Sec6.0 Report for EPA in application of WTP-ccam to GCWW

DRAFT Using WTP-ccam to Simulate Water Treatment Plant Operation: A Case Study at Greater Cincinnati Water Works Water Resources Adaptation Program (WRAP) National Risk Management Research Laboratory Office of Research and Development (ORD) United States Environmental Protection Agency Cincinnati, Ohio May 2011 Prepared By Zhiwei Li and Steven G Buchberger University of Cincinnati Robert M Clark Environmental Engineering and Public Health Consultant Y Jeffrey Yang US Environmental Protection Agency DRAFT DISCLAIMER This report demonstrates an interactive computer platform, the water treatment plant – climate change adaptation model (WTP-ccam) version 1.0, applied to the Richard Miller drinking water treatment plant of the Greater Cincinnati Water Works (GCWW) The WTP-ccam was developed for the United States Environmental Protection Agency (USEPA) under the EPA Contract EP-C-05-056 by: • • University of Cincinnati, Cincinnati, Ohio Environmental Engineering and Public Health Consultant, Cincinnati, Ohio This report may be of educational value to various individuals in the water treatment industry, but each individual must interpret and adapt the results to fit their own practice Although a diligent effort has been made to assure that the results obtained are reasonable, the results in this document are experimental and have their own limitations The USEPA and its contractors shall not be liable for any direct, indirect, consequential, or incidental damages resulting from the use of this report ii DRAFT ACKNOWLEDGMENT This report is a task of work assignment 0-35 of the USEPA Water Resources Adaptation Program (WRAP) supported by the EPA contract EP-C-05-056 The authors are grateful to the following individuals for providing assistance and information to develop this example application: Jeff Swertfeger, Jeff Vogt and Yeongho Lee of the Greater Cincinnati Water Works iii DRAFT FORWARD To protect human health and the environment, USEPA has implemented research programs to develop data and tools to support utilities and local governments in adapting to changes in climate and population As a part of the multi-scale infrastructure characterization project in the EPA Water Resources Adaptation Program, an interactive computer platform, WTP-ccam, has been developed to simulate municipal water treatment plant operation The platform can be used to link water treatment operations with various climate change scenarios through variation of source water qualities To validate and illustrate application of the WTP-ccam platform, a case study was established at the Greater Cincinnati Water Works (GCWW)’s Richard Miller water treatment plant This report summarizes the validation, data preparation, and application of the WTP-ccam platform In addition, this report provides background on the design and operation of the Miller plant, assessment of impact of climate change on performance of water treatment, adaptation of plant operation to climate change scenarios and associated adaptation cost analysis iv DRAFT List of Figures Fig 2.1 Unit Process Treatment Train for the GCWW Miller water treatment plant … Fig 2.2 Time series of influent and blended effluent TOC ………… ………………… Fig 2.3 Time series of plant inflow, mass inflow and active number of contactors …… Fig 2.4 Time series of active contactors and blended effluent TOC concentration ……… Fig 2.5 Time series of plant inflow and EBCT ………………………………………… Fig 2.6 Simulation processing train for the Miller plant………………………………… 10 Fig 2.7 Influent water inputs for sample period 10…………………………………… 11 Fig 2.8 Input window for flocculation basin…………………………………………… 11 Fig 2.9 Input window for lime addition………………………………………………… 11 Fig 3.1 Normal probability plots for logarithm of pH and TOC………………………… 13 Fig 3.2 Location of WTPs using Ohio River…………………………………………… 13 Fig 3.3 Setting up Monte Carlo simulation at the Miller plant………………………… 16 Fig 3.4 Manual input window for influent water quality statistics……………………… 16 Fig 3.5 Example format of influent water quality data file……………………………… 17 Fig 3.6 Performance of Miller plant between baseline and future scenarios…………… 18 Fig 4.1 CDF of new GAC reactivation period for noncompliance events under the climate change (future) scenario……………………………….……… 20 Fig 4.2 Working chart for GAC unit annual cost………………………………………… 22 Fig 4.3 CDF for net annual adaptation cost for the noncompliance events under the climate change (future) scenario …………………………………… 22 Fig 4.4 Net annual costs versus TOC increment……………………………………… 23 Fig 4.5 Change and range of raw water TOC…………………………………………… 23 Fig 4.6 Range of annual cost in coming years…………………………………………… 24 Fig 5.1 Logistic model fitted to full-scale field data and to RSSCT data……………… 29 Fig 5.2 Comparison of fitting GAC models with field measurements…………………… 31 v DRAFT List of Tables Table 2.1 Miller WTP Unit Process Design Parameters …… …………………………… Table 2.2 Statistics of full-scale field measurements ………………………….