Predicting disinfection performance in continuous flow systems from batch disinfection kinetics

8)

Pergamon

Waf. Sci. Tech. Vol. 38, No.6, pp. 171-179.1998. IAWQ

Pll: S0273-1223(98)OO579-4

o 1998 Published by Elsevier Science Ltd. Printed in Great Britain. All rights reserved 0273-1223/98 $19'00 + 0'00

PREDICTING DISINFECTION PERFORMANCE IN CONTINUOUS FLOW SYSTEMS FROM BATCH DISINFECTION KINETICS Charles N. Haas*, Josh Joffe** Mark Heath***, Joseph Jacangelot and Vma Anmangandla:j: I

• School ofEnvironmental Science. Engineering and Policy. Drexel University. Philadelphia, PA 19104. USA •• Malcolm Pimie. Inc., One Intemational Boulevard, Mahwah, NJ 07495-0018. USA ···2415 NE42ndSt., Ponland. OR 97213, USA t Montgomery Watson Engineers. 560 Herndon Parkway Suite 300. Herndon, VA 22070-5240. USA t PRe Environmental Management Inc., 1800 JFK Boulevard, 6th Floor, Philadelphia, PA 19103, USA

ABSTRACT Disinfection processes have often been characterized by the "cr" concept i.e., the product of disinfectant residual and contact time (perhaps as a function of pH, temperature, and other water quality variations) produces a given level of disinfection. The objective of this work was to develop and validate the use of reaction kinetic models for disinfection process design. Using bench scale (batch) kinetic information. and hydraulic characterization of pilot scale continuous disinfection processes. predictions of continuous process performance were made using a segregated flow model. These predictions were compared to independent experimental measurements of actual inactivation in pilot scale processes. Preammoniation, free residual chlorination. and ozonation were used on two waters from Portland, Oregon (US). Organisms used were Giardia mum. bacteriophage MS2, and Escherichia coli. @ 1998 Published by Elsevier Science Ltd. All rights reserved

KEYWORDS Disinfection; ozone; chlorine; reactor engineering; Giardia, INTRODUCTION Regulations being implemented under the U.S. Safe Drinking Water Act require that all surface water suppliers in the U.S, filter and/or disinfect to protect the health of their customers. The filtration and disinfection treatment requirements for public water systems using surface water sources or groundwater under the direct influence of surface water were called the Surface Water Treatment Rule (Pontius. 1993). The Guidance Manualfor Compliance with the Filtration and Disinfection Requirements for Public Water Systems Using Surface Water Sources (1991) of the Environmental Protection Agency (EPA) was written in 171

172

C. N. HAAS tt al.

order for utilities to implement the requirements of the SWTR (Malcolm Pimie and HDR Engineering, 1991).

The cr tables of the SWTR's Guidance Manual were based on a number of simplifying assumptions. First, much of the data was derived from studies conducted in buffered demand-free water which may be difficult to extrapolate to actual waters. Second, a simple Chick-Watson relationship for the microbial inactivation was assumed (with the dilution coefficient equal to one). More accurate disinfection kinetic models that include decay of the disinfectant residual can be used (Haas and Joffe, 1994). These models are functions of the particular water, disinfectant, and organism of interest (Haas et al., 1996). This cr credit was based on the tlO values obtained for a particular reactor; therefore, utilities were required to develop empirical tracer methods. The tlO was defined as the time for 10 percent of the tracer mass to achieve breakthrough. This t10 approach was based on a plug flow model which only crudely accounts for nonidealities in flow. In addition to relying on the cr tables to calculate disinfection credit, the SWTR allowed utilities to demonstrate the effectiveness of their disinfection systems through performance of pilot-scale studies, which can lead to a large expense, perhaps prohibitively so for smaller utilities. The objective of this study was to formulate a reactor engineering approach to the analysis of disinfection kinetics, and to apply the resulting model to the problem of predicting continuous flow system performance from bench laboratory inactivation studies (perhaps eliminating the aforementioned costs for small utilities). Tracer experiments were performed on pilot scale disinfection contaetors. The predicted and observed pilot inactivations were compared as a means of validation of this approach. Lab-scale batch disinfection experiments were performed at Drexel University. In parallel work, continuous• flow pilot disinfection experiments were conducted at Portland. Oregon. The tracer experiments were also conducted at the Portland site. Several combinations of water, disinfectant, and organism were used for the experiments. The water quality characteristics for the experimental waters used are given in Table I. Table I. Water quality characteristics of the experimental waters Total Organic Carbon (TOC), mgIL True Color (units) Ammonia (mg/L as N) Total Hardness (mgIL as CaC03) Total AlItalinity (mgIL as CaCOJ) Turbidity (NTI1)

