Optimization of well field management to mitigate groundwater contamination using a simulation model and evolutionary algorithm

Two-step approach based multi-objective groundwater remediation using enhanced random vector functional link integrated with evolutionary marine predator algorithm

We here propose a two-step approach-based simulation-optimization model for multi-objective groundwater remediation using enhanced random vector functional link (ERVFL) and evolutionary marine predator algorithm (EMPA). In this study, groundwater flow and solute transport models are developed using MODFLOW and MT3DMS. The ERVFL network is used to approximate the flow and transport models, enhancing the computational performance. This study also improves the robustness of the ERVFL network using a kernel density estimator (KDE) based weighted least square approach. We further develop the EMPA by modifying the marine predator algorithm (MPA) using elite opposition-based learning, biological evolution operators, and elimination mechanisms. In the multi-objective version of EMPA, the non-dominated/Pareto-optimal solutions are stored in an external repository using an archive controller and adaptive grid mechanism to promote better convergence and diversity of the Pareto front. The proposed methodologies are applied for multi-objective groundwater remediation of a hypothetical unconfined aquifer based on the two-step method. The first step directly integrates flow and transport models with EMPA and finds the optimal locations of pumping wells by minimizing the percent of contaminant mass remaining in the aquifer. In the second step, the ERVL-based proxy model is integrated with EMPA and used for multi-objective optimization while explicitly using the pumping well locations obtained in the first step. The multi-objective optimization generates a Pareto-optimal solution representing the relationship between the rate of pumping and the amount of contaminant mass in the aquifer. Further analyses show a significant advantage of the two-step approach over a traditional method for multi-objective groundwater remediation.

Comparison of parallel optimization algorithms on computationally expensive groundwater remediation designs

Contaminated groundwater resources threaten human health and destroy ecosystems in many areas worldwide. Groundwater remediation is crucial for environmental recovery; however, it can be cost prohibitive. Planning a cost-effective remediation design can take a long time, as it may involve the evaluation of many management decisions, and the corresponding simulation models are computationally demanding. Parallel optimization can facilitate much faster management decisions for cost-effective groundwater remediation design using complex pollutant transport models. However, the efficiency of different parallel optimization algorithms varies depending on both the search strategy and parallelism. In this paper, we show the performance of a parallel surrogate-based optimization algorithm called parallel stochastic radial basis function (p-SRBF), which has not been previously used on contaminant remediation problems. The two case problems involve two superfund sites (i.e., the Umatilla Aquifer and Blaine Aquifer), and one objective evaluation takes 5 and 30 min for the two problems, respectively. Exceptional speedup (superlinear) is achieved with 4 to 16 cores, and excellent speedup is achieved using up to 64 cores, obtaining a good solution at 80 % efficiency. We compare our p-SRBF with three different parallel derivative-free optimization algorithms, including genetic algorithm, mesh adaptive direct search, and asynchronous parallel pattern search optimization, in terms of objective quality, computational reduction and robust behavior across multiple trials. p-SRBF outperforms the other algorithms, as it finds the best solution in both the Umatilla and Blaine cases and reduces the computational budget by at least 50 % in both problems. Additionally, statistical comparisons show that the p-SRBF results are better than those of the alternative algorithms at the 5 % significant level. This study enriches theoretical real-world groundwater remediation methods. The results demonstrate that p-SRBF is promising for environmental management problems that involve computationally expensive models.

Effect of hydraulic retention time on the electro-bioremediation of nitrate in saline groundwater

Bioelectrochemical systems (BES) have proven their capability to treat nitrate-contaminated saline groundwater and simultaneously recover value-added chemicals (such as disinfection products) within a circular economy-based approach. In this study, the effect of the hydraulic retention time (HRT) on nitrate and salinity removal, as well as on free chlorine production, was investigated in a 3-compartment BES working in galvanostatic mode with the perspective of process intensification and future scale-up. Reducing the HRT from 30.1 ± 2.3 to 2.4 ± 0.2 h led to a corresponding increase in nitrate removal rates (from 17 ± 1 up to 131 ± 1 mgNO₃^--N L^-1d^-1), although a progressive decrease in desalination efficiency (from 77 ± 13 to 12 ± 2 %) was observed. Nitrate concentration and salinity close to threshold limits indicated by the World Health Organization for drinking water, as well as significant chlorine production were achieved with an HRT of 4.9 ± 0.4 h. At such HRT, specific energy consumption was low (6.8·10^-2 ± 0.3·10^-2 kWh g^-1NO₃^--N_removed), considering that the supplied energy supports three processes simultaneously. A logarithmic equation correlated well with nitrate removal rates at the applied HRTs and may be used to predict BES behaviour with different HRTs. The bacterial community of the bio-cathode under galvanostatic mode was dominated by a few populations, including the genera Rhizobium, Bosea, Fontibacter and Gordonia. The results provide useful information for the scale-up of BES treating multi-contaminated groundwater.

Human-Health and Environmental Risks of Heavy Metal Contamination in Soil and Groundwater at a Riverside Site, China

The contaminated site is considered a high-risk pollution source due to the accumulation of industrial waste and wastewater, which affects the soil and groundwater environment. In this study, through soil and groundwater investigation, we outlined the characteristics of heavy metal contamination in the soil and groundwater of the contaminated site, assessed the health risk of the contaminated site to humans, and established a numerical model to predict the ecological and environmental risks of the site. The results of the study showed that the maximum contamination concentration of pollutants (lead, arsenic, cadmium) in the soil all exceeded the Chinese environmental standard (GB36600-2018, Grade II), that the maximum contamination concentration (cadmium, Cd) of the groundwater exceeded the Chinese environmental standard (GB14848–2017, Grade IV), and that the heavy metal pollution was mainly concentrated in the production area of the site and the waste-residue stockpiles. The total carcinogenic risk and non-carcinogenic hazard quotient of the site’s soil heavy metal contaminants exceed the human acceptable limit, and there is a human health risk. However, the groundwater in the area where the site is located is prohibited from exploitation, and there is no volatility of the contaminants and no exposure pathway to the groundwater, so there is no risk to human health. The simulation prediction results show that, with the passage of time, the site groundwater pollutants as a whole migrate from south to north, affecting the northern surface water bodies after about 12 years, and there is a high ecological and environmental risk. The above findings provide a scientific basis for the study of the soil and groundwater at the riverside contaminated site.

Efficient, parallelized global optimization of groundwater pumping in a regional aquifer with land subsidence constraints

The design of groundwater exploitation schedules with constraints on pumping-induced land subsidence is a computationally intensive task. Physical process-based groundwater flow and land subsidence simulations are high-dimensional, nonlinear, dynamic and computationally demanding, as they require solving large systems of partial differential equations (PDEs). This work is the first application of a parallelized surrogate-based global optimization algorithm to mitigate land subsidence issues by controlling the pumping schedule of multiple groundwater wellfields over space and time. The application was demonstrated in a 6500 km² region in China, involving a large-scale coupled groundwater flow-land subsidence model that is computationally expensive in terms of computational resources, including runtime and CPU memory for one single evaluation. In addition, the optimization problem contains 50 decision variables and up to 13 constraints, which adds to the computational effort, thus an efficient optimization is required. The results show that parallel DYSOC (dynamic search with surrogate-based constrained optimization) can achieve an approximately 100% parallel efficiency when upscaling computing resources. Compared with two other widely used optimization algorithms, DYSOC is 2-6 times faster, achieving computational cost savings of at least 50%. The findings demonstrate that the integration of surrogate constraints and dynamic search process can aid in the exploration and exploitation of the search space and accelerate the search for optimal solutions to complicated problems.