Optimization Strategies for in Situ Groundwater Remediation by a Vertical Circulation Well Based on Particle‐Tracking and Node‐Dependent Finite Difference Methods
Abstract:Both theory and application of multispecies reactive transport for in situ groundwater bioremediation involving a vertical circulation well (VCW) are not fully understood despite its importance and common usage in aquifer remediation practices. This study proposes novel approaches including two methods for design and remediation prediction of a VCW system, which involves multispecies, multiphase, and microbially enhanced reactive transport process. One is particle-tracking method, which depicts the trajectorie… Show more
“…The Grid Peclet number (P e = vΔx/D) and Courant number (C r = vΔt/Δx) can be used to analyze the effects of oscillations and numerical dispersion on the developed FD models (). P e ≤ 2 and C r ≤ 1 have been met in all directions of the model, showing that the numerical errors of the FD models can be neglected (Zhu et al 2020b). The results in Fig.…”
Section: Numerical Solution and Veri Cationmentioning
Solute transport in low-permeability geological formations such as clay is a complex and ubiquitous phenomenon. Traditional solute transport models have been proved to be inadequate for describing the processes of solute transport in low-permeability media due to transient non-Darcian flow. In this study, we developed a numerical model with non-Darcian flow, aquifer storage effect and temporally exponential hydraulic head to investigate the features of solute transport. The impacts of different parameters of the threshold pressure gradient, the specific storage and time-dependent hydraulic head on breakthrough curves (BTCs) were thoroughly analyzed. New insights indicate, firstly, for non-Darcian flow, a slight change of the threshold pressure gradient will lead to considerable change of BTCs, meaning that the impact of non-Darcian flow on solute transport in low-permeability media should not be ignored; secondly, aquifer storage effect leads to a large flow velocity near flow inlet; thirdly, a larger power index β means that hydraulic head increases faster, also resulting in faster solute transport; finally, the sensitivity analysis shows that the threshold pressure gradient plays a crucial role in solute transport of low-permeability porous. In general, the effects of transient non-Darcian flow are not negligible for describing the solute transport process in low-permeability media.
“…The Grid Peclet number (P e = vΔx/D) and Courant number (C r = vΔt/Δx) can be used to analyze the effects of oscillations and numerical dispersion on the developed FD models (). P e ≤ 2 and C r ≤ 1 have been met in all directions of the model, showing that the numerical errors of the FD models can be neglected (Zhu et al 2020b). The results in Fig.…”
Section: Numerical Solution and Veri Cationmentioning
Solute transport in low-permeability geological formations such as clay is a complex and ubiquitous phenomenon. Traditional solute transport models have been proved to be inadequate for describing the processes of solute transport in low-permeability media due to transient non-Darcian flow. In this study, we developed a numerical model with non-Darcian flow, aquifer storage effect and temporally exponential hydraulic head to investigate the features of solute transport. The impacts of different parameters of the threshold pressure gradient, the specific storage and time-dependent hydraulic head on breakthrough curves (BTCs) were thoroughly analyzed. New insights indicate, firstly, for non-Darcian flow, a slight change of the threshold pressure gradient will lead to considerable change of BTCs, meaning that the impact of non-Darcian flow on solute transport in low-permeability media should not be ignored; secondly, aquifer storage effect leads to a large flow velocity near flow inlet; thirdly, a larger power index β means that hydraulic head increases faster, also resulting in faster solute transport; finally, the sensitivity analysis shows that the threshold pressure gradient plays a crucial role in solute transport of low-permeability porous. In general, the effects of transient non-Darcian flow are not negligible for describing the solute transport process in low-permeability media.
“…It can be segmented into three parts, the upstream capture zone, the circulation zone, and the downstream release zone 7. In order to characterize of groundwater features surround the GCW, two indicators are usually applied 8 , 13 , 16 : the radius of influence (R) and the particle recovery ratio (P r ). Figure 2 Indicators of GCW operation efficiency.…”
Section: Methodsmentioning
confidence: 99%
“…Structure and operation parameters: The distance between the top of the upper screen section and the bottom of the lower screen section (L) and the total length of the two screen sections (a) were used as the indicators of the circulation well structure parameters; the pumping rate (Q) was used as the main indicator of operating parameters 12 , 16 .…”
Section: Methodsmentioning
confidence: 99%
“…This research primarily focused on defining hydrogeological parameters such as the horizontal conductivity (K H ), vertical hydraulic conductivity (K V ), the vertical heterogeneity (K H /K V ), aquifer thickness (M), porosity (n), specific yield (μ), and hydraulic gradient (I). ② Structure and operation parameters: The distance between the top of the upper screen section and the bottom of the lower screen section (L) and the total length of the two screen sections (a) were used as www.nature.com/scientificreports/ the indicators of the circulation well structure parameters; the pumping rate (Q) was used as the main indicator of operating parameters 12,16 .…”
Section: Influence Factorsmentioning
confidence: 99%
“…The Finite-Difference Method (FDM) and Finite-Element Method (FEM) are the most frequently used numerical techniques in groundwater flow simulation. Particle-tracking and node-dependent finite difference methods have also been employed for the design and remediation prediction of GCW systems in confined aquifers 11 , 15 , 16 .…”
The optimal design of groundwater circulation wells (GCWs) is challenging. The key to purifying groundwater using this technique is its proficiency and productivity. However, traditional numerical simulation methods are limited by long modeling times, random optimization schemes, and optimization results that are not comprehensive. To address these issues, this study introduced an innovative approach for the optimal design of a GCW using machine learning methods. The FloPy package was used to create and implement the MODFLOW and MODPATH models. Subsequently, the formulated models were employed to calculate the characteristic indicators of the effectiveness of the GCW operation, including the radius of influence (R) and the ratio of particle recovery (Pr). A detailed collection of 3000 datasets, including measures of operational efficiency and key elements in machine learning, was meticulously compiled into documents through model execution. The optimization models were trained and evaluated using multiple linear regression (MLR), artificial neural networks (ANN), and support vector machines (SVM). The models produced by the three approaches exhibited notable correlations between anticipated outcomes and datasets. For the optimal design of circulating well parameters, machine learning methods not only improve the optimization speed, but also expand the scope of parameter optimization. Consequently, these models were applied to optimize the configuration of the GCW at a site in Xi’an. The optimal scheme for R (Q = 293.17 m3/d, a = 6.09 m, L = 7.28 m) and optimal scheme for Pr (Q = 300 m3/d, a = 3.64 m, L = 1 m) were obtained. The combination of numerical simulations and machine learning is an effective tool for optimizing and predicting the GCW remediation effect.
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