Pump-and-treat optimization using well locations and pumping rates as decision variables

Abstract

A new optimization formulation for dynamic groundwater remediation management is developed by simultaneously using well locations and the corresponding pumping rates as the decision variables. The genetic algorithm is applied to search for optimal pumping rates and the discrete space of well locations. The optimization model is applied to hypothetical, three-dimensional, contaminated aquifer systems with homogeneous and heterogeneous porous media properties. Optimal well locations and pumping rates obtained with the moving-well model are less expensive than solutions obtained with a comparable fixed-well model. Optimization with a linear objective function formulation identifies some of the optimal well locations obtained with a nonlinear formulation but results in higher pumping rates than the nonlinear formulation and ignores the higher drawdowns produced in low-permeability areas. Optimal well locations are found along the mass centerline of the contaminant plumes and in high-permeability areas in the heterogeneous system. Dynamic pumping rates and well locations produce more cost-effective solutions relative to a static model. The well location search path and convergence behavior indicate that the genetic algorithm is an effective alternative solution scheme and that well location optimization is more important than pumping rate optimization.