Postprint version. Published in Proceedings of the 2007 Conference of the Environmental and Water Resources Institute: Tampa, FL, May 15, 2007, pages 1-10.
Copyright © 2007 American Society of Civil Engineers.
NOTE: At the time of publication, the author Misgana K.Muleta was not yet affiliated with Cal Poly.
The definitive version is available at http://dx.doi.org/10.1061/40927(243)172.
Control of sewer overflows, the leading cause of water pollution in the nation’s water bodies, is vital to reducing risks to public health and protecting the environment. The most common solutions for mitigating sewer overflows include adding storage volume, increasing conduit capacity, expanding pumping capacity, and implementation of real time operational controls to more effectively utilize existing system storage. Obviously, comprehensive modeling and analysis of these sewer systems becomes necessary for developing sound cost-effective and reliable solutions for enhancing system integrity and performance to convey sewer flows without causing overflows. However, identification of the optimal remedial solution that effectively circumvents overflow problems with the least expenditure is a daunting task. The current practice involves a tedious trial-and-error evaluation procedure that seldom leads to the most effective or most economical solutions. Another emerging design approach utilizes single objective optimization that identifies the solution that best satisfies a predefined criterion. The performance criterion used with single objective optimization subjectively lumps the economics objective with metrics that measure effectiveness of the remedial solution from the perspective of avoiding overflows (e.g., minimizing the number of flooding events or reducing the flooding volume). Consequently, the design solution identified using single objective optimization depends on the weights subjectively placed on the two incommensurable and conflicting objectives, and may not represent the global optimal solution. A preferable approach is to seek tradeoff solutions commonly referred to as non-dominated solutions or Pareto-optimal solutions. The methodology proposed here links an extended version of the EPA SWMM 5 model, a comprehensive drainage network simulator, with NSGA-II, an evolutionary multiobjective optimization method with a proven history of identifying Pareto-optimal solutions for a wide range of engineering problems. The method should prove useful to any wastewater utility attempting to improve system integrity, reliability and performance and optimize its capital improvement program.
Civil and Environmental Engineering