Department of Civil Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada.
Environ Pollut. 2009 Aug-Sep;157(8-9):2485-92. doi: 10.1016/j.envpol.2009.03.005. Epub 2009 Apr 9.
This study provides a coupled simulation-optimization approach for optimal design of petroleum-contaminated groundwater remediation under uncertainty. Compared to the previous approaches, it has the advantages of: (1) addressing the stochasticity of the modeling parameters in simulating the flow and transport of NAPLs in groundwater, (2) providing a direct and response-rapid bridge between remediation strategies (pumping rates) and remediation performance (contaminant concentrations) through the created proxy models, (3) alleviating the computational cost in searching for optimal solutions, and (4) giving confidence levels for the obtained optimal remediation strategies. The approach is applied to a practical site in Canada for demonstrating its performance. The results show that mitigating the effects of uncertainty on optimal remediation strategies (through enhancing the confidence level) would lead to the rise of remediation cost due to the increase in the total pumping rate.
本研究提供了一种耦合模拟-优化方法，用于在不确定性下优化石油污染地下水修复的设计。与以往的方法相比，它具有以下优势：（1）在模拟地下水非水相液体（NAPLs）的流动和运移时，解决了模型参数的随机性问题；（2）通过创建代理模型，在修复策略（抽提速率）和修复效果（污染物浓度）之间建立了直接的、响应迅速的桥梁；（3）减轻了搜索最优解的计算成本；（4）为获得的最优修复策略提供了置信水平。该方法应用于加拿大的一个实际场地，以展示其性能。结果表明，通过提高置信水平来减轻不确定性对最优修复策略的影响，会由于总抽提速率的增加而导致修复成本的上升。
