dgpi

Pei Peclet number LvDei

= Advective transportDispersive transport

R Dimensionless radius in particle of radius R

r/R

St Stanton number kfτ(1-ε)Rεφ =

mass transfer into particleadvective transport in the column

T Dimensionless time based on mass throughput

tτ(Dg+1)

Z Dimensionless distance along column

z/L

Note: The variables are identified in the Definition of Terms section at the end of the report. Dimensionless parameters of special relevance to this study are briefly discussed here. The solute distribution parameter (Dg) is a measure of the affinity of the solute for the adsorbent. Thus large values of Dg correspond to strong adsorption. Equation (3) is written in terms of the mass of solute fed compared to the mass required to saturate the media. The total mass of solute fed is: Mt = ML + Ms (Equation 9) where ML is the mass in the liquid phase and Ms is the mass adsorbed to the solid phase. Divide by ML to get:

MtML

= 1 + Dg (Equation 10)

The total mass fed is: Mt = Qtco = Vtco (Equation 11)


19

At exhaustion the total mass of solute in the liquid phase (ML) in the bed is a product of the bed volume, the void fraction (ε) and solute concentration: ML = Vbedεco (Equation 12) Combining these terms:

MtML

= Vtco

Vbedεco = (1+Dg) (Equation 13)

Therefore, the number of bed volumes of feed to saturate the media is:

N = Vt

Vbed = ε(1+Dg) (Equation 14)

Hand, Crittenden and Thacker (1984) presented analytical solutions to equations (3) through (11) for the following cases:

• Irreversible adsorption, 1/n = 0.0 • Linear adsorption isotherm, 1/n = 1.0 • Liquid phase mass transfer control, 1/n ≤ 1.0 and Bi ≤ 1.0

For most applications, however, solution of the equations developed for the HSDM must be solved numerically. Hand, Crittenden and Thacker. (1984) noted that only three of the dimensionless parameters must be considered to provide solutions to the HSDM, and they selected the parameters 1/n, Bi and St. They subsequently used a numerical technique to generate a large number of values of these three parameters and developed an empirical relationship to predict the minimum St (Stmin) to achieve constant pattern conditions: Stmin = Ao(Bi) + A1 (Equation 15) The relationship between throughput (T) and dimensionless concentration is:

T(Bi, 1/n, Stmin) = Ao + A1(c/co)A2 + A3

1.01-(c/co)A4 (Equation 16)

Values for Ao, A1, A2, A3, and A4 were tabulated for both equations. These solutions are intended for design use, and in particular can be used to calculate the minimum empty bed contact time (EBCTmin) for a packed bed adsorber:

EBCTmin = StminφRkf(1-ε) (Equation 17)

Equation 17 will be used in later chapters to determine the empty bed contact time of the mass transfer zone.


20

Development of Scaling Relationships for Column Performance The factors that might affect the performance of columns containing media of different sizes (Crittenden, Berrigan and Hand, 1986) include: (1) the effects of backwashing, (2) physical characteristics of the adsorption media, (3) equilibrium adsorption capacity, and( 4) adsorption kinetics. Column backwashing is used to remove suspended solids that accumulate in the adsorbent media. Laboratory columns were fed filtered water; hence backwashing was not needed or practiced in lab experiments. They found that the physical characteristics of the adsorbent (density and void fraction) have little impact on the similarities between small-scale columns and large columns. The equilibrium adsorption capacity of the media is important in scaling studies. It is important that the capacity of small diameter media be similar to that of larger diameter media. Similar capacities exist if the Freundlich isotherm constants are similar, which emphasizes the importance of having reliable measurements of these parameters. In particular, it is important that various sizes of a particular media be given sufficient equilibration time so that mass transfer resistance is eliminated in measuring their isotherm characteristics (Thacker et al. 1984; Randtke and Snoeyink, 1983). The final phenomenon that may change with media size is adsorption kinetics. Scaling of systems in which the liquid phase mass transfer controls the adsorption process (low Bi) is not likely to be affected by differences in the surface diffusion coefficients (Ds) between large and small adsorbent particles. Crittenden, Berrigan and Hand. (1986) note that development of scaling equations requires assumptions of the relationship between adsorbent particle radius and the surface diffusion coefficient. They note that some studies have found that Ds is independent of the adsorbent particle size, however, sometimes the diffusion constant depends on particle radius. They also showed that if Ds is constant and independent of particle radius, the ratio of EBCTs for small columns (SC) and large columns (LC) depends on the square of the ratio of these radii.

EBCTSCEBCTLC

=

RSC

RLC

2 =

tSCtLC

(Equation 18)

In a column test, the time to complete a column study depends on the EBCT and the number of bed volumes of solution fed to the column. Thus, the ratio of the time to conduct a column test is also proportional to the square of the adsorbent particle diameters. If Ds depends on particle size the analysis is more complicated as intraparticle mass transfer then depends on R. In this case the ratio of EBCTs is:

EBCTSCEBCTLC

=

RSC

RLC

2 DsLCDsSC

= tSCtLC

(Equation 19)

Cummings and Summers (1994) generalized this relationship by recognizing that:

DsLCDsSC

=

RSC

RLC

x (Equation 20)


21

where x is the diffusivity factor. Combining equations (21) and (22) gives:

EBCTSCEBCTLC

=

RSC

RLC

2-x =

tSCtLC

(Equation 21)

where x = 0 for constant intraparticle diffusivity. If Ds is linearly dependent on particle size, x = 1. The St and Pe remain equal between the small scale and full-scale columns only if the surface diffusivity is independent of particle size. If this is the case, the hydraulic loading of the columns (flow per unit area) is inversely proportional to particle size (Crittenden, Luft and Hand, 1987b):

vSCvLC

= RLCRSC

(Equation 22)

When Ds decreases with particle radius, the film transfer effects (represented by St) and dispersive effects (represented by Pe) produce conflicting scaling equations. Crittenden, Luft and Hand (1987b) state that since these two effects account for a small portion of the length of the mass transfer zone, the hydraulic loading rate can be chosen for convenience as long as it does not overemphasize the impact of dispersion. It is important to maintain hydraulic similarity between the small and large columns by maintaining similar values for the Reynolds number (Re) (Crittenden, Luft and Hand, 1987b; Cummings and Summers, 1994). In a porous media Re is based on particle radius and is calculated as:

Re = vρL2R

µε (Equation 23)

A minimum value of ReSC in the range of 0.02 to 0.13 is needed to establish a minimum velocity to limit external mass transfer resistance (Cummings and Summers 1994). If the small and large columns are operated to maintain a constant ratio of Re, the following relation results:

ReSCminReLC

= 2vSCRSC/µε2vLCRLC/µε (Equation 24)

Canceling terms and rearranging gives the ratio of the hydraulic loading of the two columns:

vSCvLC

= RLCRSC

ReSCminReLC

(Equation 25)

It is possible to summarize this discussion very succinctly. The dimensional similarity required for the RSSCT requires two principal forms of similarity. First, the ratio of the EBCTs must be proportional to the ratio of the particle radii. If Ds is independent of particle size, then:


22

EBCTSCEBCTLC

=

RSC

RLC

2 =

tSCtLC

(Equation 26)

If Ds depends on particle size, the ratio of EBCTs also depends on the ratio of surface diffusion coefficient:

EBCTSCEBCTLC

=

RSC

RLC

2-x =

tSCtLC

(Equation 21)

Likewise, similarity in the hydraulic loading depends on whether the surface diffusivity depends on particle size. If Ds = constant:

vSCvLC

= RLCRSC

(Equation 22)

whereas if Ds depends on particle size:

vSCvLC

= RLCRSC

ReSCminReLC

(Equation 25)

The results of RSSCT column tests are generally plotted as breakthrough curves involving dimensionless concentration (C/Co) on the y-axis. The variable plotted on the x-axis can be normalized volume of liquid fed to the column, normalized mass of solute fed to the column, or adjusted time. These normalized values include number of bed volumes fed, volume of liquid fed per mass of adsorbent (Crittenden et al. 1991), mass of liquid fed per mass adsorbent (Crittenden et al. 1987a) or scaled operation time (Cummings and Summers, 1994). The scaled operation time for the small column is calculated by rearranging Equation 21:

tSC = tLC

RSC

RLC

2-x (Equation 27)

Determination of the Surface Diffusion Coefficient Development of the HSDM and other mathematical models for adsorber design highlight the importance of understanding the nature of the mass transport processes in an adsorption system. In a well-mixed batch reactor the intraparticle mass transfer rate is assumed to be much slower than transfer across the concentration boundary layer surrounding the particle, resulting in a large Bi. Under such conditions the liquid phase concentration at the surface is equal to the bulk liquid phase concentration. Determination of the mass transfer coefficients is then based on the rate at which adsorption approaches equilibrium defined as no change in concentration with respect to time. Knowledge of the surface diffusion coefficient and its dependence on adsorption particle diameter are especially important, as this value ultimately determines the scaling ratios for RSSCTs. Two methods for the determination of the surface diffusion coefficient developed by Hand, Crittenden and Thacker (1983, 1984) include (1) user oriented approximate solutions and (2) numerical solutions to the HSDM.


23

Hand, Crittenden and Thacker (1983) developed a set of empirical relationships (user-oriented solutions) for the determination of the surface diffusion coefficient. Estimation of Ds using the approximate solutions begins by measuring the equilibrium isotherm properties for the system with special emphasis placed on measuring the exponent 1/n. The next step is to select an adsorbent dose that will produce an equilibrium concentration (Ce) equal to approximately one half the initial concentration (Co). The dose of adsorbent in a batch adsorption system can be estimated from the Freundlich isotherm:

=eqMass adsorbedMass adsorbent

( ) ne

eo KCM

CCV /1=−

= (Equation 28)

The mass of adsorbent divided by volume of liquid (M/V) is the adsorbent dose, Do. This equation can be rearranged to solve for Do:

( )n

e

eoo KC

CCD /1

−= (Equation 29)

It is important to use an adsorbent dose that gives a final equilibrium liquid phase concentration of approximately 50% of the initial liquid phase concentration because the user oriented solutions to the HSDM were provided specifically for Ce/Co equal to 0.5 (Hand, Crittenden and Thacker, 1983). The adsorbent dose necessary for Ce/Co = 0.5 is as follows:

( ) no

oo CK

CD /15.0

5.0= (Equation 30)

A set of kinetic experiments is then conducted to measure the approach to equilibrium. As the concentration data are collected, they are converted to dimensionless form according to the following equation:

eo

e

CCCtC

tC−−

=)(

)( (Equation 31)

The apparatus used in this study is a differential column batch reactor, and will be discussed in the methods section. A single rate test is complete when C(t) = Ce and dimensionless concentration is zero. Dimensionless parameters appearing in the mass transfer model�s liquid and intraparticle phase mass balances are used in the determination of the surface diffusion coefficients. Of particular importance is Bi, which is the ratio of the liquid-phase mass transfer rate to the intraparticle mass transfer rate, and is defined as:

( )

εφε

sg

f

DDRk

Bi−

=1

(Equation 32)

In a well-mixed batch reactor, the intraparticle mass transfer rate is assumed to be much slower than transfer across the concentration boundary layer surrounding the particle, resulting in


24

a large Bi. The solutions to the HSDM neglect liquid phase mass transfer resistance to simplify the determination of the surface diffusion coefficient. By neglecting external mass transfer resistance, it is not necessary to find the film transfer coefficient, kf, from batch rate data. This simplifies the analysis by limiting mass transfer to internal transport mechanisms. The film transfer coefficient for a fixed-bed differential column can then be calculated as follows:

316.0Re1.12

2Sc

DRk

l

f += (Equation 33)

This correlation was developed for Re ranging from 3 to 10,000 (Hand, Crittenden and Thacker, 1983). The Reynolds number and Schmidt number (Sc) for fixed bed reactors are given as:

εµ

υρ Rl2Re = (Equation 34)

and

ll D

Scρ

µ= (Equation 35)

The liquid phase diffusivity, as determined from properties of both water and arsenate, is given by (Hayduk and Laudie, 1974):

589.014.1

51026.13

bl V

xDµ

−

= (Equation 36)

and is valid only if 15 < Vb < 500 cm3/g-mole (Crittenden, et.al., 1987a). The value of Vb for arsenate was found to be 56 cm3/g-mole based on Schroeder�s (1949) additive method. This equation gives the liquid phase diffusivity in units of cm2/s when the viscosity is given in centipoise and the molal volume in cm3/g-mole. Dg is a measure of the affinity of the solute for the adsorbent and is defined as:

o

eg CV

MqD

ε= (Equation 37)

Thus large values of Dg correspond to strong adsorption. Based on a suite of numerical solutions, Hand, Crittenden and Thacker (1983) developed an empirical relationship between dimensionless time, which is used in a direct calculation of the surface diffusion coefficient, and concentration. The analytical approximation is as follows:

33

22102 )()()(lnln tCAtCAtCAA

RtD

t s +++=

= (Equation 38)

The simplified model predictions (A0, A1, A2, A3), determined from numerical solutions of the HSDM, are based on the 1/n and Bi. Values of the constants, which give approximate solutions to the HSDM for different values of the Freundlich exponent 1/n and Bi, are tabulated in Appendix II of their paper.


25

An alternative approach, and that used in this study, is to fit the data from a differential column reactor to a numerical solution for the HSDM (Friedman, Crittenden, and Hand, 1984). The program predicts the liquid phase concentration for a batch reactor. The coupled partial differential equations representing the HSDM are solved by conversion to a system of ordinary differential using the method of orthogonal collocation then integrated by the Gear method (Friedman, Crittenden, and Hand, 1984). The mechanisms incorporated in the model program include (1) intraparticle transport described by pore and surface diffusion, (2) no surface diffusion interactions between solutes, (3) film transfer resistance at the adsorbent surface, (4) local equilibrium exists throughout the adsorbent, and (4) the ideal adsorption solution theory applies. Differential column batch reactor experiments are performed using the same procedures as the approximate solutions; but the numeric solution has little or no constraints on the equilibrium concentration, the Freundlich isotherm parameter 1/n, and the Bi. Input and output parameters for the computer code will be discussed in later sections. Use of the code reduces calculations necessary to determine surface diffusion coefficients and Bi. It also reduces the errors associated with approximations and interpolations of the constants (Ao�An). Mass Transfer Zone Considerations When the Freundlich isotherm parameter 1/n is less than 1.0, which is the case for the media studied in this project, the mass transfer zone (MTZ) will retain a constant shape as it passes through the adsorption bed once it has become fully developed. Dimensionless parameters and model calculations developed by Hand, Crittenden and Thacker (1984), which were used in the determination of the empty bed contact time of the mass transfer zone (EBCTmtz), are discussed below. When the Bi is large, the interstitial pore velocity (hydraulic loading) will have no effect on adsorber design when a fully developed mass transfer zone has been established. Bi is important in designing the experiments that are used to measure internal mass transfer rates and subsequent determination of the minimum Stanton number, Stmin.

