Engineering topics

Song, Xuehang

Publications and source records attributed to Song, Xuehang.

8 records

DOE OSTI2025

Integrating Analytical Solutions and U-Net Model for Predicting Groundwater Contaminant Plumes in Pump-and-Treat Systems

Pump-and-treat (P&T) is a common technique for groundwater remediation involving the extraction and treatment of contaminated water above ground. Optimizing the design and operation of the P&T well network is essential for maximizing the system’s effectiveness and efficiency. However, this optimization often necessitates many model evaluations, leading to computationally demanding tasks. This study introduces a novel approach that integrates analytical solutions for groundwater dynamics with the U-Net (Ronneberger et al., 2015) deep learning framework to predict groundwater contaminant plume migration under dynamic pumping conditions. By incorporating the Thiem equation (Thiem, 1906) into the input preprocessing, the U-Net model transforms sparse well data into a continuous spatial field that captures the hydraulic impacts of pumping activities. This integration enables the model to leverage both deep learning capabilities and classical physics-based groundwater theories, enhancing prediction accuracy and computational efficiency. These advancements can facilitate rapid, large-scale evaluations of P&T optimization simulations, allowing for timely and effective decision-making in well placement and system management. We demonstrate the model's robust performance across both simplified transient 2D models and a more complex 3D heterogeneous site model at the 200 West P&T facility at the Hanford Site. The U-Net-based model offers substantial computational advantages, reducing simulation times significantly compared to full physics-based models and providing a powerful tool for rapid site evaluation and P&T system optimization, such as evaluating alternative P&T well network designs. Our findings highlight the potential of advanced machine learning models to significantly enhance the efficiency and sustainability of groundwater remediation efforts, offering a novel application of U-Net architecture in environmental science.

Pump-and-treat↗

DOE OSTI2024

On the transferability of residence time distributions in two 10-km long river sections with similar hydromorphic units

Quantifying hydrologic exchange fluxes (HEFs) at the stream-groundwater interface and their residence time distributions (RTDs) in the subsurface are important for managing the water quality and ecosystem health in dynamic river corridors. However, direct simulating high-spatial resolution HEFs and RTDs can be time-consuming, especially for watershed-scale modeling. Efficient surrogate models linking RTDs to hydromorphic units (HUs) can be alternatives for simulating RTDs in large-scale models. A common concern of these surrogate models, though, is the transferability of the relationship between the RTDs and HUs from one river corridor to another. To address this issue, this work evaluates the HEFs and resulting RTD-HU relationships for two 10-km long river corridors along the Columbia River leveraging a one-way coupled three-dimensional transient surface-subsurface water transport modeling framework we previously developed. Applying such a framework at the two river corridors with similar HUs allows for quantitative comparisons of HEFs and RTDs using both statistical tests and machine learning classification models. Finally, our comparison shows that the similarity and transferability of the RTD-HU relationship is very low for the two investigated river sections, which suggests that devising a general algorithm to estimate RTDs based solely on surface water hydrodynamics and short-distance river channel topography data, as well as HU classification, might be nearly impossible.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Data and scripts associated with a manuscript on residence time distribution simulation in two 10-kilometer long river sections

This data package is associated with the publication “On the Transferability of Residence Time Distributions in Two 10-km Long River Sections with Similar Hydromorphic Units” submitted to the Journal of Hydrology (Bao et al. 2024).Quantifying hydrologic exchange fluxes (HEFs) at the stream-groundwater interface, along with their residence time distributions (RTDs) in the subsurface, is crucial for managing water quality and ecosystem health in dynamic river corridors. However, directly simulating high-spatial resolution HEFs and RTDs can be a time-consuming process, particularly for watershed-scale modeling. Efficient surrogate models that link RTDs to hydromorphic units (HUs) may serve as alternatives for simulating RTDs in large-scale models. One common concern with these surrogate models, however, is the transferability of the relationship between the RTDs and HUs from one river corridor to another. To address this, we evaluated the HEFs and the resulting RTD-HU relationships for two 10-kilometer-long river corridors along the Columbia River, using a one-way coupled three-dimensional transient surface-subsurface water transport modeling framework that we previously developed. Applying this framework to the two river corridors with similar HUs allows for quantitative comparisons of HEFs and RTDs using both statistical tests and machine learning classification models. This data package includes the model inputs files and the simulation results data. This data package contains 10 folders. The modeling simulation results data are in the folders 100H_pt_data and 300area_pt_data, for the study domain Hanford 100H and 300 area respectively. The remaining eight folders contain the scripts and data to generate the manuscript figures. The file-level metadata file (Bao_2024_Residence_Time_Distribution _flmd.csv) includes a list of all files contained in this data package and descriptions for each. The data dictionary file (Bao_2024_Residence_Time_Distribution _dd.csv) includes column header definitions and units of all tabular files.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

