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Optimization of soil vapor extraction using simulated annealing and genetic algorithms.

机译：使用模拟退火和遗传算法优化土壤蒸汽提取。

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Releases of volatile organic compounds (VOCs) to soil can pose a significant threat to human health and the environment. In situ soil vapor extraction (SVE) is used to remediate VOCs in the vadose zone. Costs for SVE systems can be several thousands to several millions of dollars, and if the SVE remediation technique is ineffective, contamination in deep soil may never be completely remediated.; One method to determine the cheapest, effective SVE system design is to use SVE simulation models. When using these models, the task to find the best design may be tedious (if not impossible) considering all possible well locations, screen depths, and vapor extraction rates. This research investigates the interfacing of heuristic optimization methods with SVE simulation codes to help determine a probable lowest cost SVE design, in minimal time, while still obtaining the required technical efficiency.; A two-dimensional analytical solution for a single SVE well operating under steady-state flow conditions was modified mathematically and with several rule-of-thumb parameters to incorporate a cost component that reflects well screen depth and vapor extraction rate. Although this ad hoc simulator should not be used in practice, it provided a quick way to evaluate different optimization methods applied to non-linear SVE equations.; Simulated annealing (SA), genetic algorithm, and simulated annealing/genetic algorithm (SAGA) hybrid optimizers were linked separately to the simulator. Comparison of several runs of these optimizers determined that a SA variant resulted in lower SVE cost and number of simulations required to find that cost. Additionally, it was found that the hybrid had better results but required much more programming.; Knowledge gained with these optimization methods was applied to a SVE coupled airflow/vapor transport simulation model (VENT3D) commonly used in industry. Because of time constraints, optimization of VENT3D using the SA variant, and not the hybrid, was investigated. When applied to a benchmark problem documented in literature, it was determined that the SA variant had comparable results to three other published optimizers. Furthermore, if the SAGA hybrid is used instead of the SA variant, it appears the hybrid could provide better results.
机译：挥发性有机化合物（VOC）向土壤的释放可能对人类健康和环境构成重大威胁。原位土壤汽提（SVE）用于修复渗流带中的VOC。 SVE系统的成本可能在数千到几百万美元之间，如果SVE修复技术无效，那么深土壤中的污染可能永远不会得到完全修复。确定最便宜，有效的SVE系统设计的一种方法是使用SVE仿真模型。当使用这些模型时，考虑所有可能的井位，筛网深度和蒸汽提取速率，找到最佳设计的任务可能很繁琐（如果不是不可能的话）。这项研究调查了启发式优化方法与SVE仿真代码的接口，以帮助在最短的时间内确定可能的最低成本的SVE设计，同时仍然获得所需的技术效率。对在稳态流量条件下运行的单个SVE井的二维分析解决方案进行了数学修改，并使用几个经验法则参数进行了修改，以合并反映井网深度和蒸汽提取速率的成本要素。尽管该特殊仿真器不应在实践中使用，但它提供了一种快速方法来评估应用于非线性SVE方程的不同优化方法。模拟退火（SA），遗传算法和模拟退火/遗传算法（SAGA）混合优化器分别链接到模拟器。通过对这些优化器的几次运行进行比较，可以确定SA变体导致了SVE成本降低，以及找到该成本所需的仿真次数。另外，发现混合具有更好的结果，但是需要更多的编程。这些优化方法获得的知识已应用于工业中常用的SVE耦合气流/蒸汽传输模拟模型（VENT3D）。由于时间限制，研究了使用SA变体而不是混合体优化VENT3D的方法。当应用于文献中记录的基准问题时，可以确定SA变体的结果可与其他三个已发布的优化器相媲美。此外，如果使用SAGA杂种代替SA变体，则看起来杂种可以提供更好的结果。

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