Bull Run 1.0-1.7
0.26-1

Willameue 0.8-7.1

NA NA

14-36 14-36 0.7-50

PROCEDURE FOR THE BATCH EXPERIMENTS Batch disinfection experiments were performed on various combinations of water, disinfectant, ~d microorganism (Table 2). Two waters (Bull Run Reservoir Water, Portland OR, and Willamette River Water, Portland OR, were studied. Free chlorine, preformed monochloramine, and ozone were used to disinfect Escherichia coli ATCC 11229 (bacteria), Giardia muris (protozoa), and bacteriophage MS2 (virus). Experiments were conducted in 2 litre beakers with temperature control. Viability was assessed by excystation (Giardia), plate count (E. coli) and plaque count (MS2). Ozone was dosed using stock ozone solutions freshly prepared. Detailed experimental procedures for the batch experiments have been reported elsewhere (Hornberger, 1993; Haas et al., 1994; Haas et aL, 1996). All experiments were conducted at ISOC.

Predicting disinfection performance

173

Table 2. Summary of batch experimental runs Disinfectant

Water

Free Chlorine Monocfiloramine

Bull Run

-OZOne Free Chlorine

Willamette River

~onocliforanune

-OZOne

Dose

(mglL) I 2 I 2

0.25 004 2 3 I 2 0.5 0.75

pH 6.52-6.54 6.93 6.37-6.55 6.70 6.53 6.53-6.93 7.30-7.34 7.17 1041 7.15-7.54 7..H-7.54 7044

Number of Exoeriments 2 I 2

I I 2 2 I I 2 2

I

PROCEDURE FOR THE Pll..OT SCALE EXPERIMENTS Pilot scale testing was conducted at two locations using the same two source waters. As with the batch experiments, free chlorine, preformed monochloramine, and ozone were used to disinfect Escherichia coli, Giardia muris, and bacteriophage MS2. Table 3 gives a summary of the pilot experiments by water, disinfectant, and concentration. Table 3. Summary of pilot plant runs-disinfectant doses (mg/l) with number of replicates in parenthesis Free Chlorine

Monochloramine

Ozone

Bull Run

1 1.5 1.9 (6)

2.5 (3) 5(3)

0.4-1 (20) 1-1.5(11)

Willamette

I 1.5 2(7) 2.5

1.5 2.5(3) 5(5)

1-1.5(12) 1.5-3(3)

Qzone disinfection experiments Ozone disinfection experiments were conducted using a specially designed pilot plant which is owned by the portland Water Bureau. The following is a description of the equipment utilized to conduct ozone disinfection experiments. Ozone was generated from pure oxygen with a small generator capable of producing approximately 2 pounds/day (0.9 kg/d) of ozone (PC! Ozone and Control Systems Inc. TN ). Ozone concentrations in the oxygen feed gas stream ranged from 0.1 to 0.75 percent by weight. Water used in pilot-scale testi~g was supplied to the ozone contactor either under natural pressure (at the Bull Run location) or by centrifugal pump (Masterflex'lV) (at the Willamette River location). The flow rate was maintained at a constant rate of 5 gpm (19 litre/min) throughout the testing period. A cylindrical, counter-current plexiglass ozone c~ntaeting column 10 inches (25 em) in diameter and 10 feet (10 m) high The ozone transfer effi~iency of .thiS ~olumn was between 95 and 98 percent. Dissolved ozone residual was calculated using the indlgo-<:olonmetnc method (Standard Methods, procedure 4500-03 B., p4-162).

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C. N. HAAS et al.