………… Table 2.3 Inflow and chemical feed levels for the Miller WTP…………………………… Table 2.4 Comparison of sampled and modeled water quality results…………………… Table 3.1 Baseline raw water inputs for the Miller water treatment plant in 1998……… 14 Table 3.2 Raw water inputs for the Miller WTP in 2050………………………………… 15 Table 4.1 Parameters for GAC contactor cost…………………………………………… 21 Table 4.2 Parameters for GAC reactivation cost………………………………………… 21 Table 5.1 Characteristics of individual GAC contactors from 46 valid datasets………… 30 Table 5.2 Statistics of parameters for logistic model……………………………………… 31 vi DRAFT TABLE OF CONTENTS Title Page ……………………………………………………………………….…… … Disclaimer ……………………………………………………………………………… Acknowledgment ………………………………………………………………….…… Forward …………………………………………………………………………….…… List of Figures …………………………………………………………………………… List of Tables …………………………………………………………………………… Introduction …………………………………………………………………… 1.1 1.2 Monte Carlo Simulation ………………………………………………… 12 Setting up Baseline Raw Water Quality………………… …….…….… 13 Projecting Raw Water Quality in 2050……………….…………………… 14 Building up Monte Carlo Simulation with WTP-ccam ………….… …… 15 Impact of Climate Change on Performance of the Miller Plant ……… … 17 Adaptation of Water Treatment to Climate Change ………………………… 19 4.1 4.2 4.3 Background …… ……….……………………………………………… GCWW Water Treatment Plant Operation ……………………………… WTP-ccam Validation at GCWW’s Treatment Plant …………………… Application of WTP-ccam in Climate Change Studies ……………………… 12 3.1 3.2 3.3 3.4 3.5 Background……….……………………………………………………… Report Organization ……………………………………………………… Background of GCWW Plant and WTP-ccam Validation …………………… 2.1 2.2 2.3 i ii iii iv v vi Adaptation Engineering.………………………………………………… 19 Cost Analysis for Adaptation Engineering ……………………… …… 20 Implication for Engineering Practice ………………………….………… 23 Customization of Logistic Model for GAC Unit Process ………… ………… 25 5.1 5.2 5.3 Overview ………………………………………… …………………… 25 Algorithm ………………………………………………….…………… 26 Calibration of the Logistic Model with GCWW Field Measurement …… 28 Summary ……………………………………………………………………… References ……………………………………………………………………… 33 vii 32 DRAFT Introduction 1.1 Background Climate change may adversely affect both surface water and ground water quality, and consequently can impact the design and operation of drinking water treatment plants Increases in precipitation and resulting increases in flows can result in problematic turbidity levels, increased levels of organic matter, high levels of bacteria, virus and parasites and increased levels of pesticides in lakes, rivers and streams Although some areas may experience increases in run-off some areas may experience droughts resulting in elevated levels of potentially toxic algae, and high concentrations of organic matter, bacteria, etc (Whitehead et al., 2009; Interlandi and Crockett, 2003; Jacobs et al., 2001) Deterioration of source water quality due to climate change will require investment in new and possibly advanced water treatment unit processes Therefore, climate change effects coupled with increasing population in urban areas will impact adversely existing and future drinking water treatment infrastructure Water utilities are concerned about the effect of climate and population changes on future water demands, source water quality and, consequently, the quality of finished drinking water The USEPA recently conducted a preliminary national assessment on water infrastructure sustainability and adaptability to climate and socioeconomic changes through a Water Resources Adaptation Program (WRAP) As a sub-task of WRAP, methodologies were developed for adapting drinking water treatment plant operations to climate change scenarios In order to assess the potential impacts of climate and socioeconomic changes on drinking water treatment, a WTP-ccam platform was developed as an extension of the basic framework in the USEPA Water Treatment Plant (WTP) Model The WTP model uses empirical correlations to predict central tendencies of natural organic matter removal, disinfection, and DBP formation for a wide variety of drinking water treatment plant configurations Changes in finished water quality parameters caused by chemical addition and/or by the unit treatment processes can be satisfactorily simulated using the WTP model (USEPA, 2005) As an upgraded version of the WTP model, the WTP-ccam platform was designed to simulate the impact of uncertainties in raw water qualities induced by climate change through Monte Carlo analysis Several new features were developed and incorporated to the WTP-ccam platform, including: (1) ability to preserve joint correlation among influent water quality parameters using a seasonal multivariate model, (2) ability to customize model parameters of a processing unit for improved modeling performance, and (3) ability to make adaptation to treatment processes and estimate associated adaptation cost To validate and illustrate application