A coupled simulation-optimization approach for groundwater remediation design under uncertainty: an application to a petroleum-contaminated site

Optimized stacking, a new method for constructing ensemble surrogate models applied to DNAPL-contaminated aquifer remediation

Surfactant-enhanced aquifer remediation (SEAR) is an appropriate method for DNAPL-contaminated aquifer remediation; However, due to the high cost of the SEAR method, finding the optimal remediation scenario is usually essential. Embedding numerical simulation models of DNAPL remediation within the optimization routines are computationally expensive, and in this situation, using surrogate models instead of numerical models is a proper alternative. Ensemble methods are also utilized to enhance the accuracy of surrogate models, and in this study, the Stacking ensemble method was applied and compared with conventional methods. First, Six machine learning methods were used as surrogate models, and various feature scaling techniques were employed, and their impact on the models' performance was evaluated. Also, Bagging and Boosting homogeneous ensemble methods were used to improve the base models' accuracy. A total of six stand-alone surrogate models and 12 homogeneous ensemble models were used as the base input models of the Stacking ensemble model. Due to the large size of the Stacking model, Bayesian hyper-parameter optimization method was used to find its optimal hyper-parameters. The results showed that the Bayesian hyper-parameter optimization method had better performance than common methods such as random search and grid search. The artificial neural network model, whose input data was scaled by the power transformer method, had the best performance with a cross-validation RMSE of 0.065. The Boosting method increased the base models' accuracy more than other homogeneous methods, and the best Boosting model had a test RMSE of 0.039. The Stacking ensemble method significantly increased the base models' accuracy and performed better than other ensemble methods. The best ensemble surrogate model constructed with Stacking had a cross-validation RMSE of 0.016. Finally, a differential evolution optimization model was used by substituting the Stacking ensemble model with the numerical model, and the optimal remediation strategy was obtained at a total cost of $ 72,706.

A coupled optimization of groundwater remediation alternatives screening under health risk assessment: An application to a petroleum-contaminated site in a typical cold industrial region in Northeastern China

Contaminated sites have been recognized as posing serious comprehensive social and environmental issues and have earned worldwide attention. China is becoming one of the largest contaminated sites remediation markets in the world and the contaminated sites in northeastern China need to rehabilitate urgently. However, remediation planning is often hindered by high financial costs resulting from incomplete assessments of pollution and inappropriate remediation plans. In-depth contaminated site assessments can provide the necessary baseline data for remediation alternatives screening. Therefore, risk assessments and remediation decisions will play crucial roles in the rehabilitation and reconstruction of contaminated sites in China. The main objectives of this study were to present a novel method for health risk assessment (HRA) and to demonstrate a multicriteria decision analysis (MCDA) based on this method to select the most suitable remediation alternatives of groundwater and to prioritize management of contaminated site. To demonstrate the HRA and MCDA processes, a typical contaminated site in Longtan, Jilin province, China, was used. The results of this research indicated that Benzene (PhH) and 1,2-Dichloroethylene (1,2-DCE) were the main organic pollutants and the vanillin plant in the north of the site was main pollution source. Pollution migrated from the north to the south and the health risk range in winter was significantly greater than in summer. Four remediation alternatives were proposed on the basis of the HRA results. The MCDA results showed that PRB was the most suitable technology for integrating the relevant environmental, social, economic, and technical aspects required for remediation. This study may help responsible agencies to strengthen local risk-based program screening frameworks for contaminated sites, to promote reconstruction projects, and to increase local public confidence of contaminated sites remediation.

Multiobjective optimization of the groundwater exploitation layout in coastal areas based on multiple surrogate models

Seawater intrusion is a common problem in coastal areas. The rational distribution of groundwater exploitation can minimize the scope of seawater intrusion and maximize groundwater exploitation. In this study, an optimization method for the groundwater exploitation layout in coastal areas was proposed. Based on the numerical simulation model of variable-density groundwater, a multiobjective groundwater management model was constructed with the objectives of maximizing groundwater exploitation and minimizing seawater intrusion. The optimization model was solved by nondominated sorted genetic algorithm-II (NSGA-II). To improve the computational efficiency of the optimization model, the surrogate models of the groundwater simulation model were built by using three different methods: kriging, support vector regression (SVR), and kernel extreme learning machines (KELM). Finally, the above methods were tested in Longkou City of China. The results show that the use of surrogate models can greatly reduce the computing time for solving seawater intrusion management problems. The surrogate model of the variable-density groundwater simulation model based on the SVR method has the best performance. The groundwater exploitation layout optimized by the above method is reasonable and can reflect the actual hydrogeological conditions in the study area. This study provides a reliable way to optimize the groundwater exploitation layout in coastal areas.