The Stanton number (St) represents the ratio of the length of the column with respect to the mass transfer zone when liquid-phase mass transfer controls the rate of adsorption. Under fully developed conditions, as St increases, the mass transfer zone becomes a smaller fraction of the bed. St is calculated as:

( )εφ

ετR

kSt f −

=1

(Equation 39)

The diffusivity modulus, Ed, represents the ratio of bed length to the height of the mass

transfer zone when the intraparticle mass transfer rate is limiting. Under fully developed conditions, as Ed increases, the mass transfer zone becomes a smaller fraction of the bed. Ed is defined as:

BiSt

RDD

Ed gs == 2

τ (Equation 40)


26

Mass throughput, t , is defined as the ratio of mass fed to the mass required to saturate the adsorber and is calculated by:

( )1+=

gDtt

τ (Equation 41)

Mass throughput must be converted into elapsed time according to:

( )tDt g 1minmin += τ (Equation 42) EBCTmtz can then be calculated using the elapsed time between C/Co = 0.05 and C/Co = 0.95. The length of the mass transfer zone may be otherwise defined depending on the treatment objective and the amount of unused capacity of the adsorbent. As an example, for a mass transfer zone defined from C/Co = 0.05 to C/Co = 0.95, the treatment objective might be 5% of the influent concentration, and 95% of the capacity of the adsorbent would be utilized. The following equation could be used if the MTZ is defined from C/Co = 0.95 to C/Co = 0.05:

min05.095.0 EBCTCCT

CCTEBCT

oomtz

=−

== (Equation 43)

Summary Comments The RSSCT procedure was developed based on a comprehensive model of the external and internal mass transport processes associated with contaminant removal in a fixed bed adsorption column. The scaling laws suggest that the EBCT, and thus the time needed to conduct a laboratory simulation of a complete breakthrough curve, depends on the ratios of the particle diameters raised to an exponent ranging from 1 to 2 as shown in Equation 21. This exponential value depends on whether the internal surface diffusion coefficient (Ds) depends on the particle size. If Ds is independent of particle (Ds = constant) x = 0 in Equation 21. Therefore, the ratio of the EBCTs for the small columns and large columns depends on the square of the ratio of particle radii. If Ds is linearly proportional to the particle radius x = 1 in Equation 21, and there is a linear relation between EBCT ratio and the ratio of the adsorbent particle radii. Although it is possible that there is a non-linear relationship between Ds and particle size, few projects generate enough information to determine the nature of this relationship, hence most investigators use an exponent of either 1 or 2 (i.e., x = 1 or 0). In doing the scaled experiments it is important to establish approximate similarity as determined by the other hydraulic and mass transfer relations. These are the surface loading (Q/A), the Reynolds number, and the Biot number. Their importance, however, is secondary to the EBCT, and this is the parameter that most investigators report in their lab scale modeling studies.


27

CHAPTER 3 RESEARCH METHODS

The research conducted for this project involved both laboratory and field investigations. The laboratory research focused on determination of fundamental parameters associated with As adsorption columns, while the field studies were performed to develop experience with column operation and to generate data that could be used to validate the RSSCT method. The experimental protocol to develop the RSSCT procedure for arsenic adsorption had four major components: (1) media selection and characterization, (2) isotherm experiments,(3) rate studies and (4) column testing. This chapter describes these laboratory and field studies. ANALYTICAL METHODS & MATERIALS The analytical methods used in this investigation all conform to those published in Standard Methods for the Examination of Water & Wastewater (18th edition) (Greenberg, Clesceri and Eaton, 1992). They are summarized in Table 3.1.

Table 3.1 Summary of analytical methods used in project.

Analyte Procedure Standard Method No. As Graphite Furnace Atomic Absorption

Spectroscopy 3500-As B.

pH Electrometic Method (Glass Electrode) 4500-H+ B. Anions Ion Chromatrography 4110 B. Arsenic was analyzed using a Varian® graphite furnace atomic absorption spectrophotometer. Arsenic analysis was conducted according to standard method 3113 B using a 50 mg/L nickel modifier solution. The modifier was used to enhance analyte signal by permitting a higher ash temperature. Arsenic reference solutions were acquired from Fisher Scientific and calibration standards were prepared in the laboratory. The detection range of this procedure is 2-50 µg/L, therefore higher concentrations had to be diluted prior to analysis. pH was determined using a three point calibration with an Orion® 720A pH meter. The pH electrode used was a Corning® �3 in 1� combination gel-filled electrode.

Selection of Media Work was conducted with four media. All were metal oxide/hydroxides. An iron coated activated alumina media (Alcan FS50) was selected as it is a fairly well characterized and widely investigated media that is commercially available for As treatment. To some extent it served as a baseline for comparison with other media. Granular Ferric Hydroxide (GFH) provided by U.S. Filter is another widely investigated media that has been considered for application by a number of water utilities in the U.S. Another Fe-based media developed by Severn-Trent, SORB-33, was selected for investigation because of intensive marketing by its manufacturer, its superior physical properties compared to GFH, and its consideration for use by a local water utility in NM.


28

Finally, a proprietary media developed by Sandia National Laboratories, Albuquerque was studied. Sandia was a collaborator on this project. Their media is a metal oxide-based media, but at present is only available in very small quantities and is currently referred to as SANS. An application for a patent for this media was submitted in 2001 that was expected to be awarded soon. More information about the media will be available after the patent has been issued. Media Preparation The GFH media is fragile and is subject to abrasion simply by backwashing or mixing with a magnetic stirring bar. It can be pulverized simply by rubbing between one�s thumb and forefinger. Furthermore, it is shipped wet as to not diminish its adsorptive capacity and cannot be allowed to dry (Driehaus et.al., 1998; Jekel and Seith, 2000). In contrast, the other three media are reasonably hard and shipped dry. They withstand more aggressive handling and consequently are easier to work with. All media were prepared for testing by grinding in a ceramic mortar and pestle, sieving to obtain appropriate size, and rinsing with deionized water to eliminate residual fines. The AA, E-33, and SANS were oven dried at 105oC. The GFH was not dried as stated above. Mesh sizes collected were 28 x 48 (0.6 - 0.3 mm diameter), 48 x 100 (0.3 � 0.15 mm diameter), and 100 x 200 (0.15 - 0.075 mm diameter). Prior to use, all media was subjected to a sequence of preparative steps to insure that the pores are completely filled with water and the surfaces were fully hydrated. This preparation included soaking the media in buffered deionized water for at least two hours. A vacuum was applied to facilitate air removal from pores within the media. Test Solutions Two types of experiments were conducted in the laboratory; batch tests and column tests. The batch tests were done to measure the equilibrium sorption characteristics of the media and the rates of the adsorption process. All batch testing was done with deionized water containing a 10 mM NaHCO3 buffered solution. pH adjustment was done with dilute solutions of HCl and NaOH. Column tests done with the larger two sizes of media (28x48 and 48x100 mesh) required very large volumes of water. The research team considered this issue at length and agreed that the column tests would be run using UNM tap water to which 3 mM NaHCO3 was added to increase the solution�s buffer capacity. This was justified as the objective of the project was to determine the validity of the RSSCT procedure, not to evaluate the effect of different water quality characteristics on media adsorption. The pH was adjusted to 7.0 with HCl and stored in a 200 L polyethylene drum. UNM tap water comes from a single deep well and had little variation in water quality over the course of the study. This water contained about 5 ug/L As and was chlorinated. Arsenic(V) was added to each batch of water to bring the influent As concentration to 100 ug/L. One mg/L of NaOCl was added to prevent microbial growth and insure that all As was present in the V oxidation state.


29

Media Characterization The adsorption media was characterized by a number of different chemical and physical methods. The objective of these tests was to gather as much information about the materials as possible. While some of this information is not especially relevant to the investigation of the RSSCT process, it does provide some additional understanding of the adsorption process. The testing included visual characterization of the media by scanning electron microscopy, measurement of its pH of zero point of charge, and measurement of surface area and pore size distribution. In addition, some of the spent media was subjected to X-ray diffraction and surface elemental analysis. These studies are described below. Scanning Electron Microscopy Samples of all three mesh sizes of virgin media were imaged by scanning electron microscopy (SEM) using a JEOL 5800LV Low Vacuum Scanning Electron Microscope. Analysis was conducted using dry media due to equipment requirements. Micrographs with magnifications of 100x, 500x, 2500x, 7500x, and 15000x were studied. Results for 7500x and 15000x are not shown due to poor resolution. SEM analyses were also performed on the 28 x 48 mesh media to observe differences between virgin and spent media and to characterize the surface of the spent media. The objective was to determine if changes occurred over the very long duration of some of these experiments that may affect arsenic adsorption or the long-term stability of adsorbed constituents. Spent media was extracted from exhausted City of Albuquerque field columns. The average influent arsenic concentration was 20 µg/L and the columns reached exhaustion between 15,000 and 40,000 bed volumes, depending on the media type. In this analysis, a JOEL JSM-6300V Scanning Microscope was used with the help of Sandia National Laboratories. The fresh media was coated with gold and the spent media was coated with carbon. Coating was applied using a SPI Module Sputter and Carbon Coater from SPI Supplies, a division of Structure Probe Inc. Coating improved the conductivity of the media allowing for improved micrographs. Surface Elemental Analysis of Spent Media

Energy dispersive x-ray spectroscopy (EDX) was utilized to determine the elemental composition of the media surface. This analysis was performed using an IXRF Systems External Scan with 5480 Imaging Interface at Sandia National Laboratories.

Measurements of Surface Areas and Pore Size Distributions Samples of the adsorption media were sent to a commercial laboratory (Porous Materials, Inc, Ithaca, NY) to measure the surface areas and pore size distributions. The tests and analyses performed by PMI include: (1) BET Gas Adsorption/Desorption, (2) BET Specific Surface Area Analysis, (3) Adsorption and Desorption Isotherms, and (4) Pierce Adsorption Mesopore Summary.


30

pH of the Zero Point of Charge Experiments were conducted to determine the pH of zero point of charge for each media. The procedure was adapted from Benjamin�s (1996) salt addition technique. This procedure involves adjusting the ionic strength of a slurry of media while measuring the initial and final pH values. Each media of each mesh size was equilibrated in a 0.01 M NaNO3 solution overnight. The pH was then adjusted using HCl or NaOH to obtain an initial pH ranging from 5.0-9.0. It often took a few hours to equilibrate the solution at the desired initial pH. Once the initial pH of the solution was stable, a 5 M NaNO3 solution was then added to increase the electrolyte concentration to 0.1 M NaNO3. This solution was equilibrated for approximately 20 minutes after which pH was measured, giving a final pH value. The change in pH was calculated for each initial pH value, where ∆pH was plotted versus pHo. The point where ∆pH = 0 is defined as the pH of zero point of charge (pHzpc). Bulk Density Values of the bulk density of the media were needed to estimate the height of the mass transfer zone in column experiments. Bulk density was determined by weighing a graduated cylinder with uncompacted media that occupied a 5 mL volume, then subtracting the weight of the graduated cylinder. This gives bulk density of the media in units of g/mL or g/cm3. Particle Density Particle densities were also needed to determine the height of the mass transfer zone. The specific gravity was determined by ASTM method D 854, "Standard Test Methods for Specific Gravity of Soil Solids by Water Pycnometer," and AASHTO T-100, "Specific Gravity of Soils" (Bowles, 1992). The procedure is summarized as follows:

1. Weigh a representative sample (approximately 2 g) of air-dried media. Put this sample into a 50 mL volumetric flask. Add deionized water to fill the flask about two-thirds full.

2. Attach the flask to a vacuum pump for at least three hours. During this time, gently agitate the mixture.

3. When the deaerating process is complete, fill with deaerated water to the volume mark. Carefully dry the neck of the flask.

4. Weigh the flask and its contents. Insert a thermometer into the flask and take a temperature reading to the nearest 1oC.

5. Empty the contents of the flask into an evaporating dish that has been previously weighed. Decant as much water as possible, then put the dish into a 105oC oven overnight. On the following day, weigh the dishes with their contents.

6. Clean the flask and fill it two-thirds full with deionized water. Apply a vacuum for at least three hours, and then fill to volume with deaerated water. Weigh the flask with deaerated water.

7. Perform the necessary calculations to compute a value of specific gravity. 8. Determine particle density from the product of specific gravity and density of water.


31

GFH Mass Correction Due to the fact that the GFH cannot be dried prior to experimentation, it has to be weighed moist. Since mass of media is important in the determination of equilibrium isotherm parameters and Ds, the mass of wet GFH must be converted to a dry mass. This required measurement of the water content. The procedure was as follows:

1. Weigh the evaporating dish 2. Weigh representative sample of moist GFH in evaporating dish 3. Oven dry sample overnight at 105oC 4. Weigh dish plus dry GFH, then determine dry mass 5. Determine mass of water (difference between wet and dry mass) 6. Determine water content defined as:

1−=−

==dry

wet

dry

drywet

dry

water

MM

MMM

MM

w (Equation 44)

The mass correction is then determined by rearranging the above equation and solving for dry mass:

w

MM wet

dry +=

1 (Equation 45)

This mass correction was performed on all GFH experiments that required a measured mass of media. ISOTHERM EXPERIMENTS The theory associated with the RSSCT concept establishes the dependence of the scaling relationships on the measurement of the internal surface diffusion coefficient (Ds). Estimation of Ds depends on knowledge of the Freundlich batch isotherm parameters, K and 1/n. The isotherm experiments were conducted for each mesh size (28x48, 48x100 and 100x200) at pH values ranging from 5.0 to 9.0. Deionized water solutions containing 10 mM NaHCO3 buffer were prepared in 30 mL screw top polyethylene or polycarbonate reactors with pH adjusted using HCl or NaOH. The adsorbent was weighed then allowed to equilibrate in the buffered solution for at least an hour prior to arsenic addition to fully hydrate all surface sites in the media. A second pH adjustment was then made because all of the media tended to alter the pH as the surfaces became hydrated. Generally the pH dropped by one to three units after fresh media was placed in the buffered solutions. For the AA equilibrium isotherms, batch reactors were spiked with As(V) concentrations varying from 100 to 5000 µg/L. A constant solid:solution ratio of 1:300 was used. Due to their higher adsorption capacities, the other three media (GFH, SORB E-33 and SANS) were spiked with As(V) concentrations ranging from 5 to 50 mg/L with a solid:solution ratio of 1:600. Batch reactors were mixed for 24 hours using end over end mixers or tumblers as shown in Figure 3.1.


32

Figure 3.1 Mixing apparatus for batch experiments. Upon completion of the equilibration period and pH measurement, the supernatant was collected using 10 mL B-D® latex free syringes and passed through a 0.45 µm Whatman® nylon membrane filter. The filtrate was then acidified to pH < 2.0 with HNO3 for preservation purposes and stored for subsequent measurement of soluble arsenic. The adsorption data were fitted to the Freundlich isotherm. Early experiments incorporated a 24 hour equilibration time, however, it was subsequently determined that this was not sufficient. The As concentration was monitored in a series of batch tests and it was determined that a minimum equilibration time of two weeks was needed. The batch isotherm experiments were subsequently repeated using a three week equilibration period, and all of the isotherm data reported here were from these experiments. Plots of the isotherm data are contained in Appendix A. Determination of Surface Diffusion Coefficients Differential column batch reactors were used to measure internal transport processes, specifically surface diffusion, as described by Hand, Crittenden and Thacker (1983). A 10 liter, 3 mM NaHCO3, pH 7, 100 µg/L As(V) batch reactor solution was prepared using deionized water. The volume of the batch reactor was chosen to be ten liters because it was required that no more than 5% of its volume can be withdrawn during all sampling, and at least ten concentration measurements should be made throughout the duration of the rate test. The initial concentration was chosen to be the same as that used in the RSSCT column studies due to the fact that Ds may have some concentration dependence (Hand, Crittenden and Thacker, 1983). The adsorbent dose used was that which made the equilibrium concentration approximately 50% of the initial concentration. The media was submerged in deionized water and vacuum was applied to rid the media of any entrapped air. Columns were packed with the required adsorbent dose and topped with glass beads. Tubing was connected from the batch reactor, through the pump, to the influent end of the column. Effluent tubing was run back into the batch reactor, to recycle the solution. All air was removed from the tubing prior to pumping to eliminate entrapped air within the differential element of media.

