Exploring and finding optimal Pump-and-Treat (P&T) strategies at chlorinated solvent-contaminated aquifer

Aqueous chlorinated solvent could affect severe groundwater contamination which requires effective remediation. Genetic Algorithm (GA) is one of widely used algorithms that help finding an optimal remediation design. In this study, we applied Simulation-Optimization modeling (S-O modeling) to identify major wells which have much contribution to optimal Pump-and-Treat (P&T) design at chlorinated solvent-contaminated aquifer. For simulation model, MODFLOW is used to simulate groundwater flow and MT3D is applied to simulate the concentration distribution of chlorinates. For optimization model, Non-dominated Sorting Genetic Algorithm-ii (NSGA-ii) is applied to estimate optimal pumping rate at pre-existed wells that could reduce the concentration of chlorinated solvent. Bi-objective function is formulated as minimizing the expense of pumping cost and the concentration of chlorinated solvent. The result of optimization shows tradeoff relationship between cost and concentration, but the number of optimal solution points is decreased in the case of higher costs. Also, the change of pumping rate is obtained subject to cost increase, and wells located near down-gradient from chlorinated solvent source zone show much change. As we simulate model which includes pumping wells, the concentration of chlorinated solvent is decreased much by pumping wells located in down-gradient from chlorinated source zone which means those have much contribution for optimal P & T design.

Acknowledgement: This work was supported by Korea Environment Industry & Technology Institute(KEITI) through "Activation of remediation technologies by application of multiple tracing techniques for remediation of groundwater in fractured rocks" funded by Korea Ministry of Environment (MOE) (Grant number:20210024800002/1485017890), Korea Environment Industry & Technology Institute(KEITI) through the Demand Responsive Water Supply Service Program funded by the Korea Ministry of Environment (MOE)(146526) and Korea Ministry of Environment as "The SEM projects; RE2020002470001/1485017133".