Download - [Advances in Chemistry] Aquatic Humic Substances Volume 219 (Influence on Fate and Treatment of Pollutants) || Effect of Coagulation, Ozonation, and Biodegradation on Activated-Carbon

40 Effect of Coagulation, Ozonation, and Biodegradation on Activated-Carbon Adsorption

Gregory W. Harrington1, Francis A. DiGiano, and Joachim Fettig2

Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, NC 27514

The ability to describe humic substance adsorption is important for the design of activated-carbon filters in water treatment. Humic solutions are composed of a multitude of unknown molecular species, and competitive adsorption among these species, as well as with trace anthropogenic organic chemicals, needs to be understood better. In this research, ideal adsorbed solution theory was used to describe an aquatic humic solution as a set of several pseudocomponents and to evaluate the effects of two treatment processes (alum coagulation and a combination of coagulation, ozonation, and biodegradation) on the solution's equilibrium and kinetic adsorption behavior.

OUR LIMITED UNDERSTANDING OF THE STRUCTURE of aquatic humic substances (HS) is manifested in our poor understanding of how these materials are adsorbed on activated carbon. When measured collectively by a surrogate parameter such as total organic carbon (TOC), HS solutions are not considered well adsorbed by activated carbon. Nevertheless, their adsorption is important because the removal of synthetic organic chemicals may be decreased by the presence of HS. Moreover, the removal of HS by adsorption

1Current address: Malcolm Pirnie, Inc., Newport News, VA 23606 2Current address: Division of Hydraulics and Sanitary Engineering, Norwegian Institute of Technology, N-7034 Trondheim-NTH, Norway

0065-2393/89/0219-0727$06.00/0 © 1989 American Chemical Society

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In Aquatic Humic Substances; Suffet, I., et al.; Advances in Chemistry; American Chemical Society: Washington, DC, 1988.

728 AQUATIC HUMIC SUBSTANCES

may become important if more stringent maximum contaminant levels (MCLs) for trihalomethanes and other chlorination byproducts are set. Water treatment facilities wil l be forced to rely more on adsorption if future M C L s cannot be met by moving the point of chlorination or increasing the efficiency of coagulation. Therefore, the ability to describe H S adsorption and its effect on the adsorption of pollutants is of utmost importance in the design of activated-carbon filters.

Because of the heterogeneous nature of HS solutions, this research was aimed at testing the applicability of a competitive adsorption model to describe equilibrium and kinetic adsorption behavior. The approach is similar to that used by Frick and Sontheimer (J) and Crittenden et al. (2), wherein an unknown mixture is described as a set of several pseudocomponents by using the ideal adsorbed solution theory (IAST) (3). This approach was used to evaluate the effects of coagulation, ozonation, and biodégradation on the adsorption behavior of HS solutions.

Preparation of Samples Raw water was obtained from Lake Drummond in southeastern Virginia. The water is highly colored, low in alkalinity (—50 m g / L as C a C 0 3 ) , and p H 4. Upon return to the laboratory, the water was prefiltered with 1.0-μπι honeycomb filters to remove leaves and sediment. The water was then stored in a cool, dark storage area prior to treatment.

The first stage of treatment used was alum coagulation. The p H of the raw water was adjusted to 6.5 by the addition of 2.4 X 10 2 M sodium carbonate. Alum was added at high mixing intensity and constant p H to a concentration of 205-210 m g / L as Al 2 (S0 4 ) 3 -18 H 2 0 to achieve destabili-zation. This step was followed by 40 min of flocculation, overnight settling, and filtration with 0.45-μιη membrane filters to achieve 76% removal of U V absorbance at 254 nm and 50% removal of T O C . The coagulated water was then stored in a refrigerator for prevention of biodégradation.

The coagulation stage was followed by ozonation and biodégradation. Distilled, deionized water was ozonated to a concentration of 25 mg of 0 3 / L and then combined with an equal volume of the coagulated sample to yield an ozone dosage of 1.15 mg of 0 3 / m g of T O C . The ozonated sample was placed in the reservoir of a recycle batch reactor, the column of which was previously seeded with return activated sludge from the local waste-water-treatment plant. The reactor was allowed to run until no further reduction in T O C was observed (10 days of run time) and the sample was then stored in a refrigerator. T O C values of each stage are given in Table I.

