WATER TREATMENT PLANT SIMULATION PROGRAM VERSION 1.21 USER'S MANUAL DRINKING WATER TECHNOLOGY BRANCH DRINKING WATER STANDARDS DIVISION OFFICE OF GROUND WATER AND DRINKING WATER UNITED STATES ENVIRONMENTAL PROTECTION AGENCY WASHINGTON, D.C. JUNE 1992 Prepared by: MALCOLM PIRNIE, INC. One International Boulevard Mahwah, New Jersey 07495-0018 2 Corporate Park Drive P.O. Box 751 White Plains, New York 10602 ------- WATER TREATMENT PLANT SIMULATION PROGRAM VERSION 121 USER'S MANUAL WORK ASSIGNMENT NO. 1-3 AMENDMENT NO. 3 CONTRACT NO. 68-CO-0062 JUNE 1992 Prepared fon USEPA - Office of Ground Water and Drinking Water Technology Transfer Prepared by: MALCOLM PIRNIE, INC. One International Boulevard Mahwah, New Jersey 07495-0018 2 Corporate Park Drive P.O. Box 751 White Plains, New York 10602 ------- TABLE OF CONTENTS Page 1.0 INTRODUCTION 1-1 1.1 Background 1-1 1.2 Purpose of This Manual 1-2 1.3 Manual Organization 1-3 1.4 PC System Requirements 1-3 2.0 DATA DEVELOPMENT AND INPUT 2-1 2.1 Main Menu Components 2-1 2.2 Simulating the Treatment Plant 2-4 2.3 Creating an Example Process Train 2-5 2.3.1 Create Process Train 2-6 2.3.2 Unit Process Parameters 2-6 2.3.3 Modify Process Train 2-7 2.3.4 Run Model 2-7 3.0 INTERPRETING MODEL OUTPUT 3-1 3.1 THM Prediction 3-1 3.2 HAA Prediction 3-2 3.3 Determination of Inactivation Ratio 3-3 3.4 Prediction of Other Parameters 3-6 REFERENCES LIST OF TABLES Table Following No. Description Page 2-1 Input Data for Example 2 2-5 2-2 Output File for Example 2 2-5 RECYCLED PAPER ------- TABLE OF CONTENTS (Continued) LIST OF FIGURES Figure Following No. Description Page 2-1 Main Menu 2-1 2-2 Interaction for Various Process Units 2-4 2-3 Process Flow Schematic for WTP 2-4 2-4 Algorithm for WTP Simulation Model 2-4 2-5 Process Flow Schematic for WTP 2-6 2-6 Flow Chart for WTP Program 2-6 2-7 Process Train Menu 2-6 2-8 Process Train Menu 2-6 2-9 Raw Water Parameters 2-6 2-10 Process Train 2-7 2-11 Predicted Water Quality Profile 2-8 LIST OF APPENDICES Appendix Description B Results of Model Verification Efforts ii RECYCLED PAPER ------- FORWARD This User's Manual for Version 1.21 of the WTP Simulation Program has been prepared to provide a basic understanding of 1) how to operate the program, and 2) the underlying assumptions and equations that are used to calculate the removal of natural organic matter (NOM) and the formation of disinfection by-products (DBPs). This manual represents the first public release of the program. The WTP Simulation Model was developed for the United States Environmental Protection Agency (USEPA) by Malcolm Pirnie, Inc. in support of the Disinfectant/Disinfection By- products (D/DBP) Rule. It was prepared with the understanding that the predictions may reflect the central tendency for treatment. It is not to be construed that the results from the model will necessarily be applicable to individual raw water quality and treatment effects at unique municipalities and agencies. This model does not replace sound engineering judgement using site-specific treatability data to evaluate the best manner in which to address the potential requirements of the D/DBP Rule for an individual application. It is understood that one limitation of the model is the extent of the data base available to verify model predictions. In a desire to systematically improve the overall predictive capability, the intent of this release is to solicit public comment on the usefulness and relative accuracy of the predictions on a case-by-case basis. To this end, a questionnaire is provided in Appendix C of this document that requests information on treatment process characteristics and DBP formation from individual utilities. The USEPA encourages constructive comments on methods to improve the model, results from verification efforts, and utility data that can be used to enhance the overall applicability of the model. Please forward comments to: Barbara M. Wysock Water Supply Technology Branch Technical Support Division Office of Ground Water and Drinking Water United States Environmental Protection Agency Cincinnati, Ohio 45268 The guidance provided herein may be of educational value to a wide variety of individuals in the water treatment industry, but each individual must adapt the results to fit their own practice. The USEPA and Malcolm Pimie, Inc. shall not be liable for any direct, indirect, consequential, or incidental damages resulting from the use of the WTP model. RECYCLED PAPER ------- 1.0 INTRODUCTION 1.1 BACKGROUND The water treatment plant simulation computer program (WTP) was developed for the U.S. Environmental Protection Agency (USEPA) by Malcolm Pirnie, Inc., in support of the Disinfectant/Disinfection By-Products (D/DBP) Rule. The primary purpose of the program is to simulate the removal of natural organics matter (NOM), the formation of disinfection by-products (DBPs), and disinfection levels in water treatment plants and distribution systems, based upon specified inputs including raw water quality, treatment process characteristics and chemical dosages. At this time, only trihalomethane (THM) and haloacetic acid (HAA) formation can be simulated. The model equations and format were developed primarily with funding from USEPA, with some support from the Metropolitan Water District of Southern California and the D/DBP Technical Advisory Workgroup (TAW) for developing specific predictive equations. WTP was developed to assist in determining the method by which treatment processes can be implemented to achieve the required levels of disinfection while maintaining compliance with potential requirements of the D/DBP Rule. The basic modeling approach includes estimation of: ¦ NOM removal by individual unit processes; ¦ disinfectant decay based upon demands exerted by NOM and other sources; and ¦ DBP formation based upon water quality throughout the treatment plant and in the distribution system. The predictive equations for water quality are described in Appendix A. The model simulates DBP formation under given treatment conditions and permits the user to evaluate the effects of changes in these conditions on the projected disinfectant decay and DBP formation. By using the model under different treatment scenarios, the user can gain an understanding of how the input variables affect disinfection and DBP formation. The WTP model was used by the USEPA to assist in evaluating treatment strategies for providing adequate disinfection, while controlling the formation of DBPs, using simulated water qualities representative of surface waters throughout the country. A copy 1 - J RECYCLED PAPER ------- of a paper describing this effort, as a part of the Regulatory Impact Analysis (RIA) for the D/DBP Rule, is provided at the end of this manual. The model is not intended as a replacement for treatability testing to evaluate the effectiveness of various processes on disinfectant decay and DBP formation in specific water supplies, but does provide a useful tool for evaluating the potential effect of different unit processes on the interrelationships between many of the new and forthcoming regulations. Users of the program should be familiar with water treatment plant operation, as well as procedures and methodologies used to disinfect water and control DBP formation. WTP, like any computer program, can not replace sound engineering judgement where input and output interpretation is required. Further, the technical adequacy of the output is primarily a function of the extent and quality of plant-specific data input, and the extent to which an individual application can be accurately simulated by predictive equations that are based upon the central tendency for treatment. 12 PURPOSE OF THIS MANUAL This manual is intended to guide the user in operating WTP and to assist in the preparation of information necessary to execute the program. The manual provides a step- by-step guide for operation, and describes how to utilize and interpret the program output. The manual includes the following components: ¦ Instructions for using the computer program; ¦ A description of the equations used in the program (Appendix A); ¦ Results of model verification efforts (Appendix B); ¦ Water treatment plant data questionnaire (Appendix C); ¦ Copy of a technical paper entitled "Simulation of Compliance Choices for the Disinfection By-Products Regulatory Impact Analysis"; and ¦ A 5.25-inch floppy disk of the executable version of WTP. It must be stressed that the model is largely empirical in nature. It can not be used as the sole tool for "full-scale" or "real-time" decisions for individual public water supplies. l -2 RECYCLED PAPER ------- 13 MANUAL ORGANIZATION This manual assumes that WTP users have a working knowledge of water treatment plants. This basic understanding is necessary to provide meaningful input data to the program and correctly interpret the output. It is not necessary for the user to have any programming knowledge or extensive computer experience. WTP operates through a user-friendly prompting program. It is assumed, however, that the user is familiar with fundamental computer operating systems. This manual will not address functions such as loading disks or connecting a printer. Operating system information of this type is usually contained in the users manual for a given computer system along with other fundamental computer operations. In addition to this introductory chapter, this user's manual contains two other chapters: ¦ Chapter 2 describes the information needed to run WTP and how the data should be input. The components of the main menu are described and explained, a diagram of a typical treatment plant is developed as an example, data input options are outlined, and a general description of how to use the program is provided. ¦ Chapter 3 provides guidance for interpretation of the output from the WTP program. Appendix A offers a description of the equations used in the program. Appendix B provides a summary of model verification efforts, and Appendix C contains a questionnaire for generating additional input data based upon actual water treatment plant operations. 1.4 PC SYSTEM REQUIREMENTS The source code for this simulation program is written in the computer language C. The executable version of this program can be run on an IBM PC or compatible hardware. The computer must be equipped with: ¦ DOS 3.1 or later versions of DOS; ¦ 8086/286/386/486 processor (a math coprocessor is not required); ¦ A minimum of 640 kilobyte random access memory (RAM); and i - 3 RECYCLED PAPER ------- ~ Graphics- capability (CGA, MCGA, EGA, EGA64, EGA-MONO, Here, ATT400, VGA, PC3270, or IBM 8514). l -4 RECYCLED PAPER ------- 2.0 DATA DEVELOPMENT AND INPUT This chapter describes how to develop and enter data to WTP. It includes sections explaining the features of the main menu, how to develop a simulated model of the specific plant being analyzed, and how to enter the proper information to activate the program. 2.1 MAIN MENU COMPONENTS The functions of the main menu are illustrated in Figure 2-1, and are described below. To select a particular function, the user must move the arrow keys until the desired function is selected. Alternately, pressing the capitalized letter shown for a function will select that function. At the completion of each function, the user is returned to the main menu. ¦ Open process train: Retrieves a process train and unit process data previously entered and saved to disk by the user. Submenu 1 - File Name: The user is presented with a list of existing file names with .WTP extensions and the user is prompted to enter the name of an existing file (without the ".WTP" extension). If the file name entered by the user exists, the unit process data are loaded into memory and the file name becomes the new working file. The user can optionally return to the main menu without entering a file name by pressing ENTER. Submenu 2 - List Process Train: A listing of the process train is presented to the user, and the user is given the option to list more detailed unit process data. Following this listing the user is returned to the main menu. ¦ New process train: Allows the user to enter a new process train with new unit process data, and replaces any previous train and data with the new train and data. An example of creating a new process train is illustrated in Section 2.3. Submenu 1 - File Name: Before any existing data are replaced in the memory, the user is presented with a list of existing file names and prompted to enter a new file name. At this point the user can optionally return to the main menu and not replace data in the memory by pressing ENTER without entering a new file name. After a new file name is entered and verified by the user, existing data are replaced in the memory and the new file name becomes the working file. 2 - 1 RECYCLED PAPER ------- U.S. Environmental Protection Agency Water Treatment Plant Model Main Menu IQpen process trainl I Run nodel 1 Mew process trainl 1 Thn & disinfection I ISaue process trainl 1 Delete data file I IMod i f y process trainl 1 Quit 1 List process trainl Highlight Desired Function and Press ENTER Uersion 1.21, June 1992 ------- Submenu 2 - Process Train: Following the new file name menu, the user is presented with a second menu to enter the new process train. Unit processes can be added and deleted from the process train in this menu. When the user is satisfied with the sequence of unit processes in the process train menu, the user selects "Done - Process Train Complete." Submenu 3 - Unit Process Data: After the process train is complete, the user is presented with a series of menus to enter specific data for each unit process. Each menu has default values for each unit process parameter which may need to be changed to meet the needs of the user application. The process train and unit process data are saved to a disk file with the name' entered in Submenu 1 with a .WTP extension. The user is then returned to the main menu. ¦ Save process train: Saves process train data and unit process data to a disk file. This function can also be used to make a duplicate copy of the data. Submenu - File Name: The user is presented with a list of existing data file names and prompted to enter an existing file name or new file name. Entering a new file name will change the working file name and save the data in the new file, thus making a duplicate of the process train data. Pressing ENTER without entering a file name will save data in the current working file. Existing data files are over-written. ¦ Modify process train: Allows the user to view and optionally change the process train and unit process data. An example of modifying a process train is illustrated in Section 2.3. Submenu 1 - Process Train: The first menu presents the process train and prompts the user to enter a specific unit process. Entering a number which corresponds to a unit process will bring up Submenu 2. Entering a negative number in Submenu 1 will delete the unit process from the process train. Entering a zero or pressing ENTER without entering a number will return to the main menu. Unit processes can not be inserted into an existing process train. Submenu 2 - Unit Process Data: This menu displays the unit process data and allows the user to change