Postprint version. Published in Proceedings of the 2008 Conference of the Environmental and Water Resources Insitute: Honolulu, Hawaii, May 12, 2008, pages 1-10.
NOTE: At the time of publication, the author Misgana K. Muleta was not yet affiliated with Cal Poly.
Excessive wet weather flow resulting from rainfall-derived inflow and infiltration (RDII) is a major source of sanitary sewer overflows (SSOs). SSOs pose serious problem to the public and the environment by causing back up into basements and sewer overflows to streets and rivers. Control of sewer overflows is, therefore, vital to reducing risks to public health and protecting the environment from water pollution. Computer modeling of sewer collection systems plays an important role in determining sound and economical remedial solutions that reduce RDII, improve system integrity, reliability and performance, and avoid overflows. This paper presents a rigorous and efficient three-step optimization methodology for use in solving the sewer overflow problem. The first step analyzes measured sewer flow and rainfall data and decomposes the flow data into dryweather flow and wet-weather flow components. The second step computes the optimal RTK parameters of the tri-triangular unit hydrograph that is commonly used to model RDII into the sewer collection system. The optimal RTK parameters are calibrated with genetic algorithm so that the simulated RDII flows closely match the RDII time series generated by decomposing the measured flow data. In the final step, the calibrated model is then used with genetic algorithm to design cost-effective solutions for existing SSO problems. Design parameters can include any combinations of pipe size, storage, slope, and pumping. The proposed wet-weather flow decomposition, optimal calibration, and optimal design models are demonstrated using an example sewer collection system. The methodology seems a good alternative to other methods proposed in the literature and should prove useful for engineers and planners that are involved in mitigating complex SSO problems.
Civil and Environmental Engineering