Optimization of a Stormwater Quality Management Pond System

摘要

Stormwater system management in urban watersheds is often achieved by effective design and use of detention ponds which help mitigate impacts of urban drainage from water quantity and quality perspectives. The costs associated with these tasks for management can be minimized considering the fulfillment of objectives of environmental and regulatory compliance. Nonlinear and mixed integer nonlinear programming (MINLP) formulations with discrete and binary variables is developed in this study to obtain an optimal design for a multiple stormwater detention pond system. The main objective considered is the minimization of cost constrained on system performance related to pollution control. Analytical probabilistic expressions in mathematically closed form for system performance depicting watershed hydrology, control system hydraulics and pollution removal processes are integrated into the optimization formulations. Gradient-based NLP and genetic algorithm-based solvers are used to obtain optimal solutions. Application of the methodology is demonstrated with a hypothetical case study system with realistic hydrologic and water quality parameter values and the benefits of solutions for effective pollution control are reported. Results from the solutions of the formulations provide optimal design parameters considering the runoff control and pollutant reduction considering environmental and regulatory constraints. A comparison of results from these formulations to those from a dynamic programming (DP) formulation developed in an earlier study indicates that limitations associated with discretization within DP can be overcome with the proposed optimal formulations.