of the WTP-ccam platform, a case study was developed at the GCWW’s Richard Miller water treatment plant This report summarizes the validation, data preparation, and application of the WTP-ccam platform in this case study viii DRAFT 1.2 Report Organization To illustrate the procedure of application of the WTP-ccam platform, this report contains the following chapters in addition to this introductory chapter:  Chapter provides background of GCWW’s Richard Miller treatment plant and analysis of its operation based on field measurements, and demonstrates the procedure for validation of the WTP-ccam in the Miller plant  Chapter introduces setting for for Monte Carlo simulation and preparation of input data to set up scenarios of climate change This chapter also demonstrates how to build up Monte Carlo simulation using collected data, and how to assess of impact of climate change on the performance of water treatment in the Miller plant  Chapter describes adaptation and associated cost of the Miller plant to climate change scenarios  Chapter introduces algorithms to estimate parameters of logistic model for GAC unit process, preparation of TOC breakthrough data, and calibration of GAC logistic model using field measurements in the Miller treatment plant ix DRAFT Background of the Miller Plant and WTP-ccam Validation 2.1 Background GCWW supplies on average 5.26 m3/s (120 MGD) of water through 5,100 km water mains to about 235,000 residential and commercial accounts Built in 1907, the GCWW’s Richard Miller Plant treats surface water from the Ohio River and supplies 88 percent of drinking water to GCWW's customers at a maximum summer capacity of 9.65 m3/s (220 MGD) As shown in Figure 2.1, the Miller plant treats the raw water through coagulation, sedimentation, biologically active rapid sand filtration, followed by granular activated carbon (GAC) processing The spent GAC is reactivated in two large on-site furnaces After chlorination disinfection, the treated water stores in clearwell for distribution The Miller plant design and operation parameters are extracted from the ICR database and listed in Table 2.1 In Table 2.1, the value for T 10 is the hydraulic retention time required in a step dose conservative tracer test for the effluent tracer concentration to reach 10 percent of the inflow tracer concentration for a treatment unit Influent Alum Rapid Mix Flocculation Pre-settling Reservoir Lime Coagulation basin Filtration GAC Cl2 Clear-well Effluent Fig 2.1 Unit Process Treatment Train for the GCWW Miller water treatment plant x DRAFT empirical parameters determined from nonlinear regression analysis, and z is either or for adjusting the cost functions for a range of USRT values The model parameters can be found from Adams and Clark (1988), which was obtained based on the costs in 1983 For consistence of comparison, all costs were converted to 2009 currency using the Producers Price Index (US BLS, 2008) The contactor cost can be further categorized by the costs of capital, process energy, building energy, maintenance material and operational and maintenance (O&M) labor The computational parameters for GAC contactors and GAC reactivation cost are listed in Table 4.1 and 4.2 Table 4.1 Parameters for GAC contactor cost Type of Cost Capital Process Building energy energy USRT volume area area a 93700 15150 b 1999.1 12 350 c 0.712 0.916 d 0.958 1 z 1 Unit cost Construction 0.08 $/kwh 0.08 $/kwh Cost 1.3y (in 2009) (in 2009) Ratio of 2009 2009ENR/1983ENR= to1983 cost R=2.16 Maintenance Material area 540 23.6 0.753 1 O&M Labor area 1160 0.3 1.068 1.152 $/hr (in 1983) 2009PPI/1983 PPI 2009 PPI/1983 PPI = 2.56 = 2.56 Table 4.2 Parameters for GAC reactivation cost Type of Cost USRT a b c d z Unit cost Ratio of 2009 to1983 cost Capital area 144000 198300.4 0.434 1 Construction Cost 1.3y 2009ENR/1983ENR = R = 2.16 Process energy Building energy Maintenance Material O&M Labor area area area area 354600 6387 0.755 1 12250 312.1 0.649 1 4456.6 0.401 1 2920 282 0.7 1 0.08 $/kwh (in 2009) 0.08 $/kwh (in 2009) $/hr (in 1983) 2009 PPI/1983PPI 2009PPI/1983PPI = 2.56 = 2.56 Natural Gas area 648400 287714.9 0.899 1 $0.0035 /scf (in 1983) 2009PPI/1983PPI = 2.56 The Miller plant (personnel communication with GCWW staff) has 12 down flow gravity contactors and two multi-hearth furnaces for onsite reactivation Each of the Miller plant contactors has a volume of 595 m3 and a surface area of 181 m2 The overall GAC loss rate through the system is 7-8% The carbon loading rate is 482 kg/day of GAC per square meter of hearth area in GAC reactivation If the capital recovery analysis is assumed a return period of 20 years with an interest rate of 5%, a cost curve can be developed to illustrate the total annual cost of the GAC system varies with GAC service time (reactivation period) Figure 4.2 shows the cost curve developed for GCWW’s Miller plant The annual cost of the GAC system decreases with increasing reactivation period It can be seen for a