any parameter. Submenu 3 - Keep Changes: At this point the user has the option to keep the changes or restore the original data. Answering "Yes" saves the changes to the current working disk file. Answering "No" restores the original data. Note: If the user wishes to have an original file and modified file then "Save process train" should be used before "Modify process train" to make a duplicate copy of the working file. 2-2 RECYCLED PAPER ------- ¦ List process train: Lists the process train and unit process data currently in memory. The user is given the option to view the data on the CRT or down load the data to a printer. An example input data screen is shown in Table 2-1 in Section 2.2. ¦ Run model: Executes the water treatment plant model. Submenu - Output Device: The user is prompted to select between viewing the model output on the CRT, printing the output to the system printer, or saving the output to a disk file. An example output screen or file is shown in Table 2-2 in Section 2.2 If the output is viewed on the CRT, a series of output screens is displayed. After each screen, the user is prompted to press ENTER to continue or press ESC to return to the main menu. If the output is saved in a disk file the user is prompted to enter a file name. The output file name will be given an .OUT extension. ¦ THM and Disinfection: Executes the water treatment plant model with a summary screen illustrating selected parameters from the output file. This menu does not have any submenu. ¦ Delete data file: Allows the user to remove data files created by program. Submenu 1 - Data File Type: Two types of data files are created by WTP. The process train/unit process data files all contain a .WTP extension while the model output files contain an .OUT extension. The user is prompted to enter which type of file is to be deleted. Submenu 2 - File Name: A list of all existing files of the type selected in Submenu 1 is displayed and the user is prompted to enter a file name to be deleted. ¦ Quit: Exits the program and returns to DOS. 2-3 RECYCLED PAPER ------- 22 SIMULATING THE TREATMENT PLANT Before WTP can be executed, a simulated version of the treatment plant process train must be developed. Data specific to this plant must also be collected for input when creating the simulated plant. WTP is an interactive computer program that consists of a main program that acts as a manager for the number of plant simulation subroutines created for the input, output, and manipulation of data. A conceptual schematic of program inputs/outputs are shown in Figure 2-2. The program algorithm (steps the program follows), for a typical simulated process train illustrated in Figure 2-3 is shown on Figure 2-4. The executable version of the computer program is interactive and menu-driven. The main menu functions permit the user to direct the program to: ¦ Create an input file; ¦ Modify an input file; ¦ Save input/output files; ¦ Perform water treatment plant simulation runs; and ¦ Print input/output files. An input file for the simulation program consists of the following: ¦ Source type (surface water or groundwater) ¦ Organic Raw Water Quality Parameters TOC; and UV-absorbance at 254 nm. ¦ Inorganic Raw Water Quality Parameters Bromide concentration; Alkalinity concentration; Total and calcium hardness concentration; and Ammonia nitrogen concentration. 2-4 RECYCLED PAPER ------- FIGURE 2-2 INTERACTION FOR VARIOUS PROCESS UNITS FOR WATER TREATMENT PLANT SIMULATION MODEL ( Raw Water \. Quality ) ( Process? (^Characteristics/ WTP MODEL /THM/HAA FormatlonX V Module J ( CT/lnactlvatlon \ I Module J Other DBP ' Formation Modules Treated Water Quality ------- FIGURE 2-3 PROCESS FLOW SCHEMA TIC FOR WTP ' Alum Chlorine Flocculation & Sedimentation Caustic nitration Clear well ------- FIGURE 2-4 INPUT FILE ¦ Water Quality ¦ Process Characteristics ALGORITHM FOR WTP SIMULA T/ON MODEL ¦ WQ at end of each unit process -THM/CI^NHpi ^Distribution system ------- ¦ Water Treatment Process Characteristics Type of unit process; Plant flow at average and peak hour conditions; Baffling characteristics and detention times; and Chemical dosages. ¦ Other raw water quality parameters Giardia cyst concentration; - pH; Turbidity; and Average and minimum temperature. The output file from a water treatment plant simulation run contains information for all the input parameters such as the raw water quality and the treatment plant process characteristics. In addition, the output contains information for calculated concentrations of the following parameters at the end of each of the unit processes for the simulated water treatment plant: ¦ Organic and inorganic water quality; ¦ Disinfectant residuals; ¦ DBPs (THMs and HAAs) formed; and ¦ Percentage of CT achieved (based upon the CT numbers provided in the USEPA Guidance Manual for Compliance With the Filtration and Disinfection Requirements for Public Water Systems Using Surface Water Sources. An example of input and output files for the program are presented in Tables 2-1 and 2-2, respectively. 23 CREATING AN EXAMPLE PROCESS TRAIN This section outlines how to create a process train, enter process parameters, and run the model. A typical process train is developed as an example. 2-5 RECYCLED PAPER ------- TABLE 2-1 INPUT DATA FOR EXAMPLE 2 Raw_water PH . 7.5 Temperature .15.0 Degrees Celsius Annual Mln Temperature . 0.5 Degrees Celsius Total Organic Carbon . 3.0 mg/L UV Absorbance at 254 .nm . 0.10 /cm Bromide . 0.10 mg/L Alkalinity .80 mg/L as Calcium Carbonate Calcium Hardness .80 mg/L as Calcium Carbonate Total Hardness .100 mg/L as Calcium Carbonate Ammonia . 0.05 mg/L as N Turbidity . 1.5 NTU Giardia . 2.0 cysts/100 L Peak Hourly Flow .20.0 M6D Flow Rate .10.0 MGD Surface Water as defined by SWTR. . Yes Alum_Coagulation Alum Dose .10.0 mg/L as A12(S04)3*14H20 Flow Rate .10.0 MGD Surface Water as defined by SWTR. .Yes Theoretical Detention Time .270.0 minutes Ratio of Mean Detention Time to Theoretical Detention Time.... . 1.0 Ratio of t(10) to Theoretical Detention Time.... . 0.5 Chlorine Chlorine Dose . 4.0 mg/L as C12 Filtration Theoretical Detention Time .15.0 minutes Ratio of Mean Detention Time to Theoretical Detention Time.... . 1.0 Ratio of t(10) to Theoretical Detention Time.... . 0.5 Basin Theoretical Detention Time .60.0 minutes Ratio of Mean Detention Time to Theoretical Detention Time.... . 1.0 Ratio of t(10) to Theoretical Detention Time.... .0.5 Caustic Caustic Dose .11.0 mg/L as HaOH Distribution Average Residence Time . 3.0 days Maximum Residence Time . 7.0 days HAA Equations (H-Haas, T-TAW) .H * ' Two sets of equations are available for HAA prediction as discussed in Section 3.2. ------- TABLE 2-2 OUTPUT FILE FOR EXAMPLE 2 CHC13 CHBrC12 CHBr2Cl CHBr3 TTHMs Location (ug/L) (ug/L) (ug/L) (ug/L) (ug/L) Raw Water 0.0 0.0 0.0 0.0 0.0 Alum Addition Basin Ef£luent 0.0 0.0 0.0 0.0 0.0 Chlorine Filtered water 5.7 3.6 1.1 0.3 10.7 Basin Effluent 8.8 5.7 1.6 0.4 16.5 Caustic Average Tap 28.4 19.0 5.3 0.6 53.3 End of System 35.8 24.0 6.7 0.7 67.2 Press ENTER to continue or Esc to return to Main Menu. HAAs Under Average Conditions Using Haas HAA Equations MCAA DCAA TCAA MBAA DBAA THAAs Location (ug/L) (ug/L) (ug/L) (ug/L) (ug/L) (ug/L) WTP Effluent... 1.0 7 . 7 4.3 0.3 0.9 14.2 Average Tap 1.0 10.6 8.9 0.4 1.2 22.1 End of System.. 1.0 11.5 11.1 0.5 1.4 25.5 Predicted water Quality Profile at Minimum Temperature and Peak Hour Flow Temp PH C12 NH2C1 Inactivation Location (C) (-) (mg/L) (mg/L) Ratio Raw Water 0.5 7.5 0.0 0.0 0.0 Alum Addition.. Basin Effluent. 0.5 7.2 0.0 0.0 0.0 Chlorine Filtered Water. 0.5 7.1 3.0 0.0 0.1 Basin Effluent. 0.5 7.1 2.9 0.0 0.4 Caustic Average Tap.. .. 0.5 8.0 1.0 0.0 0.4 End of System. . 0.5 8.1 0.3 0.0 0.4 ------- OUTPUT FILE FOR EXAMPLE 2 (CON'T) Predicted Water Quality Profile for EXAMPLE2 .WTP Under Average Conditions PH TOC UV-254 Alk Temp C12 NH2C1 NH3-N Location (-) (mg/L) (1/cm) (mg/L) (C) (mg/L) (mg/L) (mg/L) Raw Water 7.5 3.0 0.100 80 15.0 0.0 0.0 0.1 Alum Addition.. Basin Effluent. 7.2 2.3 0.048 75 15.0 0.0 0.0 0.1 Chlorine Filtered Water. 7.1 2.3 0.048 72 15.0 3.0 0.0 0.0 Basin Effluent. 7.0 2.3 0.048 71 15.0 2.9 0.0 0.0 Caustic Average Tap.. . . 8.0 2.3 0.048 84 15.0 1.0 0.0 0.0 End of System. . 8.1 2.3 0.048 83 15.0 0.3 0.0 0.0 Press ENTER to continue or Esc to return to Main Menu. Bromide Ca Hard Mg Hard Inactivation Solids Location (mg/L) (mg/L) (mg/L) Ratio (mg/L) Raw Water 0.10 80 20 0.0 0.0 Alum Addition.. Basin Effluent. 0.10 80 20 0.0 5.7 Chlorine Filtered Water. 0.10 80 20 0.5 5.7 Basin Effluent. 0.10 80 20 2.3 5.7 Caustic Average Tap 0.10 80 20 2.3 5.7 End of System.. 0.10 78 20 2.3 5.7 Press ENTER to continue or Esc to return to Main Menu. ------- A unit process flow diagram, detention times, and raw water quality data are shown in Figure 2-5. This information is needed to create the process train and enter data. To better understand the development of this process train, it is recommended that the unit process components of the process train be arranged in a sequential block diagram, as illustrated in Figure 2-6. 2J.1 Create Process Train Once the information has been collected and organized, the following procedures can. be followed to create the process train and operate the program: ¦ Step 1 - Insert the program diskette, type WTP, and press "Enter". The main menu will be displayed on the screen. ¦ Step 2 - At the main menu, select Nw prwess train and press "Enter". ¦ Step 3 - Type the name of the plant to be simulated and press "Enter". This displays the screen shown in Figure 2-7. The screen offers unit process options the user can select to create your process train. ¦ Step 4 - Highlight each unit process desired and press "Enter" to construct the process train. The options selected move to a list at the left side of the screen as in Figure 2-8. When the user has completed choosing options, the unit processes selected should match those in the block diagram in Figure 2-6. ¦ Step 5 - Select "Done - Process Train Complete" to finalize the process train. The simulated plant is now created. 232 Unit Process Parameters After the simulated plant has been created, the screen illustrated in the top of Figure 2-9 will appear. This, and a series of similar screens for each unit process selected when constructing the simulated plant, will prompt the user for specific information unique to this plant design, flow, and source water. The following sequence of steps can be used to enter the specified information for each unit process: ¦ Step 1 - Enter the requested information at the data entry point marked by the blinking cursor. Press "Enter". If the default value shown left of the arrow is correct, press "Enter" to proceed to the next parameter. ¦ Step 2 - Enter the requested information for each subsequent prompt. Press "Enter". 2-6 RECYCLED PAPER ------- FIGURE 2-5 PROCESS FLOW SCHEMATIC FOR WTP Alum = 10 mg/L Chlorin* s 4 mg/L Flocculatlon & Sadlmantatlon (270 mln) t(mean)/t(theorstlcal) e 1.0 t(10)/t (theoretical) = 0.5 Caustic = 11 mg/L nitration (15 mln) t(m«an)A(theoretlcal) = 1.0 t(10)/t(thaor«tlcal) = 0.5 Claar wall (60 mln) t(maan)/t(theoratlcal) « 1.0 t(10)A(thaoratical) = 0.5 ------- FIGURE 2-6 All diagrams start with raw water (given). RAW ------- FIGURE 2-7 Neu Process Train Unit Process Train: Raw Uater Available Unit Processes |ft luw coagulationl |Chl< II ron coagulationl Ian won i a ------- FIGURE 2-8 Unit Process Train: Raw Mater §AV?a??i3iifSi00 Bas in Ki?fi?fco Basin. Caustic Distribution Neu Process Train Available Unit Processes Ifllun coagulation] IChlorine II ron coagulationl lawwonia ------- FIGURE 2-9 a) Initial Screen = RAW WATER PARAMETERS —— pH value: 7.7 —> Type new value or press ENTER for no change b) Completed Screen ===== RAW WATER PARAMETERS pH value 7.7 —> 7.5 Temperature (Celsius) 18.0 —> 15.0 Annual Min. Temp. (Celsius) 0.5 ~> 0.5 TOC (mg/L) 3.00 —> UV-254 (1/cm) 0.10 --> Bromide (mg/L) 0.10 —> Alkalinity (mg/L as CaC03) 50.00 —> 80 Calcium Hardness (mg/L as CaC03) 100.00 —> 80 Total Hardness (mg/L as CaC03) 120.00 —> 100 Ammonia (mg/L as N) 0.01 —> .05 Turbidity (NTU) 2.00 —> 1.5 Giardia (cysts/100 L) 1.0 —> 2.0 Peak hour flow rate (MGD) 5.00 —> 20 Daily average flow rate (MGD) 2.00 —> 10 Surface Water as defined SWTR (Y/N): Y --> are the data correct (Y or N) = Type new value or press ENTER for no change ------- ¦ Step 3 - After all the process information has been entered for a specific process, the program will prompt: "are the data correct?" The initial screen with filled data is shown in the bottom of Figure 2-9. Enter "y[es]n if the information is correct. Enter "n[o]" if you need to change data, then enter "y[es]" after the corrections have been made. The program automatically proceeds to the next unit process in the simulated plant. The above steps are duplicated. After all unit process data have been entered, the simulated plant model can be run or modified from the main menu. 233 Modify Process Train The following sequence can be used to modify the input parameters in the created or retrieved process train: ¦ Step 1 - Return to main menu and highlight Of^ f^ocess train. Press "Enter". ¦ Step 2 - Type in the name assigned to the simulated process train. Press "Enter". Type "y[esj" to review the unit process data. Type "n[o]" to return to the main menu. ¦ Step 3 - Highlight MbdHy process train and press "Enter". ¦ Step 4 - Enter the number for the unit process to be modified from the ordered processes in the simulated train on the screen (Figure 2-10). Press "Enter". (Unit processes can be deleted by entering a negative number at this step.) ¦ Step 5 - Modify the input parameters for the selected unit process in the same way the process unit parameters were entered. ¦ Step 6 - Enter "y[es]" or "n[o]M for modification status. ¦ Step 7 - Enter "y[es]B or "n[o]" to select the option to edit another process data module. A "y[esj" entry invokes the process train modification menu. ¦ Step 8 - Enter "y[es]" to save the changes. 23.4 Run Model ¦ Step 1 - Return to main menu and highlight Open process train. Press "Enter". ¦ Step 2 - Type in the name assigned to the simulated process train. Press "Enter". Type "y[es]" to see the unit process data. Type "n[o]" to return to main menu. 2-7 RECYCLED PAPER ------- FIGURE 2-10 ===^== Modify Process Train == Current Working File: EXAMPLE2.WTP 0:Exit this Menu 1:Raw_Water 2:Alum_Coagulation 3:Basin 4:Chlorine 5:Filtration 6:Basin 7:Caustic 8:Distribution Enter number to modify unit process data or enter negative number to delete unit process or enter 0 to exit this menu with option to save changes > Note: A Unit Process can be deleted, or the unit process data can be modified, but a unit process cannot be inserted into an existing process train. ------- ¦ Step 3 - At main menu, highlight Eup model and press "Enter". Type "c[rt]H to send output to CRT; type "p[rinterj" to send output to printer; type "f[ilej" to send output to a file. An example of the output is shown in Table 2-2. Alternately, to see a summary of selected parameters, at Step 3 select piid disinfection and press "Enter." This will send the selected output only to the CRT. An example of the summary screen is shown in Figure 2-11. These procedures and illustrative figures provide some direction on the operation of WTP. Main menu options not specifically explained are described in Section 2.1, and are selected by highlighting and entering their function title. 