A schematic of the differential column setup is shown in Figure 3.2 and photographs of the actual column setup are shown in Figure 3.3.


33

PDifferential Element ofAdsorption Media

RecirculationPump

Fluid Reservoir(mixed)

Figure 3.2. Diagram of apparatus used in the differential column test.

The rate study was conducted for GFH, SORB E-33, and SANS media at all three mesh sizes. The batch solution was pumped into a 1 cm diameter column, which was packed with glass beads and the adsorbent media. Flow rates through the columns were kept sufficiently high to ensure a high Bi. In the approximate procedure for measuring surface diffusivities developed by Hand, Crittenden and Thacker (1983) and based on the media�s isotherm parameters, Bi must remain greater than 300 to eliminate liquid-phase mass transfer resistance. In order to use the multicomponent batch pore and surface diffusion model computer program, Bi must be greater than 30.


34

Figure 3.3. Photographs of the differential column batch reactor system.

Fifteen mL samples were extracted from the three-way valve sample port using 20 mL B-D® latex free syringes and passed through a 0.45 µm Whatman® nylon membrane filter. The filtrate was then acidified to pH < 2.0 with HNO3 for preservation purposes and stored for subsequent measurement of soluble arsenic. Five independent measurements of Ce and Co were made because of the surface diffusivity�s great dependence on these values. Samples were taken until C(t) = Ce, i.e., when dimensionless concentration was zero. Parameters used in the rate studies, which were necessary in the user-oriented approximate solutions, are shown below in Table 3.2 and Table 3.3.

Table 3.2. Constant parameters used in the rate studies.

PARAMETER VALUE UNITS Column diameter, d 1 cm Column area, A 0.785 cm2 Volume of reactor, V 10 L Fluid density, ρl 1000 kg/m3 Fluid viscosity, µ 1.12E-03 N-s/m2 Molal volume at normal boiling point, Vb 56 cm3/g*mole Free-liquid diffusion coefficient, Dl 1.06E-05 cm2/s Sphericity, φ 1 Schmidt number, Sc 1060

It was necessary to do each of these calculations for all media at all media sizes. It

became very calculation intensive; therefore the Multicomponent Batch Pore Surface Diffusion Model computer program, which uses a numerical solution, was utilized. The program requires some input parameters, most of which require little or no calculation. The input parameters necessary for all media at all sizes are shown in Table 3.4.


35

Table 3.3. Variable parameters used in the rate studies. PARAMETER UNITS Isotherm parameter, K -- Isotherm parameter, 1/n -- qe = KCo

1/n µg/g Flow rate, Q mL/min Mass of adsorbent, M g Particle radius, R m Void fraction, ε -- Surface loading, v m/s Equilibrium concentration, Ce µg/L Initial concentration, Co µg/L Interstitial velocity,υ m/s Reynold's number, Re -- Film transfer coefficient, kf m/s Solute distribution parameter, Dg -- Elapsed time, t s Concentration at time t, C(t) µg/L Dimensionless concentration, C(t) -- Dimensionless time, t -- Biot number, Bi -- Surface diffusion coefficient, Ds cm2/s

Other input parameters required were elapsed time and the corresponding liquid phase concentration (i.e., data from the batch rate study). It was also necessary to provide an initial estimate of Ds. When the data values vs. model prediction yields the smallest standard deviation, Ds is noted. The output from the program is presented in the Results section.


36

Table 3.4.

Batch pore surface diffusion model input parameters.

Media Media Size

K

1/n ρs

(g/cm3) M (g)

R (cm)

ε

Co (ug/L)

kf (cm/s)

SANS 28x48 1910 0.49 3.54 0.0810 0.0212 0.926 102.5 0.0186 48x100 1909 0.53 3.54 0.0670 0.0106 0.926 84.9 0.0249 100x200 3112 0.35 3.54 0.0827 0.0053 0.926 96.6 0.0335

E-33 28x48 2331 0.27 2.14 0.1495 0.0212 0.945 97.5 0.0188 48x100 3293 0.20 2.14 0.1180 0.0106 0.945 75.0 0.0252 100x200 3654 0.22 2.14 0.1164 0.0053 0.945 102.9 0.0339

GFH 28x48 4419 0.35 4.02 0.0581 0.0212 0.946 104.6 0.0189 48x100 4423 0.32 4.02 0.0671 0.0106 0.946 100.0 0.0252 100x200 6371 0.29 4.02 0.0506 0.0053 0.946 98.0 0.0340

LABORATORY COLUMN EXPERIMENTS All media were submerged in deionized water and de-aerated under a vacuum for at least an hour prior to column loading. The media was packed wet to avoid entrapment of air bubbles. A three mM NaHCO3, 100 µg/l As(V) feed solution was prepared using filtered Albuquerque tap water. UNM tap water is drawn from a single deep well and has little variation in water quality. It contains about four µg/L As and is chlorinated. As(V) was added to bring this concentration to 100 µg/L. One mg/L NaOCl was added to prevent microbial growth and to insure that all arsenic is present in the As(V) oxidation state. The pH was adjusted to 7.0 using dilute HCl. Feed solution was applied to columns using a Masterflex® peristaltic pump. Sigma® borosilicate glass liquid chromatography columns were used and their size varied depending on experiment parameters. The columns were operated in down flow mode. Three-way luer-lock valves were used at the column effluent for ease of sampling. Head losses in the columns containing the 100x200 mesh media were very large, typically exceeding 1000 psi. Therefore, SSI® Series II HPLC pumps were used to deliver feed solution to these columns. Stainless steel HPLC columns were used which can withstand the high pressures generated from flow through small diameter media. One-centimeter diameter columns were chosen for the small columns because they can be packed with less opportunity for formation of air bubbles in the media bed. Columns containing relatively durable media (E-33, activated alumina and SANS) were operated successfully in this mode. However, GFH is a very soft media and compressed under the very large pressure gradient. This caused even higher head losses, which led to further compaction of the media, with subsequent plugging of the column. The plugging problem could be mitigated somewhat by backwashing the columns for 15 to 30 minutes at a velocity sufficient to just fluidize the bed to remove the fines before starting the column test. Also, it was found that use of larger diameter but shorter columns to provide the same EBCT but with a lower surface application rate helped. However, the GFH media is so weak that even taking these precautions did not always eliminate plugging problems. Empty bed contact time was chosen as the control parameter because it is an effective relation of column size to flow rate. A linear relationship between surface diffusivity and


37

particle size was assumed; therefore, since the media size varied by a factor of two for each of the column sizes, the EBCT was changed accordingly. The three minute EBCT for the largest columns was chosen for two reasons: (1) three minutes is sufficiently short for a rapid test and (2) limitations of pump flow rate capacity made scaling to smaller columns possible (Crittenden, Berrigan and Hand, 1986). Column experiment parameters are shown below in Table 3.5. Photographs of the experimental setup are presented in Figure 3.4. The adsorbent media in the Large Columns referred to in this table is the same size as used in full scale adsorption columns. Thus, the column operating criteria (EBCT and surface loading rates) are the same as would be used in a full scale plant. The values for EBCT and particle radius would therefore have the subscript LC for large column in equation (23) (i.e., EBCTLC and RLC). The values for the EBCT and particle radius for the other columns would have the subscript SC, referring to small scale columns.

Table 3.5. Laboratory column experiment parameters

Columns with Large Media

Columns with Medium Media

Columns with Small Media

Particle Size (mesh) 28 x 48 48 x 100 100 x 200 Particle Diameter Range (mm)

0.6 - 0.3 0.3 - 0.15 0.15 - 0.075

Geometric Mean Particle Diameter (mm)

0.424 0.212 0.106

Column Diameter (cm) 2.5 1.5 1.0 Bed Height (cm) 7.5 7.5 2.5 Ave Influent As Conc (ug/L) 122 114 99 Flow Rate (mL/min) 16 12 5.22 Surface Loading (cm/min) 3.3 6.8 6.64 EBCT (min) 3 1.5 0.75


38

Figure 3.4. Photographs of the laboratory columns for the three different media sizes used in this study. The three systems are (clockwise from upper left) 28x48 mesh media, 100x200 mesh media, and 48x100 mesh media.

Effluent sampling was conducted through a three-way valve sample port using 20 mL B-D® latex free syringes. Approximately 15 mL of sample was collected, filtered with a 0.45 µm Whatman® nylon membrane filter, and acidified to pH < 2 with HNO3. Effluent samples were collected until the media reached exhaustion. Influent samples were taken from the feed solution at least once a week. FIELD COLUMN EXPERIMENTS Field testing of arsenic adsorption media was conducted in collaboration with the project�s participating utilities, the City of Albuquerque and the City of El Paso. A pilot experiment was set up at the City of Albuquerque�s Pino Yards facility. This testing apparatus consisted of four 28 x 48 mesh columns, one for each adsorption media, and each with an EBCT of five minutes. Column apparatus identical to those used for the laboratory columns were used for the field columns. City tap water containing about 20 ug/L of As was delivered to the columns with a Masterflex® multi-channel pump. A 40 L polyethylene drum with a float controlled water level was used as a feed tank to prevent backflow in the event of low pressure conditions in the distribution system. The water was chlorinated by the City.


39

After completing the 28 x 48 field column runs at the City of Albuquerque, subsequent small column experiments were conducted in the Environmental Engineering labs at the University of New Mexico to facilitate operation and monitoring. These columns were set up in the same manner as the laboratory 48 x 100 and 100 x 200 columns. Water from the City�s Pino Yards facility was transported to UNM in 200 L polyethylene drums. Since the EBCT of the large columns was five minutes, the smaller columns were scaled down appropriately, again assuming a linear relationship between surface diffusion and particle size. Detailed operating parameters for the field columns are shown in Table 3.6. A summary of the water quality characteristics at the Pino Yards location is presented in Table 3.7.

Table 3.6.

Pilot column operating parameters for City of Albuquerque adsorption column tests. Columns with

Large Media Columns with

Medium Media Columns with Small Media

Particle Size (mesh) 28 x 48 48 x 100 100 x 200 Particle Diameter Range (mm) 0.6 - 0.3 0.3 - 0.15 0.15 - 0.075 Geometric Mean Particle Diameter (mm)

0.424 0.212 0.106

Column Diameter (cm) 2.5 1.5 1 Bed Height (cm) 7.5 7.5 2.5 Average Influent As conc. (µg/L) 23.8 17.3 18.1 pH 7.9 7.9 7.9 Flow Rate (mL/min) 9.8 3.5 1.57 Surface Loading Rate (cm/min) 2 2 2

EBCT (min) 5 2.5 1.25


40

Table 3.7. Water quality characteristics at the City of Albuquerque Pino Yards facility (analyses

provided by the City of Albuquerque). Minimum Maximum Average Units Metals Arsenic 22 34 28 µg/L Iron 0.009 0.019 0.012 mg/L Manganese ND ND ND mg/L Selenium ND ND ND µg/L Minerals Fluoride 0.8 1.2 1.0 mg/L Alkalinity 118 128 123 mg/L as CaCO3Bicarbonate 116 127 122 mg/L as CaCO3Calcium 28 33 30 mg/L Chloride 31 36 33 mg/L Hardness 5.7 6.9 6.3 grains/gallon Magnesium 5.7 7.7 6.6 mg/L Potassium 5 7 6 mg/L Silica 65 77 70 mg/L as SiO2 Sodium 48 50 49 mg/L Sulfate 38 40 39 mg/L Total Dissolved Solids 275 298 286 mg/L Free Chlorine Residual 0.6 0.9 0.8 mg/L Conductance 404 434 421 µohms/cm pH 7.8 7.8 7.8 Standard Units Temperature 56 80 69 Fahrenheit

Similar field testing was conducted at the City of El Paso�s Canutillo well field. Three columns of dimensions 5 cm ID x 1 m long were used, each filled to a height of 0.6 m with media. They were operated in a down flow mode at an EBCT of five minutes based on the media volume. Two of the columns contained 28x48 mesh SANS media, and the third contained 28x48 mesh SORB-33. The City of El Paso conducted all analyses of water samples collected for this field project. The pH of the feed water fluctuated slightly in the range of 7.5 to 7.8. It was suggested to the City that the effect of reduced pH on column run length be investigated. Accordingly, one of the columns containing SANS media was operated at ambient pH, while the other two containing SANS and SORB-33 were fed water adjusted to pH 6.8. pH adjustment was accomplished by adding CO2(g) to the feed. When mixed with water, CO2(g) forms carbonic acid (H2CO3) which is a safe and relatively inexpensive method of lowering the solution pH.


41

CHAPTER 4 RESULTS AND DISCUSSION –

MEDIA CHARACTERISTICS & ADSORPTION PROERTIES This project involved four major areas of work: characterization of the media, measurement of the batch adsorption properties of the media, operation of laboratory adsorption columns, and operation of field adsorption columns. Results are discussed below. MEDIA CHARACTERIZATION Scanning Electron Microscopy

While not critical to evaluation of the RSSCT method, SEM analysis generates images that can assist in developing a conceptual understanding of the adsorption process. Scanning electron microscopy was performed on all media at all three sizes. Different levels of magnifications were used to investigate the media structure. Scanning electron micrographs for the virgin adsorption media are shown Figures 4.1 through 4.4 Based on the SEM observations it appears that the internal structure of the media is in the form of microfractures of less than 1-µm in width. Additional pores within the media may be of molecular dimensions. This is an indication that intraparticle diffusion dominates the adsorption process. This concept will be important in later modeling efforts.

Transmission electron microscopy was performed on thin sections of the SANS media by Sandia National Laboratories to take advantage of the enhanced resolution of this technique. Figure 4.5 shows the media to be a mix of amorphous phases with some internal crystal structure. Of special relevance to adsorption studies is the presence of large numbers of internal pores with diameters of less than 100 nm. These are smaller than can be seen with the limited resolution of the SEM micrographs.

Scanning electron micrographs were also obtained for both virgin and spent media to determine if there are visual differences at the surface, i.e. if notable mineral phases had formed at the surface of the spent media. This series of SEM analyses was conducted on all four media at 28x48 mesh and are presented in Appendix B. This examination was done to determine whether it was possible to observe changes in the media that might affect its adsorption properties. These might include precipitation of phases such as silica or carbonates on the surface that might block internal pore openings or dissolution of the metal oxide (MeOx) adsorption media. The electron micrographs collected in this study found no visually apparent differences between virgin and spent adsorption media. This suggests that if changes are occurring in the media that affect its adsorption characteristics, they are occurring at the molecular level and probably inside micropores within the media. Energy dispersive X-ray (EDX) spectroscopy was used to analyze both virgin and spent media while loaded in the scanning electron microscope. Although this method does not give specific mineral phases, it does determine what elements are present within the media at concentrations of approximately 0.5 to 5%, depending on the element. Results of the EDX analysis are tabulated in Table 4.1.


42

(a)

(b)

(c)

Figure 4.1. Scanning electron micrographs of activated alumina for (left to right) 28 x 48 mesh, 48 x 100 mesh, and 100 x 200 mesh: (a) 100x magnification (scale bar = 500 um), (b) 500x magnification (scale bar = 100 um), (c) 2500x magnification (scale bar = 20 um).

Table 4.1.

Elements detected by EDX analysis of fresh and spent adsorption media. Virgin Media Spent Media Activated Alumina Au, O, Fe, Al C, O, Fe, Al, Si, Ca, P Granular Ferric Hydroxide Au, O, Fe C, O, Fe, Si, Ca, Cl, SORB E-33 Au, O, Fe C, O, Fe, Si, SANS Au, O, Fe, Cu, Cl C, O, Fe, Cu, Cl, Si, Ca


43

(a)

(b)

(c)

Figure 4.2. Scanning electron micrographs of granular ferric hydroxide for (left to right) 28 x 48 mesh, 48 x 100 mesh, and 100 x 200 mesh: (a) 100x magnification (scale bar = 500 um), (b) 500x magnification (scale bar = 100 um), (c) 2500x magnification (scale bar = 20 um).