Chlorine/chloramine disinfection experiments Chlorine and chloramine disinfection experiments were conducted using a three-pass serpentine reactor constructed from carbon steel and painted to prevent corrosion. Raw water was delivered to the pilot-scale contactor at a constant flow rate of 3 gpm (11.4 litre/min) using natural hydraulic pressure (at the Bull Run location) or by a centrifugal pump (at the Willamelte River location). At the flow rate employed during these experiments. the overall theoretical hydraulic detention time in the chlorine contactor was 3 hours. During the chlorine disinfection experiments. chlorine was added to the contactor through an in-line static mixer. A concentrated solution of reagent-grade sodium hypochlorite was fed upstream of the static mixer into the main water stream by a metered, calibrated peristaltic pump. The static mixer was sized to provide adequate mixing and assure uniform. rapid dispersion of the concentrated chlorine solution into the flow stream. Sample taps, located at various points from the beginning to the end of the contactor. were used for withdrawing samples for chemical and microbiological analyses. The positioning of these taps was designed to allow collection of samples for microbial analyses at various contact times. For the contactor employed during this study. a series of PVC pipes were used to contain the flow after addition of disinfecting chemicals prior to its entrance into the main portion of the contactor. The overall hydraulic contact time in this influent plumbing system was approximately 7 minutes. Sample taps located at various points along the pipes allowed for samples to be withdrawn to provide sample contact times ranging from 7 seconds to 7 minutes from the time of chlorine addition. Chlorine residual analyses were performed throughout the course of each disinfection experiment according to methods 4500-CI(D) and 4500-NH 3(F), respectively (APHA et al., 1989) described for the bench-scale disinfection experiments. Test or~anisms

Spiking of test organisms. Spiking of test organisms to the disinfection reactor was conducted in a manner similar to addition of disinfectants. A concentrated mixture of microorganisms was prepared and fed to the main water stream at a known feed rate based on the microbial density in the concentrated mixture, the flow rate of water to the pilot plant, and the desired concentration of microorganisms in the disinfection reactor For each pilot-scale disinfection experiment, the test organisms were mixed together in a volume of raw water sufficient to supply the pilot plant for the duration of the experiment and fed to the main water ~tream prior to entering the disinfection contactor. The reservoir containing the suspension of test organisms 10 raw water was continuously mixed to assure the homogeneity of the mixture. The microbial mixture was fed to the disinfection reactors on a continuous basis at controlled flow rate by a peristaltic metering pump. Addition of microorganisms to the pilot plant flow occurred at a point upstream of ammonia and .chlo~ne addition. Mixture of microorganisms into the main pilot plant flow was accomplished with a static nuxer similar to those used for disinfectant mixing.

Collection and enumeration of test organisms. During each disinfection experiment, samples were collec~ed for microbial assay at the influent of the pilot plant, prior to addition of any disinfectant, and folJowlOg exposure to the disinfectants. During chlorine and chloramine experiments. samples were collected at various points along the influent plumbing system, as well as in the chlorine contactor itself. B~ause samples could be collected at several locations representing different hydraulic detention times dllOng a particular experiment, several CT products were obtained from a single experiment with a constant disinfectant dosage. During ozone disinfection experiments. due to the reactor configuration, only two samples were collected from each experiment: an influent and effluent sample. Therefore, for each experiment, only one CT product was evaluated. In order to generate several CT products, the applied ozone dose was varied from experiment to experiment, rather than the hydraulic contact time.

Predicting disinfection perfonnance

17S

Control experiments were performed to verify the recovery of the cysts from the plant in the absence of disinfectant. The same efficiency of cyst retention was observed with and without the presence of disinfectant. Therefore, controls were not performed on the concentration steps. . Because the initial Giardia cyst concentration during pilot-scale disinfection experiments was below that required to adequately conduct excystation analysis, the samples required concentration prior to analysis. This was done by membrane filtration followed by centrifugation prior to conducting the excystation assay. EVALUATION OF THE BATCH DISINFECTION KINETICS

In other work (Anmangandla, 1993), various kinetic models have been tested using nonlinear regression on the survival data from disinfection experiments for a variety of disinfectants and waters. It was found that the Hom model (Hom, 1972) with first order residual decay provided the best statistical fit to the disinfection data. This model, for batch systems, can be approximated by (Haas and Joffe, 1994): (I)

where:

=

Ni observed survival count at time i. No = observed survival count at time = O. Sb = NilNo (the survival ratio) Co the initial disinfectant concentration t disinfection contact time k* the first order residual decay rate. k, n, m fitted kinetic parameters using nonlinear regression.