2 - 8 RECYCLED PAPER ------- FIGURE 2-11 Predicted Water Quality Profile PH TOC C12 NH2C1 IR at Min. T THMs Location (-) (mg/L) (mg/L) (mg/L) & Peak Hr. Q (ug/L) Raw Water 3.0 0.0 0.0 0.00 0.0 Basin Effluent.... 2.3 0.0 0.0 0.00 0.0 Filtered Water.... 2.3 3.0 0.0 0.08 10.7 Basin Effluent.... .7.0 2.3 2.9 0.0 0.40 16.5 Average Tap Water. .8.0 2.3 1.0 0.0 0.40 53.3 tend of System .8.1 2.3 0.3 0.0 0.40 67.2 Press ENTER to return to Main Menu. ------- 3.0 INTERPRETING MODEL OUTPUT This chapter provides a description of the output of the WTP program. An example output file was presented in Table 2-2, and each of the parameters in the output file is discussed in this chapter. The output generated after the "Run Model" command contains the full output from the simulation exercise. This can be directed to the printer, disk or screen. The output generated after the "THM and Disinfection" command contains a summary of selected parameters displayed on the computer screen. 3.1 THM PREDICTION The program predicts THM formation after chlorination and chloramination. The program does not predict THM formation strictly from ozonation (in the absence of chlorination and chloramination). The program predicts TTHM formation using a single equation for total THM (TTHM) concentration. The individual THM components are predicted using four equations for the concentration of each individual THM. These proportions are then applied to the TTHM concentration to produce the individual concentrations. The equations are described in detail in Appendix A. TTHMs presented in the output file represent the concentration predicted by one TTHM equation. These concentrations also appear on the computer screen after the "THM and Disinfection" command is selected. The proportion of each individual THM concentration to the sum of the four THMs is determined from four individual THM predictive equations. An example considers a treatment process that predicts the following concentrations of individual THMs: 50 /ig/L chloroform (CHC13) 25 /ig/L bromodichloromethane (CHBrCl2) 20 /ig/L dibromochloromethane (CHBr2Cl) 5 up/L bromoform (CHBr3) 100 /ig/L TTHM by summing individual species 3 -1 RECYCLED PAPER ------- In this example, the proportion of chloroform to the TTHM concentration is 50/100, or 0.50. The proportions for the other THMs are determined in a similar manner. If the single equation predicts a TTHM concentration of 80 /*g/L, then the program will predict the following concentrations for the individual THMs: 40 /tg/L chloroform (CHC13) 20 ftg/L bromodichloromethane (CHBrCl2) 12 /tg/L dibromochloromethane (CHBr2Cl) 4 ^g/L bromoform (CHBr3) 80 /ig/L TTHM from single TTHM equation Therefore, the individual THM concentrations associated with the TTHM value of 80 /xg/L would be presented in the output file. The reason for performing the analyses in this manner is that the equation for TTHM has been determined to be more accurate than the sum of the individual species, based upon verification analyses. Because the initial efforts using the model focused upon the impact of different TTHM regulatory scenarios, the accuracy of the TTHM prediction was more important than that for the individual predictions. 3.2 HAA PREDICTION The program has the capability of predicting the concentrations of five individual haloacetic acids (HAAs) using two different sets of equations. One set of equations is referred to as the "Haas" HAA equations and the other is referred to as the "TAW" HAA equations. A complete background and description of these equations is presented in Section A.3 in Appendix A. The user is given the opportunity to select which set of equations to use in the program, and the output file will present the results using the appropriate equations. The major difference between the Haas and TAW HAA equations is the method used to calculate the concentrations at various stages of treatment. The TAW equations predict HAA concentrations on a cumulative basis which accounts for HAA formation through sequential treatment processes. In other words, the concentrations at a given point in the treatment process is calculated by summing the concentrations predicted through each treatment process. 3-2 RECYCLED PAPER ------- The Haas equations predict HAA concentrations on a "static" basis. The statistical correlations predict individual HAA levels based on concentrations of individual THM concentrations and, therefore, do not account for the cumulative effect of changing water quality parameters on the final levels of HAAs formed. Because of the method used by the Haas equations to calculate HAA concentrations, the predicted levels of HAAs may decrease from the plant effluent to the average distribution system, under some treatment scenarios. For example, if the pH in the distribution system is > 7.5, the Haas equations will predict low levels of HAAs. If low pH values exist during treatment (e.g.< 6.0 during coagulation), however, the Haas HAA equations may predict HAA levels at the plant effluent higher than those predicted at the average distribution system. The TAW equations do not demonstrate this trend. Using the above scenario, the TAW equations would predict a level of HAA formation through the treatment plant based on the predicted water quality parameters. If the pH is increased entering the distribution system, the formation rate will decrease, but the concentrations of HAAs at the average distribution system will always be greater than or equal to the plant effluent concentration. 3.3 DETERMINATION OF INACTIVATION RATIO The inactivation ratio is used to evaluate whether a system meets disinfection requirements for surface waters or ground waters. For surface water systems (or ground water systems under the influence of surface water), the Surface Water Treatment Rule (SWTR) requires a 3-log (99.9 percent) removal/inactivation of Giardia cysts and a 4-log (99.99 percent) removal/inactivation of viruses. For ground water systems (not under the influence of surface waters) it is anticipated that the Ground Water Disinfection Rule (GWDR) may require a 4-log removal/inactivation of viruses. In the WTP model, the type of source water (i.e., surface or ground water) is specified in the input file. For surface water systems using coagulation and filtration, the model provides a 2.5-log removal credit for Giardia and a 2.0-log removal for viruses. Therefore, the required inactivations for such systems are 0.5-log for Giardia and 2.0-log for viruses. For ground water systems using coagulation and filtration, the model provides a 2.0 log removal of viruses and therefore the required virus inactivation for such systems is assumed as 2.0-log. 3 - 3 RECYCLED PAPER ------- Although the SWTR currently requires surface waters (or ground waters under the influence of surface water) to achieve a minimum 3-log removal/inactivation of Giardia and a 4.0-log removal/inactivation of viruses, the USEPA recommends that utilities achieve greater inactivations depending on the Giardia concentration in the raw water. According to the SWTR, the recommended levels of removal/inactivation are based on the raw water Giardia concentrations as follows: Daily Average Giardia Cyst Recommended Giardia Recommended Virus Concentration/100 L Removal/Inactivation Removal/inactivation For surface waters, the program establishes the recommended level of Giardia removal/inactivation based on the raw water concentration of Giardia in the input file. For example, if a Giardia concentration in the range of 1 to 10 cysts is input, the removal/inactivation requirement will be 4-log for Giardia and 5-log for viruses. The log removal credit through filtration for Giardia and viruses is similar to that discussed above. Therefore, a system with a required 4-log and 5-log removal/inactivation for Giardia and viruses, respectively, would be required to provide a 1.5-log inactivation of Giardia and a 3-log inactivation of viruses. The inactivation ratio is defined as the level of inactivation (calculated as CT) achieved through a given process divided by the required amount of inactivation for Giardia or viruses from the SWTR or GWDR. If the value of the inactivation ratio at the treatment plant effluent (representing the first customer) is equal to or greater than 1.0, the system meets the disinfection requirements. For example, if the required CT value to meet a required level of inactivation is 100, and the calculated CT value through a given treatment process is 80, the resulting inactivation ratio for that process is 80/100, or 0.80. A complete 1 - Recent surveys (i.e., Le Chevallier, et al.. 1991) of the occurrence of Giardia in surface water supplies indicate that Giardia concentrations in some supplies exceed the maximum range (10-100) specified by USEPA in the SWTR. Therefore, linear extrapolation of CT the criteria were made to include 6- and 7- log removal/inactivations for higher raw water Giardia concentrations. < 1 1-10 10-100 100-1,000' 1,000-10,000' 3-log 4-log 5-log 6-log 7-log 4-log 5-log 6-log 7-log 8-log 3-4 RECYCLED PAPER ------- description of the inactivation ratio algorithm (for Giardia and viruses) is presented in Appendix A. It is important to note that when free chlorine is used as the primary disinfectant in a surface water system using coagulation and filtration, a 0.5-log inactivation of Giardia will provide greater than 2.0-log inactivation of viruses. Similarly, for a non-filtering surface water, a 3.0-log inactivation of Giardia will provide a greater than 4.0-log inactivation of viruses. As a result, when free chlorine is used as the primary disinfectant, the level of inactivation required for Giardia is always greater than the corresponding level of inactivation required for viruses. If chloramines are used as the primary disinfectant in a surface water treated with coagulation and filtration, however, a 0.5-log inactivation of Giardia will not provide greater than 2.0-log inactivation of viruses. In this case, the model will still set the required level of inactivation based on Giardia inactivation, not upon viruses. Thus, the inactivation ratio would provide an inaccurate description of the system's disinfection requirements. In subsequent versions, the model will use the higher of the two levels of inactivation for either Giardia or viruses as the required inactivation. It should also be noted that different types of filtration (i.e., direct, slow sand and diatomaceous earth) can provide different removal of Giardia cysts and viruses than coagulation and filtration systems. The current version of the model, however, does not account for different removals that may be associated with other types of filtration systems. It is intended that subsequent versions of the model will address this issue. Because the WTP model does not yet contain equations for the decay of ozone or chlorine dioxide, inactivation ratios are not calculated for these two disinfectants. When these equations are added to the model, it will be possible to calculate inactivation ratios from the use of these disinfectants. The output file for a given modeled treatment system presents the inactivation ratio under two different scenarios. The first scenario describes average temperature and average flow conditions while the second describes minimum temperature and peak hourly flow conditions. The second scenario represents the most stringent disinfection conditions and, therefore, represents the conditions under which plants would most likely design their treatment systems to meet the disinfection requirements. The inactivation ratios visually displayed on the summary screen after the "THM and Disinfection" command is selected, are the inactivation ratios predicted under the minimum temperature/peak flow conditions. 3-5 RECYCLED PAPER ------- 3.4 PREDICTION OF OTHER PARAMETERS The following describes the remaining parameters in the output from a simulation run: ¦ gH - The predicted pH value at the effluent of a given unit process. ¦ TOC - The predicted TOC concentration (in mg/L) at the effluent of a given unit process. ¦ UV-254 - The predicted UV-254 value (in 1/cm) at the effluent of a given unit process. ¦ Alk - The predicted alkalinity (in mg/L as calcium carbonate) at the effluent of a given unit process. ¦ Temp - The average temperature (in °C) through a given unit process. Changes in temperature through a process train are not calculated in this version of the program. ¦ C12 - The predicted free chlorine concentration (in mg/L) at the effluent of a given unit process. ¦ NH2C1 - The predicted combined chlorine concentration (in mg/L) at the effluent of a given unit process. ¦ NH3-N - The predicted concentration of ammonia (in mg/L as N) at the effluent of a given unit process. ¦ Bromide - The predicted bromide concentration (in mg/L) through a given unit process. (Note: bromide removal is not calculated in any unit process because of the lack of information on the fate of bromide in water treatment). ¦ Ca Hard - The predicted calcium hardness (in mg/L as calcium carbonate) in the effluent of a given unit process. Calcium hardness is removed when the solubility of calcium carbonate is exceeded. ¦ Mp Hard - The predicted magnesium hardness (in mg/L as calcium carbonate) in the effluent of a given unit process. Magnesium hardness is removed when the solubility of magnesium hydroxide is exceeded. ¦ Solids - The predicted concentration of solids (in mg/L) produced by the addition of either alum or ferric chloride or by the precipitation of calcium carbonate or magnesium hydroxide. The rate of solids production (lbs/day) can be obtained by multiplying this result by the plant flow rate. 3-6 RECYCLED PAPER ------- REFERENCES G. L. Amy, B. C. Alleman and C. B. Cluff (1990). "Removal of Dissolved Organic Matter by NanofUtration." J. Env. Eng.. 116(1), p. 200. G.L. Amy, J.H. Greenfield, and WJ. Cooper (1990). "Organic Halide Formation During Water Treatment Under Free Chlorine Versus Chloramination Conditions." in Water Chlorination: Chemistry. Environmental Impact and Health Effects. Vol. 6.; R.L. Jolley, et al„ eds; Lewis Publishers, Chelsa, MI. G. L. Amy, P. A. Chadik and Z. K. Chowdhuiy (1987). "Developing Models for Predicting Trihalomethane Formation Potential and Kinetics." J. AWWA. 79(7), p. 89. B. Batchelor (1989). "A Kinetic Model for Formation of Disinfection By-Products.", Internal USEPA Report. B. Batchelor, G. Fusilier and E. H. Murray (1987). "Developing Haloform Formation Potential Tests." J. AWWA. 79(1), p. 50. R.J. Bull and F.C. Kopfler (1991). Health Effects of Disinfectants and Disinfection By- products. AWWA Research Foundation, Denver, CO. P.A. Chadik and G.L. Amy (1983). "Removing Trihalomethane Precursors from Various Natural Waters by Metal Coagulants." Journal AWWA. 75(10), p532. T. J. Christ and J. D. Dietz (1988). "Influence of Bromide Ion Upon Trihalomethane Formation and Speciation." Proc. 1988 AWWA Nat'l Conference, Orlando, FL. R.M. Clark and S. Regli (1991). "The Basis for Giardia CT Values in the Surface Water Treatment Rule; Inactivation by Chlorine." In: Guidance Manual for Compliance with the Filtration and Disinfection Requirements for Public Water Systems Using Surface Water Supplies; USEPA, Washington, D.C. R. M. Clark (1987). "Modeling TOC Removal by GAC: The General Logistic Function." J. AWWA. 79(1), p. 33. R. M. Clark, J. M. Symons and J. C. Ireland (1986). "Evaluating Field Scale GAC Systems for Drinking Water." J. Env. Eng.. 112(4), p. 744. J. Corollo Engineers (1989). "Pilot Study Final Report; Union Hills Water Treatment Plant Water Quality Enhancement Study." Prepared for the City of Phoenix, AZ. K.G. Denbigh and J.C.R. Turner (1971). Chemical Reactor Theory: An Introduction. 