The SEM coating process added an abundance of gold and carbon to the virgin and spent media, respectively. Since all four media are metal hydroxides, oxygen is present in all media. Iron is present in GFH, SORB E-33, and SANS because they are ferric hydroxides. Iron and aluminum are present in the activated alumina because it is an aluminum hydroxide coated with an iron oxide. Finally, the SANS media showed iron and copper, which are components of the media. These elements are present in both the fresh and spent media because the composition of the media does not significantly change upon reaching exhaustion in a column study.


44

(a)

(b)

(c)

Figure 4.3. Scanning electron micrographs of SORB-33 for (left to right) 28 x 48 mesh, 48 x 100 mesh, and 100 x 200 mesh: (a) 100x magnification (scale bar = 500 um), (b) 500x magnification (scale bar = 100 um), (c) 2500x magnification (scale bar = 20 um).

The spent media shows more constituents; i.e. calcium and silica; because of relatively high concentrations of these constituents in City of Albuquerque tap water. It should be noted that arsenic was not detected by EDX spectroscopy on any of the spent media as the adsorbed arsenic concentration was below the detection level of the method. This is due to the fact that the arsenic concentration in Albuquerque�s tap water is on the order of tens of micrograms per liter, whereas calcium (Ca and CaCO3) and silica (SiO2) are present at concentrations on the order of tens to hundreds of milligrams per liter. It was hypothesized that the presence of silica precipitates on the surface might serve to encapsulate and further stabilize adsorbed arsenic. Formation of a silica phase might also reduce the capacity of the media by covering potential arsenic adsorption sites. However, the SEM photos showed no evidence of SiO2 formation on any of the adsorbents.


45

(a)

(b)

(c)

Figure 4.4. Scanning electron micrographs of SANS for (left to right) 28 x 48 mesh, 48 x 100 mesh, and 100 x 200 mesh: (a) 100x magnification (scale bar = 500 um), (b) 500x magnification (scale bar = 100 um), (c) 2500x magnification (scale bar = 20 um).


46

Figure 4.5. Transmission electron micrograph image of SANS media.


47

BET Analysis BET analyses were conducted in order to determine surface areas, pore sizes and pore size distribution of each of the media. Data collected from BET surface analyses by Porous Materials, Inc. (Ithaca, NY) include pore size distribution, pore volume, and pore surface area of all media. Data was compiled and results are presented in Figure 4.6 through Figure 4.9.

0.000

0.002

0.004

0.006

0.008

0 200 400 600 800 1000 1200 1400

Average Pore Diam e te r, Angstrom s

Por

e S

ize

Diu

stri

buti

on b

y V

olu

me

(dV

p/dD

p), c

m3 /g

/Ang

stro

m

A A

GFH

E -33

S A NS

Figure 4.6. Pore size distribution as a plot of pore volume vs. pore diameter.

0

2

4

6

8

10

12

0 200 400 600 800 1000 1200 1400

Ave rage Pore Diam eter, Angs trom s

Por

e S

ize

Dis

trib

utio

n b

y S

urfa

ce

Are

a(d

Sp/

dDp)

, m2 /g

/Ang

stro

m

AA

GFH

E-33

SA NS

Figure 4.7. Pore size distribution as a plot of surface area vs. pore diameter.


48

0

50

100

150

200

250

300

0 200 400 600 800 1000 1200 1400

Average Pore Diam e te r, Angstrom s

Cum

ulat

ive

Por

e S

urfa

ce A

rea,

m2 /g

AA

GFH

E-33

SA NS

Figure 4.8. Cumulative pore surface area vs. pore diameter.

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

20-4

0

40-1

00

100-

150

150-

200

200-

300

300-

400

400-

500

> 5

00

Pore Diameter, Angstroms

Pore

Vol

ume,

cm

3 /g

AAGFHE-33SANS

Figure 4.9. Pore volume plotted as a range of pore diameters.

The pore size distribution for the adsorption media are presented in terms of either the

internal pore volume versus pore diameter (Figure 4.6) or the internal surface area versus pore diameter (Figure 4.7). Figure 4.8 and Figure 4.9 present plots of the cumulative distribution of pore volume and surface area, respectively, as a function of pore diameter. Numerical results for total BET surface area, total pore volume, and average pore diameter are given in Table 4.2.


49

Table 4.2.

Summary of BET surface analyses of adsorption media (results provided by PMI, Ithaca, NY).

Media

Total BET Surface Area

(m2/g)

Total Pore Volume (cm3/g)

Average Pore Diameter

(Å) Activated Alumina 136 0.19 56 Granular Ferric Hydroxide 274 0.24 34 SORB E-33 113 0.44 157 SANS 135 0.26 63

These results are consistent with the literature values of specific surface area of 50-300

m2/g reported for activated alumina by Clifford (1999) and 250-300 m2/g for granular ferric hydroxide reported by Driehaus et al. (1998). Values of specific surface area for SORB E-33 and SANS have not been previously reported. The measurements are consistent with the presence of internal porosity for all media as the specific surface area of solid sphere of diameter 0.112 mm is less than 0.05 m2/g. It should be noted that the specific surface area for GAC is much larger than that for the ferric hydroxide media. Specific surface areas for GAC are on the order of 1000 m2/g. A quantitative summary of pore diameters for each media is given in Table 4.3.

Table 4.3. Summary of pore diameters of adsorption media in Angstroms and microns (results

provided by PMI, Ithaca, NY). Average Pore

Diameter Maximum Pore

Diameter Minimum Pore

Diameter Media Å nm Å nm Å nm AA 56 5.59 1660 166 15 1.48

GFH 34 3.44 1716 172 11 1.14 E-33 157 1.57 1284 128 15 1.49 SANS 63 6.34 1176 118 11 1.14

These results show that virtually all of the internal surface area is associated with pores smaller than 10 nm (100 angstroms). This is consistent with the scanning electron micrographs which do not have sufficient resolution to detect these pores. Also, based on pore diameter determinations, it is unlikely that even the largest pores, which are on the order of tenths of microns, will be visible on either set of micrographs. Another observation from this data is that both the E-33 and SANS media have larger average pore diameters and greater total pore volumes than the GFH and AA media. If intraparticle mass transfer is controlled by pore diffusion, it would be reasonable to expect that larger pore diameters would result in improved mass transfer rates. However, if intraparticle mass transfer is controlled by surface diffusion, there should be little increase in mass transfer rates by media with larger pore diameters.


50

pH of the Zero Point of Charge While not directly relevant to the success of this study, the pHzpc is important to the overall adsorption process because coulombic forces are responsible for the initial attraction between the MeOx surface and anionic As(V) species. The pH of zero point of charge experiments were difficult to conduct because the virgin media was quite acidic. The salt addition method relies on measuring the change in solution pH as the ionic strength changes, but this was obscured by the acidity of the media. The pHzpc is identified as the point where ∆pH = 0. To remedy problems associated with the acidity of the media, each sample of media was washed with approximately 100 bed volumes of buffered deionized water. Results obtained are depicted graphically in Figure 4.10 through Figure 4.13. The results are summarized in Table 4.4. These values are approximately one pH unit lower than that reported elsewhere (Chwirka, Thomson and Stomp, 2000; Clifford, 1999). This is believed to be the result of the acidic nature of the media. An example of the use of pHzpc values was shown in studies with activated alumina. The optimum pH for arsenic adsorption onto activated alumina ranges from 5.5 to 6 (Chwirka, Thomson and Stomp, 2000; Clifford, 1999; Driehaus, Jekel and Hildebrandt, 1998). Below pH of about 4.5 the activated alumina begins to dissolve, resulting in a loss of media. At higher pH, activated alumina loses its positive charge, limiting electrostatic attraction between the positive surface and negative arsenate. Also at higher pH, there is an increase in OH- in solution, which competes with arsenate for adsorption sites. The concept of pH dependent adsorption of arsenate onto metal oxide media will be discussed in more detail in the batch isotherm results section of this report.

AA 48x100

-0 .4

-0 .2

0

0.2

0.4

5 5.5 6 6.5 7 7.5 8 8.5 9

pH

∆pH

Figure 4.10. Change in pH vs. initial pH using salt addition method for AA 48x100.


51

GFH 48x100

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

5 5.5 6 6.5 7 7.5 8 8 .5 9

pH

∆p

H

Figure 4.11. Change in pH vs. initial pH using salt addition method for GFH 48x100.

E-33 48x100

-0 .4

-0 .2

0

0.2

0.4

5 5.5 6 6.5 7 7.5 8 8.5 9

pH

∆pH

Figure 4.12. Change in pH vs. initial pH using salt addition method for E-33 48x100.


52

SANS48x100

-0.4

-0.2

0

0.2

0.4

5 5.5 6 6.5 7 7.5 8 8.5 9

pH

∆pH

Figure 4.13. Change in pH vs. initial pH using salt addition method for SANS 48x100.

Table 4.4. Summary of measurements of the pHzpc.

Media pHzpc AA 6.4 GFH 7.0 E-33 7.7 SANS 7.3

Media Density & Correction for Moisture Content of GFH Media densities were necessary for surface diffusion calculations and modeling. The resulting measurements of bulk and solid phase density are given in Table 4.5. Particle densities were also calculated from PMI data by taking the inverse of the cumulative particle volume, which was given in units of cm3/g. It was PMI�s particle density data that was used in later modeling efforts. These values are also reported in Table 4.5.

Table 4.5. Summary of density measurements.

Media

Dry Bulk Density (g/cm3)

Measured Particle Density (g/cm3)

Particle Density (g/cm3) by PMI

GFH 0.89 3.98 4.02 SORB E-33 0.50 3.83 2.14 SANS 0.75 3.83 3.54


53

The water content of the moist GFH was approximately 155%. This is higher than would be expected for the shipped GFH. The GFH used in these experiments was sieved, rinsed, and then submerged in deionized water for storage. Therefore, when it was finally weighed for an experiment, the water content was well over 100% because of residual water on the spatula. Because this media is shipped wet and must remain wet it was necessary to adjust the adsorption densities for the water content to allow comparison of other media which are shipped and handled on a dry mass basis. BATCH ISOTHERM RESULTS Batch equilibrium isotherms were conducted to determine the adsorption capacities of the various adsorption media. Data was applied to the Freundlich isotherm model. In the Freundlich isotherm model, the parameter K is often considered a measure of the adsorption capacity, whereas the parameter 1/n is a measure of the strength of adsorption, or the adsorption bond strength. Initial batch isotherms were run for 24 hours. Upon analysis of the 24-hour isotherm data, fairly large variations in the Freundlich constants (K and 1/n) were observed. There are at least three possible explanations for this variability. The first is experimental error. Because these media have such a large capacity for arsenic, a small error in either sample preparation or analyses can result in large differences in sorption density. Also, 24 hours may not have been a sufficient equilibrium time. The second explanation is that simple logarithmic linearization of the Freundlich equation will result in a satisfactory curve fit for many different types of curves. Further, as the curve flattens (i.e., 1/n < 1) the statistical confidence in the value of 1/n decreases. The third explanation is that there is in fact variability in the sorption characteristics of the media with particle size. This seems to be most likely for the Fe(OH)3 coated activated alumina, as it seems plausible that most of the coating will be near the outer surface of the original particles. Grinding them will expose a larger fraction of the interior, uncoated surfaces. The first approach to reduce the variability in the measured values of the Freundlich constants was to lengthen the equilibration time. Preliminary kinetic studies were conducted to determine a proper equilibration period. These studies consisted of 500-mL batch reactors set up similar to the isotherm experiments. Concentrations were measured over a period of two weeks, when the arsenic concentrations appeared to reach equilibrium. It was then decided that 24 hours was, in fact, not a sufficient equilibration time. Therefore, the batch isotherm experiments were duplicated, and allowed to mix for three weeks. The three-week isotherms provided more satisfactory results (Table 4.6). Plots of the adsorption isotherms are presented in Appendix A.


54

Table 4.6. Freundlich parameters for batch adsorption studies using a three week equilibration

period. GFH 3-Week Equilibrium Isotherm Parameters 28x48 48x100 100x200 K 1/n K 1/n K 1/n pH 5 4448 0.39 4432 0.35 1668 0.51 pH 6 4332 0.40 5217 0.33 7040 0.33 pH 7 4419 0.35 4423 0.32 6371 0.29 pH 8 2295 0.41 3992 0.33 4754 0.30 pH 9 1789 0.43 2329 0.41 3396 0.33 SORB E-33 3-Week Equilibrium Isotherm Parameters 28x48 48x100 100x200 K 1/n K 1/n K 1/n pH 5 2087 0.29 3954 0.21 3710 0.24 pH 6 2285 0.28 4094 0.20 4214 0.22 pH 7 2331 0.27 3923 0.20 3654 0.22 pH 8 2182 0.27 3115 0.22 5348 0.17 pH 9 2315 0.27 3293 0.21 3671 0.22 SANS 3-Week Equilibrium Isotherm Parameters 28x48 48x100 100x200 K 1/n K 1/n K 1/n pH 5 5969 0.27 1902 0.48 1618 0.44 pH 6 3025 0.40 3018 0.41 2246 0.41 pH 7 1910 0.49 1909 0.53 3112 0.35 pH 8 2317 0.40 2557 0.41 3611 0.31 pH 9 893 0.55 1348 0.47 2282 0.39

Because it is difficult to compare the adsorptive properties for media using only the Freundlich constants (K and 1/n), the calculated equilibrium adsorption capacities of each media in a solution containing 100-µg/L As(V) are shown in Figure 4.14 through Figure 4.16. The adsorption capacity, or adsorbed arsenic concentration, is measured in units of µg As per g adsorbent.


55

GFH

0.E+00

1.E+04

2.E+04

3.E+04

4.E+04

5 6 7 8 9

pH

Ads

orbe

d A

s C

onc

( µ

g/g)

28x48

48x100

100x200

Figure 4.14. Adsorption capacity for GFH in equilibrium with 100 ug/L As(V) as a function of pH (calculated from Freundlich isotherm constants).

Figure 4.14 indicates that for all media sizes, adsorption of arsenate onto GFH is greatest at pH 6. This is in agreement with the measured value for the pHzpc for GFH, which is pH 7. Below pH 7, the GFH surface is positively charged and thus electrostatic attraction between the positively charged surface and the negatively charged arsenate ions is greatest. It is shown here that the adsorption capacity does not change significantly with particle size, which is one of the requirements for similarity between small- and large-scale columns. This confirms the assumption that adsorptive capacity does not change significantly with particle size due to the fact that most of the surface area is associated with internal adsorbent pores.

SORB E-33

0.E+00

5.E+03

1.E+04

2.E+04

5 6 7 8 9

pH

Ads

orbe

d A

s C

onc

(µ

g/g)

28x48

48x100

100x200

Figure 4.15. Adsorption capacity for E-33 media in equilibrium with 100 ug/L As(V) as a function of pH (calculated from Freundlich isotherm constants).


56

Figure 4.15 illustrates that the adsorption capacity of E-33 has no clear dependence on pH, thus proving that arsenic adsorption onto E-33 is due mainly to covalent bonding preferentially over electrostatic attraction. There is also not a significant change in adsorption capacity with a change in particle size.

