Drinking Water Treatment Plant Design Incorporating Variability and Uncertainty

Both inherent natural variability and model parameter uncertainty must be considered in the development of robust and reliable designs for drinking water treatment. This study presents an optimization framework for investigating the effects of five variable influent parameters and three uncertain model parameters on the least-cost treatment plant configuration contact, direct, or nonsweep conventional filtration that reliably satisfies an effluent particulate matter concentration constraint. Incorporating variability and uncertainty within the decision-making framework generates information for investigating: 1 impacts on total cost and treatment reliability; 2 shifts on the least-cost treatment configuration for providing reliable treatment; and 3 the importance of the individual variable and uncertain parameter distributions for reliably satisfying an effluent water quality constraint. Increasing the magnitude of influent variability and model parameter uncertainty results in a greater expected design cost due, generally, to increases in process sizing required to reliably satisfy the effluent concentration constraint. The inclusion of variability and uncertainty can also produce a shift in the locations of the least-cost configuration regions, which are dependent on the expected influent water quality and the magnitude of variability and uncertainty. The additional information provided by incorporating the variable and uncertain parameters illustrates that parameter distributions related to the primary removal mechanism are critical, and that contact and direct filtration are more sensitive to variability and uncertainty than conventional filtration. DOI: 10.1061/ ASCE 0733-9372 2007 133:3 303 CE Database subject headings: Potable water; Water treatment plants; Stochastic processes; Optimization; Uncertainty principles. Introduction and Objectives Decision making related to drinking water treatment is, in general, a complex process that must satisfy multiple treatment objectives through the design of integrated treatment and waste processes while constraining costs. Additionally, there is inherent variability that must be considered, yet cannot be controlled at least not easily , such as flow rate, influent water quality concentrations, and temperature. The effects of these variations must be considered to ensure that the effluent water quality reliably satisfies the appropriate regulatory statutes. More formalized approaches that utilize mathematical models to assist in the design process add additional complexities. The majority of process models require some of the model parameters to be estimated using experimental or field data, or gathered from expert elicitaOffice of Research and Development, National Homeland Security Research Center, U.S. Environmental Protection Agency, MS 163, 26 W. Martin Luther King Dr., Cincinnati, OH 45268 corresponding author . E-mail: [email protected] H. John Heinz III Professor of Environmental Engineering, Dept. of Civil and Environmental Engineering and Dept. of Engineering and Public Policy, Carnegie Mellon Univ., Pittsburgh, PA 15213. President, Center for Uncertain Systems: Tools for Optimization and Management, Vishwamitra Research Institute, 34 North Cass Ave., Westmont, IL 60559. Note. Discussion open until August 1, 2007. 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 possible publication on October 19, 2005; approved on June 14, 2006. This paper is part of the Journal of Environmental Engineering, Vol. 133, No. 3, March 1, 2007. ©ASCE, ISSN 0733-9372/2007/3-303–312/ $25.00.