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首页> 外文期刊>Desalination and water treatment >Simulated annealing based simulation-optimization approach for identification of unknown contaminant sources in groundwater aquifers
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Simulated annealing based simulation-optimization approach for identification of unknown contaminant sources in groundwater aquifers

机译：基于模拟退火的模拟优化方法，用于识别地下水含水层中的未知污染物源

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摘要

The exact location and release history of groundwater pollutant sources is often unknown. Identification of unknown release histories is usually carried out by inversion of the equations governing flow and transport over time and space. This is an ill posed problem. Solution of this ill-posed inversion is complicated due to the inherent non-uniqueness of solutions, uncertainties in modelling the flow and transport processes in the aquifer and unavoidable concentration measurement errors. Several methods to solve the ill posed inversion problem have been suggested in past. The simulation-optimization approach using global heuristic search optimization methods has been found to be the most effective with regards to accuracy of solutions. However, these methods are computationally intensive. A linked simulation-optimization based methodology using a variant of simulated annealing (SA) algorithm is linked to the numerical models used to simulate flow (MODFLOW) and transport processes (MT3DMS). The objective function minimizes the difference between observed and simulated contaminant concentration for optimal values of the decision variables representing the unknown source flux magnitude, duration and timing. The developed methodology is tested for an illustrative study area. The SA based source identification methodology is demonstrated to perform more efficiently compared to other methods based on genetic algorithm.
机译：地下水污染物源的确切位置和释放历史通常是未知的。未知释放历史的鉴定通常通过反演控制随时间和空间流动和输送的方程式来进行。这是一个不适的问题。由于溶液固有的非唯一性，在含水层中的流动和输运过程建模中的不确定性以及不可避免的浓度测量误差，这种不适定反演的解决方案很复杂。过去已经提出了几种解决不适定反演问题的方法。在解决方案的准确性方面，已经发现使用全局启发式搜索优化方法的模拟优化方法是最有效的。但是，这些方法的计算量很大。使用模拟退火（SA）算法的变体的基于链接模拟优化的方法与用于模拟流量（MODFLOW）和传输过程（MT3DMS）的数值模型链接。对于代表未知源通量幅度，持续时间和时序的决策变量的最佳值，目标函数可将观察到的污染物浓度与模拟污染物浓度之间的差异最小化。对开发的方法进行了说明性研究领域的测试。与基于遗传算法的其他方法相比，基于SA的源识别方法具有更高的执行效率。

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  • 来源
    《Desalination and water treatment》 |2011年第3期|p.79-85|共7页
  • 作者

    Manish K. Jha; Bithin Datta;

  • 作者单位

    School of Engineering and Physical Sciences, James Cook University, Townsville, QLD, 4811, Australia,Co-operative Research Centre for Contamination Assessment and Remediation of Environment, Salisbury South, SA, 5106, Australia;

    School of Engineering and Physical Sciences, James Cook University, Townsville, QLD, 4811, Australia,Co-operative Research Centre for Contamination Assessment and Remediation of Environment, Salisbury South, SA, 5106, Australia;

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  • 正文语种 eng
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  • 关键词

    source identification; groundwater pollution; genetic algorithm; adaptive simulated annealing;

    机译：来源识别;地下水污染;遗传算法自适应模拟退火;

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