SANS

0.E+00

1.E+04

2.E+04

3.E+04

5 6 7 8 9

pH

Ads

orbe

d A

s C

onc

( µ

g/g)

28x48

48x100

100x200

Figure 4.16. Adsorption capacity for SANS media in equilibrium with 100 ug/L As(V) as a function of pH (calculated from Freundlich isotherm constants). For SANS media, there are variations in adsorption capacity with changes in pH. The 48 x 100 and 100 x 200 mesh SANS show maximum adsorption capacity at pH 7, which is in agreement with the measures pHzpc value of 7.4. The 28 x 48 mesh SANS exhibit maximum capacity at pH 5. Again, it is in agreement with pHzpc, since adsorption capacity decreases with increasing pH, but the fact that it differs from the other two media sizes is inexplicable thus far. In comparison of particle size, there is no general trend in capacity as a function of particle size. At pH 5, the 28 x 48 mesh SANS exhibits the highest capacity. In the pH range of 6 to 8, the 48 x 100 mesh SANS demonstrates the highest capacity. Finally, at pH 9, the 100 x 200 mesh SANS defeats the larger sizes in capacity, though not significantly. It should be noted that although the differences in capacity are visible in the above diagrams, the differences are not significant enough to change the overall adsorption mechanisms of the arsenate-metal-oxide system. The change in capacity with pH or particle size is far less than an order of magnitude. Orders of magnitude differences at the molecular level can be very insignificant. Keeping this in mind, these results show that adsorption capacity does not change significantly with pH or with particle size. The invariability of capacity with pH demonstrates that covalent bonding is taking place, and that the adsorption mechanism is not solely electrostatic attraction. Also, since the adsorption capacity of the media does not vary greatly with particle size, the specific surface area does not change significantly either. This supports the hypothesis that most of the adsorption sites are internal, thus demonstrating the media�s high internal porosity. As a direct comparison between the different media at the same particle size, similar plots of capacity vs. pH are shown in Figure 4.17 through Figure 4.19.


57

28x48

0.E+00

1.E+04

2.E+04

3.E+04

4 5 6 7 8 9 10

pH

Ads

orbe

d A

s C

onc

( µg/g

)

GFHE-33SANS

Figure 4.17. Comparison of adsorption capacity for media size 28 x 48 in equilibrium with 100 ug/L As(V) as a function of pH (calculated with Freundlich constants).

48x100

0.E+00

1.E+04

2.E+04

3.E+04

4 5 6 7 8 9 10

pH

Ads

orbe

d A

s C

onc

( µg/

g)

GFHE-33SANS



58

100x200

0.E+00

1.E+04

2.E+04

3.E+04

4.E+04

4 5 6 7 8 9 10

pH

Ads

orbe

d A

s C

onc

( µg/

g)

GFHE-33SANS


Figure 4.17 through Figure 4.19 indicate that GFH outperforms the other media in

adsorption capacity at nearly all mesh sizes and pH. This may be explained due to the higher specific surface area of the GFH compared to other media. A higher adsorption capacity for GFH compared to the other media was found not to be true in laboratory column studies, however. An explanation for this misleading finding is the fact that adsorption isotherm experiments are highly dependent on mass of media. Since the GFH had to be stored wet, it was not possible to accurately determine its dry weight. Therefore, more confidence lies in the SANS and E-33 capacity data. Evaluations of these data show that SANS outperforms E-33 in adsorption capacity at all media sizes and all pH values. The surface diffusion coefficients strongly depend on the isotherm parameters; therefore, these parameters need to be determined as accurately as possible. Since the isotherm parameter, K, determines the capacity of the adsorbent, significant errors in K will cause the greatest error in using the HSDM for design. The isotherm exponent, 1/n, affects the shape of the breakthrough curve and influences the relative importance of the two mass transfer mechanisms with regard to control of the adsorption rate. As 1/n increases, the liquid phase mass transfer resistance will become more important for a given Bi and vice versa (Hand, Crittenden and Thacker, 1984). MEASUREMENTS OF SURFACE DIFFUSION COEFFICIENTS Surface diffusion coefficients were determined using the differential column batch reactor method as described by Hand, Crittenden and Thacker, (1983). Solutions to the homogeneous surface diffusion model for batch reactors and fixed beds were published by Hand, Crittenden and Thacker, (1983). These solutions were used to determine surface diffusion


59

coefficients based on Freundlich isotherm parameters and other dimensionless parameters, such as the Biot number. Initial attempts at the determination of surface diffusion coefficients consisted of using the approximate, analytical method as described previously. Local surface diffusion coefficients were determined for each time interval in the differential column batch reactor experiments. These values were then averaged over the entire experiment to determine the effective surface diffusion coefficient. The Biot number was also averaged over the entire experiment. The analytical method had very specific limitations concerning the Freundlich isotherm exponent, the Biot number, and the allowable range of both equilibrium and dimensionless concentration. This method proved to be very calculation intensive and is merely an approximate solution requiring interpolation of model parameters (Ao�An). Uncertainties in parameter estimation can result in errors in the calculations. Due to the many constraints and limitations of the analytical method, a numerical method was used which reduced many potential sources of error. The rate data was modeled using the Multicomponent Batch Pore Surface Diffusion Model (BPSDM) computer code developed by Gary Friedman, John Crittenden, and David Hand at Michigan Technological University in Houghton, Michigan. Surface diffusion coefficients, input as �guesses,� were varied until the smallest vector of relative standard deviations between model predictions and experimental data were found. Model predictions compared to data values with corresponding surface diffusion coefficients for all media at all mesh sizes are shown in Figure 4.20 through Figure 4.28.

S ANS 28x 48

0

0.2

0.4

0.6

0.8

1

0 2 4 6 8 10 12 14

Tim e (d ays )

C/C

o

Data Values

Model Predic tion

Ds = 4.2E-12 cm 2/s

Figure 4.20. Batch rate data and BPSDM prediction for SANS, 28x48 mesh.


60

SANS 48x100

0

0.2

0.4

0.6

0.8

1

0 2 4 6 8 10 12 14

T im e (days )

C/C

o

Data Values

Model Predic tion

Ds = 8.0E-13 cm 2/s


S ANS 100x200

0

0.2

0.4

0.6

0.8

1

0 2 4 6 8 10 12 14

Tim e (d ays)

C/C

o

Data Values

Model Predic tion

Ds = 2.1E-12 cm 2/s



61

E-33 28x48

0

0.2

0.4

0.6

0.8

1

0 2 4 6 8 10 12 14

T im e (days )

C/C

o

Data Values

Model Predic tion

Ds = 1.7E-11 cm 2/s

Figure 4.23. Batch rate data and BPSDM prediction for E-33, 28x48 mesh.

E-33 48x100

0

0.2

0.4

0.6

0.8

1

0 2 4 6 8 10 12 14

Tim e (d ays)

C/C

o

Data Values

Model Predic tion

Ds = 6.8E-12 cm 2/s

Figure 4.24. Batch rate data and BPSDM prediction for E-33, 48 x 100 mesh.


62

E-33 100x 200

0

0.2

0.4

0.6

0.8

1

0 2 4 6 8 10 12 14

Tim e (days)

C/C

o

Data Values

Model Predic tion

Ds = 5.5E-12 cm 2/s

Figure 4.25. Batch rate data and BPSDM prediction for E-33, 100 x 200 mesh.

GF H 28x48

0

0.2

0.4

0.6

0.8

1

0 2 4 6 8 1 0 12 14

Tim e (d ays )

C/C

o

Data Values

Model Predic tio n

Ds = 2.4E-12 cm 2/s

Figure 4.26. Batch rate data and BPSDM prediction for GFH, 28x48 mesh.

The measurements of the surface diffusion coefficients are summarized in Table 4.7. The table includes values for the Biot number (Bi) and the solute distribution parameter (Dg). A Biot number greater than 30 should be maintained in the differential column batch reactor experiments to insure that intraparticle diffusion is the rate limiting mass transfer step. The solute distribution parameter, Dg, can be used to calculate the number of bed volumes treated until exhaustion and this value can be compared to column data.


63

GFH 48x 100

0

0.2

0.4

0.6

0.8

1

0 2 4 6 8 1 0 1 2 14

Tim e (d ays )

C/C

o

Data Values

Model Predic tio n

D s = 1.2E-12 cm 2/s

Figure 4.27. Batch rate data and BPSDM prediction for GFH, 48 x 100 mesh.

GFH 1 00x 200

0

0.2

0.4

0.6

0.8

1

0 2 4 6 8 1 0 1 2 1 4

Tim e (da ys )

C/C

o

Data Values

Model Predic tio n

Ds = 8 .7E-13 cm 2/s

Figure 4.28. Batch rate data and BPSDM prediction for GFH, 100 x 200 mesh.


64

Table 4.7. Summary of measurements of surface diffusion coefficients (Ds).

Media Media Size Ds (cm2/s) Bi Dg

% Standard Deviation

SANS 28x48 1.81E-12 341 1.459 17.8 48x100 3.25E-13 970 1.586 14.1 100x200 1.25E-13 252 1.319 32.0

E-33 28x48 1.01E-11 224 1.237 19.9 48x100 1.98E-12 604 1.229 24.5 100x200 9.30E-13 917 1.146 14.5

GFH 28x48 2.40E-12 193 1.249 14.1 48x100 1.20E-12 287 1.296 6.1 100x200 8.70E-13 210 1.243 6.8

Measured values of Ds are plotted as a function of particle diameter in Figure 4.29. First, note that the error bars do not represent the results of replicate analyses but rather the standard deviation of the measurement of Ds based on fitting the data to the numerical solution using the BPSDM. Each experiment, including arsenic analysis, takes over three weeks to complete. Therefore, replicates could only be run for the SANS media.

The most important information from the experiments to measure the surface diffusion coefficient (Ds) is not the value of the parameter, but rather whether or not the value is dependent on the radius of the adsorbent particles. The most significant result is that there is a strong correlation between Ds and particle radius for the E-33 and the SANS media. Reference to the values for Ds in Table 4.7 show it increasing by approximately a factor of 10 as the geometric mean particle radius increases from 0.106 mm to 0.424 mm. Somewhat surprising is the result showing that Ds appears to be independent of particle size for GFH. The investigators do not know of any reason why there should be a difference between the three media. Because there are only three points for each curve and a fairly large relative standard error in the Ds values for each data point, it is not possible to determine whether the dependence of Ds on particle diameter for the E-33 and SANS media is linear or non-linear. Recall the scaling relationship presented previously:

EBCTSCEBCTLC

=

RSC

RLC

2-x =

tSCtLC

(Equation 21)

If Ds is independent of particle radius x = 0, whereas if there is a linear relationship x = 1. Because of the very long duration of the laboratory and field adsorption column experiments, they were begun assuming a linear relationship between particle diameter and EBCT (i.e. x = 1). While this appears to have been a reasonable assumption for E-33 and SANS media, it may not be valid for the GFH media. Finally, it is interesting to note that the values of Ds for the E-33 and SANS media differ by approximately a factor of 10. The cause of this difference and the significance is not well known.


65

0.0E+00

2.0E-12

4.0E-12

6.0E-12

8.0E-12

1.0E-11

1.2E-11

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45

Particle Radius (mm)

Ds

(cm

^2/s

)

Sans

Sans II

E-33

GFH

Figure 4.29. Dependence of the surface diffusion coefficient (Ds) on particle diameter (error bars represent the standard deviation of the determinations).


66


67

CHAPTER 5 RESULTS AND DISCUSSION – COLUMN EXPERIMENTS

Based on previous experience with As adsorption columns it was anticipated that the column experiments using the large media (28 x 48 mesh) would take a very long time to complete. Accordingly, they were started early in the project. Initially they consisted of bench scale columns run in the laboratory using spiked and buffered UNM tap water. As experience operating these columns was gained, they were expanded to include small pilot scale units operated by the City of El Paso and the City of Albuquerque. Because of the long duration and the need to commence, a linear relationship was assumed between the particle diameters and EBCT. This corresponds to a value of x = 1 in equation 21 (Chapter 2). The physical interpretation of this is that the surface diffusion coefficient (Ds) is linearly dependent on particle diameter. This approximate relationship was subsequently found in measurements of Ds for the E-33 media using the differential column batch reactor system. However, similar measurements of Ds for the GFH and SANS media suggest either a weak dependence of Ds on particle diameter, or possibly no dependence. In other words, Ds may be constant for these media. This chapter describes the results of the laboratory and field testing of the As adsorption columns. LAB TESTING WITH UNM TAP WATER The linear scaling relationships of the EBCT between the large and small columns were selected based on an assumed linear relationship between surface diffusivities and adsorption particle diameters (i.e., x = 1). Since the 48 x 100 mesh media is half the diameter of the 28 x 48 media (0.225 mm vs. 0.45 mm), the corresponding EBCT was also halved (1.5 min vs. 3.0 min). The same methodology was used in the design of the 100 x 200 columns. The results of the column experiments are presented as breakthrough curves which are a plot of the normalized effluent arsenic concentration (C/Co) versus the number of bed volumes of water treated. All experiments were conducted with UNM tap water containing a three mM NaHCO3 buffer adjusted to pH 7.0. The feed solutions were spiked with 100 ug/L As. 1.0 mg/L of NaOCl was added to prevent microbial growth and assure that As was in the (V) oxidation state. Activated alumina was the first media to be evaluated for the validation of the RSSCT process. As apparent in Figure 5.1, the data is very scattered, but it is apparent breakthrough occurs within a few hundred bed volumes. Due to the unsuccessful performance of the activated alumina compared to the other media, no further studies were conducted with this media.


68

AA

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

0 10000 20000 30000 40000 50000

Bed Volumes Treated

C/C

o

28x48 (3 min EBCT) 48x100 (1.5 min EBCT) 100x200 (0.75 minEBCT)

Figure 5.1. Laboratory column breakthrough curves for activated alumina, As(V) Co = 100 ug/L, pH 7. The remainder of the laboratory breakthrough curves exhibit less data scatter than that found in the activated alumina columns. The breakthrough curves that follow (Figure 5.2 through Figure 5.7) exhibit a peak in the 48x100 effluent arsenic concentrations between 30,000 and 40,000 bed volumes. Since samples were analyzed in groups of approximately 10,000 bed volumes, this peak was assumed to be due to analytical error rather than reflecting a true peak because it did not appear for either of the other two mesh sizes. Another explanation may lie in the influent feed water. There may have been a change in water quality for that particular data set, causing arsenic to be displaced from the adsorption media. To correct for possible analytical or water quality errors, erroneous data points were removed from the breakthrough curves as shown in Figure 5.3, Figure 5.5 and Figure 5.7. Breakthrough curves are displayed first with raw data, then again with erroneous data removed. GFH breakthrough curves are presented in Figure 5.2 and Figure 5.3, E-33 breakthrough curves in Figure 5.4 and Figure 5.5, and finally SANS breakthrough curves in Figure 5.6 and Figure 5.7.


69

GFH

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 10000 20000 30000 40000 50000

Bed Volumes Treated

C/C

o

28x48 (3 m in EBCT) 48x100 (1.5 m in EBCT) 100x200 (0.75 min EBCT)

Figure 5.2. Laboratory column breakthrough curves for GFH (raw data), As(V) Co = 100 ug/L, pH 7.

GFH

0

0.2

0.4

0.6

0.8

1

1.2

0 10000 20000 30000 40000 50000

Bed Volumes Treated

C/C

o

28x48 (3 m in EBCT) 48x100 (1.5 m in EBCT) 100x200 (0.75 m in EBCT)

Figure 5.3. Laboratory column breakthrough curves for GFH (erroneous data removed for 48x100 mesh column), As(V) Co = 100 ug/L, pH 7.