Adsorption Isotherms Equilibrium studies were performed by using the bottle point technique for determining adsorption isotherms. Granular activated carbon (GAC, F-400)

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40. HARRINGTON ET AL. Coagulation, Ozonation, and Biodégradation 729

Table I. Total Organic Carbon Levels in the Humic Mixtures Tested

TOC Humic Mixture (mg/L) Prefiltered 43.8 Alum coagulated 21.7 Ozonated and biostabilized 7.0

was washed, dried, stored, and ground to a 200-325 mesh size to ensure that a representative sample of carbon was used (4). In order to adequately describe the isotherms, bottles were filled with powdered carbon dosages ranging from 5 to 4000 mg/L. Samples were buffered with a 5 m M phosphate buffer to yield a p H of 6.5. Each bottle was then filled with 100 m L of sample and tumbled for 7-10 days at 23 °C. After the period of tumbling, the powdered carbon was removed by using 0.45-μιη membrane filters and the T O C of each sample was measured. Each component in a given mixture must satisfy the following mass balance equation for each bottle:

* = "MÂT- ω

where q{ is the surface loading of component i at equilibrium, C{ is the bulk liquid-phase concentration of component i at equilibrium, C 0 i is the initial liquid-phase concentration of component i , and M/V is the carbon dosage.

Prediction of Multicomponent Equilibrium IAST was assumed capable of describing the multicomponent nature of the resulting HS isotherms through the use of the following five equations:

qT = Σ Qi (2)

% = — for i = 1 to Ν (3) QT

ι N

— = Σ—0 (4)

C, = z,C,° for i = 1 to Ν (5) T l A d , TMads (iP d[\ll (C, 0)]

~W = ~W = I Â M t f J Ï * 1 f o r ' = l t o N ( 6 )

Total surface loading, qT, is defined as the sum of individual solute surface loadings by equation 2; the surface mole fraction, z t , of an individual solute is defined by equation 3. Equation 4 defines the surface loading of

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the mixture as a function of single-solute surface loadings, q®, achieved when the single-solute systems adsorb at the same temperature and spreading pressure as the mixture. By equating the chemical potentials of a solute in the adsorbed and liquid phases, one arrives at equation 5, where would be in equilibrium with q? in a single-solute system. Equations 4 and 5 are key equations in IAST, because both assume that the adsorbed phase forms an ideal solution. Finally, equation 6 equates the spreading pressure of the mixture, ττ τ , with the spreading pressures of the single-solute systems, ir t °. A a d s is the adsorbent surface area per unit mass of adsorbent. The spreading pressure of a single-solute system is evaluated by the integral shown in equation 6.

Each H S solution was assumed to contain a set of several individual pseudocomponents, each having a single-solute adsorption behavior that could by described by the Freundlich isotherm equation. The linearized form of the Freundlich equation is given by

In (q?) = In (K.) + ^ j In (C °) (7)

where K f and ni are the Freundlich isotherm constant and exponent, respectively, for component i. When equation 7 is substituted into equation 6, the following expression results:

niqo = njqj° for j = 2 to Ν (8)

Combining equations 1-5, 7 and 8 yields the following objective function, which was derived by Crittenden et al. (2):

F i = 0 = C 0 t < ' y %~

Thus, the equilibrium state of each isotherm bottle may be described by a set of Ν equations when the Freundlich parameters, nt and K„ and the initial concentration, C 0 „ of each component are known and when the carbon dosage, M / V , is also known. The set of equations may be solved by a Newton-Raphson algorithm, as shown by Crittenden et al. (2).

In this case, the Freundlich constants and initial concentrations of each component were unknown, although the initial concentration of the total mixture was known. Therefore, a search routine was combined with the Newton-Raphson algorithm to find the Freundlich constants and initial con-

Ν

Ν

Σ M for i = 1 to Ν

(9) Dow

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centrations of the pseudocomponents that yielded the best fit to the isotherm of the total mixture. The objective of the search routine was to minimize

SSR =

where SSR is the residual sum of squares, Nohs is the number of observations, ^ T f . o b s

a n d C T i c a l c are the observed and calculated concentrations of the mixture in bottle i , and σ { is the standard deviation of replicate measurements O f C Ti.obs-

Because of the unknown nature of HS mixtures, their concentrations must be measured through the use of surrogate parameters such as T O C . The IAST equations (equations 2-6), however, are based on a thermodynamic derivation in which concentrations are expressed in molar rather than mass units. If the Freundlich model (equation 7) is used to describe single-solute adsorption on a T O C basis, equation 8 is still valid on a molar basis. However, equation 9 is arrived at only by assuming that each component contains the same number of carbon atoms per molecule. The need for this assumption produces a dilemma in using T O C data in the IAST model to search for Freundlich adsorption constants of individual components if, in fact, these components do not all contain the same number of carbon atoms per molecule. This dilemma has not been adequately addressed by others who have used the IAST model (5, 6). On-going work by this research group is addressing further manipulation of the IAST equations to include the number of carbon atoms per molecule for each unknown fraction. However, for the purposes of this chapter, we assume that all fractions have the same number of carbon atoms per molecule.

