Application of a Multiobjective Genetic Algorithm to Reduce Wet Weather Sanitary Sewer Overflows and Surcharge

Sanitary sewer overflow (SSO) is the discharge of wastewater from the collection network into the environment. SSOs are significant environmental and public health hazards. The EPA estimates that up to 75,000 SSO events occur in the United States each year. One of the main causes of SSOs is excessive rainfall derived inflow and infiltration into the sanitary sewer network. This study applies a multiobjective genetic algorithm (MOGA) to identify near-optimal solutions to maximize the reduction of the number of SSOs, volume of SSOs, and surcharge, while minimizing costs for sewer rehabilitation. Three approaches are investigated: (1) enhancing the flow capacity of the collection and conveyance system by pipe diameter increase, (2) peak flow reduction using decentralized inline storage tanks, and (3) combining pipe capacity enhancement with inline storages. The approach is tested in a 15.3-square-kilometer sewer network located west of downtown San Antonio, Texas. The results show that the Combination strategy outperforms both the pipe capacity enhancement and the inline storage strategies. Reduction of surcharge lowers the pressure in the entire network and also provides substantial reduction of SSOs volumes.