70

SORB E-33

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0 10000 20000 30000 40000 50000 60000

Bed Volumes Treated

C/C

o

28x48 (3 m in EBCT) 48x100 (1.5 m in EBCT 100x200 (0.75 m in EBCT)

Figure 5.4. Laboratory column breakthrough curves for E-33 (raw data), As(V) Co = 100 ug/L, pH 7.

SORB E-33

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0 10000 20000 30000 40000 50000 60000

Bed Volumes Treated

C/C

o

28x48 (3 m in EBCT) 48x100 (1 .5 m in EBCT) 100x200 (0.75 m in EBCT)

Figure 5.5. Laboratory column breakthrough curves for E-33 (erroneous data removed for 48x100 mesh column), As(V) Co = 100 ug/L, pH 7.


71

SANS

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0 20000 40000 60000 80000

Bed Volumes Treated

C/C

o

28x48 (3 min EBCT) 48x100 (1.5 min EBCT) 100x200 (0.75 min EBCT)

Figure 5.6. Laboratory column breakthrough curves for SANS (raw data), As(V) Co = 100 ug/L, pH 7.

SANS

0

0.2

0.4

0.6

0.8

1

1.2

0 20000 40000 60000 80000

Bed Volumes Treated

C/C

o


Figure 5.7. Laboratory column breakthrough curves for SANS (erroneous data removed for 48x100 mesh column), As(V) Co = 100 ug/L, pH 7.


72

The data scatter that exists is attributed to influent and effluent variability. First, there were some problems maintaining a constant influent arsenic concentration. Some of the influent variability may be due to the logistics of preparing large volumes (typically 200 L) of water two to three times a week. There were also unexplained decreases in arsenic concentration in the influent that was thought to be due to a small but persistent presence of particulates in the tap water. A filter was attached to the tap water source, which eliminated much of the variability by the adsorption of arsenic onto particulates in the feed water. A second source of variability was analytical uncertainty associated with measurement of very low arsenic concentrations (< 5 ug/L). The portions of a breakthrough curve of most importance are actual breakthrough of the media to the treatment objective and exhaustion of the media to where it has nearly reached its ultimate adsorption capacity. The number of bed volumes treated to the MCL (C/Co = 0.1) and inflection point (C/Co = 0.5), along with the total adsorption capacities based on the breakthrough curves are shown in Table 5.1 through Table 5.3. The use of the inflection point or mid-point in the breakthrough curve is a useful measure for comparing the performance of different media because this point is located on the steepest part of the curve and is much easier to identify with confidence than either the breakthrough point (C/Co = 0.1) or the exhaustion point (C/Co = 0.9).

Table 5.1. Performance characteristics of GFH laboratory columns

Bed Volumes Treated Media Size Breakthrough to

MCL (C/Co=0.1) Inflection Point

(C/Co=0.5)

Total Column Capacity (µg As/g media)

28x48 15,000 41,000 4400 48x100 25,000 37,000 3800 100x200 20,000 37,000 4300

Table 5.2.

Performance characteristics of SORB E-33 laboratory columns Bed Volumes Treated

Media Size Breakthrough to MCL (C/Co=0.1)

Inflection Point (C/Co=0.5)


28x48 22,000 40,000 7300 48x100 18,000 36,000 6300 100x200 27,000 45,000 8400

Table 5.3.

Performance characteristics of SANS laboratory columns Bed Volumes Treated

Media Size Breakthrough to MCL (C/Co=0.1)

Inflection Point (C/Co=0.5)


28x48 25,000 42,000 6400 48x100 29,000 41,000 5500 100x200 46,000 55,000 10300


73

These tables give a useful comparison between media types and their performance characteristics in adsorption columns. As discussed in Chapter 2, similarity is achieved (i.e., the RSSCT method validated) if the breakthrough curves of columns containing different media sizes are identical. Even with the data scatter, it is apparent that the laboratory column breakthrough curves are not exactly identical with differing particle size. This is attributed to the fact that the surface diffusion coefficients are not linearly dependent on particle size as was assumed in column scaling and design. Consequently, scaling factors were reevaluated based on measured values of the diffusivity factor (x). This term is defined in terms of the ratio of surface diffusion coefficients and particle radii in equation 20:

DsLCDsSC

=

RSC

RLC

x (Equation 20)

Equation 20 was used to calculate the diffusivity factor for each media depending on the surface diffusivity and particle diameter. Diffusivity factors corresponding to 28 x 48 and 100 x 200 mesh columns are given in Table 5.4 for each media. Also included are the corresponding EBCTs.

Table 5.4. Calculated diffusivity factors and corresponding EBCTsc.

Diffusivity factor, x EBCT28 (min) EBCT100 (min)

GFH 0.732 3.0 0.517 E-33 0.814 3.0 0.580 SANS 0.500 3.0 0.375

NOTE: Linear assumption (x = 1) yielded EBCT100 = 0.75 min.

Since surface diffusion coefficients are characteristic of a specific adsorbent/adsorbate system, the diffusivity factor will differ for each media. Therefore, scaling factors will also differ between media type. To achieve perfect similarity, it is likely that EBCTs (i.e., scaling relationships) will have to be modified depending on the media�s diffusivity factor. Another explanation for the conflicting breakthrough curves is the difference in source water for the batch isotherms, surface diffusion experiments and column experiments. The isotherms and surface diffusion coefficient measurements were conducted using buffered, deionized water at specific pH values. Thus, the only possible interferences or competing ions might be associated with the carbonate buffer. In contrast, the laboratory columns were run with filtered tap water, adjusted to pH 7, and spiked with arsenate to a concentration of 100 µg/L. Though the water was filtered, there are still a number of other constituents present at higher concentrations than arsenate that might compete for sorption sites, notably silica. Competing ions will reduce the capacity of the media for As. These competing water constituents will be discussed in the following section.


74

FIELD COLUMN EXPERIMENTS Arsenic adsorption columns were operated by the City of Albuquerque and the City of El Paso. The columns operated by the City of Albuquerque were located at the water utility�s administrative offices in an area of town containing high As levels in tap water. The columns operated by the City of El Paso were located at a relatively isolated well field located approximately 15 miles north of town. The results of this testing are described below. Testing by the City of El Paso In November, 2002, three small As adsorption columns were installed at a well operated by the City of El Pasot. The columns are 5 cm ID x 1 m long, and contained about 0.6 m of media. They were operated in a down flow mode at an empty bed contact time of five minutes based on the media volume. Two of the columns contained 28x48 mesh SANS media, and the third contained 28x48 mesh E-33. The City of El Paso�s conducted all analyses of water samples collected for this field project. The pH of the feed water fluctuated slightly in the range of 7.5 to 7.8. The intent of the experiment was to adjust the influent solution of one of the SANS columns and the E-33 column to pH 6.8 using CO2(g), while the other SANS column would be operated at ambient pH. A plumbing problem resulted in all columns being fed low pH water for approximately the first two months of operation. This was corrected and the second SANS column continued receiving water at ambient pH until exhaustion. The results are presented in Figure 5.8. Note that monitoring of the SANS column treating water at ambient pH was discontinued after exhaustion was reached. These results suggest a chromatographic peaking due to displacement of As by another constituent, however, it is not known if this is a true effect.


75

0

5

10

15

20

25

30

35

40

45

50

0.0E+00 2.0E+04 4.0E+04 6.0E+04 8.0E+04 1.0E+05 1.2E+05 1.4E+05 1.6E+05

Bed Volumes Treated

Ars

enic

Con

c. (u

g/L)

Arsenic In

E-33 Low pHSANS Low pH

SANS High pH

Figure 5.8. Arsenic breakthrough curves for El Paso adsorption columns. One column treated water at ambient pH, the other two treated water adjusted to pH 6.8 using CO2(g).

The El Paso columns were monitored for a host of other constituents including pH, silica, iron, copper, fluoride and phosphates. Breakthrough curves for these other parameters are presented in Appendix A The silica results are of special interest because it is often reported to be a competing ion for adsorption sites on MeOx media. The silica monitoring results are summarized in Figure 5.9. The data show that the silica concentration is approximately 1,200 times greater than the As concentration on a molar basis (0.5 mM SiO2 compared to 0.4 uM As). Second, the data also show almost instantaneous silica breakthrough, with the columns providing no removal of silica after less than 2,000 bed volumes. Thus, although silica may affect As adsorption, it seems unlikely that it directly competes for surface adsorption sites because once exhaustion is reached, no further As removal would be expected.


76

20

25

30

35

40

45

0.0E+00 2.0E+04 4.0E+04 6.0E+04 8.0E+04 1.0E+05 1.2E+05 1.4E+05 1.6E+05

Bed Volumes of Water Treated

Silic

a C

onc.

(mg/

L)

Silica In, mg/LE-33 Low pHSANS Low pHSANS High pH

Figure 5.9. Silica monitoring results for El Paso adsorption columns. One column treated water at ambient pH, the other two treated water adjusted to pH 6.8 using CO2(g). The results of the El Paso adsorption columns presented an unexpected complication for this research project. Although started early in the project and operated continuously throughout its duration, the low pH columns never produced a complete breakthrough curve. In fact, the SANS media never reached the breakthrough point (C/Co = 0.1). Thus, it was neither logistically feasible nor possible within the time constraints of the project to simulate the performance of this media using the RSSCT process. The results, however, are of significant value because they demonstrate the remarkable capacity of new As adsorption media when operated at low pH. Furthermore, the experience gained from using CO2(g) to adjust pH is of considerable value; it is a cheap and safe way of adjusting the pH without significantly affecting other water quality characteristics. Testing by the City of Albuquerque The pilot columns operated at the City of Albuquerque�s Pino Yards facility were designed under the same assumption of a linear dependence of surface diffusivity on particle size. Field columns were conducted under the same methodology as the laboratory column experiments, only differing in the source water. Albuquerque�s Pino Yard tap water was fed to the columns with no pH adjustment or addition of arsenate. Influent arsenic concentrations


77

averaged around 20 µg/L. Extensive water quality data at this site was provided by the City of Albuquerque. Characteristics relevant to arsenic adsorption are presented in Table 5.5. Constituents in Albuquerque�s tap water that may compete with arsenic for adsorption sites include fluoride and silica.

Breakthrough curves for the field columns were obtained in the same manner as the laboratory columns by plotting normalized concentration versus bed volumes treated. Results for field columns and laboratory columns using GFH, E-33, and SANS are displayed in Figure 5.10 through Figure 5.12. Note that the field columns were conducted at UNM using water collected from the Pino Yards facility.

Table 5.5. Water quality at the City of Albuquerque Pino Yards facility

Minimum Maximum Average Units Metals Arsenic 22 34 28 µg/L Iron 0.009 0.019 0.012 mg/L Manganese ND ND ND mg/L Selenium ND ND ND µg/L Minerals Fluoride 0.8 1.2 1.0 mg/L Alkalinity 118 128 123 mg/L as CaCO3Bicarbonate 116 127 122 mg/L as CaCO3Calcium 28 33 30 mg/L Chloride 31 36 33 mg/L Hardness 5.7 6.9 6.3 grains/gallon Magnesium 5.7 7.7 6.6 mg/L Potassium 5 7 6 mg/L Silica 65 77 70 mg/L as SiO2 Sodium 48 50 49 mg/L Sulfate 38 40 39 mg/L Total Dissolved Solids 275 298 286 mg/L Free Chlorine Residual 0.6 0.9 0.8 mg/L Conductance 404 434 421 µohms/cm pH 7.8 7.8 7.8 Standard Units Temperature 56 80 69 Fahrenheit


78

GFH

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0 10000 20000 30000 40000

Bed Volumes Fed

C/C

o


Figure 5.10. Field column breakthrough curves for GFH, City of Albuquerque Pino Yards tap water.

SORB E-33

0

0.2

0.4

0.6

0.8

1

0 10000 20000 30000 40000

Bed Volumes Fed

C/C

o


Figure 5.11. Field column breakthrough curves for SORB E-33, City of Albuquerque Pino Yards tap water.


79

SANS

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0 10000 20000 30000 40000

Bed Volumes Fed

C/C

o


Figure 5.12. Field column breakthrough curves for SANS, City of Albuquerque Pino Yards tap water. The performance trends of the media are comparable to studies conducted in the laboratory using spiked UNM tap water. Minor fluctuations in effluent arsenic concentration can be attributed either to changes in the source water or analytical variance, as these samples were analyzed on multiple occasions. Tabulated results of bed volumes treated to the MCL (C/Co = 0.5) and the total adsorption capacities based on the breakthrough curves are shown in Table 5.6 through Table 5.8.

Table 5.6. Performance characteristics of GFH field columns.

Bed Volumes Treated Media Size Breakthrough to Mid-Point

(C/Co=0.5)

Total Column Capacity

(�g As/g media) 28x48 15,000 570 48x100 7,000 220 100x200 10,000 180

Table 5.7.

Performance characteristics of E-33 field columns. Bed Volumes Treated

Media Size Breakthrough to Mid-Point (C/Co=0.5)


(�g As/g media) 28x48 > 40,000 1800 48x100 16,000 700 100x200 15,000 450


80

Table 5.8. Performance characteristics of SANS field columns.

Bed Volumes Treated Media Size Breakthrough to Mid-Point

(C/Co=0.5)


(�g As/g media) 28x48 21,000 870 48x100 7,000 310 100x200 17,000 450

As with the laboratory columns, the field column breakthrough curves do not exhibit perfect similarity. The discrepancies in field column breakthrough curves are due to: (1) surface diffusion coefficients that are not linearly proportional to particle size; and (2) diffusivity factors that vary with media type. Another explanation for the inconsistent breakthrough curves with respect to the field columns is the fact that the surface diffusion coefficient is concentration dependent. Surface diffusion coefficients were determined based on an initial arsenic concentration of 100 µg/L. Initial concentrations of arsenic for the field columns were only around 28 µg/L. The change in surface diffusion coefficients for lower concentrations will change the scaling factors, leading to modified EBCTs for the small scale columns. At this point in the discussion there is value in comparing the adsorbed concentration of As in the columns to that calculated by the Freundlich isotherm constants. In all cases the equilibrium adsorbed concentrations predicted by the Freundlich equation are much greater than observed in the column experiments. This is attributed to two principal factors. First, the batch adsorption experiments were allowed to equilibrate for three weeks whereas the column experiments had EBCTs of five minutes of less. Thus, they do not represent equilibrium conditions. Second, the batch equilibrium isotherms were determined using buffered deionized water containing no competing ions. In contrast the column experiments were run on tap water containing several likely competing ions. MASS TRANSFER ZONE CONSIDERATIONS The numerical method described in Chapter 2 to estimate the height of the mass transfer zone within adsorption columns was very calculation intensive with many sources for error. Another method proposed by Hand, Crittenden and Thacker (1984) involves the use of the column breakthrough curves and a calculated value for the velocity of the mass transfer zone using:

1+

=g

mtz DvV (Equation 46)

Elapsed times were calculated based the number of bed volumes treated between C/Co = 0.1 and C/Co = 0.5. The height of the mass transfer zone was then calculated as: ( ) mtzCCCCmtz Vtth

oo*2 1.0/5.0/ == −= (Equation 47)


81

The two methods used in the determination of the height of the mass transfer zone gave conflicting results, in some cases by an order of magnitude. The mass transfer zones were evaluated instead by considering the dimensionless parameters for the adsorption columns. First, the rate limiting mass transfer step must be identified. The column experiments utilize much slower flow rates than the differential column batch reactor experiments; therefore, surface diffusion will not necessarily be the rate limiting transport step. The rate limiting transfer step is determined from the Biot number. As stated previously, when the Biot number is large (Bi > 30), the intraparticle mass transfer rate becomes the rate-limiting step and when the Biot number is small (Bi < 0.5), liquid phase mass transfer is rate limiting. If liquid phase mass transfer is rate limiting, the Stanton number represents the ratio of the length of the column with respect to the mass transfer zone. On the other hand, if intraparticle mass transfer is rate limiting, the diffusivity modulus represents the ratio of the bed length with respect to the mass transfer zone. These dimensionless parameters are tabulated in Table 5.9.

