A stochastic optimization model based on adaptive feedback correction process and surrogate model uncertainty for DNAPL-contaminated groundwater remediation design

2018 ◽  
Vol 32 (11) ◽  
pp. 3195-3206 ◽  
Author(s):  
Xue Jiang ◽  
Wenxi Lu ◽  
Jin Na ◽  
Zeyu Hou ◽  
Yanxin Wang ◽  
...  
Keyword(s):  
Stochastic Optimization ◽  
Model Uncertainty ◽  
Optimization Model ◽  
Surrogate Model ◽  
Groundwater Remediation ◽  
Contaminated Groundwater ◽  
Adaptive Feedback ◽  
Correction Process ◽  
Remediation Design ◽  
Feedback Correction

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.

scholarly journals Optimal Latin hypercube sampling-based surrogate model in NAPLs contaminated groundwater remediation optimization process

2017 ◽  
Vol 18 (1) ◽  
pp. 333-346 ◽  
Author(s):  
Jiannan Luo ◽  
Yefei Ji ◽  
Wenxi Lu ◽  
He Wang
Keyword(s):  
Surrogate Model ◽  
Groundwater Remediation ◽  
Model Performance ◽  
Latin Hypercube Sampling ◽  
Feasible Region ◽  
Contaminated Groundwater ◽  
Chance Constrained Programming ◽  
Latin Hypercube ◽  
Optimal Latin Hypercube Sampling ◽  
The Impact

Abstract A surrogate model based groundwater optimization model was developed to solve the non-aqueous phase liquids (NAPLs) contaminated groundwater remediation optimization problem. To illustrate the impact of sampling method improvement to the surrogate model performance improvement, aiming at a nitrobenzene contaminated groundwater remediation problem, optimal Latin hypercube sampling (OLHS) method was introduced to sample data in the input variables feasible region, and a radial basis function artificial neural network was used to construct a surrogate model. Considering the surrogate model's uncertainty, a chance-constrained programming (CCP) model was constructed, and it was solved by genetic algorithm. The results showed the following, for the problem considered in this study. (1) Compared with the Latin hypercube sampling (LHS) method, the OLHS method improves the space-filling degree of sample points considerably. (2) The effects of the two sampling methods on surrogate model performance were analyzed through comparison of goodness of fit, residual and uncertainty. The results indicated that the OLHS-based surrogate model performed better than the LHS-based surrogate model. (3) The optimal remediation strategies at 99%, 95%, 90%, 85%, 80% and 50% confidence levels were obtained, which showed that the remediation cost increased with the confidence level. This work would be helpful for increasing surrogate model performance and lowering the risk of a groundwater remediation strategy.

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