Many-Objective Design Of Engineered Injection And Extraction Sequences To Optimize In Situ Remediation Of Contaminated Groundwater
Piscopo, Amy N 1 ; Neupauer, Roseanna M 2 ; Kasprzyk, Joseph R 3 ; Mays, David C 4
1 University of Å·ÃÀ¿Ú±¬ÊÓƵ Boulder
2 University of Å·ÃÀ¿Ú±¬ÊÓƵ Boulder
3 Pennsylvania State University
4 University of Å·ÃÀ¿Ú±¬ÊÓƵ Denver
Groundwater is an important resource that is often contaminated by various industrial and agricultural sources. Techniques to remediate contaminated groundwater exist but could be improved. Specifically, in situ remediation is a favorable form of groundwater remediation, in which a treatment solution is injected into the contaminated aquifer to degrade the contaminated groundwater in place. However, the degradation that occurs during in situ remediation is limited to areas where the treatment solution and groundwater contaminant contact each other, which are often small and invariable. Natural phenomena, such as ambient groundwater flow and natural heterogeneity, provide a degree of spreading which can influence the position of the treatment solution relative to the contaminant, allowing for degradation reactions to occur. Engineered injection and extraction (EIE) is a novel technique that can enhance spreading significantly, using a sequence of injections and extractions of clean water at wells that surround the groundwater contaminant plume and the added treatment solution. In consequence, EIE leads to more contaminant degradation than natural phenomena and ultimately reduces the duration of treatment. While the improvement over natural phenomena is significant, EIE can likely increase contaminant degradation beyond the amount demonstrated previously, since prior work was conducted for one unique sequence of EIE. New approaches are needed to optimize the EIE sequence according to engineering performance objectives and constraints, where the primary objective is to maximize contaminant degradation, for example. This study develops a multi-objective evolutionary algorithm (MOEA), a search algorithm based on the mechanics of natural selection and genetics, for that purpose. The ultimate value of the MOEA lies in its ability to determine the optimal EIE sequence for any contaminated site (e.g. for sites with varied degrees of aquifer heterogeneity, aqueous versus sorbing contaminants, numbers of wells, and locations of wells); therefore, it is a valuable tool that expands the relevance and applicability of EIE.