An Online Prototype Toolset for Predicting and Optimizing P&T Performance (FY23 Status Report)

A new web-based toolset is being developed to support ongoing remediation optimization efforts and implementation of an adaptive site management strategy for the 200 West Area Pump-and-Treat (P&T) system at the Hanford Site. This toolset, comprising the well performance index tool and the well optimization pre-screening tool, will offer a user-friendly interface to predict and optimize the P&T well network’s performance at a preliminary level. Efforts in fiscal year (FY) 2023 focused on three main components: updating the existing deep learning model for predicting P&T performance, designing and developing a prototype of a web-based performance index tool, and initiating the conceptual design of the well optimization pre-screening tool. The well performance index tool is based on a pre-trained deep learning model that allows users to select a target contaminant and well screen length, then visualize the predicted performance of potential new wells across the site. The well optimization pre-screening tool includes two separate modules: the pre-computed scenario viewer, which organizes and visualizes offline optimization simulation results, and the quick analysis module, which provides real-time model prediction using user-specified well locations. In FY24, the plan is to add web-based applications to SOCRATES for both the well performance prediction tool and the optimization prescreening tool, with accompanying user and theory guides. These tools are intended to enable an accessible, easily applied, and transparent approach to remedy planning and decision-making.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Part I: Predicting performance of Purolite A532E resins for remediation of comingled contaminants in groundwater

Ion exchange (IX) resins are used in pump-and-treat (P&T) facilities to remove soluble groundwater contaminants. However, natural anions present at concentrations orders of magnitude higher than contaminants can compete for IX sites and impact resin lifecycles. Here, the Hanford Site’s 200 West Area P&T facility (Washington State, USA) was selected as a case study because it currently uses two IX resins: Purolite® A532E (A532E) to remove pertechnetate (TcO 4 - ) and DOWEX 21K (DOWEX) to remove uranium from groundwater. Nitrate (NO 3 - ), sulfate (SO 4 2- ), chloride (Cl - ), and carbonate (CO 3 2- ) anions have been identified to potentially compete for A532E and DOWEX IX sites. Hanford-relevant anion groundwater concentrations were used to design a series of laboratory-scale batch experiments to evaluate the impact of competing anions on resin performance and potential kinetic effects. These data are then modeled to obtain Cl--normalized equilibrium exchange coefficients (K) needed to predict IX resin performance. The work is presented in two parts, with IX performance evaluated for A532E in Part I and DOWEX in Part II. Part I results demonstrate that TcO 4 - uptake is not impacted by NO 3 - , SO 4 2- , Cl - , CO 3 2- (as HCO 3 - ) and U(VI) carbonate anions, with K TcO4-/Cl- > 4,000, likely due to the high selectivity of A532E trihexylammonium sites for the large, weakly hydrated TcO 4 - anion. Other anion K values were K NO3-/Cl- = 20, K SO4--/Cl- = 0.2, K HCO3-/Cl- = 0.09, K U/Cl- = 370–1000. These K values provide conservative parameters for predicting A532E performance, and demonstrate that, under these test conditions, A532E will remove TcO 4 - from current and future influent streams to meet groundwater treatment objectives.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Part II: Predicting performance of $\mathrm{DOWEX 21K}$ resin for remediation of comingled contaminants in groundwater