Prior to determining pseudocomponent properties, the number of statistically valid pseudocomponents was determined. This procedure was begun by determining the root mean square error (RMSE) of the fit obtained using one adsorbing pseudocomponent. The value of R M S E was calculated from

Γ S S R N , I 0 5

R M S E- - [ r t J (11> where IV a d s is the number of adsorbing pseudocomponents, p N a d $ is the number of adjustable parameters for N a d s pseudocomponents, SSR i V a d s is the residual sum of squares for N a d s pseudocomponents, and N o b s is the number of observations. For the IAST-Freundlich model used in this work,

( 1 2 )

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Equation 11 shows that the value of R M S E can increase with increasing values of N^s. The remainder of the procedure involved calculating R M S E N a d s for succeeding values of N a d s and comparing the values obtained. For the isotherms presented here, R M S E N a d j for IV a d s = 3 was always greater than or not sufficiently smaller than the value of R M S E N a d t for Nads = 2. Thus, the largest number of statistically valid adsorbing pseudocomponents was limited to two.

Another modeling technique, reported by Jayaraj and Tien (7), reduces the number of parameters to

= A U - 1 (13)

and, as a result, reduces computational limits to the maximum number of pseudocomponents. However, this reduction in parameters is obtained by arbitrarily assigning Freundlich parameters to pseudocomponents and searching only for pseudocomponent concentrations, whereas the technique employed in this work searches for all of these parameters. Statistical l imitations to the Jayaraj and Tien technique are unknown, but the feet that they exist is demonstrated by the inability of this technique to produce a unique description of one of the wastewaters tested when using five adsorbing pseudocomponents (7). The use of three or four adsorbing pseudocomponents may have produced a more valid result. In any event, further research is required to determine the statistical limitations to this approach.

Rate Tests and Kinetic Models

External diffusion rates were studied through the use of the minicolumn rate test (8). This test uses a high flow rate and a short G A G bed length to allow for the domination of external mass transfer. External mass transfer coefficients were calculated from

where Q is the volumetric flow rate through the column, M is the mass of G A G in the column, A e f f is the effective external specific surface area of the adsorbent (as determined by the use of a solute with a known diffusion coefficient), and CTt and CTJ=0 are the effluent plateau and influent concentrations of the mixture, respectively. Free liquid diffusivities (Dt) were calculated from Gnielinskis correlation; these measures of D z were independent of the equilibrium parameters. Gnielinski's correlation was used because experiments by Roberts et al. (8) determined that the particle shape factor and, therefore, A e f f were not affected by the Reynolds number. The Dt

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obtained in this manner describes an average free liquid diffusivity for the mixture. No attempt was made to describe rates of diffusion for each pseudocomponent.

Data from batch rate tests were used in a heterogeneous diffusion model to determine the internal diffusion characteristics of a given mixture. The kinetic model incorporates pore and surface diffusion mechanisms and an IAST description of multicomponent equilibrium. Thus, the model not only requires batch rate data as input; it also requires the input of pseudocomponent equilibrium parameters. In addition, an external mass-transfer coefficient is required, and this requirement implies that the free liquid diffusivity must also be known. When these inputs are combined with G A C characteristics (particle radius, apparent density, pore void fraction, and dosage), average diffusion coefficients can be determined by guessing values of pore diffusivity (DF) and surface diffusivity (Ds) until an adequate description of the rate data is achieved. This step is best accomplished by fixing one of the coefficients and then adjusting the other coefficient until the smallest residual sum of squares is obtained.

