5 research outputs found
Numerical modelling of the dynamics of chlorinated solvent pollution in aquifers and their remediation with engineered nano-particles: An integrated approach
[EN] The global water shortage is one of the main environmental concerns in the 21st century. The
main source of drinking water is the groundwater that flows in the subsurface. The increased
agriculture and industrial activities in the last few decades have been proven to be detrimental
for groundwater. While these water resources are limited, the scarcity is further triggered by
the loss of quality due to anthropogenic activities such as waste deposition and landfill leakage.
Contaminants from the anthropogenic waste often migrates through the sub-surface and reach
an underlying aquifer. The occurrence of these contaminants threatens the quality of water
resources and often requires remediation efforts. Several in-situ and ex-situ remediation
methodologies have been developed and tested in the last decades; recently, the use of
Engineered Nano-Particles (ENPs) for in-situ contaminant degradation have gained a lot of
interest in the field of groundwater remediation. These ENPs have been found to be effective
due to their high reactive surface area, minimal disruption of the groundwater system and their
aggressive contaminant degradation capabilities. However, the field scale implementation of
this remediation technique is often challenging, as each polluted site require a custom design
and strategy of remediation.
The field scale remediation of groundwater using ENPs requires a lot of scientific investigation
and technical resources, owing to complexity and the limited accessibility of the contamination-
groundwater system. Therefore, it is necessary to develop a robust remediation strategy which
includes laboratory scale and field scale studies as well as application of a numerical approach.
The success in the remediation effort is often limited by lack of detailed understanding of the
contaminant and hydrogeological properties of the aquifer. While, the information of
contamination-aquifer dynamics can be studied at field, knowledge on the continuous and
consistent contamination behavior on both temporal and spatial scale is often missing. The use
of an integrated numerical model can be helpful for bridging the gap between the field studies
and the relevant insights required for groundwater remediation
Role of the clay lenses within sandy aquifers in the migration pathway of infiltrating DNAPL plume: A numerical investigation
The use of numerical based multi-phase fluid flow simulation can
significantly aid in the development of an effective remediation strategy for
groundwater systems contaminated with Dense Non Aqueous Phase Liquid (DNAPL).
Incorporating the lithological heterogeneities of the aquifer into the model
domain is a crucial aspect in the development of robust numerical simulators.
Previous research studies have attempted to incorporate lithological
heterogeneities into the domain; however, most of these numerical simulators
are based on Finite Volume Method (FVM) and Finite Difference Method (FDM)
which have limited applicability in the field-scale aquifers. Finite Element
Method (FEM) can be highly useful in developing the field-scale simulation of
DNAPL infiltration due to its consistent accuracy on irregular study domain,
and the availability of higher orders of basis functions.
In this research work, FEM based model has been developed to simulate the
DNAPL infiltration in a hypothetical field-scale aquifer. The model results
demonstrate the effect of meso-scale heterogeneities, specifically clay lenses,
on the migration and accumulation of Dense Non Aqueous Phase Liquid (DNAPL)
within the aquifer. Furthermore, this research provides valuable insights for
the development of an appropriate remediation strategy for a general
contaminated aquifer
Dynamic weights enabled Physics-Informed Neural Network for simulating the mobility of Engineered Nano-particles in a contaminated aquifer
Numerous polluted groundwater sites across the globe require an active
remediation strategy to restore natural environmental conditions and local
ecosystem. The Engineered Nano-particles (ENPs) have emerged as an efficient
reactive agent for the in-situ degradation of groundwater contaminants. While
the performance of these ENPs has been highly promising on the laboratory
scale, their application in real field case conditions is still limited. The
complex transport and retention mechanisms of ENPs hinder the development of an
efficient remediation strategy. Therefore, a predictive tool for understanding
the transport and retention behavior of ENPs is highly required. The existing
tools in the literature are dominated with numerical simulators, which have
limited flexibility and accuracy in the presence of sparse datasets. This work
uses a dynamic, weight-enabled Physics-Informed Neural Network (dw-PINN)
framework to model the nano-particle behavior within an aquifer. The result
from the forward model demonstrates the effective capability of dw-PINN in
accurately predicting the ENPs mobility. The model verification step shows that
the relative mean square error (MSE) of the predicted ENPs concentration using
dw-PINN converges to a minimum value of . In the subsequent step,
the result from the inverse model estimates the governing parameters of ENPs
mobility with reasonable accuracy. The research demonstrates the tool's
capability to provide predictive insights for developing an efficient
groundwater remediation strategy.Comment: 5 pages, 3 Figures, Conference paper accepted in NeurIPS 2022
Workshop: Tackling Climate Change with Machine Learnin
End-to-End Integrated Simulation for Predicting the Fate of Contaminant and Remediating Nano-Particles in a Polluted Aquifer
Groundwater contamination caused by Dense Non-Aqueous Phase Liquid (DNAPL)
has an adverse impact on human health and environment. Remediation techniques,
such as the in-situ injection of nano Zero Valent Iron (nZVI) particles, are
widely used in mitigating DNAPL-induced groundwater contamination. However, an
effective remediation strategy requires predictive insights and understanding
of the physiochemical interaction of nZVI and contamination along with the
porous media properties. While several stand-alone models are widely used for
predictive modeling, the integration of these models for better scalability and
accuracy is still rarely utilized. This study presents an end-to-end integrated
modeling framework for the remediation of DNAPL-contaminated aquifers using
nZVI. The framework simulates the migration pathway of DNAPL and subsequently
its dissolution in groundwater resulting in an aqueous contaminant plume.
Additionally, the framework includes simulations of nZVI mobility, transport,
and reactive behavior, allowing for the prediction of the radius of influence
and efficiency of nZVI for contaminant degradation. The framework has been
applied to a hypothetical 2-dimensional and heterogeneous silty sand aquifer,
considering trichloroethylene (TCE) as the DNAPL contaminant and carboxymethyl
cellulose (CMC) coated nZVI for remediation. The results demonstrate the
framework's capability to provide comprehensive insights into the contaminant's
behavior and the effectiveness of the remediation strategy. The proposed
modeling framework serves as a reference for future studies and can be expanded
to incorporate real field data and complex geometries for upscaled
applications.Comment: 33 page
Bayesian Physics-Informed Neural Network for the Forward and Inverse Simulation of Engineered Nano-particles Mobility in a Contaminated Aquifer
Globally, there are many polluted groundwater sites that need an active
remediation plan for the restoration of local ecosystem and environment.
Engineered nanoparticles (ENPs) have proven to be an effective reactive agent
for the in-situ degradation of pollutants in groundwater. While the performance
of these ENPs has been highly promising on the laboratory scale, their
application in real field case conditions is still limited. The complex
transport and retention mechanisms of ENPs hinder the development of an
efficient remediation strategy. Therefore, a predictive tool to comprehend the
transport and retention behavior of ENPs is highly required. The existing tools
in the literature are dominated with numerical simulators, which have limited
flexibility and accuracy in the presence of sparse datasets and the aquifer
heterogeneity. This work uses a Bayesian Physics-Informed Neural Network
(B-PINN) framework to model the nano-particles mobility within an aquifer. The
result from the forward model demonstrates the effective capability of B-PINN
in accurately predicting the ENPs mobility and quantifying the uncertainty. The
inverse model output is then used to predict the governing parameters for the
ENPs mobility in a small-scale aquifer. The research demonstrates the
capability of the tool to provide predictive insights for developing an
efficient groundwater remediation strategy.Comment: To be submitted to a NeurIPS 2023 workshop. arXiv admin note:
substantial text overlap with arXiv:2211.0352