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Reducing Long-Term Remedial Costs by Transport Modeling Optimization

机译：通过运输模型优化降低长期补救成本

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The Department of Defense (DoD) Environmental Security Technology Certification Program and the Environmental Protection Agency sponsored a project to evaluate the benefits and utility of contaminant transport simulation-optimization algorithms against traditional (trial and error) modeling approaches. Three pump-and-treat facilities operated by the DoD were selected for inclusion in the project. Three optimization formulations were developed for each facility and solved independently by three modeling teams (two using simulation-optimization algorithms and one applying trial-and-error methods). The results clearly indicate that simulation-optimization methods are able to search a wider range of well locations and flow rates and identify better solutions than current trial-and-error approaches. The solutions found were 5% to 50% better than those obtained using trial-and-error (measured using optimal objective function values), with an average improvement of ～20%. This translated into potential savings ranging from $600,000 to $10,000,000 for the three sites. In nearly all cases, the cost savings easily outweighed the costs of the optimization. To reduce computational requirements, in some cases the simulation-optimization groups applied multiple mathematical algorithms, solved a series of modified subproblems, and/or fit "meta-models" such as neural networks or regression models to replace time-consuming simulation models in the optimization algorithm. The optimal solutions did not account for the uncertainties inherent in the modeling process. This project illustrates that transport simulation-optimization techniques are practical for real problems. However, applying the techniques in an efficient manner requires expertise and should involve iterative modification to the formulations based on interim results.
机译：国防部（DoD）环境安全技术认证计划和环境保护局发起了一个项目，以对照传统的（试验和错误）建模方法评估污染物传输模拟优化算法的收益和效用。国防部运营的三个抽水处理设施被选入该项目。为每个设施开发了三种优化公式，并由三个建模团队独立解决（两个使用仿真优化算法，一个使用试错法）。结果清楚地表明，与当前的反复试验方法相比，模拟优化方法能够搜索更大范围的井位和流速，并找到更好的解决方案。发现的解决方案比使用反复试验（使用最佳目标函数值进行测量）获得的解决方案好5％至50％，平均改进约20％。这三个站点的潜在节省从60万美元到1000万美元不等。在几乎所有情况下，节省的成本很容易超过优化的成本。为了减少计算需求，在某些情况下，仿真优化小组应用了多种数学算法，解决了一系列修改后的子问题，并且/或者拟合了诸如神经网络或回归模型之类的“元模型”，以取代耗时的仿真模型。优化算法。最佳解决方案未考虑建模过程中固有的不确定性。该项目说明了运输仿真优化技术对实际问题的实用性。但是，以有效方式应用这些技术需要专业知识，并且应包括根据中期结果对配方进行迭代修改。

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