2nd Edition, Cambridge University Press, Cambridge, UK. H. Dharmarajah, N.L. Patania, J.G. Jacangelo, and E.M. Aieta (1991) "Empirical Modeling of Chlorine and Chloramine Residual Decay." in Water Quality for the new Decade. 1991 Annual Conference Proceedings, AWWA. R -1 RECYCLED PAPER ------- J. K. Edzwald, W. C. Becker and K. L. Wattier (1985). "Surrogate Parameters for Monitoring Organic Matter and THM Precursors." J. AWWA. 77(4), p. 122. J. K. Edzwald (1984). Removal of Trihalomethane Precursors bv Direct Filtration and Conventional Treatment. USEPA Municipal Environmental Research Laboratory, Rept. No. EPA-600/2-84/068, NTIS Publ. No. PB84-163278. B. A. Engerholm and G. L. Amy (1983). "A Predictive Model for Chloroform Formation from Humic Acid." J. AWWA. 75(8), p. 418. P. Fair (1990). Letter to S. Regli, USEPA. G.W. Harrington, Z.K. Chowdhury, and D.M. Owen (1991). "Integral Water Treatment Plant Model: A Computer Model to Simulate Organics Removal and Trihalomethane Formation." in Water Quality for the New Decade. 1991 Annual Conference Proceedings, AWWA. S.A. Hubbs and G.C. Holdren (1986). Chloro-Organic Water Quality Changes Resulting from Modification of Water Treatment Practices. AWWA Research Foundation, Denver, CO. R.E. Hubel and J.K. Edzwald (1987). "Removing Trihalomethane Precursors by Coagulation." Journal AWWA. 79(7), p98. H. Hudson (1981). Water Clarification Processes: Practical Design and Evaluation. Van Nostrand Reinhold, New York, NY. M.C. Kavanaugh (1978). "Modified Coagulation for Improved Removal of Trihalomethane Precursors." Journal AWWA. 70(11), pl63. D.G. Kleinbaum and L.L. Kupper (1978). Applied Regression Analysis and Other Multivariate Methods. Duxbury Press, Boston, MA. W.R. Knocke, S. West and R.C. Hoehn (1986). "Effects of Low Temperature on the Removal of Trihalomethane Precursors by Coagulation." Journal AWWA. 78(4), pl89. S. W. Krasner (1991). Internal Correspondence to Distribution, February 1, 1991. M. W. Le Chavallier, W.D. Norton and R. G. Lee (1991) "Occurrence of Giardia and Cryptosporidium in Surface Water Supplies". Applied and Environmental Microbiology. 57(9), p. 2610. O. Levenspiel (1972). Chemical Reaction Engineering. 2nd Edition, John Wiley & Sons, New York, NY. B. W. Lykins, R. M. Clark and J. Q. Adams (1988). "Granular Activated Carbon for Controlling THMs." J. AWWA. 80(5), p. 85. R -2 ------- Malcolm Pimie, Inc. and Metropolitan Water District of Southern California (1991). Evaluation of SDWA Impacts on Metropolitan Member Agencies. Malcolm Pirnie, Inc. and City of San Diego (1990). Water Quality Report. Malcolm Pirnie, Inc. and City of Phoenix (1989). Water Quality Master Plan. James M. Montgomery Consulting Engineers and AWWA (1992). Mathematical Modeling of the Formation of THMs and HAAs in Chlorinated Natural Waters. James M. Montgomery Consulting Engineers and Metropolitan Water District of Southern California (1989). Disinfection Bv-Products in U.S. Drinking Waters. United States Environmental Protection Agency and Association of Metropolitan Water Agencies; Cincinnati, OH and Washington, DC. J.C. Morris and R.A. Isaac (1985). "A Critical Review of Kinetic and Thermodynamic Constants for the Aqueous Chlorine-Ammonia System." in Water Chlorination: Chemistry. Environmental Impact and Health Effects. Vol 4; lewis Publishers, Chelsa MI. R. G. Quails and J. D. Johnson (1983). "Kinetics of the Short-Term Consumption of Chlorine by Fulvic Acid." Environ. Sci. Technol.. 17(11), p. 692. M.J. Semmens and T.K. Field (1980). "Coagulation: Experiences in Organics Removal." Journal AWWA. 72(8), p476. P. C. Singer (1988). Alternative Oxidant and Disinfectant Treatment Strategies for Controlling Trihalomethane Formation. USEPA Risk Reduction Engineering Laboratory, Rept. No. EPA/600/2-88/044, NTIS Publ. No. PB88-238928. P. C. Singer and S. D. Chang (1989). "Correlations Between Trihalomethanes and Total Organic Halides Formed During Water Treatment." J. AWWA. 81(8), p. 61. W. Stumm and J. J. Morgan (1981). Aquatic Chemistry: An Introduction Emphasizing Chemical Equilibria in Natural Waters. John Wiley and Sons, New York, NY. J. M. Symons (1991). Correspondence to D. Owen, March 1, 1991. J. S. Taylor, D. M. Thompson and J. K. Carswell (1987). "Applying Membrane Processes to Groundwater Sources for Trihalomethane Precursor Control." J. AWWA. 79(8), p. 72. J. S. Taylor, L. A. Mulford, W. M. Barrett, S. J. Duranceau and D. K. Smith (1989). Cost and Performance of Membranes for Organic Control in Small Systems: Flagler Beach and Punta Gorda. Florida. USEPA Risk Reduction Engineering Laboratory. S. M. Teefy and P. C. Singer (1990). "Performance and Analysis of Tracer Tests to Determine Compliance with the SWTR." J. AWWA. 82(12), p. 88. R - 3 RECYCLED PAPER ------- United States Environmental Protection Agency (1991). Guidance Manual for Compliance with the Filtration and Disinfection Requirements for Public Water Systems Using Surface Water Supplies. J.S. Young and P.C. Singer (1979). "Chloroform Formation in Public Water Supplies: A Case Study." Journal AWWA. 71(12), p87. R -4 ------- APPENDIX A DESCRIPTION OF MODEL EQUATIONS ------- A.1 INTRODUCTION This appendix presents the basis of a model that simulates disinfection by-product (DBP) formation and removal of natural organic matter (NOM) in water treatment plants. The purpose of the model is to: ¦ determine DBP levels that can be achieved by existing treatment technologies, given the requirements for microbiological safety; ¦ identify those technologies that may be considered best available technology (BAT) for DBP control; and ¦ provide a tool that will assess the impacts of new regulations on DBP formation in existing treatment plants. The basic modeling approach begins with the estimation of DBP precursor removal by individual process units in the process train of interest. The fate of applied disinfectant through the treatment process train is analyzed and the concentration of the disinfectant at the beginning and end of a process unit is determined. The final step involves the calculation of DBP formation based on water quality through the process train. At this time, the model is only capable of simulating THM and HAA formation because of the lack of information on the kinetics of formation and decay of other DBPs. At this time, the formation of only trihalomethanes (THMs) and haloacetic acids (HAAs) can be predicted. The formation of THMs have been studied much more extensively than HAAs, and therefore the database for THM formation is considerably more robust. Insufficient data are available to develop equations to predict other DBPs at this time. This appendix summarizes the development of equations for simulating: ¦ total and individual THM concentrations; ¦ total and individual HAA concentrations; ¦ removal of total organic carbon (TOC) and ultraviolet absorbance at 254 nm (UV-254); ¦ changes in alkalinity and pH; and ¦ decay of chlorine and chloramines. a -1 RECYCLED PAPER ------- A2 EQUATIONS FOR THM FORMATION A2.1 Published Models for THM Formation Numerous studies have used linear regression techniques to correlate trihalomethane formation potential (THMFP) with TOC and UV-254. While these results showed good correlations, general use of these regression equations is limited because they do not include parameters such as chlorine dose, pH, temperature and time (Edzwald, et al.. 1985; Singer and Chang, 1989; Lykins, et al.. 1988; Batchelor, et al.. 1987). Additional studies have taken a more thorough approach for modeling kinetics of THM formation. Engerholm and Amy (1983) developed a model that simulates the rate of chloroform formation from pH, temperature, TOC and the ratio between chlorine dose and TOC. The following equation was developed from chlorination of a peat soil humic acid extract: CHCl} = kjk/TOC) 0.95 [TOC I \0.28 (A-l) where CHC13 is the chloroform concentration in /ig/L, TOC is the total organic carbon concentration in mg/L, Cl2 is the chlorine dose in mg/L and t is the reaction time in hours. The value of k, is pH dependent while the values of z and k2 are temperature dependent. Amy, Chadik and Chowdhury (1987) studied chlorination of 13 natural waters from various locations throughout the United States. These chlorination studies produced the following equation: THM = 0.00309[(TOC)(UV-254)J0U°(Cl2)04O9(t)0265(T)106(pH -2.6)071$(Br +1)0036 (A-2) where THM is the TTHM concentration in /imole/L, TOC is the total organic carbon concentration in mg/L, UV-254 is the absorbance of ultraviolet light at a 254 nm wavelength (in cm"1), Cl2 is the chlorine dose in mg/L, t is the reaction time in hours, T is the temperature in degrees Centigrade and Br is the bromide concentration in mg/L. Raw A-2 RECYCLED PAPER ------- water samples were filtered through a 0.45 /*m filter. Therefore, the TOC used in the model actually could be considered DOC by operational definition. A third model, developed by Christ and Dietz (1988), was based on chlorination studies involving two natural waters from Florida. The following equation was generated: THM = 0.06Sf7TOQft9JO(>^,^(7)0652fC//^Br)aMV)a2^ (A"3) where THM is the TTHM concentration in /ig/L, TOC is the total organic carbon concentration in mg/L, T is the temperature in degrees Centigrade, Cl2 is the chlorine dose in mg/L, Br is the bromide concentration in mg/L and t is the reaction time in hours. Table A-l shows a comparison of test conditions for each of the three equations shown above. This comparison demonstrates that Equation A-2 was based upon the most thorough database incorporating the largest number of observations and widest range of parameter values. A fourth model has been developed for the USEPA that is based on a mechanistic framework rather than an empirical framework (Batchelor, 1989). This model simulates the formation and, where applicable, decay of four THMs, five non-THM DBPs and total organic halogen (TOX). Simulations are based on TOC, pH, time, bromide and chlorine dose. Temperature was not considered due to lack of data. Parameter values in the model were based on results observed for water from the Ohio River at Cincinnati, Ohio. Additional tests conducted on water from the Edisto River in South Carolina showed that the parameter values developed for Ohio River water were not applicable to Edisto River water. Tests should be conducted to develop parameter values from a group of waters so that the model can be used to describe the general case. Temperature should be a variable in these tests. Equation A-2 was selected as the basis for THM formation in the WTP program. A22 Refinement of Equation A-2 One limitation associated with Equation A-2 is its simulation of TTHM formation in /imoles/L. The molecular weights of the four THM species vary significantly (119 for CHClj to 252 for CHBr3) and, therefore, conversion from /xmoles/L to /ig/L is difficult. In order to convert the TTHM concentrations calculated by Equation A-2 from /tmole/L to A - 3 RECYCLED PAPER ------- TABLE A-1 COMPARISON OF TRIHALOMETHANE FORMATION MODELS Authors Published in Number of Natural Waters Tested Number of Data Points r* Range of Values Tested TOC (mg/L) UV-254 (1/cm) Chlorine Dose (mg/L) Bromide (mg/L) PH Temperature CO Time (hr) Engerholm and Amy 1983 01 648 0.99 2.5 - 10.0 N.U. 3 5-60 N.U. 5.5 - 8.5 10-35 1 - 96 Amy, Chadik and Chowdhury 1987 13 1090 0.90 3.0- 13.8 0.063 - 0.489 1.5-69 0.01 - 1.245 4.6 - 9.8 10-30 0.1 - 168 Christ and Dietz 1988 2 180 N.R. * CNJ 1 CO CO N.U. 3 8-25 0.087 - 0.500 7-9 15-30 2-144 Notes: 1 Humic acid extracted from a peat soil was used during this effort. 2 N.R. - Not Reported taba-i.wki 3 N.U. « Not Used in this study. 06/07/92 ------- /tg/L, the database used to develop Equation A-2 was used to develop a model which describes the average molecular weight of the THM species formed. Using a stepwise variable selection procedure, the following equation was developed with multiple linear regression: AMW = 105.3(Br +lfM{UV-254)-<>™ (A_4> where AMW is the average molecular weight of the trihalomethane species formed. The results calculated by Equations A-2 and A-4 can be multiplied together to calculate TTHM formation in ng/l*. A23 Development of Models for Formation of THM Species The database used to develop Equation A-2 was used to formulate new empirical equations for individual THM species. The relationship between the various THM species and the controlling variables such as pH, temperature, chlorine dosage, TOC and bromide exhibit nonlinear behavior. An appropriate empirical relationship for such a function can be of the following form: Y = AfXjNX/fXJ (A-5) where A, a, b and c are empirical constants; X„ X2 and X3 are independent variables and Y is the dependent variable. This relationship can be linearized by taking logarithms of both sides of the above equation. The resulting equation therefore becomes: ln(y) = ln(/4) + aln(Xj) + b\n(XJ + cln(Xj) (A-6) Multiple regression analysis can be performed to correlate ln(Y) with a linear combination of the independent variables. This analysis determines the intercept, ln(A), and the slopes for the independent variables (a, b and c). These constants can then be used to describe the equation shown in Equation A-5. a - 4 RECYCLED PAPER ------- As a first step of the modeling effort, dependent and independent variables were defined as shown in Table A-2. The experiments showed that THM formation from the chlorination of NOM virtually stops at pH 2.6 (Amy, et al.. 1987). Therefore, 2.6 was subtracted from pH so that THM formation is not calculated at a pH value of 2.6. Similarly, the variable Br + 1 was used to ensure no formation of brominated THM species when bromide is not present in the water (the natural log of 1.0 is 0). The definition of X« could be improved by adding stoichiometric coefficients to Fe, Mn, and NH3. This was not determined, however, until after the modeling effort and this improvement has not yet been tested. The development of an appropriate regression equation consisted of selecting the most significant variable forms from the list above and then performing a stepwise multiple linear regression analysis using the selected variable forms. The selection of the appropriate variable forms was done by developing a Pearson's Correlation matrix (Table A-3) for all of the above variable forms on the entire data base. A Pearson's Correlation matrix shows the correlation coefficients among all the variables in the matrix. The importance of a particular variable in the regression equation is shown by the correlation coefficient, considering the