reactivation period shorter than 90 days, the annual cost increases rapidly with a decreasing reactivation period WTP-ccam uses the curve to estimate the adaptation cost through interpolations based on GAC reactivation period xxviii DRAFT 40 Annual cost, million $ 30 20 10 Baseline condition 0 100 200 300 Reactivation period, days 400 Fig 4.2 Working chart for GAC unit annual cost For ease of comparison, net annual cost is used for cost analysis, which is defined as the difference between the annual cost calculated with Figure 4.2 and the base annual cost (13.6 million dollars calculated with average GAC reactivation period of 180 days at the Miller plant) Therefore, the CDF of net annual cost can be computed using Figure 4.1 and 4.2 as shown in Figure 4.3 The net annual cost for complete control of TOC violation are up to 7.0 million dollars for 0.02mg/L safety margin and up to 7.8 million dollars for 0.20mg/L safety margin If the plant performance criterion allows a 10% risk of TOC violations, the net annual cost would reduce to 3.4 million dollars for 0.02mg/L safety margin and 4.4 million dollars for 0.20mg/L safety margin CDF 0.8 0.6 0.4 Safety margin: 0.02mg/L Safety margin: 0.20mg/L 0.2 0 Net annual cost, million $ 10 Fig 4.3 CDF for net annual adaptation cost for the noncompliance events under the climate change (future) scenario Since the adapted GAC reactivation period is a function of TOC concentration, the relations between the net annual cost and TOC concentration can be established using GAC reactivation period For ease of comparison, TOC concentration is expressed as increment over the compliance criteria (gap between TOC concentration and compliance criteria) Figure 4.4 xxix DRAFT reveals that the net annual cost in GAC processing increases linearly with increasing TOC increment over the compliance, given by, Y = 5.92 X + 0.62 (4.6) where, Y is net annual cost in million US dollar and X is the TOC increment R = 0.9985 indicates highly linear relationship for the two variables Net annual cost, m$ 0 0.2 0.4 0.6 0.8 1.2 TOC increment over compliance, mg/L Fig 4.4 Net annual costs versus TOC increment 4.3 Implication for Engineering Practice The implication for engineering practice may be drawn by results based on the following assumptions: 1) the rate of variation for averages of raw water quality parameters is assumed to keep the same as described in Section 3.3 until the year of 2100; 2) the coefficient of variation for water quality parameters keeps the same as the baseline condition; and 3) the range of change of raw water quality parameters is limited by a lower boundary µ − 2σ and a upper boundary µ + 2σ Figure 4.5 demonstrates the change and range of raw water TOC from 2000 to 2100 Similarly, change of other water quality parameters are established based on Section 3.3 Fig 4.5 Change and range of raw water TOC xxx DRAFT WTP-ccam is applied to determine whether adaptation of GAC reactivation period is required for ensuring TOC compliance at finished water for the corresponding lower boundary, average and upper boundary of raw water qualities, respectively If adaptation is required, the adjusted GAC reactivation period and corresponding annual cost are computed Figure 4.6 shows the range of annual cost in GAC processing for the years to come The results indicate that the annual cost would keep a basic annual cost level if the input raw water qualities not lead to a TOC noncompliance event The difference between the cost curve computed by upper raw water boundary and the basic cost line implies the risk of violation of TOC compliance The risk of TOC violation increases as raw water qualities deteriorate in years to come The risk of TOC violation is greater than 50% when the cost curve calculated by the mean raw water qualities is greater than the basic cost The annual cost in 2050 is 14.0 million dollars, greater than the basic cost 13.6 million dollars This result is consistent to the result in section 3.5 that the risk of TOC violation is 55% in 2050 Therefore, planning decisions may be drawn from Figure 4.6 in practice to control the violation of TOC compliance criteria Fig 4.6 Range of annual cost in coming years xxxi DRAFT Customization of Logistic Model for GAC Unit Process 5.1 Overview GAC treatment has been used as an alternative for reducing organic contamination in water supplies since early 1970’s (Roberts and Summers, 1982) The performance of GAC for TOC removal has been studied using TOC breakthrough experiments in GAC columns under different conditions, such as GAC sources or pretreatment configurations Roberts and Summers (1982) found that complete removal of TOC by GAC cannot be achieved under water treatment conditions An immediate, partial breakthrough of TOC can be observed, even using a column filled with fresh GAC, which indicates that a portion of the influent TOC is not amenable to removal by GAC treatment With increased service time, the effluent TOC concentration rises