Bayesian Physics-Informed Neural Network for the Forward and Inverse Simulation of Engineered Nano-particles Mobility in a Contaminated Aquifer

TL;DR

This paper introduces a Bayesian Physics-Informed Neural Network framework to accurately model and predict the transport and retention of engineered nanoparticles in contaminated aquifers, aiding groundwater remediation efforts.

Contribution

It presents a novel B-PINN approach that effectively models ENPs mobility, incorporating uncertainty quantification and enabling inverse parameter estimation in complex aquifer systems.

Findings

B-PINN accurately predicts ENPs mobility with quantified uncertainty.

The inverse model estimates key parameters governing nanoparticle transport.

The framework enhances predictive capabilities over traditional numerical simulators.

Abstract

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.