Unfortunately, the search for DP and D s in the heterogeneous diffusion model wil l not yield a unique set of DP and Ds values that describe the test data. At the two extremes, the heterogeneous model simplifies to homogeneous diffusion; that is, a pore diffusion model (PDM) is easily obtained by setting D$ equal to zero and a surface diffusion model (SDM) is obtained by setting DP equal to zero. Fettig and Sontheimer (6) presented a methodology by which another experimental measurement can yield a unique combination of Ds and DP. The procedure entails the collection of equilibrium data for the HS mixture remaining after the rate test. The objective of the kinetic modeling is to find the combination of Ds and DP that allows the proper amount of adsorption of each pseudocomponent to occur during the rate test such that the remaining concentrations in solution, which become the initial concentrations for subsequent equilibrium modeling, yield the best equilibrium description of the residual mixture. The resulting values of Ds and DP will be referred to as those of the heterogeneous diffusion model (HDM).

Both types of rate tests employed 18-20 mesh G A C (F-400), prepared and stored as described previously. The minicolumn experiments were performed in a lucite minicolumn with the G A C packed between two layers of glass beads. This bed was fixed between two stainless steel screens that were 5 cm apart. Solutions were fed from a constant-head reservoir at 25 m L / m i n for 10 min. A particle shape factor of 1.54 was obtained with p-nitrophenol (PNP) in calibration runs.

For the batch rate tests, 3 L of a given mixture was placed in a 4-L glass beaker and allowed to contact the G A C for 190-225 h. Mixing was provided by a polyvinyl chloride) impeller with an attached chamber to allow flow-through contact with the G A C . G A C dosages were designed to

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achieve approximately 90% removal of the total initial concentration at the equilibrium state. The HS mixture remaining at the end of the rate test was immediately used in a bottle point isotherm experiment to describe the equilibrium behavior of the residual H S solution.

Equilibrium Results

Figure 1 shows that the equilibrium adsorption behavior of the coagulated HS solution changes when the initial total concentration is changed. This observation, not seen in single-solute systems, confirms the multicomponent nature of H S solutions. With the technique described earlier, a best fit was obtained for the data having an initial T O C of 10.85 mg/L. This fit yielded a set of one nonadsorbing pseudocomponent and two adsorbing pseudocomponents, each having the Freundlich constants and percent initial concentrations given in Table II for the coagulated HS mixture. The pseudo-component properties listed in Table II should apply to both of the mixtures studied in Figure 1, because the only difference between the two mixtures was their initial total concentration.

Initial pseudocomponent concentrations can be calculated from Table II by knowing the initial total concentration. Thus, the IAST model should be able to predict the isotherm of the more concentrated solution (TOC = 21.70 mg/L) from the Freundlich constants and the percent initial concentrations of the pseudocomponents as obtained by the best fit of the dilute solution (TOC = 10.85 mg/L). As Figure 1 shows, the IAST model makes a very reasonable prediction (dashed line) in the upper and lower regions of the more concentrated isotherm, but overpredicts adsorbability in the middle region. Additional data points in the middle region may improve the performance of the IAST model.

By assuming that the IAST model makes adequate predictions of the effect of initial concentration on H S isotherms, the model can be used to compare how various treatments change HS adsorbability. For instance, Figure 2 shows isotherm data for uncoagulated and coagulated HS solutions that reveal definite improvement of adsorbability upon coagulation. However, the data points do not reveal whether the improvement was due to decreased initial concentration, changes in solution composition, or both.

The model was used to test whether the observed shift in isotherm position upon coagulation could be accounted for solely by the decrease of initial concentration. First, best fits were obtained for both sets of isotherm data to yield the pseudocomponent parameters shown in Table II. As noted in the previous paragraph, the parameters obtained for the coagulated HS mixture can be used by the IAST model to predict the position of the coagulated HS isotherm at various initial concentrations. A comparison between the best fit of the prefiltered HS isotherm at an initial concentration of 14.60 mg of T O C / L and the prediction of the coagulated HS isotherm at

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Table II. Ideal Adsorbed Solution Theory Equilibrium Model Results

Humic Mixture Pseudocomponent 1 Κ I/n % CT.O

Pseudocomponent 2 Κ I/n % CT.O

Nonadsorbing Pseudocomponent

% CT.o Prefiltered" Coagulated Ozonated and biostabilized

25 0.36 92 31 0.27 84 23 0.17 71

127 0.20 12 87 0.14 25

8 4 4

°A second pseudocomponent was found to be statistically insignificant for the prefiltered mixture.

an initial concentration of 14.60 mg of T O C / L should indicate whether the improvement in adsorbability upon coagulation was merely due to the decrease of initial concentration. For instance, if the prediction of the coagulated HS isotherm were to match or closely agree with the best fit of the prefiltered HS isotherm at the indicated initial concentrations, then the improvement in adsorbability could be attributed to the decrease of initial concentration.

