Extensions and Verification of the Water Treatment Plant Model for

Chapter 4

Downloaded by CORNELL UNIV on July 31, 2012 | http://pubs.acs.org Publication Date: August 15, 2000 | doi: 10.1021/bk-2000-0761.ch004

Extensions and Verification of the Water Treatment Plant Model for Disinfection By-Product Formation 1

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Gabriele Solarik , R. Scott Summers , Jinsik Sohn , Warren J. Swanson , Zaid K. Chowdhury , and Gary L. A m y 2

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1

Center for Drinking Water Optimization, Department of Civil, Environmental and Architectural Engineering, University of Colorado at Boulder, Campus Box 421, Boulder, CO 80309-0421 Malcolm Pirnie, Ind., 432 North 44th Street, Suite 400, Phoenix, AZ 85008 2

Introduction The U.S. Environmental Protection Agency (USEPA) Water Treatment Plant (WTP) model was developed in 1992 and used to support the Disinfectant/Disinfection By-product (D/DBP) Reg/Neg process in 1993-94 (1). The model predicts (1) the behavior of water quality parameters that impact the formation of disinfection by-products (DBPs) and (2) the formation of DBPs. This version of the model was limited in several ways: (a) many existing process, inactivation, D B P formation, and disinfectant decay algorithms within the W T P model were limited and/or outdated; (b) new process, inactivation, D B P formation, and disinfectant decay algorithms needed to be added; and (c) multiple points of chlorination, parallel treatment trains, and the distribution system were not modeled.

Objectives The overall objectives of this project are to modify existing model algorithms and to extend the model with new algorithms. The specific objective of this paper is to report the results from the modification, extension, and verification of the model for conventional and softening treatment with chlorination. New algorithms were developed and verified for advanced treatment processes and alternative disinfectants. However, these results will be presented in more detail in future publications. © 2000 American Chemical Society

In Natural Organic Matter and Disinfection By-Products; Barrett, S., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2000.

47

Approach The WTP model uses empirical correlations to predict central tendencies for natural organic matter (NOM) removal, disinfection, and D B P formation in a treatment plant. N O M characteristics such as total organic carbon (TOC), ultraviolet absorbance at 254 nm ( U V A ) , and specific U V A (SUVA) are used to predict trihalomethane (THM) and haloacetic acid ( H A A ) formation. The original model and its verification are discussed by Harrington et al. (2). The algorithms were generally developed using multiple linear regression, and the following regression parameters are given for the equations: the multiple correlation coefficient (R ), which measures the strength of the correlation by indicating the proportion of the variability in the dependent variable that is explained by all predictor variables combined; the adjusted correlation coefficient (R ), which is the multiple correlation coefficient adjusted by the number of predictor variables; the standard estimate of error (SEE), which measures the amount of scatter in the vertical direction of the data (i.e., around the dependent variable) about the regression plane; the F-statistic, which can be used to assess the goodness of fit using the F-test of the variance accounted for by regression; and the number of data points (n) used in equation development (3). Algorithm equations, together with data ranges for the input parameters, are given in this paper. These data ranges represent the boundary conditions within which the equations were developed and should be used. However, the W T P model does not restrict the use of the equations outside these boundary conditions. It is the user's responsibility to apply the model in an appropriate manner. A l l verification results presented herein were developed using independent data, i.e., data that were not used in the development of the predictive equations. The predicted results used only measured input variables, unless otherwise noted. The errors in predictions of the verification data set are reported either as the standard error (SE), which is an estimate of the variance or as the average error (AE), which expresses the absolute difference between the measured and predicted value as a percentage of the measured value. The equations for the SE and the A E are as follows: 2