and eventually reaches a steady state value, which indicates that the GAC becomes saturated with organics They also observed that the effluent TOC seldom reaches the influent concentration but is lower than the influent level This constant steady-state removal usually is attributed to biodegradation (USEPA, 1996) During early stages of operation, the ratio of effluent to influent TOC concentration (called “fraction remaining”) generally ranges from 0.1 to 0.5, depending on composition of the organic constituents and EBCT/bed depth For steady-state removal, the fraction remaining varies from 0.6 to 0.9 with corresponding range of service times from 3,000 to 14,000 measured in bed volumes Various methods have been used to quantify breakthrough behavior including full-scale GAC columns, pilot-plant columns, numerical modeling using data from equilibrium and kinetic lab tests, non-scaled mini-column tests and a bench-scale experiment called the rapid small-scale column test (RSSCT) Among these, pilot columns and RSSCT are the most common methods to evaluate GAC treatment performance (USEPA, 1996) Pilot columns utilize the same GAC media, column length and influent water as the full-scale system This method has been shown to be accurate and reliable (Speth, 1989) However, a valid pilot column test requires equivalent operation time and, hence, higher costs than other methods As an alternative, the RSSCT method uses mass transfer models to downsize the full-scale contactor to a small-scale column Similitude with large-scale contactors is assured by properly selecting the GAC particle size, hydraulic loading and empty bed contact time (EBCT) of the small-scale contactor (Crittenden et al 1991; Zachman and Summers 2010) The advantage of RSSCT is its short operation time, normally less than 15 percent of the time required to test the full-scale contactor The drawback of RSSCT method is that it cannot simulate long-term biodegradation in the GAC media WTP-ccam platform primarily uses empirical correlations to predict central tendencies of NOM removal, disinfection, and DBP formation in a treatment plant The algorithms were generally developed using multiple linear regression techniques As a result, the empirical correlations usually consist of independent variables and empirical constants These statistical models generally work well for providing the central tendencies However, they may not provide sufficiently accurate predictions for a specific utility As a new feature, therefore, WTPccam provides options to customize the empirical constants in regression equations using sitespecific treatment study data For multiple parallel GAC contactors with staggered reactivation cycles, it is simulated with Equations 4.1 for WTP-ccam, which is actually integration of a logistic model when number of contactors in the parallel system is greater than ten Model parameters are estimated with xxxii DRAFT Equation 4.2 to 4.4 Blending the treated effluent from multiple parallel GAC columns having staggered reactivation cycles leads to a near steady-state TOC output concentration (USEPA, 1999) According to the analysis for the operation in the Miller treatment plant in Section 2.2, however, the parallel GAC system was not operated in a style with staggered reactivation cycles Additionally, seasonality has been detected for plant inflows, influent TOC concentrations, and number of contactors in use As a result, Equation 4.1 with parameters estimated with Equation 4.2-4.4 may not well present the operation in the Miller plant Therefore, the GAC simulation model and the associated parameters are calibrated with field operational data from GCWW’s Miller treatment plant (Li et al., 2010; Li et al., 2011) 5.2 Algorithm Adsorption processes in water treatment usually conform to one of the well-studied adsorption isotherms given by the Freundlich, Langmuir, or Brunauer-Emmett-Teller (BET) models For a single GAC contactor, the logistic function describes the characteristic 'S' shape of breakthrough curves seen in most plots of TOC concentration versus GAC runtime (Oulman, 1980; Clark, 1987a, 1987b; USEPA, 2005) The mathematical form of the logistic function closely approximates the Langmuir isotherm To model experimental breakthrough behavior at a single GAC column, Chowdhury et al (1996) and Summers et al (1998) applied a modified dimensionless version of the logistic function, which is also used by WTP-ccam platform, given by, f ( t) = TOCeff TOCin = a + be − d ×t (5.1) Where, f ( t ) is defined as TOC fraction remaining; t is total continuous GAC runtime; a, b and d are model parameters that may be determined experimentally by a best fit to the breakthrough data instead of estimation with Equation 4.2-4.4 The parameter a represents the asymptotic steady-state value of f(t) Parameters a and b govern the intercept of the logistic curve while the parameter d affects the steepness of the logistic curve The parameter d is proportional to the influent