Optimal remediation design and simulation of groundwater flow coupled to contaminant transport using genetic algorithm and radial point collocation method (RPCM)

The simulation-optimisation models of groundwater and contaminant transport can be a powerful tool in the management of groundwater resources and remediation design. In this study, using Multiquadratic Radial Basis Function (MRBF) a coupled groundwater flow and reactive transport of contaminant and oxidant was developed in the framework of the Meshfree method. The parameter analysis has determined the optimum shape parameter (0.97), and the results of the model were compared with a physical sandbox model which were in good agreement. The genetic algorithm approach was used to find the optimum design of the remediation using permanganate as an oxidant. To find the optimum design we considered two objectives and two constraints. The results revealed that the breakthrough of contaminant to the downstream area of interest and the concentration of the contaminant in this area is reduced significantly with optimisation.

Application of ensemble surrogates and adaptive sequential sampling to optimal groundwater remediation design at DNAPLs-contaminated sites

In this study, we aimed to develop an optimal groundwater remediation design for sites contaminated by dense non-aqueous phase liquids by using an ensemble of surrogates and adaptive sequential sampling. Compared with previous approaches, our proposed method has the following advantages: (1) a surrogate surfactant-enhanced aquifer remediation simulation model is constructed using a Gaussian process; (2) the accuracy of the surrogate model is improved by constructing ensemble surrogates using five different surrogate modelling techniques, i.e., polynomial response surface, radial basis function, Kriging, support vector regression, and Gaussian process; (3) we conducted comparisons and analyses based on 31 surrogate models derived from different combinations of the five surrogate modelling techniques; and (4) the reliability of the optimal solution was improved by implementing adaptive sequential sampling. The two proposed methods were applied to a hypothetical perchloroethylene-contaminated site in order to demonstrate their performance. The results showed that the best surrogate model integrated all five of the surrogate modelling methods, with an R² value of 0.9913 and a root mean squared error of 0.0159, thereby demonstrating the advantage of using ensemble surrogates. In addition, the reliability of the optimization model solution was improved by adaptive sequential sampling, which avoided false solutions.

Optimization-based multicriteria decision analysis for identification of desired petroleum-contaminated groundwater remediation strategies

The conventional multicriteria decision analysis (MCDA) methods used for pollution control generally depend on the data currently available. This could limit their real-world applications, especially where the input data (e.g., the most cost-effective remediation cost and eventual contaminant concentration) might vary by scenario. This study proposes an optimization-based MCDA (OMCDA) framework to address such a challenge. It is capable of (1) capturing various preferences of decision-makers, (2) screening and analyzing the performance of various optimized remediation strategies under changeable scenarios, and (3) compromising incongruous decision analysis results. A real-world case study is employed for demonstration, where four scenarios are considered with each one corresponding to a set of weights representative of the preference of the decision-makers. Four criteria are selected, i.e., optimal total pumping rate, remediation cost, contaminant concentration, and fitting error. Their values are determined through running optimization and optimization-based simulation procedures. Four sets of the most desired groundwater remediation strategies are identified, implying specific pumping rates under varied scenarios. Results indicate that the best action lies in groups 32 and 16 for the 5-year, groups 49 and 36 for the 10-year, groups 26 and 13 for the 15-year, and groups 47 and 13 for the 20-year remediation.

Design of optimal groundwater remediation systems under flexible environmental-standard constraints

In developing optimal groundwater remediation strategies, limited effort has been exerted to solve the uncertainty in environmental quality standards. When such uncertainty is not considered, either over optimistic or over pessimistic optimization strategies may be developed, probably leading to the formulation of rigid remediation strategies. This study advances a mathematical programming modeling approach for optimizing groundwater remediation design. This approach not only prevents the formulation of over optimistic and over pessimistic optimization strategies but also provides a satisfaction level that indicates the degree to which the environmental quality standard is satisfied. Therefore the approach may be expected to be significantly more acknowledged by the decision maker than those who do not consider standard uncertainty. The proposed approach is applied to a petroleum-contaminated site in western Canada. Results from the case study show that (1) the peak benzene concentrations can always satisfy the environmental standard under the optimal strategy, (2) the pumping rates of all wells decrease under a relaxed standard or long-term remediation approach, (3) the pumping rates are less affected by environmental quality constraints under short-term remediation, and (4) increased flexible environmental standards have a reduced effect on the optimal remediation strategy.