Sanitary sewer overflows (SSOs) is the unintentional discharge of untreated sewage from the sanitary sewer system and pose serious risk to public health and to the environment. Rehabilitation plans to reduce SSOs involve increasing conveyance capacity and shaving peak flow using detention storages. Identifying the best location for rehabilitating the sanitary sewer network is a difficult task because of the great length of sanitary sewer systems. This study utilized single and multiobjective genetic algorithms (GAs) to design rehabilitation strategies for SSOs reduction in an existing sewer network. The Nondominated Sorting Genetic Algorithm II was linked to the EPA-SWMM to generate non-dominated sets of solutions that characterizes the tradeoffs between reduction in number of SSOs and cost (Case I), and the tradeoff between of volume of SSOs and cost (Case II). The results show that, when maximizing the reduction of number SSOs, the algorithm target first regions of the network with higher density of SSOs. When maximizing the reduction of volume of SSOs, the solutions prioritize the nodes with the largest overflow volumes. The tested approach provides a range of options to decision makers that seek to reduce or eliminate SSOs in an existing sanitary sewer system.
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This project was funded by the San Antonio Water System (SAWS) under the inter-local agreement: Development of an optimization framework for sanitary sewer overflow reduction, contract No CD-M-14-040-MR. The authors would like to express their gratitude to all SAWS engineers who facilitated the research through positive participation and provision of required data.
Financial assistance from the U.S. Department of Agriculture/National Institute of Food and Agriculture (Award Number: 2014-38422-22088) to support students’ experiential learning is gratefully acknowledged.
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Ogidan, O., Giacomoni, M. Multiobjective Genetic Optimization Approach to Identify Pipe Segment Replacements and Inline Storages to Reduce Sanitary Sewer Overflows. Water Resour Manage 30, 3707–3722 (2016). https://doi.org/10.1007/s11269-016-1373-z
- Sanitary sewer rehabilitation
- Multiobjective optimization
- Sanitary sewer overflow
- Genetic algorithm