The results of the actual comparison, shown in Figure 2, reveal that the prediction (dashed line) is in poor agreement with the best fit (solid line), thereby implying that the decrease of initial concentration upon coagulation is not enough to explain the change in isotherm position. Therefore, coagulation must also create compositional changes, such as the preferential removal of larger and more poorly adsorbed organic molecules, that result in improved adsorbability.

The combined effect of ozonation and biodégradation was also studied through the use of the same comparison technique. The results of this comparison, depicted in Figure 3, show that the prediction of the coagulated HS isotherm at an initial concentration of 7.00 mg of T O C / L (dashed line) is quite similar to the best fit of the ozonated and biodegraded H S isotherm at an initial concentration of 7.00 mg of T O C / L (solid line). A slight decrease in adsorbability upon ozonation and biodégradation is observed in the middle region of the comparison, although the upper and lower regions are virtually the same. Ozonation is known to decrease the adsorption capacity for HS by creating solutions that are highly polar in nature. Therefore, these results indicate that such polar components are preferentially removed by the bio-degradation process to produce a solution similar in adsorbability to the coagulated solution.

Table II may be used to compare the equilibrium properties of each solution in a quantitative manner. The fact that coagulation caused the IAST modeling procedure to call for the addition of a second and more strongly adsorbing pseudocomponent (i.e., in the Freundlich model, a larger Κ and a smaller 1/n) suggests improved adsorbability of the HS mixture. The percentage of the nonadsorbing pseudocomponent also decreased. Subsequent ozonation and biodégradation, however, caused the IAST model to

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ED

H

UM

ICS

COT

= 1

0·9

M9

Toc/

L

Δ D

AT

A:

OZ

ON

AT

ED

(1

.2:1

) A

ND

B

IOST

AB

ILIZ

ED

H

UM

ICS

CO

T =

7.00

m

g T

OC

/L

PRE

DIC

TIO

N:

CO

AG

UL

AT

ED

H

UM

ICS

CO

T =

7.00

m

g T

OC

/L

BE

ST

FIT:

O

ZO

NA

TE

D

(1.2

:1)

AN

D

BIO

STA

BIL

IZE

D

HU

MIC

S C

OT

= 7.

00

mg

TO

C/L

I ί

I I

~T~

10

ι 1—ι—

ι ι ι

ι ι

1 10

E

QU

ILIB

RIU

M

LIQ

UID

P

HA

SE

CO

NC

EN

TR

AT

ION

(m

g T

OC

/L) τ

ι ι—

I I

I 10

Fig

ure

3. E

ffec

t of

ozo

nat

ion

and

bio

dégr

adat

ion

on t

he c

oagu

late

d h

um

ic-s

ubs

tan

ce i

soth

erm

. Th

e da

shed

li

ne

show

s th

e I A

ST p

redi

ctio

n o

f th

e co

agu

late

d is

oth

erm

at

the

sam

e in

itia

l co

nce

ntr

atio

n a

s th

e oz

onat

ed

and

bios

tabi

lize

d is

oth

erm

. Th

e pr

edic

tion

was

base

d o

n pa

ram

eter

s ob

tain

ed f

rom

the

bes

t fit

of

the

coag

ula

ted

isot

her

m.

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40. HARRINGTON ET AL. Coagulation, Ozonation, and Biodégradation

predict further changes in Freundlich parameters and percent compositions that are less easily explained. The decrease in the Freundlich 1/n value for both pseudocomponents and the increase in percent composition of Pseu-docomponent 2, the more strongly adsorbing pseudocomponent, from 12% to 25% can be taken as evidence of increased adsorbability. However, the Freundlich Κ value for both pseudocomponents decreases significantly upon ozonation and biodégradation, a result leading to the conclusion that adsorbability has decreased. Therefore, the Freundlich constants and the percent compositions do not necessarily yield conclusive results on their own.

Kinetic Results The results of the minicolumn and batch rate tests (see Tables III and IV) revealed small changes in average external and homogeneous internal diffusion rates upon treatment. Coagulation was found to increase the average rate of external diffusion, as was expected because of a decrease of average molecular size. Although ozonation and biodégradation would be expected to produce even smaller molecules, this result was not manifested by any further increase in Dt. However, the batch rate tests showed an increase in average internal diffusion rates with each treatment stage.

