Predicting Remediation Efficiency of LNAPLs using Surrogate Polynomial Chaos Expansion Model and Global Sensitivity Analysis
Predicting Remediation Efficiency of LNAPLs using Surrogate Polynomial Chaos Expansion Model and Global Sensitivity Analysis
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DOI：
10.1016/j.advwatres.2022.104179
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发表时间：
2022-03
影响因子：
4.7
通讯作者：
Taehoon Kim;W. Han;J. Piao;P. Kang;Jehyun Shin
中科院分区：
文献类型：
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作者：
Taehoon Kim;W. Han;J. Piao;P. Kang;Jehyun Shin
多相、多组分数值模拟器评估了由苯、甲苯、乙苯和二甲苯组成的轻质非水相液体 (LNAPL) 的去除效率。 LNAPL 溢出、自然分布和修复阶段的场景是在全物理数值模型中设计的。对于 LNAPL 修复，采用了多相萃取 (MPE) 和蒸汽注入技术。 LNAPL 的去除效率是通过系统地改变决定修复井配置的 6 个因素来计算的。然后，通过代表 4 个场景案例的 600 个训练数据集，开发了以数学方式预测去除效率的代理多项式混沌展开 (PCE) 模型；情景案例中考虑了不同的渗透率和 SI 井的位置。 PCE 模型用于 Sobol 全局敏感性分析和随机蒙特卡罗预测。因此，MPE 井的深度被认为是决定 LNAPL 去除效率的最重要因素。当 MPE 井位于地下水位以下 1.5 m 时，去除效率最大化。此外，现场渗透率也显着改变了影响因素的贡献。本研究提出了一个通用框架，通过结合先进的数值模型、基于 PCE 的替代模型和敏感性分析，有效预测 LNAPL 修复效率并确定关键影响因素。
A multi-phase and multi-component numerical simulator assessed the removal efficiencies of light non-aqueous phase liquid (LNAPL) consisting of benzene, toluene, ethylbenzene, and xylene-p. Scenarios of the LNAPL-spilling, natural distribution, and remediation stages were designed in the full-physics numerical modeling. For LNAPLs remediation, a multi-phase extraction (MPE) and a steam injection technique were employed. The removal efficiencies of LNAPLs were computed by systematically varying 6 factors that determine the configuration of the remediation wells. Then, surrogate polynomial chaos expansion (PCE) models mathematically predicting the removal efficiencies were developed through 600 training datasets representing 4 scenario cases; different permeability and the location of SI well were considered in the scenario cases. The PCE models were utilized for Sobol global sensitivity analysis and stochastic Monte Carlo prediction. As a result, the depth of the MPE well was identified as the most significant factor in determining the removal efficiency of the LNAPLs. The removal efficiency was maximized when the MPE well was positioned 1.5 m below the groundwater table. Additionally, the contributions of influencing factors were significantly changed by the field permeability. This study proposed a general framework that efficiently predicts LNAPLs remediation efficiency and identifies key influencing factors by combining advanced numerical modeling, PCE-based surrogate modeling, and sensitivity analyses.