Multi-Objective Design of Natural Attenuation with Active Remediation Systems Under Uncertainty Using Genetic Algorithms

The objective of this study to analyze the effects of parameter uncertainty on the optimal design of cost-effective and reliable remediation plans that use natural attenuation with active remediation. An optimization framework is used identify appropri ate remediation strategies that are inexpensive and effective under several scenarios of parameter uncertainty, thus assessing the potential of natural attenuation with active remediation. The overall simulation-optimization framework combines a contaminant fate and transport simulation model and spatially -correlated random field generator with the enhanced multi-objective robust genetic algorithm (EMRGA) optimization approach. The optimization problem minimizes the expected cost of the natural attenuatio n-active remediation system while minimizing the expected dissolved contaminant concentration, given uncertainty and heterogeneity in system parameters. Decision variables are the extraction well locations and rates of the active remediation system. The EMRGA evolves a set of Pareto -optimal solutions that represent the best designs that identify the trade-off between the expected cost and clean -up level. The optimization model is applied to a problem based on a field site located in Eglin Air Force Base, Florida, contaminated with benzene. The optimization model is subjected to a series of cases, which vary the model parameters and degree of uncertainty associated with these parameters. The uncertain parameters examined in this study are the heterogeneous hydraulic conductivity, hydraulic gradient, and first -order benzene degradation rate. The results indicate that hydraulic conductivity is the most sensitive parameter, increasing the difficulty in achieving lower cleanup levels and higher remediation reliabilities.

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