Remediation Of Uranium-Contaminated Groundwater Using Engineered Injection And Extraction
Greene, John A 1 ; Neupauer, Roseanna M 2 ; Ye, Ming 3 ; Kasprzyk, Joseph R 4 ; Mays, David C 5
1 ¾«Æ·SMÔÚÏßӰƬ
2 ¾«Æ·SMÔÚÏßӰƬ
3 Florida State University
4 ¾«Æ·SMÔÚÏßӰƬ
5 University of Colorado Denver
During in situ remediation of contaminated groundwater, a treatment chemical is injected into the contaminated groundwater to react with and degrade the contaminant, with reactions occurring where the treatment chemical contacts the contaminant. Traditional in situ groundwater remediation relies on background groundwater flow for spreading of treatment chemicals into contaminant plumes. Engineered Injection and Extraction (EIE), in which time-varying induced flow fields are used to actively spread the treatment chemical into the contaminant plume, has been developed to increase contact between the contaminant and treatment chemical, thereby enhancing contaminant degradation. EIE has been investigated for contaminants that degrade through irreversible bimolecular reaction with a treatment chemical, but has not been investigated for a contaminant governed by reversible reactions. In this study, we investigate the effectiveness of EIE on remediation of uranium-contaminated groundwater. One technique for remediation of uranium-contaminated groundwater is in situ immobilization, which relies on geochemical processes, such as reduction of soluble U(VI) into insoluble U(IV) or sorption of soluble U(VI) to aquifer media. Reactions that govern uranium immobilization are favorable under certain pH values. In this study, we investigate the ability for EIE to facilitate and sustain favorable conditions to immobilize uranium during remediation, and to prevent re-mobilization of uranium into the aqueous phase after active remediation has ended. The simulations in this investigation are conducted using a semi-synthetic model based on physical and chemical conditions at the Naturita former uranium processing site in southwest Colorado. The EIE design is optimized for the Naturita site using the Borg multi-objective evolutionary algorithm.