2


adj

r AE

100

(Measured - Predicted] Measured

J


49

Results and Discussion


Conventional Treatment by Coagulation and Softening

Coagulation and Softening pH The WTP model equations to predict p H changes due to coagulation and softening were not revised and are calculated based on raw water alkalinity, coagulant dose, and carbonate chemistry (4). The model uses only equilibrium considerations and does not take into account the kinetics of processes such as calcium carbonate precipitation or carbon dioxide dissolution. The model assumes that the water treatment plant is, in effect, a closed system. Figure 1 shows the verification of settled water p H by coagulation (both alum and iron), using independent bench-scale jar test batch data (5,6). The line on this graph, and all subsequent verification graphs (unless otherwise noted), indicates the line of equality, i.e., any data points on this line indicate a perfect prediction. For alum coagulation, comparison between the measured and predicted data shows that pH is, in general, underpredicted, i.e., the model overpredicts the depression of pH by coagulant addition. The SE for p H verification for the waters coagulated with alum is 0.5 units, and the model underpredicted settled water p H by an A E of 5 percent. Similar results were found in the 1992 version of the model (2). For iron coagulation, the p H predictions are within the ranges shown for alum coagulation; however, too few data are available to conclude any consistent trends. Coagulation p H is used as an input parameter into the models calculating settled water T O C and U V A . Thus the underprediction of pH can lead to a propagation of error in settled water quality. The consistent underprediction of coagulation p H can be remedied by recalibration of the model or by the possible inclusion of an open carbonate chemistry system, as suggested by Tseng and Edwards (7). Prior to use or recalibration, the model results will be compared to results from pilot- and full-scale plants, where a flow-through system is used. Following the verification with pilotand full-scale data, the p H models will be recalibrated i f necessary.

TOC In the 1992 version of the W T P model, T O C removal by alum and iron coagulation was predicted by an empirical equation based on the raw water T O C , coagulant dose, and coagulant pH. In the current version of the W T P model, T O C removal by coagulation is predicted using the semi-empirical sorption model proposed by Edwards (8). The model proposed by Edwards was based on dissolved organic carbon (DOC); however, the author showed it to predict T O C removal nearly as well. The model does not consider particulate organic matter (POC), which, together with D O C comprises TOC. The model divides T O C into fractions that are sorbable and nonsorbable by the coagulant. The nonsorbable fraction cannot be removed by coagulation, and T O C removal is attributed solely to the sorbable fraction. The model is designed to handle


50 8.5

8.0

Χ

7

SE Settled Water pH Ο Alum Coagulation, 24 Waters, η = 147 0 . 5 -1 i · Iron Coagulation, 4 Waters, η = 8 0.5

s. °

5

Ο.

Ρίο

ϊ

7.0

-j

~Ό

O

· /

O

CD


6.5

6.0

-1

5.5

5.5

8^

Ο >

ι—ι—Γ

ο

•>—ι—«—"—ι—ι—ι—ι—>—ι——ι—r

' I

1

6.0

6.5

7.0

1

8.0

7.5

8.5

Measured pH Figure 1. Verification of Settled Water pH Prediction

both alum and iron coagulation. The model uses similar input parameters as were used previously (raw water TOC, coagulant dose, and coagulation pH), but also uses calculated model coefficients and the raw water S U V A . The raw water S U V A is a more refined indicator of N O M characteristics and indicates the humic content in the water. Coagulation typically preferentially removes humic ΝΟΜ. Thus, a water with a low humic content, indicated by a low S U V A , is expected to have a large nonsorbable fraction, whereas a water with a higher humic content and a higher S U V A will have a lower nonsorbable fraction (9). Additional information and detail about the equations can be found in (8). Coagulation: TOC =TOC settled

where TOC TOC

s e a l c d

sorb eq

+ TOC sorb.eq

ι

υ

υ

nonsorb

= settled water T O C (mg/L) = sorbable T O C remaining in solution at equilibrium (mg/L) = f(pH , coag. type, Dose , S U V A , T O C ) (based on Langmuir adsorption model) coag

TOC

n(

coag

r a w

raw

= non-sorbable T O C concentration (mg/L) = TOP

* F

where F„onsorb = K , * S U V A +K Kj has a negative value) r a w

2

(K, and K are coagulant dependent; 2


51 2

Alum: (R TOC

settled

TOC

r a w

SUVA Dose pH

adj

2

Iron: (R


(ulj

TOC

settled

TOC

r a w

pH

< 26.5

c o a g

< 6.11

r a w

< 1.51

< 8.0

= settled T O C (mg/L): 0.9 < T O C

r e m o v e d

= raw water T O C (mg/L): 2.3 < T O C r a w

coag

= 0.988, SEE = 0.47 mg/L, η = 250)

r a w

< 26

Extensions and Verification of the Water Treatment Plant Model for

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