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A multi-objective optimization framework for surfactant-enhanced remediation of DNAPL contaminations

机译:表面活性剂增强的DNAPL污染修复的多目标优化框架

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The occurrence of Dense Non-Aqueous Phase Liquid (DNAPL) contaminations in the subsurface is a threat for drinkwater resources in the western world. Surfactant-Enhanced Aquifer Remediation (SEAR) is widely considered as one of the most promising techniques to remediate DNAPL contaminations in-situ, be it with considerable additional costs compared to classical pump-and-treat remediations. A cost-effective design of the remediation set-up is therefore essential. In this work, a pilot SEAR test is executed at a DNAPL contaminated site in Belgium in order to collect data for the calibration of a multi-phase multi-component model. The calibrated model is used to assess a series of scenario-analyses for the full-scale remediation of the site. The remediation variables that were varied were the injection and extraction rate, the injection and extraction duration, and the surfactant injection concentrations. A constrained multi-objective optimization of the model was applied to obtain a Pareto set of optimal remediation strategies with different weights for the two objectives of the remediation: (ⅰ) the maximal removal of DNAPL and (ⅱ) a total minimal cost. These Pareto curves can help decision makers to select an optimal remediation strategy in terms of cost and remediation efficiency. The Pareto front shows a considerable trade-off between the total remediation cost and the removed DNAPL mass.
机译:在地下,密集的非水相液体(DNAPL)污染的发生是对西方世界饮用水资源的威胁。表面活性剂增强含水层修复(SEAR)被广泛认为是就地修复DNAPL污染物的最有前途的技术之一,与传统的泵送处理相比,它具有相当大的额外成本。因此,具有成本效益的补救措施设计至关重要。在这项工作中,在比利时的DNAPL污染现场执行了SEAR试点测试,以收集用于校准多相多组分模型的数据。校准后的模型用于评估针对现场全面修复的一系列方案分析。修复变量有所不同,分别是进样和萃取速率,进样和萃取时间以及表面活性剂的进样浓度。针对该修复的两个目标,应用模型的受限多目标优化来获得帕累托最优补救策略集,这些策略具有不同的权重:(ⅰ)DNAPL的最大去除率和(ⅱ)总的最小成本。这些Pareto曲线可以帮助决策者根据成本和修复效率选择最佳的修复策略。 Pareto前沿显示了总修复成本与DNAPL去除量之间的重大折衷。

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