| dc.description.abstract | Access to clean drinking water is very important to the health and well-being of the population.
Mathematical modeling, optimization, and online estimation are needed to solve challenging
problems in water network applications such as the requirement to meet the new dynamic
regulations in the Safe Drinking Water Act and the Clean Water Act. This includes providing
sufficient capacity to satisfy uncertain and changing water demands, maintaining consistent water
quality, and identifying and responding to abnormal events. In most of these applications, reliable
knowledge of the water flow velocity is necessary. However, in practice, few measurements are
usually available. This work uses a nonlinear optimization framework to estimate the unknown
water demands and velocities from limited measurements. The problem is formulated as a
constrained nonlinear least squares estimation problem. The constraints represent the basic
governing mass and energy conservation laws as well as some operational constraints. Given the
limited number of flow measurements, the estimation problem is ill-posed. Non-unique solutions
may exist in which many demand profiles can match the limited number of measurements. Offline
estimates of the demand patterns based on historical data are used to regularize the problem and
force a unique solution. In the first phase of this project, a hydraulic model was developed for
water distribution systems. This model showed very good agreement when it was validated against
the simulator EPANET using 3 case studies. In the second phase, the estimation formulation was
tested using the same 3 case studies with different sensor configurations. In each of the case
studies, estimation results are reasonable with fewer sensors than the available degrees of freedom. | en |
