Optimization of intermittent pumping schedules for aquifer remediation using a genetic algorithm

Using a genetic algorithm (GA), optimal intermittent pumping schedules were established to simulate pump-and-treat remediation of a contaminated aquif er with known hydraulic limitations and a water miscible contaminant, locat ed within the Duke Forest in Durham, North Carolina. The objectives of the optimization model were to minimize total costs, minimize health risks,and maximize the amount of contaminant removed from the aquifer. Stochastic gro und water and contaminant transport models were required to provide estimat es of contaminant concentrations at pumping wells. Optimization model simul ations defined a tradeoff curve between the pumping cost and the amount of contaminant extracted from the aquifer. For this specific aquifer/miscible contaminant combination, the model simulations indicated that pump-and-trea t remediation using intermittent pumping schedules for each pumping well pr oduced significant reductions in predicted contaminant concentrations and a ssociated health risks at a reasonable cost, after a remediation time of tw o years.