TOC concentration (USEPA, 2005) Assuming identical flow rates through GAC contactors of equal size, the blended TOC fraction remaining is found as the arithmetic average of the TOC breakthrough curves for each individual contactor operated in parallel (Roberts and Summers 1982), f ( t) = TOC eff m = ∑ fi ( t ) TOCin m i =1 (5.2) Where, m is number of contactors in the parallel system; f ( t ) is the average TOC fraction remaining in the blended effluent from m contactors; fi ( t ) is TOC fraction remaining in the effluent from contactor i at time t When TOC breakthrough, f(t) versus t, dataset are available from field measurements or GAC treatment studies, WTP-ccam applies a modified Gauss-Newton method to estimate model parameters a, b and d by fitting the non-linear regression function (Equation 5.1) through least xxxiii DRAFT square analysis based on Hartley (1961) Hartley’s method is an iterative nonlinear least squares algorithm with objective function, n Min Q ( a, b, d ) = ∑ yk − f ( t k ; a, b, d )  (5.3) k =1 Where, f ( t; a, b, d ) is defined in Equation 5.1; a, b and d are the unknown parameters whose values are to be estimated; tk and yk are the measured breakthrough data representing, respectively, GAC runtime and TOC fraction remaining; n is the number of field data points Each iteration of the method provides a vector of corrected model parameters, θ j = θ j - + vD where, θ j = 1, 2,3, (5.4) is a vector of parameters to be estimated, a θ θ =  b ÷ ÷ , j -1 d ÷   represents the parameter vector from the previous iteration (initialized with typical values from the literature), and θ j represents the corrected parameter vector at the current iteration; D is a correction vector to the initial parameters as a solution from the Gauss-Newton equations, minimize Q ( a , b, d )  D1  D =  D2 ÷ ÷; D ÷  3 v is a value from to to during each iteration The Gauss-Newton equation is given by, (5.5) AD = R Where, A is the Gauss-Newton coefficient matrix, defined as,  n  ∂f 2  2∑  ÷  k =1  ∂a   n ∂f ∂f A =  2∑  k =1 ∂b ∂a  n  ∂f ∂f  ∑  k =1 ∂d ∂a ∂f ∂f ∂ k =1 a ∂b n 2∑ n  ∂f  2∑  ÷ k =1  ∂b  ∂f ∂f ∂ k =1 d ∂b n 2∑ ∂f ∂f  ÷ ∂ k =1 a ∂d ÷ ÷ n ∂f ∂f ÷ 2∑ k =1 ∂b ∂d ÷ 2÷ n  ∂f  ÷ 2∑  ÷ ÷ k =1  ∂d   n 2∑ (note: f = f ( t; a , b, d ) ) (5.6) and R is a right-hand-side vector of Gauss-Newton equation, defined by,  ∂Q   − ∂a ÷  ÷ ∂Q ÷ R = −  ∂b ÷  ÷  − ∂Q ÷ ÷  ∂d  (note: Q = Q ( a, b, d ) ) (5.7) The vector D is obtained by solving Equation 5.5 while v is estimated from the parabola through the points Q ( ν = ) , Q ( ν = 0.5) , and Q ( ν = 1) , given by (Hartley, 1961), xxxiv DRAFT ν=  Q ( ν = ) − Q ( ν = 1)   +  ÷     Q ( ν = 1) − 2Q ( ν = 0.5 ) + Q ( ν = )  (5.8) Where, Q ( ν = ) , Q ( ν = 0.5) , and Q ( ν = 1) represent the Q ( a1 , b1 , d1 ) values evaluated with ν = , ν = 0.5 and ν = in Equation 5.3 From Equation 5.4, the final parameter estimates for a, b and d are obtained when the preset precision requirement for ∆Q ( a, b, d ) is met Otherwise, the iteration process is repeated 5.3 Calibration of the Logistic Model with GCWW Field Measurement GCWW provided weekly values of influent and effluent TOC concentrations for each of the 12 full-scale GAC contactors Using the 76-month sampling period from January 2004 to April 2010, a total of 87 individual TOC breakthrough datasets were identified The final collection of breakthrough datasets for the GAC contactors was expressed in dimensionless form as TOC fraction remaining versus service runtime The 87 datasets from the full-scale contactors were further classified on the basis of their runtime length According to USEPA (1996), GAC pilot studies should be terminated if either of the following two conditions are met: (1) the effluent TOC concentration is 70 percent of the average influent TOC concentration on two consecutive sampling dates separated by at least two weeks, or (2) 50 percent TOC breakthrough occurs and a concentration plateau is reached in which the effluent TOC concentration does not increase by more than 10 percent of the average influent TOC concentration over 60 days Full-scale field measurements from the GCWW operation which met either of these EPA criteria were considered “valid” for logistic model calibration and parameter estimation In all, 46 datasets were identified as valid out of the 87 total individual TOC breakthrough datasets from the 12 full-scale GAC contactors Figures 5.1a and 5.1b illustrate the behavior of TOC breakthrough curves for two of the 46 valid datasets Also shown are plots of the fitted logistic model for a single contactor (based on Equation 5.1) Two sets of RSSCT experimental data were obtained from GCWW The TOC breakthrough behavior including influent and effluent TOC concentrations, and GAC column runtimes were recorded and rescaled to represent prototype conditions at the GCWW Miller plant Figures 5.1c and 5.1d illustrate the TOC breakthrough behavior, including the logistic model fitted to the RSSCT data Each of the 12 GAC