Optimization of Denser Nonaqueous Phase Liquids-contaminated groundwater remediation based on Kriging surrogate model

2013 ◽  
Vol 8 (2) ◽  
pp. 304-314 ◽  
Author(s):  
Wenxi Lu ◽  
Haibo Chu ◽  
Ying Zhao ◽  
Jiannan Luo
Keyword(s):  
Simulation Model ◽  
Optimization Model ◽  
Surrogate Model ◽  
Groundwater Remediation ◽  
Groundwater Resources ◽  
Optimal Strategies ◽  
Nonaqueous Phase Liquids ◽  
Kriging Surrogate Model ◽  
Aquifer Remediation ◽  
Remediation Process

Spillage of large amounts of Denser Nonaqueous Phase Liquids (DNAPLs) had resulted in serious pollution of groundwater resources throughout the world; a large number of studies had demonstrated surfactant-enhanced remediation is a more effective approach to remediate DNAPLs contaminations. In this paper, the remediation optimization process was carried out in three steps. Firstly, a water-oil-surfactant simulation model had been firstly established to simulate a surfactant enhanced aquifer remediation process. The Kriging surrogate model had been developed to get a similar input–output relationship with simulation model. In the final, a nonlinear optimization model was formulated for the minimum cost, and Kriging surrogate model had been embedded into the optimization model as a constrained condition. What is more, simulated annealing method was used to solve the optimization model and give the optimal Surfactant-Enhanced Aquifer Remediation strategy. The results showed Kriging surrogate model had reduced computational burden and make the optimization model easy to solve, and the optimal strategies gave an effective guide to contaminants remediation process.

Adaptive Kriging surrogate model for the optimization design of a dense non-aqueous phase liquid-contaminated groundwater remediation process

2014 ◽  
Vol 15 (2) ◽  
pp. 263-270 ◽  
Author(s):  
Haibo Chu ◽  
Wenxi Lu
Keyword(s):  
Simulation Model ◽  
Aqueous Phase ◽  
Surrogate Model ◽  
Optimization Design ◽  
Integrated Approach ◽  
Surrogate Models ◽  
Kriging Surrogate Model ◽  
The Common ◽  
Adaptive Kriging ◽  
Remediation Process

The optimization model needs to call the simulation model to calculate the response under different conditions for many times, and this is computationally expensive and time-consuming. To solve this problem, surrogate models can be used to yield insight into the functional relationship between the design variables and the responses, instead of simulation models in the optimization. In this paper, an integrated optimization method based on adaptive Kriging surrogate models was proposed and applied to the cost optimization of a surfactant enhanced aquifer remediation process for dense non-aqueous phase liquids (DNAPLs). First, the initial samples were created by Latin hypercube sampling, and then the responses corresponding to the initial samples were computed by a simulation model. The initial Kriging model was derived through these samples. Secondly, the adaptive Kriging surrogate model was proposed based on updating initial Kriging with new samples via infill sampling criteria. The results showed that it had improved the accuracy of the surrogate model, and the added samples had provided more information about the simulation model than the common samples. Even with the same number of samples, the adaptive Kriging surrogate model performed better than the common Kriging surrogate model, which was built only once. What's more, the integrated approach not only greatly reduced the computational burden, but also determined the actual optimal DNAPLs remediation strategy.

scholarly journals Uncertainty Analysis in the Optimization Process of Groundwater Exploitation Scheme Based on SVR Method—A Case Study of Hetao Plain

2018 ◽  
Author(s):  
Yongkai An ◽  
Wenxi Lu ◽  
Xueman Yan
Keyword(s):  
Uncertainty Analysis ◽  
Simulation Model ◽  
Optimization Model ◽  
Surrogate Model ◽  
Surrogate Models ◽  
Groundwater Table ◽  
Pumping Rate ◽  
Groundwater Exploitation ◽  
Computational Load ◽  
Hetao Plain

This paper introduces a surrogate model to reduce the huge computational load in the process of simulation-optimization and uncertainty analysis. First, the groundwater numerical simulation model was established, calibrated and verified in the northeast of Hetao Plain. Second, two surrogate models of simulation model were established using support vector regression (SVR) method, one (surrogate model A, SMA) was used to describe the corresponding relationship between the pumping rate and average groundwater table drawdown, and another (surrogate model B, SMB) was used to express the corresponding relationship between the hydrogeological parameter values and average groundwater table drawdown. Third, an optimization model was established to search an optimal groundwater exploitation scheme using the maximum total pumping rate as objective function and the limitative average groundwater table drawdown as constraint condition, the SMA was invoked by the optimization model for obtaining the optimal groundwater exploitation scheme. Finally, the SMB was invoked in the process of uncertainty analysis for assessing the reliability of optimal groundwater exploitation scheme. Results show that the relative error and root mean square error between simulation model and the two surrogate models are both less than 5%, which is a high approximation accuracy. The SVR surrogate model developed in this study could not only considerably reduce the computational load, but also maintain high computational accuracy. The optimal total pumping rate is 7947 m3/d and the reliability of optimal scheme is 40.21%. This can thus provide an effective method for identifying an optimal groundwater exploitation scheme and assessing the reliability of scheme quickly and accurately.

