In situ bioremediation of perchlorate-contaminated groundwater using a multi-objective parallel evolutionary algorithm

Abstract

Combining horizontal flow treatment wells (HFTWs) with in situ biodegradation is an innovative approach with the potential to remediate perchlorate-contaminated groundwater. A model has been developed that combines the groundwater flow induced by HFTWs with biodegradation processes that result from using the HFTWs to mix electron donor into perchlorate-contaminated groundwater. The model can be used to select engineering design parameters that optimize performance under given site conditions. In particular, one desires to design a system that 1) maximizes perchlorate destruction, 2) minimizes treatment expense, and 3) attains regulatory limits on downgradient contaminant concentrations. Unfortunately, for a relatively complex technology like in situ bioremediation, system optimization is not straightforward. In this study, a general multi-objective parallel evolutionary algorithm call GENMOP is developed and used to stochastically determine design parameter values (flow rate, well spacing, concentration of injected electron donor, and injection schedule) in order to maximize perchlorate destruction while minimizing cost. Results indicate that the relationship between perchlorate mass removal and operating cost is positively correlated and nonlinear. For equivalent operating times and costs, the solutions show that the technology achieves higher perchlorate mass removals for a site having both higher hydraulic conductivity as well as higher initial perchlorate concentrations.