Table 5.9. Dimensionless parameters representing the height of the mass transfer zone.

Media Type

Media Size

Bi

St

Stmin

Ed

28 x 48 4.2 13.8 2.1 3.3 48 x 100 9.9 28.0 3.6 2.8

GFH

100 x 200 8.3 42.4 3.0 5.1 28 x 48 3.0 13.8 1.1 4.6 48 x 100 6.3 28.0 1.8 4.5

E-33

100 x 200 5.8 42.4 1.6 7.4 28 x 48 4.7 13.9 3.8 2.9 48 x 100 20.9 28.3 16.8 1.4

Lab Columns

SANS

100 x 200 8.7 42.7 4.3 4.9 28 x 48 10.2 18.9 5.1 1.9 48 x 100 11.7 29.7 4.2 2.5

GFH

100 x 200 12.3 48.9 4.4 4.0 28 x 48 4.2 18.9 1.5 4.5 48 x 100 6.7 29.7 1.9 4.4

E-33

100 x 200 4.6 48.9 1.3 10.6 28 x 48 9.8 19.1 4.9 2.0 48 x 100 35.4 29.9 17.7 0.8

Field Columns

SANS

100 x 200 10.6 49.2 3.8 4.6 The Biot numbers in the column studies range between 3.0 and 30; therefore it is unclear which dimensionless parameter (St or Ed) best represents the height of the mass transfer zone in relation to the bed height. It must be mentioned that the Stanton numbers calculated are higher than the minimum Stanton numbers required to achieve constant pattern conditions in the adsorber. Therefore, it can be assumed that the mass transfer zones are fully developed and travel through the column in a constant pattern. Also, both St and Ed are greater than one for all columns. Therefore, whichever mass transfer mechanism is rate limiting, the bed height is greater than the mass transfer zone height based on these dimensionless parameters.


82


83

CHAPTER 6 CONCLUSIONS

Adsorption of arsenic (As) onto various MeOx media is a promising treatment technology that offers many advantages over other As treatment methods available to small utilities. Perhaps its greatest advantage is its simplicity, as most current arsenic adsorption processes are intended to operate without media regeneration. This project used three commercially available media (Alcan�s FS-50, US Filter�s GFH, and Severn Trent�s E-33) and a new media, SANS, developed by Sandia National Laboratories, Albuquerque. The project involved significant in-kind contributions from Sandia as well as the Cities of El Paso and Albuquerque. The project focused on development of a method of rapidly evaluating the performance of As adsorption media through use of Rapid Small Scale Column Tests (RSSCTs). This type of testing was originally developed to allow scale modeling of the adsorption of organic constituents onto GAC. In order to determine whether the RSSCT process works for As adsorption onto MeOx media, it is necessary to develop an understanding of the transport and bonding processes that affect adsorption column performance. Thus, a considerable amount of attention is given to these processes in this report. While it is clear that coulombic forces play an important role in As adsorption, both arsenate (H2AsO4

- and HAsO42-) and arsenite (H3AsO4) molecules will also form covalent bonds

with metal oxide surfaces. Initial bonding between the As and MeOx surfaces occurs rapidly and is probably dominated by electrostatic attraction. Thus, a positive surface will attract and retain negative As(V) species, whereas a negative surface will not. Inner sphere covalent bonds form with time that are much stronger than electrostatic bonds and more specific for As species. Because the surface charge of MeOx surfaces is pH dependent, most good As adsorbents have high pH of zero point of charge (pHzpc); when pH < pHzpc surface sites are predominately positively charged whereas when pH > pHzpc surface sites are mostly negatively charged. Thus, there is a strong pH dependence on the adsorption process that is not present when considering adsorption of organic solutes by activated carbon. This introduces another variable into the process that causes substantial complexity. Research by other investigators has shown that As-MeOx bonds are dominated by mono- and bi-dentate bonds. It is likely that the bi-dentate bonds are slower to form but are much stronger than the monodentate bonds. The transport processes that are involved in an adsorption column appear to be the same whether the process involves removal of organics by activated carbon or adsorption of As onto MeOx. These processes include both external and internal mass transfer phenomenon. Mathematical modeling emphasizes the importance of internal transport, and especially internal migration by surface diffusion. This transport within the media may occur by either pore diffusion or surface diffusion. The latter has been shown by previous investigations to often be the rate limiting step in activated carbon adsorption processes. It is not believed that the different bonding mechanisms between As adsorption onto MeOx and adsorption of organics onto activated carbon will affect this internal transport. This project involved extensive characterization of the surfaces of the adsorbents. Scanning and transmission electron microscopy showed that the internal pores of the media are very small. BET isotherm analyses were used to measure the total surface areas of the media which ranged from ~100 m2/g for E-33 to 270 m2/g for GFH. This indicates that most of the


84

surface�s adsorption sites lie within the pores themselves. Considering the media�s extensive internal pore structure, it is believed that the rate of adsorption is limited by internal mass transfer processes. The pore size distributions were also determined and, confirming the results of the electron microscopy, found average pore diameters ranging from 3 nm (GFH) to 16 nm (E-33). This dimension can be compared to the diameter of an H4AsO4 molecule, which is approximately 0.5 nm. Pilot testing to evaluate As adsorption onto MeOx media is a difficult and time consuming process. Measurement of the equilibrium adsorption properties, as is done in determining Freundlich isotherms, provides only a qualitative comparison of different media. This is because isotherm tests are done in a batch system using a small volume of water. The system is allowed to equilibrate for many days to approximate chemical equilibrium. In contrast, a column system is operated at a brief EBCT of a few minutes, during which many tens of thousands of bed volumes of water are passed through the column. Further complexity is introduced by the possible presence of competing ions or constituents that may change the surface chemistry of the adsorbent. Phosphate is an example of an ion which competes with As for adsorption sites on most iron based adsorption media, while silica or calcium carbonate (CaCO3) are constituents that may precipitate on the surface of an adsorbent media, thereby reducing its adsorptive capacity. To date column tests appear to be the only method of determining the performance of an adsorption media. However, these take a very long time to complete and consequently are quite costly. The RSSCT process is based on scaling relations developed for adsorption of organics onto granular activated carbon wherein external and/or internal mass transfer processes are the principal rate limiting steps in an adsorption column. These relations can be used to establish similarities in performance between small columns containing small media and larger columns with larger media. Small diameter particles have less internal mass transfer resistance because internal distances are smaller, hence they can be operated at shorter empty bed contact times (EBCTs). The primary advantages of the RSSCT procedure for evaluating adsorption media include:

• Time and cost savings • Rapid comparison of different media • Rapid determination of performance characteristics for different water qualities • Optimization of the adsorption process; particularly with respect to operating pH and

effects of interferences and competing constituents on process performance.

The primary objective of this research was to determine if the RSSCT process could be applied to arsenic adsorption onto MeOx media, while in turn using RSSCTs to compare the performance of different arsenic adsorption media. In order to validate the RSSCT process, extensive batch and column experiments were necessary. To begin, adsorption isotherm experiments were conducted and data were fitted to the Freundlich isotherm model to determine the adsorption characteristics (K and 1/n) of each media. These values were needed for the determination of the surface diffusion coefficients, as they were important input parameters for use of the Multicomponent Batch Pore Surface Diffusion Model. Other required input was concentration vs. time data from the differential column batch reactor rate studies. This data was used to determine surface diffusion coefficients for three different sizes of each media. It is the


85

surface diffusion coefficients� dependence on particle size that determine the scaling relationships used in the design of small- and large-scale column experiments. Due to time constraints, the laboratory and field column experiments performed were designed based on the assumption of a linear relationship between the surface diffusivity and particle size, resulting in a linear scaling relationship between EBCT and particle size. For example, when particle size doubled, EBCT also doubled. Breakthrough curves were similar for the different particle sizes, although the scaled performance was not identical. Measurements of the internal surface diffusion coefficient using differential column batch reactors were conducted for three different media sizes; 28 x 48 mesh, 48 x 100 mesh, and 100 x 200 mesh. Two of the media (E-33 and SANS) have surface diffusion coefficients (Ds) that are dependent on adsorbent media size, while this coefficient appears not vary with particle size for GFH. As only three different media sizes were tested, it was not possible to determine with certainty whether the dependence of Ds on media size was linear or not. Comparison of the measurements of Ds with the results of the column testing suggest that further effort is needed to determine the exact nature of the scaling relationship for the RSSCT process. At the same time, this project found very good agreement between the performance of columns using full size media (28x48 mesh size) and columns using very fine media (48x100 mesh and 100x200 mesh) and operated at correspondingly reduced EBCTs. In particular, both the batch equilibrium studies and the adsorption column studies showed that the adsorption properties of the four media tested do not change significantly with a change in particle size. Although the breakthrough curves obtained were not identical, they showed strong similarities in shape and in the relative performance of the different media. Other experimental considerations could aid in improved prediction of full-scale column performance. Of most importance is the source water used in experimentation. The batch isotherm experiments and differential column batch reactor experiments were conducted using buffered, deionized water containing only As and a NaHCO3 buffer. The column experiments, on the other hand, were performed using tap water with little or no alterations in water chemistry. The tap water from the University of New Mexico, City of Albuquerque, and well water from a well in El Paso were used for the column studies. The performance of the adsorption columns was different for each. It is clear that pH is by far the most important parameter affecting column performance. The effects of other constituents could not be determined due to the lengthy testing process associated with column studies. Nevertheless, since the performance of adsorption media is strongly dependent on the chemistry of the water being treated, it is important to conduct all experiments for candidate media with the actual water requiring treatment. One of the motivations for development of an RSSCT protocol was to reduce the cost, complexity, and time required to conduct experiments to determine the effects of competing solutes in adsorption processes.

There is a second benefit of the RSSCT protocol. Because the scaled down adsorbent particles are much smaller, the adsorption columns can also be smaller. The volume of the columns decreases approximately as a function of the particle radius cubed. Thus, the volume of water needed for a full duration breakthrough test is reduced proportionately. In many cases, this allows water to be transported to the laboratory for testing where the adsorption process can be conducted under controlled conditions of temperature, pH, and flow rate. The quality of the testing is further improved by the ability to collect samples more frequently to generate a more complete breakthrough curve.


86

To further validate the RSSCT procedure, continued experimentation is suggested for the consideration of competitive adsorption, inconsistencies in source water chemistry, and finally the modification of scaling equations based on these considerations. For example, multicomponent isotherm experiments should be conducted and duplicated to ensure accuracy in the isotherm parameters, since modeling efforts are largely dependent on these parameters. Errors in the isotherm parameters will compound errors in surface diffusion coefficients and scaling relationships. In addition, general experimentation on the surface diffusion coefficient should be conducted to determine its sensitivity to variable water quality. Also, accurate measurements of the height of the mass transfer zone would be beneficial. If precise values were known, it would enable the researcher to use a minimum bed height in columns studies to further reduce run time, water use, and media use. The testing conducted in this project allows a relative comparison of the four arsenic adsorption media evaluated. A common phenomenon of As adsorption column experiments is that they exhibit considerable leakage which makes accurate determination of the breakthrough (C/Co = 0.1) and exhaustion (C/Co = 0.9) points difficult. An endpoint that is easier to detect is the inflection or mid-point in the breakthrough curve where C/Co = 0.5. The activated alumina exhibited poor performance in the early stages of testing, therefore it was excluded from further evaluation. In laboratory column studies using UNM tap water spiked with As and adjusted to pH 7.0, the GFH reached the mid-point after 41,000 bed volumes, the E-33 after an average of 40,000 bed volumes, and the SANS media after an average of 42,000 bed volumes. Testing with City of Albuquerque water at ambient pH (7.8) found that GFH reached the mid-point after 15,000 BVs, E-33 treated more than 40,000 BVs, and the SANS media treated 21,000 BVs of water. Experiments conducted at El Paso with E-33 and SANS media treating well water adjusted to pH 6.8 found extraordinarily long column runs, likely the longest reported for any As adsorption study in the U.S. At the conclusion of this study both media had treated 115,000 BVs of water. The breakthrough curve for E-33 was just approaching the mid-point and SANS media had not reached breakthrough (C/Co = 0.1). The variability in results is notable. While all three media gave similar performance for UNM tap water at pH 7.0, E-33 provided significantly longer runs treating City of Albuquerque tap water at pH 7.8, and SANS media was better at treating El Paso well water at pH 6.8. While it is clear that the RSSCT concept as applied to As adsorption columns should not be used for design of full-scale systems, it does have enormous value in studies conducted to compare the effects of different variables on the treatment process. Three examples of situations where RSSCT would be especially appropriate for a utility include:

• RSSCT testing allows direct comparison of the relative adsorption capacity of different media for a source water.

• RSSCT testing can determine the relative increase in adsorption capacity that can be obtained by reducing the pH of the source water to see if improved performance would offset the additional complexity and cost associated with pH adjustment.

• RSSCT testing can be conducted to measure the effects of constituents which interfere with the As adsorption process such as phosphate or silica.

Measurements of Ds for SANS and E-33 media show that it depends on the particle size. This means there should be a linear scaling relationship between particle radius and EBCT. This relationship allows investigators to reduce the duration of a column study by a factor of four by


87

running experiments with 100x200 mesh media instead of 28x48 mesh media. Therefore, the time to complete a column run, can be reduced by a factor of 16. The dependence of Ds for GFH was not as clear, however, laboratory and field scale column studies with different size media suggest that the linear scaling relationship is also valid for this media.


88


89

APPENDIX A – GRAPHICAL ISOTHERM RESULTS

Figure A.1. Linear regression of three-week isotherm data for SORB E-33, 28x48, pH 5 – pH 9.

E-33 28x48 pH 5

y = 0.285x + 3.3196R2 = 0.94

3.0

3.4

3.8

4.2

4.6

5.0

0.0 1.0 2.0 3.0 4.0 5.0

log Ce

log

q

E-33 28x48 pH 6

y = 0.2788x + 3.3589R2 = 0.8744

3.0

3.4

3.8

4.2

4.6

5.0

0.0 1.0 2.0 3.0 4.0 5.0

log Ce

log

q

E-33 28x48 pH 7

y = 0.2686x + 3.3675R2 = 0.8833

3.0

3.4

3.8

4.2

4.6

0.0 1.0 2.0 3.0 4.0 5.0

log Ce

log

q

E-33 28x48 pH 8

y = 0.2702x + 3.3389R2 = 0.9467

3.0

3.4

3.8

4.2

4.6

0.0 1.0 2.0 3.0 4.0 5.0

log Ce

log

q

E-33 28x48 pH 9

y = 0.2728x + 3.3645R2 = 0.9503

3.0

3.4

3.8

4.2

4.6

0.0 1.0 2.0 3.0 4.0 5.0

log Ce

log

q


90

Figure A.2. Linear regression of three-week isotherm data for SORB E-33, 48x100, pH 5 - pH 9.

