Water Quality Model Calibration under Unknown Demands P. M. R. Jonkergouw 1 ; S.-T. Khu 2 ; Z. S. Kapelan 3 ; and D. A. Savić 4 Abstract: It has often been cited that a water distribution system WDS hydraulic model needs to be highly accurate before it may be used in combination with a water quality model WQM to simulate the dispersion and decay of a residual disinfectant. However, even a well-calibrated WDS hydraulic model may not have data relating to the specific water demands during a given period, which may impede WQM calibration. This study examines using residual disinfectant data to calibrate a WQM under unknown or uncertain demands by calibrating a residential demand multiplier pattern DMP in tandem with the WQM parameters. Two artificial scenarios and one real case study are investigated. The artificial scenarios are used to 1 verify the proposed methodology under ideal conditions and 2 validate the proposed methodology when the hydraulic model and calibration data contain realistic errors. The real case study uses residual chlorine data and a WDS model for which a hydraulic and WQM calibration had been performed previously. The estimated demands from the real case study are validated using tracer test data. Results from the artificial case studies may be summarized as follows: 1 the proposed methodology can estimate the demands and calibrate WQM parameters correctly, although increasing model and calibration data errors adversely affect calibration results; 2 the calibrated WDS models reproduce the true residual chlorine concentrations with very little error. Results from the real case study indicate that the original WQM calibration was performed using underestimated WDS demands. Tracer test data confirm that the calibrated DMP provides good hydraulic velocities. The calibrated WDS model from the real case study is in good agreement with measured residual chlorine concentrations. The mean absolute error between the simulated chlorine concentrations from the calibrated network model and the observed values is 0.059 mg / L. DOI: 10.1061/ASCE0733-94962008134:4326 CE Database subject headings: Water quality; Calibration; Water distribution systems; Hydraulic models. Introduction Chlorine is widely used as a drinking water disinfectant because of its applicability, relatively low cost, effectiveness, and ability to provide disinfectant residual throughout a water distribution system WDS. However, a major drawback of chlorine as a dis- infectant is the formation of harmful disinfection by-products and possible odor problems. Therefore, the use of chlorine as a disin- fectant becomes a trade off: An increased chlorine dosage in- creases disinfection by-product formation, while a lower chlorine dosage has diminished disinfection efficiency. In addition, chlo- rine undergoes chemical reactions in the bulk of the water and at the pipe wall, which causes the concentration to decay over time, thereby necessitating even higher chlorine dosages at the water treatment plant in order to maintain a residual chlorine concentra- tion capable of preventing microbial regrowth and limiting bio- film augmentation throughout the WDS. The complex behavior of chlorine dispersion and decay throughout a WDS can be modeled using a chlorine decay model coupled with a hydraulic WDS model. Results from water quality model WQM simulations may be used to optimize the chlorine dosing at the water treatment plant or to determine optimum chlo- rine booster locations Cozzolino et al. 2005. However, a model requires calibration before useful results may be obtained Walski 1983. While the calibration of water quantity WDS models has been researched extensively Kapelan et al. 2003, 2006, only a limited number of studies have been dedicated to calibrating WQM parameters. Vasconcelos et al. 1997 calibrated global wall correlation coefficients for various WDS models through trial and error, Maier et al. 2000 used a reverse-calibration method, and Munavalli and Mohan Kumar 2003, 2005 applied an automated Gauss-Newton minimization technique to calibrate WQM parameters under dynamic state conditions. The preferred method for WDS WQM calibration requires an extended period simulation EPS and, thus, one or more demand multiplier pat- terns DMPsassuming demand-driven WDS modeling soft- ware. Previous studies have assumed that the underlying hydraulic model flows and demands from the WDS are correct prior to WQM calibration. It has been shown that the reliability of results from WQM parameter calibration is not only largely de- pendent on the quality of the calibration data Maier et al. 2000, but also on the accuracy of the underlying hydraulic model Vas- concelos et al. 1997. Thus, errors in a WDS hydraulic model could result in the incorrect calibration of WQM parameters. While nothing may be done about the quality of the residual disinfectant data, the accuracy of the underlying hydraulic model may be improved upon through calibration of demands using re- 1 Research Student, Center for Water Systems, Univ. of Exeter, Exeter EX4 4QF, U.K. E-mail: P.M.R.Jonkergouw@exeter.ac.uk 2 Senior Lecturer, Center for Water Systems, Univ. of Exeter, Exeter EX4 4QF, U.K. E-mail: S.T.Khu@exeter.ac.uk 3 Senior Lecturer, Center for Water Systems, Univ. of Exeter, Exeter EX4 4QF, U.K. E-mail: Z.Kapelan@exeter.ac.uk 4 Codirector, Center for Water Systems, Univ. of Exeter, Exeter EX4 4QF, U.K. E-mail: D.Savic@exeter.ac.uk Note. Discussion open until December 1, 2008. Separate discussions must be submitted for individual papers. To extend the closing date by one month, a written request must be filed with the ASCE Managing Editor. The manuscript for this paper was submitted for review and pos- sible publication on September 19, 2006; approved on July 23, 2007. This paper is part of the Journal of Water Resources Planning and Manage- ment, Vol. 134, No. 4, July 1, 2008. ©ASCE, ISSN 0733-9496/2008/4- 326–336/$25.00. 326 / JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT © ASCE / JULY/AUGUST 2008 J. Water Resour. Plann. Manage. 2008.134:326-336. Downloaded from ascelibrary.org by University of Surrey on 01/23/15. Copyright ASCE. For personal use only; all rights reserved.