contactors yielded three or four valid TOC breakthrough datasets Applying the nonlinear regression algorithm, the valid TOC breakthrough datasets were used to estimate parameters of the logistic model for the single GAC contactor, given in Equation 5.1 The estimated model parameters a, b and d of the logistic function were further averaged for each contactor and also for all 12 GAC contactors The parameter estimates are summarized in Table 5.1 along with some basic information for each contactor including average influent TOC concentration, EBCT statistics, average runtime statistics, number of valid datasets, and percentage of time in use during the 76-month sampling period The runtimes for the GAC contactors range from 214 days to 340 days with an average of 285 days The global average of the estimated parameters a, b and d are 0.782, 8.191 and 0.029 day-1, respectively xxxv DRAFT (a) 0.8 0.6 0.4 Full-scale dataset Logistic model 0.2 TOC fract ion remaining TOC fraction remaining 0 80 160 240 (b) 0.8 0.6 0.4 Full-scale dat aset Logist ic model 0.2 320 Cont actor runtime, days 160 240 320 Contactor runt ime, days 1 (c) 0.8 0.6 0.4 RSSCT dataset Logist ic model 0.2 TOC fraction remaining TOC fract ion remaining 80 (d) 0.8 0.6 0.4 RSSCT dat aset Logist ic model 0.2 0 80 160 240 320 Cont actor runt ime, days 80 160 240 320 Cont act or runtime, days Fig 5.1 Logistic model fitted to full-scale field data and to RSSCT data The logistic model parameters estimated from the RSSCT datasets are listed in Table 5.2 Also shown are parameter estimates from the non-valid and the valid full-scale breakthrough data sets The RSSCT datasets produced parameter estimates which, in two cases (for a and d), are closer to the results obtained from non-valid datasets than the valid full-scale datasets The RSSCT data yielded the lowest estimates for parameter a among all three types of breakthrough datasets Mathematically, the parameter a governs the asymptotic high plateau of the breakthrough curve Consequently, a logistic model calibrated with RSSCT data may not replicate the high extremes in the historical record of effluent TOC observed at the Miller plant Simulations of blended average TOC fraction remaining are compared in Figure 5.2 for two versions of the logistic model against the actual full-scale measurements for the 76-month monitoring period In the system-wide model, all 12 GAC contactors use the same three global average parameters obtained from the 46 valid full-scale datasets: a=0.782; b=8.191, d=0.029 day-1, as listed in Table 5.2 In the RSSCT-based model, all 12 GAC contactors use the same average parameter values from the two RSSCT datasets: a=0.624; b=7.477, d=0.034 day-1, also shown in Table 5.2 Equation 5.2 was used to compute the blended average TOC fraction xxxvi DRAFT remaining for the system-wide and the RSSCT models In both cases, the exact historical sequencing and corresponding runtimes of active GAC contactors at the Miller plant were followed Figure 5.2 shows that the system-wide logistic model closely replicates (R=0.97) the performance history of full-scale field measurements at the Miller plant The good agreement between model and observation is expected since the system-wide logistic model was calibrated using the full-scale datasets Model residual sum of squares (RSS) defined by Equation 5.3 is 0.58 In contrast, the RSSCT-based model is unable to replicate the performance of the multiGAC contactor bank when the TOC fraction remaining is high The poor performance in the RSSCT-based model results from underestimation of the parameter a, leading to a systematic bias in predictions from the logistic model and a relatively high RSS value of 2.24 Table 5.1 Characteristics of individual GAC contactors from 46 valid datasets (2004-2010) GAC Contacto r ID TOCin EBCT (minutes) Runtime (days) Time in Use (1/day) No of Valid Datasets (mg/L) Avg Min Max Avg Min Max 1.74 17.6 12.1 24.4 292 273 305 0.832 8.334 0.027 69.3 1.69 16.7 13.8 21.9 290 235 319 1.71 16.7 13.8 21.9 284 256 312 0.741 9.557 0.031 80.2 0.759 8.124 0.029 4 1.77 17.5 12.1 24.4 293 270 73.0 312 0.770 9.419 0.030 77.4 1.80 16.7 12.1 24.4 289 1.76 16.7 13.8 21.9 282 263 326 0.773 7.502 0.032 72.0 248 312 0.795 6.440 0.032 1.77 17.2 12.1 24.4 70.3 272 214 319 0.811 0.027 1.74 17.2 12.1 68.3 24.4 277 249 305 0.801 7.478 0.028 1.70 17.1 69.4 12.1 24.4 275 242 298 0.799 7.176 0.027 10 1.78 70.3 16.6 13.2 21.9 279 224 305 0.765 6.988 0.026 66.7 a b 8.838 d (%) 11 1.78 17.3 12.1 24.4 293 228 340 0.776 8.588 0.027 79.1 12 1.77 17.5 12.1 24.4 290 263 326 0.767 9.843 0.031 76.5 Systemwide average 1.75 17.1 12.6 23.6 285 247 315 0.782 8.191 0.029 3.8 72.7 Coef of variation 0.02 0.02 0.06 0.05 0.03 0.08 0.04 0.03 0.13 0.08 0.10 0.06 Note: “Time in Use” is based on the full record of 87 datasets All other columns are based on the 46 valid datasets xxxvii DRAFT Table 5.2 Statistics of parameters for logistic model Data Source Average Stan Dev Coef of Var Minimum