Nonlinear optimization for improving the operation of sewer systems: the Bogotá Case Study

Sewer systems are considered as complex large-scale systems that traditionally collect and transport stormwater and sanitary sewage out from urban areas. Each subsystem is in itself composed of a large number of elements with time-varying behavior, exhibiting several operating modes and subject to changes due to external conditions and operational constraints. Sewer systems are mainly operated using pumping stations and pollutants are removed from sewage by treatment plants before water is released into the environment. When a sewer overflow occurs, e.g., caused by a strong rainfall, sewage is discharged directly into the environment with some dilution but without treatment. An efficient use of storage capacities and pumping stations can help to minimize the environmental pollution caused by sewer overflows. In this paper a nonlinear predictive control approach is presented to improve the operation of sewer systems. To deal with the nonlinear and non-differentiable features of the used prediction model, a pattern search method is proposed to solve the underlying optimization problem. The technique proposed is implemented on a part of the sewer system of Bogotá, Colombia. Simulation results illustrate the potential of the approach.