A Field Demonstration of the Simulation Optimization Approach for Remediation System Design
A Field Demonstration of the Simulation Optimization Approach for Remediation System Design
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DOI：
10.1111/j.1745-6584.2002.tb02653.x
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发表时间：
2002-05
期刊：
影响因子：
2.6
通讯作者：
C. Zheng;P. Wang
中科院分区：
文献类型：
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作者：
C. Zheng;P. Wang
尽管地下水修复设计的模拟/优化（S/O）方法的理论发展已经取得了重大进展，但其在大型现场问题中的应用仍然有限。为了证明 S/O 方法在实际现场条件下的适用性和实用性，在马萨诸塞州科德角的马萨诸塞州军事保留地进行了一个优化示范项目，其中包括设计一个用于遏制和清理大型三氯乙烯 (TCE) 羽流的泵处理系统。本研究中使用的优化技术基于进化算法和响应函数方法，以提高计算效率。 S/O 分析与仅基于模拟的传统试错分析并行进行。本研究的结果表明，在试错设计中假设的相同抽水量下，不仅可以去除更多的 TCE 质量，而且通过减少所需的井数和采用动态抽水，还可以节省大量成本。尽管模型规模超过50万个节点，规划周期长达30年，优化建模还是在桌面PC上成功进行。该现场示范项目清楚地说明了在修复系统设计中应用优化技术的潜在好处。
While significant progress has been made in the theoretical development of the simulation/optimization (S/O) approach for ground water remediation design, its application to large, field‐scale problems has remained limited. To demonstrate the applicability and usefulness of the S/O approach under real field conditions, an optimization demonstration project was conducted at the Massachusetts Military Reservation in Cape Cod, Massachusetts, involving the design of a pump‐and‐treat system for the containment and cleanup of a large trichloroethylene (TCE) plume. The optimization techniques used in this study are based on evolutionary algorithms coupled with a response function approach for greater computational efficiency. The S/O analysis was performed parallel to a conventional trial‐and‐error analysis based on simulation alone. The results of this study demonstrate that not only would it be possible to remove more TCE mass under the same amount of pumping assumed in the trial‐and‐error design, but also substantial cost savings could be achieved by reducing the number of wells needed and adapting dynamic pumping. In spite of the large model size of more than 500,000 nodes and a long planning horizon of 30 years, the optimization modeling was carried out successfully on desktop PCs. This field demonstration project clearly illustrates the potential benefits of applying optimization techniques in remediation system design.