Maximum 125 30 0.24 80 207 Logistic curve, Parameter a 0.650 0.099 0.15 0.447 0.914 Logistic curve, Parameter b 8.785 4.774 0.54 3.520 29.70 Logistic curve, Parameter d (1/day) 0.037 0.013 0.35 0.021 0.071 285 30 0.11 214 340 Logistic curve, Parameter a 0.782 0.041 0.05 0.679 0.856 Logistic curve, Parameter b 8.191 2.283 0.28 4.656 14.167 Logistic curve, Parameter d (1/day) 0.029 0.006 0.20 0.021 0.045 201 12.7 0.06 192 210 Logistic curve, Parameter a 0.624 0.028 0.06 0.604 0.644 Logistic curve, Parameter b 7.477 2.826 0.38 5.448 9.445 Logistic curve, Parameter d (1/day) 0.034 0.004 0.11 0.031 0.036 Non-valid Full-Scale Datasets (n=41) GAC runtime, (days) Valid Full-Scale Datasets (n=46) GAC runtime, (days) RSSCT Datasets (n=2) GAC runtime, (days) TOC fraction remaining Field measurements System-wide model RSSCT-based model 0.8 0.6 0.4 0.2 1/1/04 1/1/05 1/1/06 1/1/07 1/1/08 1/1/09 1/1/10 Date Fig 5.2 Comparison of fitting GAC models with field measurements xxxviii 1/1/11 DRAFT Summary • WTP-ccam platform, upgraded from the USEPA WTP model, is applied to GCWW’s Richard Miller water treatment plant to demonstrate a method to assess the impact of climate change on drinking water treatment infrastructure • Validation tests show that the WTP-ccam platform provides a reasonable replication of operation in the GCWW’s Miller water treatment plant through comparing to the field data from the USEPA’s ICR database • The impact of climate change induced raw water quality deterioration on water treatment infrastructure was studied using Monte Carlo analysis Results show that the existing treatment train at the Miller plant meets the TOC compliance requirements for a baseline scenario but has high risk of TOC compliance violation under the future climate-change scenario • Adaptation to the operation of the Miller plant through reducing the GAC reactivation period is effective to enhance TOC removal The results show that TOC compliance can be met after the adaptation made to the existing WTP operations • A cost model was set up for the Miller plant to link annual costs and reactivation period for the GAC processing unit Results show that much higher net annual cost is required for complete control of TOC compliance violation but the net annual cost would reduce significantly if a given small risk of violation is permitted • The risk of TOC compliance violation increases as the projected raw water quality deteriorates in years to come The method of adaptation and associated cost analysis to climate change can be useful in planning for infrastructure adaptation due to climate change • The logistic model for TOC breakthrough can be calibrated with full-scale GAC contactor data using Hartley's (1961) nonlinear parameter estimation algorithm Results show that the calibrated logistic model very accurately replicates historical operation of TOC removal in the GAC contactor bank at the Greater Cincinnati Water Works Miller treatment plant over a 76 month period with large fluctuations in flow rate and influent loading • Estimates of the three logistic curve parameters are very sensitive to the runtime length of the breakthrough dataset The logistic model based on RSSCT breakthrough datasets is unable to replicate the high TOC extremes in the historical record of GAC operation at the Miller plant The poor performance of the logistic breakthrough model is attributed, in this case, to very low estimates of the parameter "a" The cause(s) of these anomalous low parameter estimates, based on data from RSSCT experimental runs, is unknown and should be investigated xxxix DRAFT References Adams, J Q and Clark, R M (1988) “Development of Cost Equations for GAC Treatment Systems.” Journal of Environmental Engineering, 114 (3): 672-688 Bras, R.L and Rodriguez-Iturbe, I (1984) “Random Functions and Hydrology.” 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Rep No EPA 814-B-96-003, Office of Water, Washington, D.C xli DRAFT Whitehead, P G., Wilby, R L., Battarbee, R W., Kernan, M and Wade, A J (2009) “A review of the potential impacts of climate change on surface water quality” Hydrological Sciences 54 (1): 101-123 Whitehead, P G., Wilby, R L., Butterfield, D and Wade, A J (2006) “Impacts of climate change on in-stream nitrogen in a lowland chalk stream: An appraisal of adaptation strategies” Science of the Total Environment 365: 260–273 xlii ... achieved during operation by adjusting the number of active GAC contactors to meet GCWW? ??s operational goals including offsetting of the seasonal variation of the inflow, as shown in Figure 2.3 Influent... platform in this case study viii DRAFT 1.2 Report Organization To illustrate the procedure of application of the WTP-ccam platform, this report contains the following chapters in addition to this... of Cincinnati, Cincinnati, Ohio Environmental Engineering and Public Health Consultant, Cincinnati, Ohio This report may be of educational value to various individuals in the water treatment industry,