Toward closed-loop remediation: A dynamic optimization approach for hydraulic conductivity estimation and...

In the remediation of contaminated sites, the accuracy of the hydraulic conductivity field (K-field) is a critical factor affecting remediation efficiency. Traditional methods, which rely on sparse historical data, short-term pumping tests, and static interpolation methods, often fail to capture heterogeneity of K-fields due to limited monitoring data. This inaccuracy leads to suboptimal remediation designs, resulting in increased costs and reduced efficiency. To address these limitations, this study proposes a dynamic iterative optimization framework that integrates parameter inversion with remediation plan design into a closed-loop system of simulation-observation-update-optimization. The framework iteratively updates the K-field using pilot point parameterization and simulation-optimization techniques, while dynamically adjusting the remediation strategy based on real-time monitoring data. Numerical experiments conducted on a virtual contaminated site demonstrate that the proposed framework significantly improves the accuracy of K-field characterization, as evidenced by decreasing logarithmic root mean square error (LRMSE) and increasing spatial correlation coefficient (SCC) over iterations. Critically, when compared with a conventional static remediation plan that is designed once and executed without updates, the dynamic framework achieves a substantially higher contaminant removal rate while simultaneously reducing the total pumping volume. These results highlight the framework' s potential to enhance remediation effectiveness and reduce operational costs in heterogeneous aquifers, offering a practical and adaptive solution for complex contaminated site management.