Matott L Shawn, Singh Anshuman, Rabideau Alan J
University at Buffalo, Center for Computational Research, Buffalo, NY, United States.
Department of Civil Engineering, National Institute of Technology, Patna, India.
J Contam Hydrol. 2017 May;200:35-48. doi: 10.1016/j.jconhyd.2017.03.006. Epub 2017 Mar 21.
Predictive modeling of the transport and remediation of groundwater contaminants requires an accurate description of the sorption process, which is usually provided by fitting an isotherm model to site-specific laboratory data. Commonly used calibration procedures, listed in order of increasing sophistication, include: trial-and-error, linearization, non-linear regression, global search, and hybrid global-local search. Given the considerable variability in fitting procedures applied in published isotherm studies, we investigated the importance of algorithm selection through a series of numerical experiments involving 13 previously published sorption datasets. These datasets, considered representative of state-of-the-art for isotherm experiments, had been previously analyzed using trial-and-error, linearization, or non-linear regression methods. The isotherm expressions were re-fit using a 3-stage hybrid global-local search procedure (i.e. global search using particle swarm optimization followed by Powell's derivative free local search method and Gauss-Marquardt-Levenberg non-linear regression). The re-fitted expressions were then compared to previously published fits in terms of the optimized weighted sum of squared residuals (WSSR) fitness function, the final estimated parameters, and the influence on contaminant transport predictions - where easily computed concentration-dependent contaminant retardation factors served as a surrogate measure of likely transport behavior. Results suggest that many of the previously published calibrated isotherm parameter sets were local minima. In some cases, the updated hybrid global-local search yielded order-of-magnitude reductions in the fitness function. In particular, of the candidate isotherms, the Polanyi-type models were most likely to benefit from the use of the hybrid fitting procedure. In some cases, improvements in fitness function were associated with slight (<10%) changes in parameter values, but in other cases significant (>50%) changes in parameter values were noted. Despite these differences, the influence of isotherm misspecification on contaminant transport predictions was quite variable and difficult to predict from inspection of the isotherms.
地下水污染物迁移与修复的预测模型需要对吸附过程进行准确描述,通常是通过将等温线模型拟合到特定场地的实验室数据来实现。按复杂程度递增顺序列出的常用校准程序包括:试错法、线性化、非线性回归、全局搜索和混合全局-局部搜索。鉴于已发表的等温线研究中应用的拟合程序存在很大差异,我们通过一系列数值实验研究了算法选择的重要性,这些实验涉及13个先前发表的吸附数据集。这些数据集被认为代表了等温线实验的最新水平,之前已使用试错法、线性化或非线性回归方法进行了分析。使用三阶段混合全局-局部搜索程序(即使用粒子群优化进行全局搜索,随后是鲍威尔无导数局部搜索方法和高斯-马尔可夫-列文伯格非线性回归)重新拟合等温线表达式。然后,根据优化的加权平方残差(WSSR)适应度函数、最终估计参数以及对污染物迁移预测的影响,将重新拟合的表达式与先前发表的拟合结果进行比较——其中易于计算的浓度依赖性污染物阻滞因子用作可能迁移行为的替代度量。结果表明,许多先前发表的校准等温线参数集是局部最小值。在某些情况下,更新后的混合全局-局部搜索使适应度函数降低了几个数量级。特别是,在候选等温线中,波兰尼型模型最有可能从混合拟合程序的使用中受益。在某些情况下,适应度函数的改善与参数值的轻微(<10%)变化相关,但在其他情况下,参数值出现了显著(>50%)变化。尽管存在这些差异,等温线错误设定对污染物迁移预测的影响差异很大,难以通过检查等温线来预测。