variable as a single predictor. A high correlation between two independent variables indicates that if one is selected, the addition of the other will not improve the significance of the regression. In a stepwise regression analysis, the most significant independent variable describing the dependent variable is taken into consideration first. Variables are added one at a time according to the highest remaining correlation coefficient after the previously selected variable is removed. Addition of independent variables increases the overall correlation coefficient, but as the degrees of freedom decrease (as a result of increasing the number of variables), the significance of the regression equation (portrayed by the F value) decreases. In selecting independent variable forms for describing chloroform (Y,) formation, only one occurrence of each of the controlling variables was desired. Table A-2 shows that the correlation coefficient between Yl and X3 is higher (r = 0.603) than that between Y, and either X, (r = 0.557), X2 (r = 0.589) or X4 (r = 0.303). Therefore, X3 was selected as the form of the independent variable incorporating TOC and UV-254. Because X3 includes both UV-254 and TOC, no other variables were desired for describing these precursors. Similarly, X5 was selected instead of to represent chlorine dose. A -5 RECYCLED PAPER ------- TABLE A-2 VARIABLES DEFINED IN DEVELOPMENT OF TRIHALOMETHANE SPECIES Dependent Variables , I CHC1, ^ Yl ~ H 119 .38 j Y2 = ln| r CHBrCl2 k 163.83 • ln( CHBr2Cl> 208.28, r. - in( CHBr> | 4 \ 252.73 J where CHCI3 is the chloroform concentration in pg/L, CHBrCI2 is the dichlorobromomethane concentration in vg/L, CHBr2CI is the chlorodibromomethane concentration in jjg/L and CHBr3 is the bromoform concentration in fjg/L The constants used in the above equations are the molecular weights of the respective species. Independent Variables X1 = In (TOC) X. = In {UV-254) X3 = In [ (UV-254) (TOC)] *4 = = m/ uv~254\ \ TOC j X5 = In(Cl2 Dose) Xn = In (t) X9 = In ipH-2.6) X6 = In (Cl2 Dose-Fe-Mn-NH3) xR ? ln(r) Jf10 = ln(Br + l) Xxl = In (Br) *12 = = In/———\ \ UV-254} *13 - -ln(. Br TOC where TOC is the total organic carbon concentration in mg/L, UV-254 is the UV absorbance at 254 nm in cm'1, Cl2 Dose is the free chlorine dose in mg/L, Fe is the iron concentration in mg/L, Mn is the manganese concentration in mg/L, NH3 is the ammonia concentration in mg/L, t is the reaction time in hours, T is the temperature in degrees centigrade, pH is the pH of reaction and Br is the bromide ion concentration in mg/L. TABA-2.WP 06/07/92 ------- TABLE A-3 PEARSON'S CORRELATION MATRIX FOR THM EQUATION VARIABLES Variable Variable Name Definition X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X1 Ln(TOC) 1.000 0.818 0.938 0.133 0.556 0.549 -0.006 -0.013 0.009 0.091 0.089 -0.260 -0.248 X2 Ln(UV254) 0.818 1.000 0.967 0.679 0.486 0.469 -0.009 -0.003 -0.238 0.041 -0.033 -0.445 -0.305 X3 Ln(UV254*TOC) 0.938 0.967 1.000 0.467 0.541 0.527 -0.008 -0.007 -0.139 0.066 0.020 -0.384 -0.294 X4 Ln(UV254/TOC) 0.133 0.679 0.467 1.000 0.128 0.107 -0.007 0.011 -0.421 -0.046 -0.170 -0.434 -0.207 X5 Ln(CI2 dose) 0.556 0.486 0.541 0.128 1.000 0.997 0.115 0.266 0.191 0.278 0.265 0.035 0.072 X6 Ln(CI2 Dose - Fe - Mn - NH3) 0.549 0.469 0.527 0.107 0.997 1.000 0.117 0.270 0.195 0.271 0.249 0.028 0.059 X7 Ln(Reaction Time) -0.006 -0.009 -0.008 -0.007 0.115 0.117 1.000 0.036 0.043 0.041 0.035 0.035 0.037 X8 Ln(temperature) -0.013 -0.003 -0.007 0.011 0.266 0.270 0.036 1.000 0.563 0.248 0.207 0.186 0.205 X9 Ln(pH - 2.6) 0.009 -0.238 -0.139 -0.421 0.191 0.195 0.043 0.563 1.000 0.255 0.294 0.362 0.283 X10 Ln(Br + 1.0) 0.091 0.041 0.066 -0.046 0.278 0.271 0.041 0.248 0.255 1.000 0.876 0.768 0.822 X11 Ln(Br) 0.089 -0.033 0.020 -0.170 0.265 0.249 0.035 0.207 0.294 0.876 1.000 0.910 0.943 X12 Ln(Br/UV254) -0.260 -0.445 -0.384 -0.434 0.035 0.028 0.035 0.186 0.362 0.768 0.910 1.000 0.972 X13 Ln(Br/TOC) -0.248 -0.305 -0.294 -0.207 0.072 0.059 0.037 0.205 0.283 0.822 0.943 0.972 1.000 Y1 Ln(CHCI3/119.38) 0.557 0.589 0.603 0.303 0.607 0.605 0.547 0.322 0.113 -0.101 -0.118 -0.351 0.301 Y2 Ln(CHCI2Br/163.8) 0.261 0.159 0.212 -0.059 0.527 0.513 0.494 0.377 0.379 0.611 0.715 0.575 0.608 Y3 Ln(CHCIBr2/208.3) -0.027 -0.166 -0.112 -0.252 0.250 0.233 0.222 0.260 0.400 0.769 0.921 0.894 0.905 Y4 Ln(CHBr3/252.7) -0.131 -0.212 -0.186 -0.198 0.121 0.108 0.118 0.196 0.374 0.850 0.823 0.825 0.844 TABA-3.WK1 06/07/92 ------- Variables X10 to X13 define the variable forms for bromine incorporation. However, the selection of X3, which contains UV-254 and TOC, makes it less desirable to select X12 or X13 since these forms contain UV-254 or TOC. Selection X12 or X13 would result in a multi-colinearity problem with X3. Y, had a slightly higher correlation coefficient for X„ (r = -0.118) than X10 (r = -0.101), however, the choice of variable X10 ensures that the formation of chloroform will continue in the absence of bromide. A similar approach was followed to select independent variables for the other THM species. The elimination of independent variables which are highly correlated with each other is important in avoiding multi-colinearity. This problem is minimized by using the correlation matrix. In this effort, however, the elimination of all such variables was not possible. For example, X5 is highly correlated with Xj (r = 0.56) because the chlorine dose used in the development of the database was based on the TOC concentration. Both of these variables are of prime importance in THM formation and, therefore, were considered in the equation in spite of the high correlation between them. The stepwise multiple linear regression analysis described above was performed on the data base, resulting in the following equations: CHCl3 = 0.21S[(UV-254)(TOC)]0616(Cl2 Dose)039l(t)0:l65(T)l ls(j)H-2.6)0SO0(Br *l)""3 (A-7) [r = 0.941, adj r2 = 0.884, SEE = 0.313, F = 713 (a < 0.001), n = 561] CHBrCl2 = 0.863[(l/K-254)(rOC)]0177(C/2 Dose)°™(t)°*\T)0™(pH-2.6f ™(Br)om (A-8) [r = 0.945, adj r2 = 0.892, SEE = 0.360, F = 768 (a < 0.001), n = 561] CHBr2Cl = 2.57|-^^j^,M(C/2 Dose^^itf^iTf^tpH-2.6)l3\Br)^ (A-9) [r = 0.962, adj r2 = 0.926, SEE = 0.657, F = 1160 (a < 0.001), n = 561] CHBr3 = 61 A(UV-254)om(Cl2 Dose)^"\t)on\T)*^(pH-2.6)lM^^n (A-10) [r = 0.810, adj r2 = 0.652, SEE = 1.454, F = 176 (a < 0.001), n = 561] A -6 RECYCLED PAPER ------- The number of data points (n), the correlation coefficient (r), the adjusted coefficient of determination (adj r2), the standard error of estimate (SEE) and the F-statistic at significance level of a are shown beneath each equation. An equation developed by regression analysis has strong descriptive capabilities if it shows a high correlation coefficient. The F values for Equations A-7 through A-10 are significant at the 99.9 percent confidence level as shown by a less than 0.001. The correlation coefficients, however, are highest for chloroform and gradually decrease with increasing bromine incorporation. This indicates a better quality of fit for the chloroform equation and relatively worse qualities of fit for the brominated species. The use of Equations A-7 through A-10 is limited in the following respects: ¦ the equations were based on THM formation in untreated waters and the applicability of these equations to treated waters is unknown; ¦ the database used for developing the equations had waters primarily low in bromide, so the use of these equations for high bromide waters may be limited; ¦ the equations were based on data collected from thirteen waters (Amy, et. al., 1987) and, therefore, represent the best generalized method of simulating THM formation. While a generalized approach is needed for the purposes of this document, THM formation rates are site-specific due to variation in NOM characteristics. Users of these equations are, therefore, encouraged to develop site-specific parameter values instead of using the general parameter values shown; and ¦ bromine incorporation during THM formation is a function of chlorine dosage. Excess chlorination does not result in bromine incorporation as high as would be realized under more moderate chlorination schemes. The database used for developing the equations contained experiments with higher chlorine dosages than would normally be expected in water treatment. Therefore, the equations are expected to underpredict bromine incorporation. The same limitations apply to the use of Equations A-2 and A-4. \2A Calculation of Trihalomethane Formation Through Multiple Treatment Processes The calculation of trihalomethane formation in water treatment plants would be relatively simple if water quality conditions were constant from the point of chlorination to the end of the distribution system. This calculation would involve the use of Equations A-2 A-7 RECYCLED PAPER ------- and A-4 for total trihalomethanes and the use of Equations A-7 through A-10 for the individual trihalomethane species. However, water quality conditions are not normally constant from the point of chlorine application to the end of the distribution system. An example of this is shown on Figure A-l, where chlorine is applied at time to prior to Basin 1. The removal of NOM at time t, produces TOC and UV-254 levels in Basin 2 that are less than those in Basin 1. The THM formation in these basins is calculated as follows: ¦ TOC„ UV-254, and t, are input into Equations A-2, A-4 and A-7 through A-10 to calculate the THM concentrations at t,. ¦ TOQ, UV-2542 and t, are input into Equations A-2, A-4 and A-7 through A-10. The results are subtracted from the results obtained when TOQ, UV-2542 and tj are input into Equations A-2, A-4 and A-7 through A-10. These subtractions produce an estimate of THM formation in Basin 2. ¦ The estimate of THM formation in Basin 2 is added to the estimate of THM formation in Basin 1. A graphical representation of this procedure is shown in Figure A-l. Similar procedures are followed for changes in other variables such as pH. The calculation of THM formation in systems employing two points of chlorination is also an involved process. An example of this calculation is shown on Figure A-2. The THM formation in these basins is calculated as follows: ¦ Equations A-2, A-4 and A-7 through A-10 are used to calculate the THM concentrations at t,. ¦ After calculating THM concentrations at t„ the cumulative time was reset to zero and Equations A-2, A-4 and A-7 through A-10 are used to calculate THM formation in Basin 2. The cumulative time was not reset to zero in the example shown on Figure A-l. ¦ The estimate of THM formation in Basin 2 is added to the estimate of THM formation in Basin 1. A graphical representation of this procedure is shown in Figure A-2. This calculation method is necessary because THM formation is based only on the chlorine dose and not on the chlorine residual. This is another limitation of the THM formation models. a - 8 RECYCLED PAPER ------- FIGURE A-1 CALCULATION OF TRIHALOMETHANE FORMATION Chlorine Basin 1 1 Basin 2 1 TOC(2) < TOC(1) UV-254(2) < UV-254(1) PH(2) = PH(1) Br(2) = Br(1) T (2) = T(1) *2 C o ca *-> c 0 O c o O CD C (0 o E o (0 Time ------- FIGURE A-2 CALCULATION OF TRIHALOMETHANE FORMATION FOR TWO POINTS OF CHLORINATION Chlorine Chlorine Basin 1 1 ^ TOC(2) UV-254(2) PH(2) Br(2) T(2) TOC(1) UV-254(1) PH(1) Br(1) T(1) c 0 1 +-> c Q) O C o O 0) c cfl a> E o cd sz Time ------- A25 Trihalomethane Formation in the Presence of Chloramines The formation of trihalomethanes is possible in the presence of a chloramine residual and has been observed (Hubbs and Holdren, 1986). This formation is possible for two reasons: ¦ The reaction between free chlorine and ammonia to produce monochloramine is a reversible reaction (Morris and Isaac, 1985). Therefore, free chlorine and monochloramine can coexist in an equilibrium state. The ability of free chlorine present in such a system to form trihalomethanes depends on the rate of the forward reaction relative to the rate of the reverse reaction. ¦ Mixing conditions in the treatment plant are not sufficient to bring free chlorine and ammonia into contact with each other instantaneously. The presence of free chlorine for this limited period of time may result in trihalomethane formation. Studies have not been conducted to evaluate the conditions under which trihalomethane formation is observed during chloramination. Based on the nature of the reversible reaction between free chlorine and ammonia, however, it can be speculated that free chlorine is present in higher concentrations when the chlorine to ammonia ratio is increased. Also, because the rate of the reaction between free chlorine and ammonia is optimal at pH 8.2, it can be speculated that free chlorine is present at higher concentrations when pH conditions deviate from pH 8.2. Even with this speculation, there are insufficient data available to develop a model of trihalomethane formation during chloramination. Nevertheless, because trihalomethane formation is observed during chloramination, the model must account for it. A review of the data produced by Hubbs and Holdren (1986) indicates that trihalomethane formation observed during chloramination was approximately 10 percent of the formation observed during free chlorination. Bull and Kopfler (1991) used an estimate developed by Amy, et al. (1990) that trihalomethane formation in chloraminated waters "would approximate 20 percent of that observed if the same waters were chlorinated." Based on this review, the model assumes that the rate of trihalomethane formation during chloramination is 20 percent of the rate of trihalomethane formation during chlorination. a -9 RECYCLED PAPER ------- A3 EQUATIONS FOR HAA FORMATION A3.1 Equations Based on Utility Survey A nationwide survey was conducted to evaluate DBP concentrations in treatment plant effluents (JMM and Metropolitan, 1989). During this survey, finished water samples were collected from 35 utilities nationwide and analyzed for THMs and other DBPs. Four samples were collected from each of the participating utilities representing four seasons. These data were statistically analyzed at the Illinois Institute of Technology to develop correlations between THMs and other DBPs. The general relationship between HAA and THM concentrations took the following form (Haas, 1990): f(DBP) = A + B(Ln CHCL,) + C(Ln CHCl2Br) + D(Ln CHClBr2) + E(TOC) + F(pH) + G(CHBr3 Class) (A-ll) Where: A, B, C, D, and E are coefficients having the general format: |C,| Coefficient = (Constant) + |XiX2x3| | Cj | |C,| Where: | x, x2 x31 = A vector of descriptor variables used to classify the disinfection category; and IQ| | Cj | = A vector of constants associated with each independent variable. 