Risk-Based Design of a Sanitary Sewer Overflow Control Plan

摘要

Detailed flow and rainfall measurements, accompanied by long-term simulation, were used to identify the inflow and infiltration response characteristics of the sanitary sewer collection system in Vallejo CA. This response has produced sanitary sewer overflows on numerous occasions throughout various locations in the collection system. A mix of collection system rehabilitation, capacity upgrades, storage and increased treatment capacity may be used to control future wet-weather flows to a regulatory standard. A major limitation to optimizing the design of these wet-weather controls is a relatively high uncertainty regarding the effectiveness of collection system rehabilitation to control wet-weather flows. The ability of collection system rehabilitation activities to control wet-weather flows will in turn affect the performance of all downstream controls, including conveyance, storage and treatment. If rehabilitation effectiveness is over-estimated, overall control performance will not meet design standards, and if underestimated, significant overexpenditure of resources is possible. A risk-based approach was used to identify the importance of rehabilitation effectiveness on the overall design. Probability density functions of wet-weather pollutant control for storage, treatment and rehabilitation were derived from direct observations and used to estimate the overall reliability of various mixes of design alternatives. Based on this analysis, a concentrated smallscale rehabilitation project was used to reduce the uncertainty associated with estimating the performance of rehabilitation for sanitary sewer overflow (SSO) control. rnThe results of the rehabilitation test were used to refine the risk-based design of the SSO control plan. Actual rehabilitation costs were used to estimate a costeffectiveness relationship for this specific collection system. A process model was used to link the performance and costs of storage, treatment, conveyance and rehabilitation. This design model was formulated as a nonlinear optimization problem, and a generalized reduced gradient search algorithm was used to find the best mix of alternatives to meet various regulatory goals in a cost-effective manner. Various levels of cost and risk were then generated with this model for meeting performance expectations, aiding the design team by concentrating their efforts on an array of non-dominated solutions.