As noted earlier, results from the I AST equilibrium and H D M kinetic models should predict the equilibrium behavior of an H S solution remaining at the end of a batch rate test. Data from adsorption rate tests of the coagulated H S mixture and the coagulated, ozonated, and biostabilized H S mixture are presented in Figure 4, with the accompanying H D M predictions. Figure 5 presents isotherm data obtained for the coagulated HS mixture before and after 190 h of batch rate testing. This figure also shows the IAST model prediction (solid line) of the isotherm for the HS mixture remaining after the kinetic testing. A good agreement between the prediction and the data indicates that the proper internal diflusion coefficients were

Table III. Minicolumn Rate Test Results Humic Mixture D, (cm2/s)

Prefiltered 1.1 X 10~6

Coagulated 2.0 X ίο- 6

Ozonated and biostabilized 2.0 X 10 6

Table IV. Kinetic Model Results

PDM Dp (cm2/s)

SDM D s (cm2/s)

HDM Humic Mixture

PDM Dp (cm2/s)

SDM D s (cm2/s) Dp (cm2/ί ή Ds (cm2ls)

Prefiltered0

Coagulated Ozonated and biostabilized

3.2 Χ 10"7

4.0 x ΙΟ"7

6.3 Χ ΙΟ"7

6.1 X 10"" 6.8 x 10"" 2.0 x 1010

4.0 x 10 6.3 X 10

7 2.2 7 1.6

x 10"m

χ 101 7

•An HDM test was not performed for the prefiltered humic mixture.

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c ο a Ω

ς/3

C CO

CO

Ζ Ο Μ

CO

Fig

ure

4. E

ffec

t of

ozo

nat

ion

and

bio

dégr

adat

ion

on th

e in

tern

al d

iffu

sion

ra

te o

f coa

gula

ted

hu

mic

su

bsta

nce

s.

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LIB

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10

10

2H

10

Ί—I

I M

I

"Π"

Τ I

Μ

I IJ

• Ο

Ο

ADSORBATE:

COAGULATED

HUMICS

DATA

: BEFORE

BATCH

RATE

TEST

COT

= 10

.9

mg

TOC/L

DATA

: AFTER

BATCH

RATE

TEST

COT

= 4.

23

mg

TOC/L

PRED

ICTI

ON:

AFTER

BATCH

RATE

TEST

CQT

= 4.

23

mg

TOC/L

1—I

i M

10

I

I Ϊ

I ι

ι ι ι

I

Γ 1

10

EQUI

LIBR

IUM

LIQU

ID

PHASE

CONCENTRATION

(mg

TOC/L)

10

Fig

ure

5. P

redi

ctio

n o

f th

e is

oth

erm

for

coag

ula

ted

hu

mic

su

bsta

nce

s re

mai

nin

g af

ter

a ba

tch

ra

te t

est

. Th

e pr

edic

tion

was

mad

e fr

om F

reun

dlic

h pa

ram

eter

s ob

tain

ed f

rom

the

orig

inal

mix

ture

(•)

and

fro

m p

erce

nt

con

cen

trat

ion

s fo

r th

e re

sidu

al m

ixtu

re a

s pr

edic

ted

by t

he h

eter

ogen

eou

s di

ffu

sion

mod

el.

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ded

by U

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LIB

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used in the H D M to yield pseudocomponent concentrations that provided the proper input to the IAST equilibrium model. Similar results were obtained for the ozonated and biodegraded mixture, as shown in Figure 6. Table V shows that the H D M predicted the preferential adsorption of the more strongly adsorbed pseudocomponent (Pseudocomponent 2) during both batch rate tests.

The values of D s and DP that yielded these predictions are posted in Table IV and show that, according to this modeling approach, pore diffusion was the dominant mechanism for internal HS transport. However, all three kinetic models ( P D M , S D M , and H D M ) provided an adequate description of the rate data. Moreover, a simplifying assumption was made that each pseudocomponent had the same diffusion coefficient, even though this situation is unlikely. Thus, caution is needed in stating that pore diffusion is actually the dominant mechanism. Nevertheless, this result agrees with recent discussion (8) that the macromolecular nature of HS mixtures may provide a tremendous obstacle to mass transport in the adsorbed phase and thereby force pore diffusion to account for transport.