= = =

=

The k* values were calculated by regression of the disinfectant residual decay observations. These values were found to vary by combination of disinfectant and water. For each disinfectant-water-organism combination, the inactivation parameters were determined by nonlinear regression using log-transformed survival ratio as the dependent variable. Confidence limits were determined using a critical F statistic criterion applied to the sum of squares ratio (Bates and Watts, 1988). Procedures for these determinations have been described elsewhere (Haas, 1988; Anmangandla, 1993; Haas et al., 1996). These parameter confidence limits were then used to construct prediction intervals for the pilot survival tests as discussed below. EVALUATION OF THE Pll..OT TRACER STUDIES

Ira&er testin~ procedure Hydraulic characterization was accomplished through tracer tests conducted on each disinfection reactor under the flow, temperature, and water quality conditions that were employed during the disinfection experiments. Both step and pulse inputs were used for the tests. For the ozone contactor, a continuous feed of sodium chloride was employed. The tracer's passage through the contaCtor was monitored by measurement of specific conductivity at the contactor effluent. For the chlorine/chloramine reactor, two methods were employed. Specific conductivity measurements, with sodium chloride, were used to determine residence time distributions in the influent. At the longer contact times, rhodamine WT was employed as a tracer. The passage of this fluorescent dye through the reactor was monitored using a fluorometer.

176

C. N. HAAS et al.

organisms. Introduction of the tracer The tracer injection point was the same location used for spiking the sample taps used for collection of occurred at 1=0, following which, samples were collected from the same e times throughout the length residenc provide to chosen samples for microbial analysis. Sampling taps were of the reactors. Analysis of the breakthroul:h curves tracer concentration as a function of The goal of tracer testing is to provide a profile of the breakthrough ough curves leads to the hydraulic breakthr the of Analysis ion. time known as the residence time distribut n fit to the axial dispersion regressio r nonlinea a work this In reactor. tion disinfec the of characterization was used to characterize 1984) at., et rp Westerte model (Levenspiel, 1972; Nauman and Buffham, 1983; d to the CSTR in compare as fit superior a give to observed was This 1997). at., et reactor hydraulics (Haas series model (Figure I). 1.6 1.4

...

1.2

-'S cClI:: IJ c::

0.8

8

0.4

c::

()

0.6

0.2

0

0

100

200

300

400

500

timeCminutes) compared to Iitted axial dispersion model. Figure I. Observed pulse tracer curve for chlorination contactor port 6

VERIFICATION OF THE EXTRAPOLATION PROCEDURE FOR CONTINUOUS FLOW SYSTEMS e time distribution for a c~ntinuous Given process rate models determined in batch systems, and a residenc system, such as the pIlot scale flow us continuo a of ance perform predict flow system, it is possible to 1958) and was apparently first erts, disinfectant reactors. The general procedure was outlined in (Danckw Haas (1987). It assume~ a by later and (1977) Chao and applied to disinfection systems by Trussell disinfection reactor by the follOWing completely segregated system, and estimates the survival ratio in a equation: co

Sprtd=

JS

b

(t) E(t) dt

(2)

o where;

spred = predicted pilot survival ratio initial condition Sb(t) predicted batch survival at time t, given influent concentration as ion distribut time e residenc the for E(t) = normalized density function

=

The term E(t)dt is the proFrtio n of Sb(t) is the same as Si* from Equation I for the batch kinetic model. residence time distribu!lon cu,:"e. fitted the from d evaluate is this t+dt; and t between entering fluid exiting c parameters correspondlOg hydrauli and ) These equations were a function of the predicted kinetic (k,n,m,C

Predicting disinfection perfonnance

In

to a particular combination of water, disinfectant, and organism, as well as location in the pilot reactor. The complete segregation model is one extreme of micromixedness behavior. Prior work has suggested that, while there is difference between the predictions from the perfect segregation assumption and the perfect micromixed assumption, the numerical differences are less than those associated with variations in the residence time distribution itself (Haas, 1987). The integration of Equation 2 was performed using the trapezoidal method. Narrower time steps were used for the peak region of the residence time distribution curves for greater accuracy. An example survival plot for the pilot study is given in Figure 2. 2 mgIL Free Chlorine

i-

0.1

~----..

0.01

'.

0.001

iii

'.

0.0001

-....... -.......

•....

.......--...

.

•

10. 6

o

O.S

I

--

- --".-::,

..

- - Si-(Predicted) _ - Si-(Maximum) .. . Si"fMmiOluOl) Si (Observed)

10"

10"

-.......

.....

•

I.S

11lc:la (minules)

2

2.5

3

Figure 2. Survival Ratio (Si) vs. Residence Time (lbeta) for MS2 virus with Willamette River water. The Hom model fit with 95% confidence bands are taken from the batch kinetics. Observed values are from the pilot study.

35_-....1----+---........-----1---+ 30

2S

:=~

~

III

.g

batch predicts greater inactivation

batch predicts less inactivation

20

15

10

5

O-l...._~

-10

0

10

20

30

In(observed)-ln(predided) Figure 3. Histogram of differences between pilot and predicted survivals.

