Many-Objective Design of Engineered Injection and Extraction Sequences for In Situ Remediation of Contaminated Groundwater

Although in situ treatment methods exist for groundwater contamination remediation, it is often difficult to increase the efficiency of the reactions under time constraints. During in situ remediation, a treatment solution is injected into the contaminated region of the aquifer to react with and degrade the groundwater contaminant. Ideally, the treatment solution should be spread throughout the contaminated region to increase its contact with the contaminant, leading to more opportunities for degradation reactions. Engineered injection and extraction (EIE) is a promising technique for generating spreading, in which transient flow fields are induced using a sequence of injections and extractions of clean water at wells surrounding the contaminant plumes. While simulations of in situ remediation using a single unique sequence of EIE have shown that spreading due to EIE can enhance reaction, a new approach is needed to design high-quality EIE designs that consider multiple engineering performance objectives. This study uses a multiobjective evolutionary algorithm (MOEA) to determine a set of EIE designs that balance multiple objectives, such as maximizing the amount of reaction while minimizing the amounts of required treatment solution and extracted groundwater contaminant. Such tradeoff surfaces allow stakeholders to determine how EIE designs perform under various degrees of aquifer heterogeneity, types of contaminant (aqueous or sorbed), number of wells, and location of wells.