Modeling Limitations When modeling a complicated process such as multicomponent adsorption, the procedure is likely to have limitations. First, several of the assumptions used to derive the IAST model are violated by the HS-activated-carbon system. For instance, an adsorbed-phase solution of HS is not likely to be in conformance with the assumption of an ideal adsorbed solution, and activated carbon is certainly not the inert solid-phase material it is assumed to be. In addition, the IAST model assumes that adsorption takes place by physical means and ignores the phenomenon of chemisorption. The poly-electrolytic nature of HS molecules, combined with the ionic surfaces of activated carbon, implies that chemisorption of humic substances is highly likely. Thus, the model wil l not work if some mechanism other than physical adsorption is primarily responsible for the removal of H S from solution. Finally, the intraparticle diffusion coefficients listed here are dependent upon equilibrium characterizations and may not represent the true kinetic properties of an HS mixture.

The ultimate goal of this approach is to provide a means to predict the effects of H S mixtures on the adsorption of pollutants in water-treatment systems. Thus, the severity of such limitations will be tested when this approach is eventually extended to predicting breakthrough curves of pollutants in HS solutions.

Summary The equilibrium adsorption behavior of aquatic HS solutions was described by the use of the multicomponent IAST model. The IAST model is simplistic

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ο < θ α ο ζ ο St

(Τ

Ι—

Ζ Lu α ζ ο ο Lu

CO

< I Ο

ίο

3-

10

y 10

d

ο CO

§ 2 m

_j σ

~ Γ

ADSORBATE:

OZONATED

(1.2

:1)

AND

BIOS

TABI

LIZE

D HUMICS

Δ DA

TA:

BEFORE

BATCH

RATE

TEST

COT

-=

7

00

mg

TOC/L

A

DATA

: AFTER

BATCH

RATE

TEST

COT

= 3-

42

mg

TOC/L

PRED

ICTI

ON:

AFTER

BATCH

RATE

TEST

CQT

=•

3.42

m

g TOC/L

Δ Δ

Δ

10

1 π—

ι—ι—

Γ 10

EQUI

LIBR

IUM

LIQU

ID

PHASE

CONCENTRATION

(mg

TOC/L)

Fig

ure

6. P

redi

ctio

n o

f th

e is

oth

erm

for

ozon

ated

and

hio

degr

aded

hu

mic

su

bsta

nce

s re

mai

nin

g aft

er a

bat

ch

rate

test

. Th

e pr

edic

tion

was

mad

e fr

om F

reun

dlic

h pa

ram

eter

s ob

tain

edfr

om th

e or

igin

al m

ixtu

re (Δ

) an

dfro

m

perc

ent

con

cen

trat

ion

s fo

r th

e re

sidu

al m

ixtu

re a

s pr

edic

ted

by t

he h

eter

ogen

eou

s di

ffu

sion

mo

del

.

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ded

by U

CSF

LIB

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Table V. Mixture Compositions Before and After Batch Rate Testing

Humic Mixture Pseudocomponent Ie Pseudocomponent 2*

Nonadsorbing Pseudocomponent

Humic Mixture Before After" Before After" Before* After" Coagulated 84 92 12 0 4 8 Ozonated and

biostabilized 71 87 25 5 4 8 NOTE: All values are % CT,0. •Freundlich parameters for each adsorbing pseudocomponent are listed in Table II and were not changed during the course of kinetic testing. B̂efore batch rate test; determined by the IAST equilibrium model.

'After batch rate test; predicted by the HDM.

in that the pseudocomponents comprising an H S mixture are assumed to have the same number of carbon atoms per molecule. This deficiency in modeling needs more attention. Nevertheless, the model was used to evaluate the adsorption equilibrium and kinetic effects of two different treatment processes—coagulation and a combination of ozonation and biodégradation following coagulation.

The IAST model provided a reasonable prediction of changes in the equilibrium adsorption behavior of the coagulated HS solution upon dilution or concentration. This result allows the study of treatment effects without the interference of initial concentration effects. Coagulation improved the adsorbability of the HS mixture by changing its composition, as well as by decreasing its initial concentration. The improvement due to compositional changes is probably a result of the preferential removal of larger-molecular-weight species by the coagulation process. After ozonation and biodégradation, the HS mixture showed no significant changes from the coagulated mixture in terms of adsorption equilibrium behavior. Therefore, since ozonation is thought to reduce adsorbability because of the production of polar organic species, biodégradation must remove these compounds to yield the observed results.