The selectivity of ion exchange (IX) resins for aqueous contaminant removal can be impacted by changing concentrations of competing natural groundwater ions. In a two-part investigation, the Hanford Site 200 West Area pump-and-treat (P&T) facility in Washington State, USA is used as a case study to evaluate the performance of two IX resins for groundwater treatment: Purolite® A532E for pertechnetate (TcO 4 - ) removal, explored in Part I, and DOWEX 21K (DOWEX) for uranium (U) removal. In Part II, DOWEX selectivity for U, as uranyl carbonate species, and uptake kinetics is quantified in a series of laboratory-scale aqueous batch experiments containing Hanford-relevant concentrations of competing anions nitrate (NO 3 - ), sulfate (SO 4 2- ), chloride (Cl - ), and carbonate (CO 3 2- ), as well as co-mingled contaminant TcO 4 - . Here the results demonstrate that DOWEX trimethylammonium functional groups are highly selective for U carbonate species (85–100 % uptake) under all conditions investigated. Only NO 3 - concentrations of 100 mM were shown to decrease U removal, with the extent (85–99 %) depending on competing anion concentrations present in solution. However, at the highest NO 3 - concentrations reported for groundwaters treated at the P&T facility (25 mM), the effect on U uptake is minimal. The batch sorption results are modeled to obtain chloride normalized equilibrium exchange coefficients (K) for predicting DOWEX performance: K SO4--/Cl- = 2.0, K NO3-/Cl- = 5.0, K HCO3-/Cl- = 1.5, K TcO4-/Cl- = 2,000, and K U/Cl- = 50,000. These K values predict little effect of current and future influent chemistries on U removal by DOWEX, where both uranyl carbonate species and TcO 4 - are removed such that effluent concentrations meet groundwater treatment requirements.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Vertical Hydrologic Exchange Flows Control Methane Emissions from Riverbed Sediments

CH 4 emissions from inland waters are highly uncertain in the current global CH 4 budget, especially for streams, rivers, and other lotic systems. Previous studies have attributed the strong spatiotemporal heterogeneity of riverine CH 4 to environmental factors such as sediment type, water level, temperature, or particulate organic carbon abundance through correlation analysis. However, a mechanistic understanding of the basis for such heterogeneity is lacking. Here, in this study, we combine sediment CH 4 data from the Hanford reach of the Columbia River with a biogeochemical-transport model to show that vertical hydrologic exchange flows (VHEFs), driven by the difference between river stage and groundwater level, determine CH 4 flux at the sediment–water interface. CH 4 fluxes show a nonlinear relationship with the magnitude of VHEFs, where high VHEFs introduce O 2 into riverbed sediments, which inhibit CH 4 production and induce CH 4 oxidation, and low VHEFs cause transient reduction in CH 4 flux (relative to production) due to reduced advective CH 4 transport. In addition, VHEFs lead to the hysteresis of temperature rise and CH 4 emissions because high river discharge caused by snowmelt in spring leads to strong downwelling flow that offsets increasing CH 4 production with temperature rise. Our findings reveal how the interplay between in-stream hydrologic flux besides fluvial-wetland connectivity and microbial metabolic pathways that compete with methanogenic pathways can produce complex patterns in CH 4 production and emission in riverbed alluvial sediments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Predicting future well performance for environmental remediation design using deep learning

Here in this study, we developed a deep learning (DL) framework with a multi-channel three-dimensional convolutional neural network (MC3D-CNN) to predict well performance and thereby assist future environmental remediation design. Such prediction of extraction well performance at designated locations is critical for configuring pump-and-treat (P&T) well network design and operation, setting reasonable target closure dates for overall remedying, and estimating remedy costs. The framework is developed with operational and monitoring data routinely collected during P&T remedy operations, including well extraction and injection rates as well as in situ contaminant concentrations. Traditionally, the collected data were rarely used for purposes other than assessing past well performance and the accuracy of the conceptual site model. However, recent advances in data-driven computational approaches enable better use of the large datasets to inform future well performance, enhance site characterization, and improve remediation planning. In this study, we established a DL framework to integrate transient three-dimensional contaminant plumes and multiple aquifer properties (e.g., hydraulic conductivity and hydrostratigraphic maps) to identify characteristic patterns controlling and representing extraction well mass recovery, aiming at providing future mass recovery estimates for existing wells and candidate wells at any proposed locations. We evaluated our framework by using a realistic synthetic dataset generated from a well-calibrated flow and transport model used in the 200 West Area of the U.S. Department of Energy’s Hanford Site in southeastern Washington state. The multi-channel feature in our framework allows integration of various types and temporal densities of training datasets for DL model development. Overall, we found that the trained DL model achieved an accuracy of over 90% in ranking extraction well performance in validation datasets, and over 80% in predicting high-performance-ranking well locations. This data-informed approach provides a flexible tool to support adaptive site management, streamline decision-making, and potentially reduce remediation time and costs. Our DL framework can be used as a filtering tool to improve the current P&T network optimization design by reducing the number of candidate well locations.

54 ENVIRONMENTAL SCIENCES↗