10,1 In this vector of coefficients, the first row (C,) is multiplied by the value of x„ the second row (Cj) is multiplied by the value of x2 and the third row (Q) is multiplied by the value of x3. The sum of these three products would be the numerical value of the coefficient. In other words, whenever a column vector of coefficients appears in one of the regression equations, it is multiplied by the row vector | x, x2 x3 | to determine the numerical value of the coefficient. The values for x„ x2 and x3 are determined from the following table and are a function of the disinfection practices of a given treatment plant: a -10 RECYCLED PAPER ------- Descriptor Variables by Disinfection Type Disinfection Group x3 Free Chlorine 1 0 0 Post-Ammoniation 0 1 0 Concurrent 0 0 1 Other -1 -1 -1 Because many of the reported CHBr3 concentrations were below detection limit, the . regression analysis used a variable called "Br3 Class". This variable was based on coding into classes spaced on a logarithmic scale. In this manner, the large number of bromoform observations below detection limits could be included in the regression without biasing the results. The values of Br3 Class are listed in Table A-4. As shown in the table, all observations having a bromoform concentration less than 0.101 /ig/L (the detection-limit used for the survey samples) were assigned a value of 1 for Br3 Class. All observations having a bromoform concentration greater than 0.101 ng/L but less than 0.126 ^g/L were assigned a value of 2 for Br3 Class, and so on. The class boundaries increased by a factor of 1.25 until all observations were included, with the highest value of Br3 Class corresponding to the highest observed bromoform concentration (81.6 ng/L) in the 35- Utility DBP Study data base. The constants developed during this regression analysis are listed in Table A-5 for the various HAA species. The example below illustrates the use of this model to predict the concentration of DCAA for a utility in which the only disinfectant employed was free chlorine. Given: [CHC1J = 16.69 fig/L [CHBrJ = 7.21 /ig/L (Br3 Class = 21) [TOC] = 4.01 mg/L pH = 7.63 Further: For free chlorine, x, = 1, x2 = 0, and x3 =0. 10.668651 (DCAA)"3 = 1.33006 + |100| j 0.64799 [ + 0.32686 Ln(CHCl3) - 0.00887 (Br3 Class) 11.248041 10.171661 - 10.147851 + TOC 11001 j 0.01770 j + pH |100| j-0.081431 (A-12) |-0.01272| - j 0.156251 a- n RECYCLED PAPER ------- TABLE A-4 VALUES OF THE INDEPENDENT VARIABLE "Br, CLASS" Upper Class Value of Number of Boundary for Variable Observations CHBr3 (fjg/L) "Br3 Class" in Class 0.101 1 46 0.126 2 4 0.158 3 2 0.197 4 2 0.247 5 4 0.308 6 1 0.385 7 3 0.482 8 3 0.602 9 7 0.753 10 5 0.941 11 5 1.176 12 9 1.470 13 6 1.837 14 3 2.296 15 4 2.871 16 3 3.588 17 1 4.485 18 1 5.607 19 1 7.008 20 4 8.760 21 3 10.950 22 1 13.688 23 0 17.110 24 5 21.388 25 2 26.734 26 3 33.418 27 3 41.773 28 5 52.216 29 0 65.270 30 3 81.587 31 1 TBLA-4.ABG 06/07/92 ------- TABLE A-5 CONSTANTS FOR DBP REGRESSION ANALYSIS DCAAA(1/3) ln(TCAA) In(MBAA) In(DBAA) In(MCAA) CONSTANT C1 C2 C3 1.33006 0.66865 0.64799 1.24804 2.47537 -2.05187 -1.27803 -0.63410 LCHC13 L BrC12 L Br2C1 TOC PH Br3 Class LC13*T0C Br3Class*T0C LBr2C1*pH 0.32686 -0.00887 1.07748 -0.42038 0.08289 0.07401 0.13276 0.20800 C1 LC13 C2 LC13 C3 LC13 -0.10960 -0.32806 0.29479 C1 LBrC12 C2 LBrC12 C3 LBrC12 0.29841 0.31497 -0.88464 0.13349 0.44408 0.10853 C1 LBr2C1 C2 LBr2C1 C3 LBr2Cl -0.24591 -0.22422 0.53450 C1 TOC C2T0C C3 TOC 0.17166 0.01770 -0.01272 0.06587 0.00956 -0.18197 C1 pH C2 pH C3 pH -0.14785 -0.08143 -0.15625 -0.00733 0.10439 -0.07219 C1 Br3Class C2 Br3Class C3 Br3Class -0.02820 -0.05424 -0.00925 YBLA-S.ABg 06/07/92 ------- Simplification of the above equation results in: (DCAA)1/3 = 1.33006 + 0.66865 + (0.32686) (LnCHCl3) + (-0.00887)(Br, Class) + (0.17166)(TOC) + (-0.14785)(pH), and DCAA = 12.1 ng/L (predicted) A3 2 Equations Based on Laboratory Studies The American Water Works Association (A WW A) Technical Advisory Workgroup (TAW) for D/DBP sponsored a laboratory study to examine the formation of THMs and HAAs as a result of chlorination. Data used for developing HAA predictive equations were derived from two experimental studies as described below: ¦ In the D/DBP TAW database, kinetic experiments were conducted on raw water samples from eight (8) utilities across the country. Each water sample was subjected to a single dose of chlorine and a single incubation temperature of 20°C. The chlorine dose was selected to maintain a free residual of 0.2 to 0.5 mg/L at the end of the 96-hour reaction time. Samples were collected at ten different reaction times over the 96-hour period and analyzed for HAAs. A series of additional experiments were conducted on two of the eight water samples to briefly investigate the impact of pH, chlorine dose, and temperature on the reaction kinetics. The entire database contains 172 observations on HAA formation. ¦ In the D/DBP TAW coagulation database, raw water samples from eighteen utilities were coagulated at different coagulant doses and at different coagulation pH values. The treated water was filtered through a 8.0 um filter paper. The filtered water sample were pH adjusted to 8.0, and was chlorinated at a 1:1 chlorine to TOC ratio, and were incubated at 20°C for sixteen hours. Formation of HAAs was measured at the end of the 16-hour reaction time. This database contained 71 observations on HAA formation. These laboratory observations on HAA formation were used to develop regression equations correlating various HAA species with the water quality parameters. The equations for MCAA, DCAA, TCAA, MBAA, and DBAA are shown in Tables A-6 to A-10. These tables also include the boundary conditions relevant to each of the equations (JMM, 1992). A .3 3 Calculation of HAA Formation Calculation of HAA formation in the model utilizes one of the two sets of empirical equations outlined above. The user is given a choice to select which set of equations to use. A - 12 RECYCLED PAPER ------- TABLE A-6 MCAA EQUATION TIME > 12 hours: MCAA = 1.634 • (TOC)0753 • (BR+0.01 J"0 085 • (PH)"1124 • (CL2DOSE) • (TIME)0,300 BOUNDARY CONDITIONS STANDARD PARAMETER MIN MAX MEAN DEVIATION CL2DOSE/Br 9.8 819.9 196.4 211.3 CL2DOSE/TOC 1.0 2.3 1.5 0.4 TOC (mg/L) 2.8 11.0 4.3 1.8 UV-254 (cm1) 0.050 0.382 0.127 0.073 BR (mg/L) 0.01 0.43 0.14 0.14 PH 5.6 9.0 7.5 0.8 CL2DOSE (mg/L) 3.0 25.3 6.9 4.9 TIME (hr) 15.8 105.0 55.0 27.5 TEMP (°C) 13.0 20.0 19.4 1.9 MCAA fcyg/L) 1.2 22.0 6.0 4.8 TBLA-S.ABG 06/07/92 ------- TABLE A-7 DCAA EQUATION DCAA = 0.605 • (TOC)0-291 • (UV-254)0 726 • (BR+0.01 J"0'568 • (CL2DOSE)0 • (TIME)0-239 • (TEMP)0665 BOUNDARY CONDITIONS STANDARD PARAMETER MIN MAX MEAN DEVIATION CL2DOSE/Br 9.8 819.9 202.3 214.3 CL2DOSE/TOC 1.0 2.3 1.6 0.4 TOC (mg/L) 2.8 11.0 4.4 1.9 UV-254 (cm1) 0.050 0.382 0.131 0.077 BR (mg/L) 0.01 0.43 0.13 0.14 PH 5.6 9.0 7.5 0.8 CL2DOSE (mg/L) 3.04 25.30 7.3 5.2 TIME (hr) 0.1 105.0 27.4 32.6 TEMP (°C) 13.0 20.0 19.5 1.8 DCAA (pq/L) 1.9 251.0 36.6 38.4 TBLA-7.ABG 06/07/92 ------- TABLE A-8 TCAA EQUATION TCAA = 87.182 • (TOC)0 355 • (UV-254)0901 • (Bl R + 0.01)-0-679 • (PH) • (CL2DOSE) °'881 • (TIME)0"2* BOUNDARY CONDITIONS STANDARD PARAMETER MIN MAX MEAN DEVIATION CL2DOSE/Br 9.8 819.9 202.3 214.3 CL2DOSE/TOC 1.0 2.3 1.6 0.4 TOC (mg/L) 2.8 11.0 4.4 1.9 UV-254 (cm1) 0.050 0.382 0.131 0.077 BR (mg/L) 0.01 0.43 0.13 0.14 PH 5.6 9.0 7.5 0.8 CL2DOSE (mg/L) 3.04 25.30 7.3 5.2 TIME (hr) 0.1 105.0 27.4 32.6 TEMP (°C) 13.0 20.0 19.5 1.8 TCAA (j/g/L) 1.7 485.0 72.6 90.1 fBU-fi.ABff 06/07/92 ------- TABLE A-9 MBAA EQUATION MBAA = 0.176 • fTOC)1,664 • (UV-254)"0'624 • (BR)0'795 • (PH) • (TIME)0145 • (TEMP)0450 BOUNDARY CONDITIONS STANDARD PARAMETER MIN MAX MEAN DEVIATION CL2DOSE/Br 9.8 192.0 39.0 50.4 CL2DOSE/TOC 1.0 2.0 1.4 0.4 TOC (mg/L) 3.0 5.9 3.8 0.9 UV-254 (cm'1) 0.050 0.110 0.077 0.018 BR (mg/L) 0.05 0.43 0.24 0.13 PH 7.0 9.0 8.1 0.5 CL2DOSE (mg/L) 3.0 10.3 5.4 2.5 TIME (hr) 0.1 103.5 31.3 33.6 TEMP (°C) 13.0 20.0 19.4 2.0 MBAA (j/g/L) 0.5 4.6 1.8 0.9 T6U-8.AB5 06/07/92 ------- TABLE A-10 DBAA EQUATION DBAA = 84.940 • (TOC)-0620 • (UV-254)0651 • (BR)1073 • (CL2DOSE)-0-2®0 • (TIME)0120 • (TEMP)0 6®7 BOUNDARY CONDITIONS PARAMETER MIN MAX MEAN STANDARD DEVIATION CL2DOSE/Br 9.8 280.0 41.1 59.9 CL2DOSE/TOC 1.0 2.0 1.4 0.4 TOC (mg/L) 3.0 5.9 3.7 0.9 UV-254 (crrT1) 0.050 0.170 0.080 0.025 BR (mg/L) 0.02 0.43 0.24 0.13 PH 5.6 9.0 8.1 0.6 CL2DOSE (mg/L) 3.0 10.3 5.2 2.4 TIME (hr) 0.1 103.5 27.6 32.8 TEMP (°C) 13.0 20.0 19.4 2.0 DBAA (jjg/L) 0.6 31.0 11.0 7.9 TBLA-IO.ABG 06/07/92 ------- The application of the equations presented in Section A.3.2 to the treatment plant parameters for calculation of HAA concentrations follow the same techniques described in Section A.2.4 for THMs. The application of the equations presented in Section A.3.2 to the treatment plant parameters is based on statistical correlations and illustrated in Section A.3.2. A.4 EQUATIONS FOR REMOVAL OF NATURAL ORGANIC MATTER A.4.1 Alum Coagulation, Flocculation, Clarification and Filtration Many investigators have evaluated the impacts of alum dose and coagulation pH on the removal of natural organic matter (NOM) at a bench-scale level (Kavanaugh, 1978; Young and Singer, 1979; Semmens and Field, 1980; Chadik and Amy, 1983; Knocke, et al.. 1986; Hubel and Edzwald, 1987). In general, these studies have found that the removal of NOM can be optimized by maintaining certain pH ranges during coagulation, flocculation and clarification. Despite extensive bench-scale testing, a complete field-scale assessment of pH and coagulant dose impacts has not been published to-date. Therefore, an effort was made to develop a model that described the removal of TOC and UV-254 in alum coagulation, flocculation, clarification and filtration plants. TOC and UV-254 were selected to quantify the NOM so that compatibility with the THM formation model was assured. Removal of NOM by alum coagulation was based on full-scale process data collected in three studies (Montgomery and Metropolitan, 1989; Singer, 1988; and Edzwald, 1984). The complete field-scale database includes 45 data points obtained from the 17 treatment plants shown on Figure A-3. Characteristics of the database are shown in Table A-ll and indicate that the database covers a wide range of raw water qualities and treatment conditions. Median TOC and UV-254 removals in this database were 26 and 68 percent, respectively. A multiple regression analysis was employed to analyze the influence of raw water TOC (TOCq), alum dose, coagulation pH (pHc), temperature and chloride on finished water TOC (TOCr) in these 17 treatment plants. Because alum dose, TOC and chloride data were lognormally distributed, the natural logarithm of these variables was used for this analysis. Neither temperature nor chloride had a statistically significant impact on TOCf in this data A - 13 RECYCLED PAPER ------- flgursU ALUM COAGULATION TREATMENT PLANT LOCATIONS . "\ > (. ¦' \ / Contra Costa County. CA «Sacramento, CA San Francisco, CA#El ^ CA .AuroraiCO Santa Clara Valley,'CA \ r' ' j ; MWD - Mills WTP, CA«mwD - Weymouth W?P, CA ~Canton, NY t. a Oneida, NY i ^Norwich, CT • Ha c ken sack, NJ •vj 7 •Newport News, VA I • Little Rock, AR ~' """ *•«., .* 1 < Big Spring, TX® •Arlington, TX i Monroe, NC REFERENCES ~ Edzwald, 1984 ¦ Singer, 1988 • JMM and MWD, 1989 O c 31 m > ------- TABLE A-11 CHARACTERISTICS OF FIELD-SCALE ALUM COAGULATION DATABASE Distribution Range Median Raw Water Quality TOC (mg/L) lognormal 1.11 -12.1 2.78 UV-254 (cm1) lognormal 0.019-0.84 0.111 PH normal 6.3 - 9.0 7.7 Alkalinity (mg/L as CaC03) lognormal 5.8 - 149 52 Hardness (mg/L as CaCOa) lognormal 10-550 93 Temperature (°C) normal 2-31 18 Turbidity (NTU) lognormal 0.4 - 26 2.4 Chloride (mg/L) lognormal 1 -640 20 Finished Water Quality TOC (mg/L) UV-254 (crrr1) Turbidity (NTU) lognormal lognormal lognormal 0.78 - 6.3 0.002 - 0.095 0.04 - 0.61 1.98 0.037 0.16 Coagulation pH normal 5.5 - 8.0 7.15 Alum Dose lognormal 1.5-55 11 0313\549\tbl«\taba-11. wp 06/07/92 ------- base. The apparent lack of significance between temperature and TOC removal is in agreement with the results of bench-scale studies performed by Knocke, et al. (1986). The regression analysis was conducted a second time without temperature and chloride. Because both the intercept and slope of the relationship between ln(alum dose) and ln(TOCf) are likely to depend on ln(TOQ,) and pHj, several interactive variables were also included in the analysis (Kleinbaum and Kupper, 1978). Without the interactive variables, the resulting model would have indicated that the slope of the relationship between ln(alum dose) and ln(TOCf) was independent of pHc and ln(TOCo). The variables examined in this analysis were those shown in Table A-12. A stepwise variable selection procedure was used to select the most statistically significant of the independent variables and include them in an equation describing the performance of the 17 treatment plants in the data base. The resulting equation and its associated statistics are as follows: In(TOC) = -0.16 + 1.16[ln(7t>C0)] -0.45[ln(a/«w dose)] /A -0.070[ln(rOC0)]tln(aiMm dose)] 1 ' +0.057(pHc)\\n(alum dose)] [r2 = 0.968, adj r2 = 0.965, SEE = 0.087, F = 297 (a < 0.001), n = 44] An analysis was performed to determine the differences between finished water TOC levels predicted by Equation A-13 and those observed in the 17 treatment plants. Figure A-4 shows that 60 and 90 percent of the predicted finished water TOC levels were within 0.2 and 0.4 mg/L, respectively, of the observed concentration. In addition, 90 percent of the predicted values differed from observed values by less than 12 percent. A sensitivity analysis of Equation A-13 is shown in Figures A-5 and A-6. These figures indicate the following: ¦ TOC removal increases with increasing alum dose, however, the magnitude of the increase becomes smaller as alum dose increases. This is consistent with observations described in the literature. ¦ TOC removal increases with decreasing coagulation pH down to pH 5.5. This is also in agreement with the literature. The range of coagulation pH levels in the database, however, did not allow for the determination of an optimal coagulation pH. A -14 RECYCLED PAPER ------- TABLE A-12 VARIABLES DEFINED IN DEVELOPMENT OF NOM REMOVAL BY ALUM COAGULATION Dependent Variable: Independent Variables: ln(TOC,) ln(TOC0) In (alum dose) pHe ln(TOCo) * ln(alum dose) ln(TOCo) * pHe In (alum dose) * pHe ln(TOC0) * ln(alum dose) pHe Notes: TOCf = Filtered water total organic carbon in mg/L TOC0 = Raw water total organic carbon in mg/L alum dose = alum dose in mg/L as AI2(S04)3*14 H20 pHc = pH of coagulation 0313\549\tbls\taba-12.wp 06/07/92 ------- ftgunM RESIDUAL PROBABILITY PLOT 99.9 99 111 O cc hi CL ID o 95 80 LU > 50 20 0.1 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 (OBSERVED EFFLUENT TOC) - (PREDICTED EFFLUENT TOC) > ------- figuraOB IMPACT OF COAGULATION pH ON TOC REMOVAL BY ALUM COAGULATION/FILTRATION ALUM DOSE (mg/l) 0 c 3J m > 01 ------- •gunOS 100% 80% O LU > o 2 LU CC O o 60% 40% 20% 0% IMPACT OF RAW WATER TOC ON TOC REMOVAL BY ALUM COAGULATION / FILTRATION 10 20 30 40 ALUM DOSE (mg/l) 50 60 > 6> ------- ¦ At alum doses greater than 10 mg/L, TOC removal increases with increasing raw water TOC levels. ¦ At pH 5.5, the maximum possible removal of TOC is approximately 55 percent for a water with a median TOC of 2.8 mg/L. Because the curves shown on Figure A-5 were developed from a regression analysis, the maximum possible removal of 55 percent is the expected average. Some utilities will observe greater removals while others will observe lower removals. A database developed by USEPA's Technical Support Division (Fair, 1990) was used to develop a relationship for UV-254 removal. Using similar techniques to those used for TOC removal, the following equation was developed: \n(UV-254} = -4.64 + 0.879[ln( t/K-25^] -0.185[ln(tf/u/M dose)] (A-14) + 0.564(pi/t) [r2 = 0.733, adj r2 = 0.702, SEE = 0.299, F = 23.8 (a < 0.001), n = 30] where UV-2540 is the raw water UV-254 in cm"1 and UV-254f is the filtered water UV-254 in cm'1. The basis for selecting the USEPA database for this equation is described elsewhere (Harrington, et al.. 1991). A lower quality of fit was obtained for Equation A-14 when compared with Equation A-13. This lower quality of fit can be expected because the removal of UV-254 may also occur as a direct result of oxidation processes conducted during the coagulation, flocculation, clarification and filtration processes. An evaluation of this phenomenon has not been conducted to date. A.4.2 Ferric Coagulation, Flocculation, Clarification and Filtration Unlike the equations for alum coagulation (based solely on full-scale observations) the equations for ferric coagulation were based on bench and pilot studies. An empirical equation for NOM removal by ferric salt coagulation was based on data collected from several bench and pilot scale studies conducted by Malcolm Pirnie, Inc (1989, 1990). and from one study conducted by John Carollo Engineers (1989). The following raw water sources and geographical locations were represented: a -15 RECYCLED PAPER ------- ¦ San Diego, California (140 observations); ¦ Central Arizona Project Water, Arizona (46 observations); ¦ Clermont, Ohio (9 observations); ¦ Akron, Ohio (9 observations); ¦ Elizabethtown, New Jersey (6 observations); and ¦ Omaha, Nebraska (5 observations). Summary statistics of this database are shown in Table A-13. The ability of the ferric coagulation equations to simulate NOM removal for the general case is not expected to be as good as the alum coagulation equations because fewer waters were included in the analysis and because the results may be skewed by the large number of observations from two sources. Using similar techniques to those used for developing the alum coagulation equations, the following equations were developed for ferric salt coagulation: log10(7£>9 = 0.316+0.89inog10(7OC0)] -0.018[log10(FeCiJ dose)] (A-15) [adj r2 = 0.742, F = 200 (a < 0.001), n = 213] log l0(UV-254} = 0.228+ 1.025[log w{UV-254j\ -O.OSSflogj^FeC/j dose)] (A-16) [adj r2 = 0.796, F = 247 (a < 0.001), n = 195] where FeCl3 dose is the ferric salt dose in mg/L as FeCl3 • 6 H20. A -16 RECYCLED PAPER ------- TABLE A-13 SUMMARY STATISTICS FOR FERRIC CHLORIDE DATABASE Standard Parameter Mimimum Maximum Mean Deviation Raw Water DOC (mg/L) 2.7 9.0 4.8 1.7 Raw Water UV-254 (1 /cm) 0.044 0.720 0.080 0.08 Raw Water pH 6.0 10.8 8.1 0.7 FeCI3 Dose (mg/L) 0.3 75 15.8 14.8 Treated Water DOC (mg/L) 1.9 8.9 3.9 1.3 Treated Water UV-254 (1/cm) 0.010 0.420 0.050 0.04 Treated Water pH 5.6 10.8 7.4 0.9 j:\0313\549Ubls\tob»-13.wp 06/07/92 ------- A.4J Precipitative Softening, Clarification and Filtration Full-scale data, including all of the parameters required for modeling, have been compiled for only twelve precipitative softening plants. TOC, UV-254, lime dose and total hardness values were available for most of the treatment plants. Seven of the plants reported calcium and magnesium hardness values in addition to total hardness. Nine field-scale observations were available for these seven plants (Table A-14). Four observations were collected from four plants in the database developed by USEPA's Technical Support Division (Fair, 1990), three observations were collected from one plant in the database developed by Metropolitan and Montgomery (1989), and two observations were collected from two plants recently evaluated by Malcolm Pirnie, Inc. (1991) Using similar techniques to those used for developing the coagulation equations, the following equations were developed for precipitative softening: WJOC) = lWflndOCo)] -0.0862[ln(ACfl)] (A*17) - 0.0482[ln(AAfg)] [r2 = 0.984, adj r2 = 0.978, SEE = 0.217, F = 120 (a < 0.0001), n = 9] In (UV-254} = 0.923MUV-254 -0.0758[ln(ACa)] (A"18> -0.198[ln(AA/g)] [r2 = 0.989, adj r2 = 0.986, SEE = 0.366, F = 183 (a < 0.0001), n = 9] where ACa = Raw Water Calcium Hardness (mg/L as CaCO}) + Calcium Hardness from Lime Addition (mg/L as CaC03) - Finished Water Calcium Hardness (mg/L as CaC03) and AMg = Raw Water Magnesium Hardness (mg/L as CaC03) - Finished Water Magnesium Hardness (mg/L as CaC03) A - 17 RECYCLED PAPER ------- TABLE A-14 CHARACTERISTICS OF FIELD-SCALE PRECIPITATIVE SOFTENING DATABASE Range Median Raw Water Quality TOC (mg/L) 2.2 -19.0 4.6 UV-254 (cm1) 0.030 - 0.697 0.123 pH 6.9 - 8.4 7.8 Alkalinity (mg/L as CaC03) 68 - 295 182 Calcium Hardness (mg/L as CaC03) 64 - 300 168 Magnesium Hardness (mg/L as CaC03) 0- 183 28 Total Hardness (mg/L as CaC03) 98 ¦ 360 247 Temperature (°C) 9 -29 23 Turbidity (NTU) 0.1-10 2.2 Finished Water Quality TOC (mg/L) 1.7-9.5 2.9 UV-254 (cm ') 0.010 - 0.232 0.060 Removals Calcium Hardness (mg/L as CaC03) 100 - 602 262 Magnesium Hardness (mg/L as CaC03) 0-90 16 Total Hardness (mg/L as CaC03) 104 - 644 346 Softening pH 8.9-11.3 10.2 Lime Dose (mg/L as CaC03) 109 - 394 228 Soda Dose (mg/L as Na2C03) 0-9.4 0 0313\549\tbls\taba-14. wp 06/07/92 ------- Because of the lack of data available for the development of the above equations, an extensive survey of field scale precipitative softening plants is recommended. A.4.4 Granular Activated Carbon TOC breakthrough curves for alum coagulated, settled and filtered water were described by the general logistic function (Clark, et al.. 1986; Clark, 1987). This function is represented by the following equation: where TOCf is the finished water TOC, TOQ is the influent TOC, 1/n is the slope of the Freundlich isotherm and t indicates the time since the beginning of the adsorption process. The two constants of the logistic function, A and r, can be determined by the procedure discussed by Clark (1987). The logistic function parameters, A and r, are a function of empty bed contact time (EBCT), the type of GAC used and water quality factors such as pH and ionic strength. A modified form of this equation is not developed at this time to account for these other factors. The existing database on granular activated carbon (GAC) adsorption of NOM should be evaluated to determine if Equation A-19 can be modified to reflect a general case. In the meantime, the model assumes that the Jefferson Parish, Louisiana case is representative of the general case, because this appears to be a median case when compared with results from other utilities. For instance, TOC removal was higher at Jefferson Parish than at Manchester, NH, Miami, FL and Metropolitan. TOC removal was lower at Jefferson Parish, however, than at Philadelphia, PA, Cincinnati, OH and Shreveport, LA. The parameters reported for the Jefferson Parish case are as follows: (A-19) 1 +Ae'rt r = 0.0743(£flC7) -0.429 (A-20) A = 0.757(EBCT)'35 (A-21) A - 18 RECYCLED PAVER ------- - = 0.316 n (A-22) The model uses Equations A-19 through A-22 to calculate a running average TOC removal based on the EBCT and the regeneration frequency. Running average removals are of interest because USEPA plans to regulate DBPs on a running annual average basis. The model, therefore, is not intended to simulate the dynamics of TOC removal by this process but, rather, simulate average water quality conditions within the treatment plant being considered. In the algorithm, the running average TOC is calculated as follows: ET. TOC, (JOCJ ,11-1 1 +Ae'rt #1-1 (A-23) RF where RF is the regeneration frequency. Because a sufficient database was not available to develop a separate equation for UV-254 removal, the model assumes that UV-254 removal is equivalent to TOC removal on a percentage basis. Because UV-254 is a surrogate measure of the more adsorbable humic, non-polar fraction of organic material, this assumption is conservative. UV-254 removal would be expected to exceed that of TOC, which is comprised of both non-polar and polar (less adsorbable) organic material. A.4.5 Membrane Processes Removal of NOM by membrane systems was based on bench scale process evaluations performed by Taylor, et al. (1987 and 1989) and Amy, et al. (1990). Relevant water quality characteristics from these studies are shown in Table A-15. Data from these three studies were combined and analyzed for relationships between nominal molecular weight cutoff (MWC) and removal of natural organic parameters. The best relationship obtained for dissolved organic carbon (DOC) removal was: [adj r2 = 0.886, n = 44] A - 19 RECYCLED PAPER ------- TABLE A-15 CHARACTERISTICS OF BENCH-SCALE, CONTINUOUS FLOW MEMBRANE STUDIES Range Median Raw Water Quality TOC (mg/L) 3.4 - 25.1 8.9 PH 5.8 - 7.9 7.2 Alkalinity (mg/L as CaC03) 20 - 338 97 Total Hardness (mg/L as CaC03) 152 - 352 231 Permeate Water Quality" TOC (mg/L) 0.2 - 16.2 2.0 PH 5.9 - 7.7 6.8 Alkalinity (mg/L as CaC03) 7-343 48 Total Hardness (mg/L as CaC03) 4 - 365 122 Operating Parameters Molecular Weight Cutoff 50 - 40,000 400 Operating Pressure (psi) 50 - 190 130 Recovery (%) 45-86 76 Product Flux (gpd/sf) 9-57 26 0313\549\tbl»\tab»-15.wp 06/07/92 ------- (A-24) where DOCf and DOQ, are the product water and feed water DOC levels, respectively. This equation had an adjusted R2 of 0.886 and was based on 44 observations from 6 different waters. Data for UV-254 removal were not reported in either study and, therefore, the model assumes that UV-254 and DOC are removed to the same extent in membrane systems. A.5 EQUATIONS FOR ALKALINITY AND pH ADJUSTMENT A.5.1 General The addition of alum in drinking water treatment consumes alkalinity and, consequently, depresses the pH. Alkalinity is defined by the following expression (Stumm and Morgan, 1981): Where [HC03 ] is the molar concentration of the bicarbonate ion, [C032] is the molar concentration of the carbonate ion, [OH ] is the molar concentration of the hydroxide ion and [H+] is the molar concentration of the hydrogen ion. The concentrations of the carbonate and bicarbonate ions are pH dependent and may be defined as: Alkalinity = [HCO3~] + 2[CO,2"] + [OH ] - [/T] (A-25) [HCOj ] - oc i Cf COj (A-26) [COj ] - fltjCj.COj (A-27) A-20 RECYCLED PAPER ------- Where: cTCOj = [H2co3] - [HCO;] + [CO]-] (A-28) a (A-29) [H+]2 * *,[//~] * (A-30) [H*]2 * KJH+] + In the above equations, [H2C03] is the molar concentration of dissolved carbon dioxide (carbonic acid), while K, and K2 are the acidity constants for carbonic acid and bicarbonate ion, respectively. At 25°C and an ionic strength of zero, the value for K, is 10"4 3 while the value for K2 is 10'10 3. The model assumes that K, and K2 are temperature dependent as follows (Stumm and Morgan, 1981): where AH0 is the standard enthalpy change of the dissociation of carbonic acid to bicarbonate, T° is the standard temperature of 298°K (25°C) and K,° is the value of the equilibrium constant at T°. Solving Equation A-31 for K, produces the following: Equations A-31 and A-32 assume that AH° is independent of temperature. Using Equation A-32, the following equations were developed for K, and K2: (A-31) (A-32) A - 21 RECYCLED PAPER ------- K{ = exp 7700 mole 8.314 'K-mole t f 1 -1] ^ 298.15° T) 14.5 (A-33) Kj = exp' 14900 mole 8.314 'Kmole t f 1 -1] \ 298.15°^ rj - 23.7 (A-34) Equations A-33 and A-34 require the use of degrees Kelvin for the temperature, T. Degrees Kelvin can be calculated by adding 273.15 to the temperature determined as degrees" Centigrade. The concentration of hydroxide ion, [OH"], is calculated as follows: [OH] = (A-35) [JH where Kv is the ion product of water and is 10"M at 25°C. Like K, and K* K» is temperature dependent. The model uses an empirical equation described in Stumm and Morgan (1981) to calculate K„ at temperatures other than 25°C (298.15°K) as follows: lo8io(^J = "44^°" + 6 0875 - 0.017067" (A-36) where T is the temperature in degrees Kelvin. Based on Equations A-25 through A-30 and on Equation A-35, Equation A-25 can be rewritten as follows: A - 22 RECYCLED PAPER ------- Alkalinity = (Oj + 2a2)CTCOj + [0H~] - [i/+] (A-37) " [HI As shown by this equation, alkalinity is dependent on [H+], Cr.coj and several equilibrium constants. The equilibrium constants are dependent on temperature. Therefore, any change in alkalinity, C-tcoj or temperature will produce a change in [H+]. Because pH is equal to -log10[H+], any change in alkalinity, CtiC0, or temperature will produce a change in pH. Electroneutrality also requires that alkalinity be defined by the following expression: where CB is the equivalent concentration of all positively charged ions except hydrogen and CA is the equivalent concentration of all negatively charged ions except hydroxide, bicarbonate and carbonate. Equation A-38 can also be written as follows: Alkalinity = CB - CA (A-38) Alkalinity = C'B + 2 [Ca2*] + [CaOH*] + 2 [Mg2*] + IMgOH1 + [NH; ] - C'A - [OCI-] (A-39) where: [CaOH'] = [Ca2*Wa,* Co2*-CaOH' (A-40) [HI A -23 RECYCLED PAPER ------- [afgoin = [HI (A-41) ^Ca1' "CaOH' = exP -72320- = exp mole [8.314—I—1(7) { mole-°K) (A-42) and ^Mgu-MgOH' ~ exP -65180- exp mole (8.314 -—](T) { mole- K) (A-43) Using Equations A-40 and A-41, Equation A-39 can be rewritten as follows: Alkalinity = c'B + [Ca2*] [Mg2*] '2 + ^Ca2'"CaOH' \ [H*] ) ' v 2 + ^Ug1' -MgOH* IH1 + m;] - c'A - [ocn (A-44) The concentration of dissolved calcium is assumed to be the following: ct.o = tCfl21 + [CaOH*] * [Ca(OH)2(eqJ (A-45) A-24 RECYCLED PAPER ------- where: [Ca^*\Krj. »«*W (A-46) and V-ctoio.