scholarly journals Optimal location of cutoff walls for seawater intrusion

2021 ◽  
Vol 11 (11) ◽  
Author(s):  
Han Zheng ◽  
Lu Wenxi ◽  
Fan Yue ◽  
Miao Tiansheng ◽  
Lin Jin ◽  
...  
Keyword(s):  
Seawater Intrusion ◽  
Optimization Model ◽  
Surrogate Model ◽  
Model Simulation ◽  
Water Sources ◽  
Optimal Location ◽  
Chlorine Concentration ◽  
Kriging Surrogate Model ◽  
Cutoff Wall ◽  
Cutoff Walls

AbstractIn this paper, the simulation–optimization method is used to study the optimal location of cutoff walls for seawater intrusion. The optimization model is based on minimizing the chlorine concentration of two water sources after 50 years. In order to reduce the computational complexity, a Kriging surrogate model simulation is coupled with the optimization model. Finally, a hypothetical case is used to evaluate the accuracy of the surrogate model and the performance of the optimization model. The results show that the outputs of the Kriging surrogate model and the variable density groundwater simulation model for the same cutoff wall design fit well, and the average relative error of the two outputs is only 2.2% which proves that it is feasible to apply the Kriging surrogate model to this problem. By solving the optimization model, the location of the cutoff wall which minimizes the sum of chlorine concentration of the two water sources after 50 years is obtained. This provides a stable and reliable method for the site selection of cutoff walls for future projects intended to prevent and control seawater intrusion.

scholarly journals A surrogate-based simulation–optimization approach for coastal aquifer management

2020 ◽  
Vol 20 (8) ◽  
pp. 3404-3418
Author(s):  
Zheng Han ◽  
Wenxi Lu ◽  
Yue Fan ◽  
Jin Lin ◽  
Qian Yuan
Keyword(s):  
Simulation Model ◽  
Groundwater Management ◽  
Optimization Model ◽  
Surrogate Model ◽  
Simulation Optimization ◽  
Surrogate Models ◽  
Variable Density ◽  
Support Vector ◽  
Aquifer Management ◽  
Groundwater Simulation

Abstract This study proposed a pumping-injection (P-I) groundwater management strategy based on a simulation–optimization (S-O) framework to mitigate seawater intrusion (SI). The methodology was applied to a real case in Longkou, China. A three-dimensional variable-density groundwater simulation model was established to simulate and predict the SI process. In the S-O framework, while solving the optimization model, it is required to call the simulation model thousands of times, which leads to enormous computational load. In this case, the Kriging and support vector regression (SVR) surrogate models were established for the simulation model respectively. Furthermore, the ensemble surrogate modeling technique was applied to construct the Kriging-SVR ensemble surrogate model. The most accurate surrogate model was selected as the substitute for the simulation model, saving considerable computing costs. The results show that the ensemble surrogate model performs better than the stand-alone surrogate models in accuracy, indicating that combining stand-alone surrogate models is a potential modeling method for the surrogate model of the variable-density groundwater simulation model. By solving the optimization model, the optimal pumping and injection schemes under different scenarios were obtained. The optimization results demonstrate that the proposed methodology is effective and stable in coastal groundwater management.

scholarly journals Updated Kriging-Assisted Shape Optimization of a Gravity Dam

Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 87
Author(s):  
Yongqiang Wang ◽  
Ye Liu ◽  
Xiaoyi Ma
Keyword(s):  
Surrogate Model ◽  
Computational Cost ◽  
Region Of Interest ◽  
Optimization Procedure ◽  
Optimal Shape ◽  
Small Sample ◽  
Gravity Dam ◽  
Kriging Surrogate Model ◽  
Gravity Dams ◽  
Sample Set

The numerical simulation of the optimal design of gravity dams is computationally expensive. Therefore, a new optimization procedure is presented in this study to reduce the computational cost for determining the optimal shape of a gravity dam. Optimization was performed using a combination of the genetic algorithm (GA) and an updated Kriging surrogate model (UKSM). First, a Kriging surrogate model (KSM) was constructed with a small sample set. Second, the minimizing the predictor strategy was used to add samples in the region of interest to update the KSM in each updating cycle until the optimization process converged. Third, an existing gravity dam was used to demonstrate the effectiveness of the GA–UKSM. The solution obtained with the GA–UKSM was compared with that obtained using the GA–KSM. The results revealed that the GA–UKSM required only 7.53% of the total number of numerical simulations required by the GA–KSM to achieve similar optimization results. Thus, the GA–UKSM can significantly improve the computational efficiency. The method adopted in this study can be used as a reference for the optimization of the design of gravity dams.

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