Kinetic tests showed a small increase in free liquid diffusivity upon coagulation and no change upon ozonation and biodégradation. In addition, the competitive diffusion models were found to be useful in describing the internal kinetics of an HS mixture, as well as in predicting the equilibrium behavior of an HS mixture remaining at the end of a batch rate test. The results show modest increases in internal diffusion rates with each treatment stage and the domination of pore diffusion as the mechanism of internal mass transfer. However, the actual diffusion mechanism is unknown without further testing.

Nomenclature

A a d s Adsorbent surface area per unit mass of adsorbent (length2/mass) AeS Effective surface area of the adsorbent per unit mass of adsorbent

(length2/mass)

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40. HARRINGTON E T AL. Coagulation, Ozonation, and Biodégradation 745

C, Equilibrium liquid-phase concentration of component i (mass/ length3)

C i° Equilibrium liquid-phase concentration of component i in a single-solute system at the same temperature and spreading pressure as the mixture (mass/length3)

CQi Initial liquid-phase concentration of component i (mass/length3) C T O Initial liquid-phase T O C concentration of the humic mixture (mass/

length3) C T l c a l c IAST-calculated equilibrium liquid-phase concentration of the mix

ture in isotherm bottle i (mass/length3) C T i o b s Observed equilibrium liquid-phase concentration of the mixture in

isotherm bottle t (mass/length3) CTt T O C concentration of the minicolumn effluent mixture at a given

time, t, of a rate test (mass/length3) C T t = 0 T O C concentration of the minicolumn influent mixture during a

rate test (mass/length3) Ό ι Free liquid diffusion coefficient (length2/time) Dp Pore diffusion coefficient (length2/time) Ds Surface diffusion coefficient (length2/time) K, Freundlich isotherm constant for component i [(mass/mass)

(length3/mass)1 / n i] Κι External mass-transfer coefficient (length/time) M Mass of carbon in an isotherm bottle or in the minicolumn reactor

(mass) i V a d s Number of adsorbing pseudocomponents in a mixture rti Freundlich isotherm exponent for component i iV0bs Number of isotherm observations pNaàs Number of adjustable parameters for N^s components in the IAST

equilibrium model q( Equilibrium solid-phase concentration of component i (mass/mass) q® Equilibrium solid-phase concentration of component i in a single-

solute system at the same temperature and spreading pressure as the mixture (mass/mass)

qT Sum of the solid-phase concentrations of each solute (mass/mass) Q Volumetric flow rate (length3/time) R Ideal gas constant (mass · length 2/ t ime 2 - temperature · mole) R M S Ε Root mean square error SSR Residual sum of squares Γ Absolute temperature (temperature) V Liquid volume in an isotherm bottle (length3) z( Surface mole fraction of component i I T , 0 Spreading pressure of component i in a single-solute system (mass/

time 2) IT7 Spreading pressure of the mixture (mass/timeMength) σ, Standard deviation of the T O C observations for isotherm bottle i

(mass/length3)

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Acknowledgments The authors thank Emery Kong for his help in setting up and performing the ozonation and biostabilization experiments. The heterogeneous diffusion model was provided by John Crittenden's group at Michigan Technological University. This research was supported by the U.S. Environmental Protection Agency's Exploratory Research Program, Grant No. R811824.

References 1. Frick, B. R.; Sontheimer, H. In Treatment of Water by Granular Activated

Carbon; American Chemical Society: Washington, DC, 1983; p 247. 2. Crittenden, J. C.; Luft, P.; Hand, D. W. Water Res. 1985, 19, 1537. 3. Radke, C. J.; Prausnitz, J. M . AIChE J. 1972, 18, 761. 4. Randtke, S. J.; Snoeyink, V. L. J. Am. Water Works Assoc. 1983, 75, 406. 5. Calligaris, M. B.; Tien, C. Can. J. Chem. Eng. 1982, 60, 772. 6. Fettig, J.; Sontheimer, H. J. Environ. Eng. Div. (Am. Soc. Civ. Eng.) 1987, 113,

795. 7. Jayaraj, K.; Tien, C. Ind. Eng. Chem. Process Des. Dev. 1984, 24, 1230. 8. Roberts, P. V.; Cornel, P.; Summers, R. S. J. Environ. Eng. Div. (Am. Soc. Civ.

Eng.) 1985, 111, 891. 9. Summers, R. S. Ph.D. Thesis, Stanford University, 1986.

RECEIVED for review October 15, 1987. ACCEPTED for publication February 9, 1988.

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