^ ~ exp(~^~) exp •159800- (A-47) mole (8.314—-—\l) V mole°Kj Combining Equations A-40, A-45 and A-46, the following relationship is obtained: r 2*1 _ *~T,Ca [Ca2l = J + KCa2* «• CaOH* + KCc*'-.CajO^, (A-48) [HI [H*f The concentration of dissolved magnesium is assumed to be the following: CTMg = [A/g2t] * [MgOH+] * [Mg{OH)Uaq) (A-49) where: [Mg(OH)2{aqJ = Wg ]K"£~"S(OHh"1) (A_50) and Combining Equations A-40, A-49 and A-50, the following relationship is obtained: A -25 RECYCLED PAPER ------- Ktig* afa€) = eXP = exp -159760- mole (8.314——\j) ( molefiKf (A-51) IMg1^ = 'TMg_ j + KMg*-MgOH' + S(0H)2M [#1 [H T (A-52) The concentration of dissolved free chlorine is assumed to be the following: CTOa = [HOCl\ + [OCr] (A-53) where: [ifocq = Igcnun (A.54) and K ^Hoci-ocr *Hoci-oci' ~ exP 13800 mole 8.314 5Kmole 1 298.15° A- -i) - 17.5 (A-55) Combining Equations A-53 and A-54, the following relationship is obtained: [OCT] = 1 . [HI (A-56> *Hoci-ocr The concentration of dissolved ammonia is assumed to be the following: A -26 RECYCLED PAPER ------- CTw} = [NH; ] + [NHJ (A-57) where: [NHJ = [NH^. «h4 -«w, [HI (A-58) and knh; -nH) " exP 52210 mole 8.314 5 K-mole, ( 1 -1) ^298.15° AT rj - 21.4 (A-S9) Combining Equations A-57 and A-58, the following relationship is obtained: m;] = 1 + ^NH'-NH, [HI (A-60) Combining Equations A-44, A-48, A-52, A-56 and A-60, the following expression may be written: The model calculates [H+] based on the equality of Equations A-37 and A-61. As shown by these equations, the model assumes that a change in [H+] occurs when a change occurs in CA\ CB\ Ctq,, Cj-mj, Ct NH3, Ctoci. C,-^ or temperature. The calculation method is described below. Calcium Carbonate Solubility The calcium ion and the carbonate ion are assumed to be in equilibrium with calcium carbonate when water quality conditions cause calcium carbonate to precipitate from solution. Under these circumstances, the molar quantity of calcium ions leaving the aqueous A -27 RECYCLED PAPER ------- Alkalinity = c'B - C'A 'T.Ca 2 + Ca —CaOH' UH j + ^Ca1' —CaOH* + m2 [/T] 'TMg ' If \ 2 + Mg1' •'MgOH' [HI J + KM^-UgOH' + Kus1'-u8(Oirh(ai) [//I [H*f 'TMi 'T.OCI j + KNH:^NUy j + [#+] [HI ^uoct-ocr (A-61) phase is equivalent to the molar quantity of carbonate ions leaving the aqueous phase. Therefore, the model assumes the following: Ccoj>pt ~ ^cOyppt (A-62) where ^ is the concentration of precipitated calcium in equilibrium with the aqueous phase and Cc03ppt is the concentration of precipitated carbonate in equilibrium with the aqueous phase. These concentrations are related to dissolved phase concentrations and Equation A-62 can be rewritten as follows: Cc " CT,Ca ~ Ccos ~ ^r.co, (A-63) where is the sum of the aqueous phase calcium concentration (C^) and the concentration of precipitated calcium in equilibrium with the aqueous phase. is the sum of the aqueous phase carbonate concentration (Crco,) and the concentration of precipitated carbonate in equilibrium with the aqueous phase. The solubility of calcium and carbonate ions in equilibrium with calcium carbonate is given by the following expression: A -28 RECYCLED PAPER ------- [C-'*][COh - KfjCaCO, (A-64) where -12530 j»,CaCOj = exp mote 8.314 1 ^298.15°* -i) - 19.1 (A-65) 'Kmole t Based on Equations A-27, A-29 and A-44, Equation A-64 can be rewritten as follows: Y WTjaH \ M pr? * *,[*•]~ K&) (A"66) [#1 J Solving Equation A-66 for CriCa yields the following: xo.CoCOj -r.Ca J + ^Ca2'-CaOH' + ^Ca2' -Ca(OH^M IH~] [J/*]2 '7, CO, *1*2 ^[JT]2 + KJ/T] + (A-67) Solving Equation A-63 for Cr.coi and substituting Equation A-67 for CrfQl yields the following: ^Tjcoy ~ Cccty CCO VCoCO, J + ^Ca2' "CaOH' + *Ca*' w [#*] ~l2 (A-68) 'r.co, ^CH2 + *,[#1 * K^j A-29 RECYCLED PAPER ------- Multiplying each term in Equation A-68 by Ct>co, yields: 0 = ^r.co, " *t>COj(CCOj ~ CJ - K. to.CaCO) j + -CcOH' + ^Ca^-CtKPB^M [HI [HI1 [h*]1 * *,[/n + (A-69) Using the quadratic formula to solve for Q- co, yields: where and t.co, -b + \/ b2 - 4ac 2a (A-70) a = l (A-71) b = C- - C™ "Co CO, (A-72) c = -A". to,CaCOs j + ^Ce1' "CaOH" + Kca*'- ra un2 [/T]2 + *,[#~] + (A-73) The model calculates from Equations A-70 through A-73 when A -30 RECYCLED PAPER ------- Y Wrxo, ' , + + Ko>--<*<*>,„, IWT2 - KJfll * un wV J (A-74) After calculating Crcon the model calculates C^q, from Equation A-63. The calculated values of Q-^oj and C, a are substituted into the alkalinity expressions in Equations A-37 and A-61. The model uses Equation A-63 and Equations A-70 through A-73 only when the aqueous phase is considered to be in equilibrium with calcium carbonate precipitate. If the precipitate is removed by a treatment process and pH conditions are changed such that the aqueous phase is undersaturated with respect to calcium carbonate precipitate, the model assumes that the aqueous phase is no longer in equilibrium with calcium carbonate precipitate. Magnesium Hydroxide Solubility The magnesium ion and the hydroxide ion are assumed to be in equilibrium with magnesium hydroxide when water quality conditions cause magnesium hydroxide to precipitate from solution. The solubility of magnesium and hydroxide ions in equilibrium with magnesium hydroxide is given by the following expression: [Mg2*] . K [H*]2 ' "WOHh. (A-75) where -113960 + 38.8 (A-76) 8.314 O Kmole Based on Equation A-52, Equation A-75 can be rewritten as follows: K toMgiOl0j (A-77) [JT]2- IH1KU, A - 31 RECYCLED PAPER ------- Solving Equation A-77 for yields the following: ^TMg ~ * [H*]KUgi. ^UgOH. + KUgt. (A-78) The model calculates from Equation A-78 when Ksomaob^ * 2 (A"?9) in j +1/1 -ug(pm2W The model uses Equation A-78 only when the aqueous phase is considered to be in equilibrium with magnesium hydroxide precipitate. If the precipitate is removed by a treatment process and pH conditions are changed such that the aqueous phase is undersaturated with respect to magnesium hydroxide precipitate, the model assumes that the aqueous phase is no longer in equilibrium with magnesium hydroxide precipitate. Calculating pH Changes An iterative procedure is used to calculate the change in pH associated with chemical - addition. This iterative procedure uses a bisection method to search for the concentration of hydrogen ions, [H+], that allows the alkalinity calculated by Equation A-37 to be equivalent to the alkalinity calculated by Equation A-61. The flowchart shown in Figure A-7 provides a detailed description of this procedure. Open System Versus Closed System The calculation of pH changes in the model is based only on equilibrium considerations. This is an important limitation of the model because the kinetics of some processes, such as calcium carbonate precipitation and carbon dioxide dissolution, may be important. A more realistic model would account for the kinetics of carbon dioxide transfer from the air into water by using an appropriate mass transfer model with the appropriate mass transfer coefficients. As an alternative to using such a kinetic model, two equilibrium models were considered for the water treatment plant model described in this paper. One A - 32 RECYCLED PAPER ------- FIGURE A-7 ------- equilibrium model, referred to as the open system model, assumes that the concentration of carbon dioxide dissolved in the water in treatment plant basins is in equilibrium with the concentration of carbon dioxide in the atmosphere above the water. Hie other equilibrium model, referred to as the closed system model, assumes no exchange of carbon dioxide between the water and the atmosphere. The water treatment plant model described in this paper assumes that the closed system model more closely approximates actual conditions in a water treatment plant and distribution system than the open system model. This assumption needs to be verified and the possibility of including the kinetics of carbon dioxide dissolution into the water treatment plant model needs further consideration, particularly for the unit processes in a water treatment plant. A£2 Alum Coagulation, Flocculation, Clarification and Filtration When alum is added to water, it dissociates according to the following equation: For the purposes of this model, all of the sulfate ions are assumed to remain dissociated and all of the aluminum ions are assumed to form insoluble aluminum hydroxide, Al(OH)3(s). Therefore, when one mole of alum is added to one liter of water, the quantity (CV - CA') decreases by six equivalents per liter and the alkalinity correspondingly decreases by six equivalents per liter (see Equation A-61). The assumption that all aluminum ions form insoluble aluminum hydroxide becomes less valid at pH levels farther from the pH of minimum solubility (pH = 5.9 at 25°C). The iterative procedure described above is used to obtain a new value for [H+] and, hence, pH. AS3 Ferric Coagulation, Flocculation, Clarification and Filtration When ferric sulfate is added to water, it dissociates according to the following equation: Aip0Jf\4H20 - 2 Al3* + 3 SO]' + 14 Hp A-80) Fe2(S0J3»H20 - 2Feyt * 3SO]~ * Hp (A-81) A -33 RECYCLED PAPER ------- For the purposes of this model, all of the sulfate ions are assumed to remain dissociated and all of the ferric ions are assumed to form insoluble ferric hydroxide, Fe(OH)3. Therefore, when one mole of ferric sulfate is added to one liter of water, the quantity (Cy - CA') decreases by six equivalents per liter and the alkalinity correspondingly decreases by six equivalents per liter (see Equation A-61). The assumption that all ferric ions form insoluble ferric hydroxide becomes less valid at pH levels farther from the pH of minimum solubility (pH = 9 at 25°C). The iterative procedure described above is used to obtain a new value for [H+] and, hence, pH. Also, the model does not account for any excess acidity that might accompany commercial ferric sulfate. When ferric chloride is added to water, it dissociates according to the following equation: For the purposes of this model, all of the chloride ions are assumed to remain dissociated and all of the ferric ions are assumed to form insoluble ferric hydroxide, Fe(OH)3. Therefore, when one mole of ferric chloride is added to one liter of water, the quantity (C„' - CA') decreases by three equivalents per liter and the alkalinity correspondingly decreases by three equivalents per liter (see Equation A*61). The assumption that all ferric ions form insoluble ferric hydroxide becomes less valid at pH levels farther from the pH of minimum solubility (pH = 9.0 at 25°C). The iterative procedure described above is used to obtain a new value for [H+] and, hence, pH. Also, the model does not account for any excess acidity that might accompany commercial ferric chloride. A.5.4 Precipitative Softening, Clarification and Filtration When added to water, lime dissociates according to the following expression: Therefore, when one mole of lime is added to one liter of water, C^q, increases by one mole per liter (see Equations A-74 and A-61). If the condition described by Equation A-74 is met FeCl3»6 H20 - Fe3* * 3 C/" + 6H20 (A-82) Ca(OH)Ut) - Ca2* + 2 OH~ (A-83) A - 34 RECYCLED PAPER ------- (this is the objective in precipitative softening), C^co, is recalculated with Equations A-70 through A-73 and Cx>Ql is recalculated with Equation A-63. If the condition described by Equation A-74 is not met, however, C^coj does not change and Cj c, increases by one mole per liter (see Equation A-61). The iterative procedure described above is used to obtain a new value for [H+] and, hence, pH. A.5.5 Chlorine Addition When chlorine gas is added to water, it reacts according to the following equation: d2 (g) + Hi° - Hocl * H* + CI (A-84) For the purposes of this model, all of the chloride ions are assumed to remain dissociated. Therefore, when one mole of chlorine gas is added to one liter of water, Cr.oa increases by one mole per liter and the quantity (CB' - CA') decreases by one equivalent per liter (see Equation A-61). The iterative procedure described above is used to obtain a new value for [H+] and, hence, pH. A.5.6 Sodium Hypochlorite Addition When sodium hypochlorite is added to water, it reacts according to the following equation: NaOCl - No* * OCl- (A-8S) For the purposes of this model, all of the sodium ions are assumed to remain dissociated. Therefore, when one mole of sodium hypochlorite is added to one liter of water, Ct qc increases by one mole per liter and the quantity (CB' - CA') increases by one equivalent per liter (see Equation A-61). The iterative procedure described above is used to obtain a new value for [H+] and, hence, pH. A - 35 RECYCLED PAPER ------- A.5.7 Potassium Permanganate Addition When potassium permanganate is added to water, it dissociates according to the following equation: KMn04 - JT + MnO; (A"86) For the purposes of this model, all of the potassium ions are assumed to remain dissociated and all of the permanganate ions are assumed to react and form insoluble manganese dioxide as shown by the following equation: MnO; + AH* + 3«r" - Mn01 * 2H20 (A"87> The assumption that all permanganate ions form insoluble manganese dioxide becomes less valid at pH levels farther from the pH of minimum solubility. When one mole of potassium permanganate is added to one liter of water, the net effect of Equations A-86 and A-87 is to increase the quantity (CB' - Cx') by four equivalents per liter. This net effect is only true if the oxidized product remains in solution. For instance, if the permanganate ion oxidizes ferrous ions to ferric ions, the ferric ions can precipitate as ferric hydroxide. The net effect of this precipitation in concert with Equations A-86 and A-87 is to increase the quantity (CB' - CA') by only one equivalent per liter rather than four equivalents per liter. The model presently assumes that the quantity (C„' - CA') increases by one equivalent per liter when one mole of potassium permanganate is added to one liter of water. Under most water treatment conditions, potassium permanganate is added in quantities that do not change alkalinity to the degree that are effected by coagulants. Therefore, this assumption is not expected to be critical in most cases. A.5.8 Sulfuric Acid Addition Sulfuric acid is typically used to decrease pH and alkalinity in drinking water treatment. When added to water, sulfuric acid dissociates according to the following equation: A-36 RECYCLED PAPER ------- H2SOa -2H* + sol' (A-88) Sulfate ions are assumed to remain completely dissociated (this assumption is valid for pH greater than 2 and when the solubility limit of calcium sulfate is not exceeded). Thus, when one mole of sulfuric acid is added to one liter of water, the quantity (C^' - CA') is decreased by two equivalents per liter (see~Equation A-61). The iterative procedure described above is used to obtain a new value for [H+] and, hence, pH. XS.9 Sodium Hydroxide (Caustic) Addition Sodium hydroxide is used to increase pH and alkalinity in drinking water treatment When added to water, sodium hydroxide dissociates according to the following equation: Sodium ions are assumed to remain completely dissociated. Thus, when one mole of sodium hydroxide is added to one liter of water, the quantity (CB' - CA') is increased by one equivalent per liter (see Equation A-61). The iterative procedure described above is used to obtain a new value for [H+] and, hence, pH. Lime is also added to increase alkalinity and pH levels in drinking water treatment. When added to water, lime dissociates according to the following expression: Therefore, when one mole of lime is added to one liter of water, C^q, increases by one mole per liter (see Equations A-74 and A-61). If the condition described by Equation A-74 is met (this is not the objective when using lime for pH adjustment), Cjcoj is recalculated with Equations A-70 through A-73 and Q. q, is recalculated with Equation A-63. If the condition described by Equation A-74 is not met, however, Ctcoi does not change and C^q, increases NaOH - Na* * OH~ (A-89) A.5.10 Calcium Hydroxide (Lime) Addition Ca(OH)m - Ca2* + 2 OtT (A-90) A - 37 RECYCLED PAPER ------- by one mole per liter (see Equation A-61). The iterative procedure described above is used to obtain a new value for [H+] and, hence, pH. A.5.11 Sodium Carbonate (Soda Ash) Addition Soda ash is another chemical added to increase alkalinity and pH levels in drinking water treatment. When added to water, soda ash dissociates according to the following expression: NafOj -2Na+ + CO] (A-91) Therefore, when one mole of soda ash is added to one liter of water, the quantity (CB' - CA') is increased by two equivalents per liter and C^co, is increased by one mole per liter (see Equations A-74 and A-61). If the condition described by Equation A*74 is met, CtiC0, is recalculated with Equations A-70 through A-73 and Ctq, is recalculated with Equation A-63. The iterative procedure described above is used to obtain a new value for [H+] and, hence, pH. A.5.12 Carbon Dioxide Addition Carbon dioxide is added to lower pH levels and increase the carbonate concentration in some precipitative softening systems. When one mole of carbon dioxide is added to one liter of water, Ct |