E-33 48x100 pH 5

y = 0.212x + 3.597R2 = 0.9827

3.0

3.5

4.0

4.5

5.0

0.0 1.0 2.0 3.0 4.0 5.0

log Ce

log

qE-33 48x100

pH 6

y = 0.204x + 3.6122R2 = 0.9661

3.0

3.5

4.0

4.5

5.0

0.0 1.0 2.0 3.0 4.0 5.0

log Ce

log

q

E-33 48x100 pH 7

y = 0.2016x + 3.5936R2 = 0.9743

3.0

3.5

4.0

4.5

5.0

0.0 1.0 2.0 3.0 4.0 5.0

log Ce

log

q

E-33 48x100 pH 8

y = 0.2242x + 3.4935R2 = 0.9643

3.0

3.5

4.0

4.5

5.0

0.0 1.0 2.0 3.0 4.0 5.0

log Ce

log

q

E-33 48x100 pH 9

y = 0.2091x + 3.5176R2 = 0.9712

3.0

3.5

4.0

4.5

5.0

0.0 1.0 2.0 3.0 4.0 5.0

log Ce

log

q


91

Figure A.3. Linear regression of three-week isotherm data for SORB E-33, 100x200, pH 5 - pH 9.

E-33 100x200 pH 5

y = 0.2379x + 3.5694R2 = 0.9629

3.0

3.5

4.0

4.5

5.0

0.0 1.0 2.0 3.0 4.0

log Ce

log

qE-33 100x200

pH 6

y = 0.2161x + 3.6247R2 = 0.9215

3.0

3.5

4.0

4.5

5.0

0.0 1.0 2.0 3.0 4.0 5.0

log Ce

log

q

E-33 100x200 pH 7

y = 0.2235x + 3.5628R2 = 0.8887

3.0

3.5

4.0

4.5

5.0

0.0 1.0 2.0 3.0 4.0 5.0

log Ce

log

q

E-33 100x200 pH 8

y = 0.1715x + 3.7282R2 = 0.9686

3.0

3.5

4.0

4.5

5.0

0.0 1.0 2.0 3.0 4.0 5.0

log Ce

log

q

E-33 100x200 pH 9

y = 0.2188x + 3.5648R2 = 0.8986

3.0

3.5

4.0

4.5

5.0

0.0 1.0 2.0 3.0 4.0 5.0

log Ce

log

q


92

Figure A.4. Linear regression of three-week isotherm data for GFH, 28x48, pH 5 – pH 9.

GFH 28x48 pH 5

y = 0.3924x + 3.6482R2 = 0.9626

3.0

3.5

4.0

4.5

5.0

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

log Ce

log

q

GFH 28x48 pH 6

y = 0.4015x + 3.6367R2 = 0.9375

3.0

3.5

4.0

4.5

5.0

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5log Ce

log

q

GFH 28x48 pH 7

y = 0.3485x + 3.6453R2 = 0.9445

3.0

3.5

4.0

4.5

5.0

0.0 1.0 2.0 3.0 4.0log Ce

log

q

GFH 28x48 pH 8

y = 0.4065x + 3.3607R2 = 0.9608

3.0

3.5

4.0

4.5

5.0

0.0 1.0 2.0 3.0 4.0log Ce

log

q

GFH 28x48 pH 9

y = 0.428x + 3.2526R2 = 0.9743

3.0

3.5

4.0

4.5

5.0

0.0 1.0 2.0 3.0 4.0

log Ce

log

q


93


GFH 48 pH 5

y = 0.3519x + 3.6466R2 = 0.9568

3.0

3.5

4.0

4.5

5.0

5.5

0.0 1.0 2.0 3.0 4.0log Ce

log

q

GFH 48 pH 6

y = 0.3325x + 3.7174R2 = 0.9577

3.0

3.4

3.8

4.2

4.6

5.0

0.0 1.0 2.0 3.0 4.0log Ce

log

q

GFH 48 pH 7

y = 0.3249x + 3.6457R2 = 0.9259

3.0

3.5

4.0

4.5

5.0

5.5

0.0 1.0 2.0 3.0 4.0 5.0log Ce

log

q

GFH 48 pH 8

y = 0.3308x + 3.6012R2 = 0.9507

3.0

3.4

3.8

4.2

4.6

5.0

0.0 1.0 2.0 3.0 4.0 5.0log Ce

log

q

GFH 48 pH 9

y = 0.4057x + 3.3672R2 = 0.964

3.0

3.4

3.8

4.2

4.6

5.0

0.0 1.0 2.0 3.0 4.0log Ce

log

q


94


GFH 100x200 pH 5

y = 0.511x + 3.2222R2 = 0.7876

3.0

3.5

4.0

4.5

5.0

0.0 1.0 2.0 3.0 4.0

log Ce

log

q

GFH 100x200 pH 6

y = 0.325x + 3.8476R2 = 0.9424

3.0

3.4

3.8

4.2

4.6

5.0

0.0 1.0 2.0 3.0 4.0log Ce

log

q

GFH 100x200 pH 7

y = 0.2939x + 3.8042R2 = 0.8878

3.0

3.5

4.0

4.5

5.0

5.5

0.0 1.0 2.0 3.0 4.0log Ce

log

q

GFH 100x200 pH 8

y = 0.3046x + 3.6771R2 = 0.9357

3.0

3.4

3.8

4.2

4.6

5.0

0.0 1.0 2.0 3.0 4.0log Ce

log

q

GFH 100x200 pH 9

y = 0.3284x + 3.531R2 = 0.9547

3.0

3.4

3.8

4.2

4.6

5.0

0.0 1.0 2.0 3.0 4.0 5.0log Ce

log

q


95

Figure A.7. Linear regression of three-week isotherm data for SANS, 28x48, pH 5 – pH 9.

SANS 28x48 pH 5

y = 0.2652x + 3.7759R2 = 0.9736

3.0

3.2

3.4

3.6

3.8

4.0

4.2

4.4

4.6

0.0 0.5 1.0 1.5 2.0 2.5 3.0

log Ce

log

q

SANS 28x48 pH 6

y = 0.3952x + 3.4807R2 = 0.9967

3.0

3.5

4.0

4.5

5.0

0.0 0.5 1.0 1.5 2.0 2.5 3.0

log Ce

log

q

SANS 28x48 pH 7

y = 0.485x + 3.281R2 = 0.9806

3.0

3.4

3.8

4.2

4.6

5.0

0.5 1.0 1.5 2.0 2.5 3.0log Ce

log

q

SANS 28x48 pH 8

y = 0.4029x + 3.3649R2 = 0.9751

3.0

3.5

4.0

4.5

5.0

1.0 1.5 2.0 2.5 3.0log Ce

log

q

SANS 28x48 pH 9

y = 0.5537x + 2.9509R2 = 0.9744

3.0

3.5

4.0

4.5

5.0

1.0 1.5 2.0 2.5 3.0log Ce

log

q


96


SANS 48x100 pH 5

y = 0.4809x + 3.2792R2 = 0.9981

3.0

3.5

4.0

4.5

5.0

0.0 0.5 1.0 1.5 2.0 2.5 3.0

log Ce

log

qSANS 48x100

pH 6

y = 0.4132x + 3.4797R2 = 0.9385

3.0

3.5

4.0

4.5

5.0

0.0 0.5 1.0 1.5 2.0 2.5 3.0

log Ce

log

q

SANS 48x100 pH 7

y = 0.5252x + 3.2808R2 = 0.9138

3.0

3.5

4.0

4.5

5.0

0.0 0.5 1.0 1.5 2.0 2.5 3.0

log Ce

log

q

SANS 48x100 pH 8

y = 0.4058x + 3.4078R2 = 0.9977

3.0

3.5

4.0

4.5

5.0

0.0 0.5 1.0 1.5 2.0 2.5 3.0

log Ce

log

q

SANS 48x100 pH 9

y = 0.4716x + 3.1296R2 = 0.9712

3.0

3.5

4.0

4.5

5.0

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

log Ce

log

q


97


SANS 100 pH 5

y = 0.442x + 3.2089R2 = 0.926

3.0

3.2

3.4

3.6

3.8

4.0

4.2

4.4

4.6

0.0 1.0 2.0 3.0 4.0log Ce

log

qSANS 100

pH 6

y = 0.4126x + 3.3515R2 = 0.9236

3.0

3.2

3.4

3.6

3.8

4.0

4.2

4.4

4.6

0.0 0.5 1.0 1.5 2.0 2.5 3.0log Ce

log

q

SANS 100 pH 7

y = 0.3543x + 3.493R2 = 0.9606

3.0

3.2

3.4

3.6

3.8

4.0

4.2

4.4

4.6

0.0 1.0 2.0 3.0 4.0log Ce

log

q

SANS 100 pH 8

y = 0.3105x + 3.5576R2 = 0.957

3.0

3.2

3.4

3.6

3.8

4.0

4.2

4.4

4.6

-1.0 0.0 1.0 2.0 3.0 4.0log Ce

log

q

SANS 100 pH 9

y = 0.3863x + 3.3584R2 = 0.9981

3.0

3.2

3.4

3.6

3.8

4.0

4.2

4.4

4.6

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5log Ce

log

q


98


99

APPENDIX B – SEM ANALYSIS OF VIRGIN AND SPENT MEDIA

SEM analyses were performed on the 28 x 48 mesh media to observe differences between virgin and spent media, to characterize the surface of the spent media, and to determine if there are visual differences at the surface. The objective was to determine if changes have occurred over the very long duration of some of these experiments that may affect arsenic adsorption or the long-term stability of adsorbed constituents. In particular, this study was conducted to determine if notable mineral phases had formed at the surface of the spent media that might affect the adsorption process.

The spent media was extracted from exhausted City of Albuquerque field columns. The average influent arsenic concentration was 20 µg/L and the columns reached exhaustion between 15,000 and 40,000 bed volumes, depending on the media type. In this analysis, a JOEL JSM-6300V Scanning Microscope was used with the help of Sandia National Laboratories. The fresh media was coated with gold and the spent media was coated with carbon. Coating was applied using a SPI Module Sputter and Carbon Coater from SPI Supplies, a division of Structure Probe Inc. Coating improved the conductivity of the media allowing for improved micrographs. This series of SEM analyses was conducted on all four media at 28x48 mesh and are shown in Figure B.1 through Figure B.4. By visual observation, there are no significant differences between virgin adsorption media and spent media. There are also no observable precipitates or surface coatings that might affect the adsorption process. This shows that the adsorption of arsenic (or other competing ions) is occurring at the molecular level.


100

Figure B.1. Scanning electron micrographs for (left to right) virgin and spent AA 28x48 mesh at magnifications of: (1) 20x (scale bare = 500 um), (b) 1000 x (scale bar = 10 um)

a

b


101

Figure B.2. Scanning electron micrographs for (left to right) virgin and spent GFH 28x48 mesh at magnifications of: (a) 20x (scale bar = 500 µm), (b) 100x (scale bar = 100µm), (c) 1000x (scale bar = 10µm), (d) 10,000x (1µm).

a

c

b

d


102

Figure B.3. Scanning electron micrographs for (left to right) virgin and spent SORB E-33 28x48 mesh at magnifications of: (a) 20x (scale bar = 500 µm), (b) 100x (scale bar = 100µm), (c) 1000x (scale bar = 10µm), (d) 10,000x (1µm).

a

c

b

d


103

Figure B.4. Scanning electron micrographs for (left to right) virgin and spent SANS 28x48 mesh at magnifications of: (a) 20x (scale bar = 500 µm), (b) 100x (scale bar = 100µm), (c) 1000x (scale bar = 10µm), (d) 10,000x (1µm).

a

c

b

d


104


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ABBREVIATIONS LIST AND DEFINITION OF TERMS

A area of column, L2 AA activated Alumina As arsenic

nAA ...0 coefficients for polynomial equation which fits model prediction curves

Bi Biot number, dimensionless BV bed volume, dimensionless

eC liquid-phase equilibrium concentration (M/L3)

oC initial liquid-phase concentration (M/L3) )(tC liquid-phase concentration as a function of time (M/L3) )(tC dimensionless liquid-phase concentration as a function of dimensionless time

),( tzC dimensionless liquid-phase concentration as a function of dimensionless axial position and time

),,( tzrC dimensionless liquid-phase concentration as a function of dimensionless radial distance, axial position, and time

),( zrC p dimensionless adsorbate concentration in adsorbent pores as a function of dimensionless radial distance and axial position

C normalized liquid phase concentration (dimensionless)

sC liquid phase concentration at exterior particle surface (M/L3)

CCA copper-chromate-arsenic treatment for wood preservation

cm centimeter

C/MF coagulation-microfiltration process for arsenic treatment

d diameter of column (L)

iD ratio of surface to pore diffusion transport (dimensionless)

lD free liquid diffusion coefficient (L2/T)

oD dosage of adsorbent (M/L3)

sD surface diffusion coefficient (L2/T) Dgi solute distribution parameter (dimensionless) Dgp pore solute distribution parameter (dimensionless) Dgs surface solute distribution parameter (dimensionless) Dgt combined solute distribution parameter (dimensionless)


110

EBCT empty bed contact time, T EBCTmin minimum empty bed contact time required for constant pattern, T EBCTmtz empty bed contact time of mass transfer zone, T Ed diffusivity modulus (dimensionless) Edp pore diffusion modulus (dimensionless) Ed surface diffusion modulus (dimensionless) EDX energy dispersive X-ray spectroscopy E-33 Arsenic adsorption media sold by Severn-Trent Fe iron FS-50 activated alumina adsorption media sold by Alcan GAC granular activated carbon GFH granular ferric hydroxide adsorption media sold by U.S. Filter hmtz height of the mass transfer zone (L) HSDM homogeneous surface diffusion model IX ion exchange K Freundlich isotherm constant, (L3/M)1/n kf film transfer coefficient (L/T) L adsorber length (L) L liter

LC subscript denoting large column

M mass of adsorbent (M) Mdry mass of dry adsorbent (M) Mwater mass of water (M) Mwet mass of wet adsorbent (M) MeOx metal oxide adsorption media mg/L milligram per liter 1/n Freundlich isotherm exponent (dimensionless) Pe Peclet number (dimensionless) pHzpc pH of the zero point of charge of a surface


111

Q volumetric flow rate (L3/T)

qe adsorbent phase concentration in equilibrium with the initial liquid-phase concentration (M/M)

),,( tzrq dimensionless adsorbent phase concentration as a function of dimensionless radial distance, axial position, and time

R particle radius (geometric mean) (L) r radial coordinate (L)

r dimensionless radial coordinate

Re Reynold�s number (dimensionless) RO reverse osmosis RSSCT rapid small scale column test

SANS proprietary adsorption media developed by Sandia National Laboratories, Albuquerque, NM

SC subscript denoting small column Sc Schmidt number (dimensionless) SEM Scanning electron microscopy St Stanton number (dimensionless) Stmin minimum Stanton number required to establish constant pattern (dimensionless) T mass throughput (dimensionless) t elapsed time (T) t dimensionless time tmin elapsed time corresponding to minEBCT (T) V reactor volume (L3) Vb molar volume at normal boiling point (cm3/mole) w water content (dimensionless) x diffusivity factor (dimensionless) z axial position in adsorber (L) z dimensionless axial position through adsorber ε void fraction (dimensionless)

φ sphericity, ratio of the surface area of the equivalent volume sphere to the actual surface area of the particle (dimensionless)

µ viscosity of the fluid (M/LT)


112

bρ bulk density of adsorbent (M/L3)

lρ density of the fluid (M/L3)

sρ solid density of adsorbent (M/L3) τ packed bed contact time (T)

minτ minimum packed bed contact time required to establish constant pattern (T) v superficial velocity (hydraulic loading) (L/T) υ interstitial velocity (L/T) µg/L microgram per liter

Note: Abbreviations in parentheses indicate the fundamental units of each parameter in terms of mass (M), length (L), and time (T).


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