Optimizing Ethanol Enhanced NAPL Remediation Using Evolutionary Algorithms

In the past decades, subsurface non-aqueous phase liquid (NAPL) contamination has been recognized as one of the most widespread and challenging environmental problems. Thus, researchers have focused their efforts on developing and testing the efficiency of remediation methodologies, able to address the unique nature of these contaminants. Recently, in-situ flooding techniques for the accelerated removal of NAPLs trapped in the subsurface have been proposed, where additives are injected together with water upgradient of the NAPL-contaminated area in order to alter the physio-chemical properties of the contaminants, such as interfacial tension, and enhance their solubilities. In this work, the efficiency of ethanol enhanced NAPL remediation is addressed. To this end, a non-linear, multi-objective optimization strategy is developed by combining a multiphase flow simulation model with evolutionary algorithms. Two conflicting optimization objectives are considered: minimizing operation cost and maximizing remediation efficiency, while preventing uncontrolled NAPL mobilization. More specifically, the first objective involves the operation cost of the procedure, which is directly proportional to the pumping rate, duration and ethanol volume used. The second represents the environmental considerations of the problem that, in this work, are described by the maximization of free product removal and the prevention of DNAPL vertical spreading.