178

C. N. HAAS et al.

One test of overall prediction adequacy is obtained by examining the differences between the observed and predicted In(survival) values. There were a total of 239 individual points from the pilot plant runs. The histogram of differences between observed and predicted log-survival ratios are plotted in Figure 3. While the distribution of residuals is skewed, the mean difference is 0.727 with a standard error of 0.451. This distribution has a median than is not statistically significantly different from zero, as determined using a Wilcoxson signed rank test (p=39%). When the differences between pilot plant measured survival and that predicted from batch kinetics were examined as a function of disinfectant, organism, or test water, in all cases the subsets of predictions did not show a statistically significant difference from zero. A second test of the adequacy of batch kinetics coupled with residence time distribution information to

predict inactivation in the continuous flow pilot plant systems may be conducted by computing the overlap between the prediction bands from the batch kinetic extrapolations and the pilot measurements, considering experimental variability. It would be anticipated that 95% of the pilot observations would have overlapping confidence bands with the 95% prediction intervals from batch kinetic extrapolation. The overlap was computed from pilot runs in which identical disinfection doses were used on the same water and the same organism and contact time; these duplicates were then used to determine a replication standard error. The total number of observations for which duplicates (or more replicates) are available is. 19~. Overall, 78.6% of the pilot plant duplicated runs had confidence bands overlapping with the bench kinetic data (Table 4). While this indicates somewhat lower than the 95% nominal coverage, this analysis presumes that replication errors were normally distributed (with respect to log nonnal survival ratios), which may not necessarily be the case. Table 4. Proportion of pilot plant experimental trials with duplicated observations with overlapping confidence bands with bench extrapolated predictions (95% level). fraction in CI N all data 192 0.786 by organism 0.740 73 E. coli 0.75 MS2 80 GilJrdia

by disinfectant preammoniation free chlorine ozone by water Bull Run Willamette River

39

0.949

70 75 47

0.786 0.827 0.723

93

0.796 0.778

99

When concordance was examined separately by water, by disinfectant, and by organism, the only noticeab~e difference was the greater concordance with Giardia data (Table 4) as compared to the other organisms. Th,IS may have been due to the study design, in which relatively longer contact times were used (to obtam substantial GilJrdiIJ inactivation). At these contact times, an extremely high level of inactivation of the other organisms was obtained, and therefore substantial extrapolation from bench kinetics, as well as the increasing uncertainties associated with enumerating very small densities of organisms, existed. The results of the Giardia pilot plant experiments with free chlorine were also compared with published US EPA "ct" tables (Malcolm Pimie and HDR Engineering, 1991). Using the estimated tlO values fro~ tr~er tests, and the effluent chlorine residual, the "ct" tables were used to compute a predicted log inacU,vauon. This, and the predicted log inactivation, were both compared to the observed log inactivation. Usmg the

Predicting disinfection perfonnance

179

Wilcoxson signed rank test to compare the log predicted (by either the extrapolation from batch reactors or the EPA "et" tables) with the log observed inactivations, it was found that the "et" predictions were biased downward (less inactivation than observed), and that this difference was statistically significant (p=O.OO2, tWO tailed test). For the prediction from batch results, the difference was less, and was not statistically significant (p=O.252, two tailed test. These results also held for the Bull Run and Willamette data examined separately.

The ultimate conclusion of this work is that the procedure outlined for prediction of pilot inactivation results

based on batch kinetic experiments results using the Hom (with disinfectant decay) model is an adequate estimation of pilot results, and therefore (at least for the organisms and disinfectants studied), the need for pilot testing except in the very largest treatment plants may be minimized. However, as noted in prior work
CONCLUSIONS It is possible to obtain information sufficient to estimate inactivation in open systems, such as pilot plants (or rull scale treatment) by the use of la~ra~ory batch systems. This may greatly simplify and facilitate the process of disinfection efficacy detemunatlon.

The replication error associated with pilot plant studies on disinfection efficiency is large in comparison with

the uncertainty associated in extrapolation from bench kinetics to pilot data. If pilot investigations are to be performed. particular attention should be given to reducing such sources of variability.

ACKNOWLEDGEMENTS nte authors would like to thank the American Water Works Association Research Foundation for funding this project (#710) and personnel at the Portland Water Bureau and Montgomery Watson Engineers. Uma Anmang andla and Joel Hornberger perfonned a significant portion of the experimental work.

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p:::..F.