Groundwater Remediation Strategy Using Global Optimization Algorithms

Abstract

The remediation of groundwater contamination by pumping and injection is generally a long-term and costly strategy. Aquifer cleanup time is a highly nonlinear and nonconvex function of pumping rates. The cleanup objective often involves minimizing or constraining the cleanup time or cleanup cost. Linear programming and nonlinear optimization cannot guarantee the global solution. In this study, four global optimization (GO) algorithms, including a popular genetic algorithm, are used to minimize both cleanup time and cleanup cost taking pumping rates and/or well locations as decision variables. Groundwater flow and particle-tracking models (MODFLOW and MODPATH) and a GO tool (GLOBE) are used. Real and hypothetical contaminated aquifers are considered for application. The results are satisfactory and show that GO techniques can be widely applied in groundwater remediation strategy and planning. The comparison of the performance of algorithms did not reveal a clear winner. The results also show that in the particle-tracking method, excluding few particles from removal can significantly reduce the cleanup time.