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- W2320125079 abstract "To cost-effectively restore contaminated sites to productive use requires integrated planning of cleanup and reuse. Ultimate reuse objectives should guide remediation decisions, instead of being dictated by pre-described remediation plans. This study links a robust groundwater remediation approach with reuse planning. A genetic algorithm optimizer that is integrated with a subsurface contaminant transport model (The Modular Groundwater Optimizer) is modified to allow for a robust search. A robust approach is used to increase the likelihood of successful remediation in the context of the uncertainties in the system. The genetic algorithm objective is to find a low cost and reliable remediation plan. The remediation costs are then reduced by the expected reuse benefits. Reuse benefits are dependent on the time required for the remediation stage and on the endpoint of remediation. As more complete remediation is reached additional reuse options may become feasible. An analysis approach is developed and will be demonstrated to explore the trade-offs between increasing costs for more complete, faster, and more reliable remediation versus the potential reuse benefits gained from implementing more profitable reuse options and from beginning reuse sooner. The case study shown is based on the Emmell's septic landfill on the National Priorities List." @default.
- W2320125079 created "2016-06-24" @default.
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- W2320125079 date "2004-06-25" @default.
- W2320125079 modified "2023-09-23" @default.
- W2320125079 title "Robust Optimal Groundwater Remediation Design and Reuse Options" @default.
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- W2320125079 doi "https://doi.org/10.1061/40737(2004)95" @default.
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