Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2021WR029959 |
Integrating process-based reactive transport modeling and machine learning for electrokinetic remediation of contaminated groundwater | |
R. Sprocati; M. Rolle | |
2021-08-02 | |
发表期刊 | Water Resources Research |
出版年 | 2021 |
英文摘要 | Advanced reactive transport models of fluid flow and solute transport in subsurface porous media are instrumental for the assessment of contaminant environmental fate and for the design of in situ remediation interventions. However, the increasing complexity of process-based reactive transport simulators often leads to long runtimes, which poses severe restrictions for tasks that require numerous model evaluations. To overcome this limitation, we demonstrate how machine learning surrogate models, trained on the outputs of a limited number of process-based reactive transport simulations, can predict the evolution of complex subsurface systems. We focus on electrokinetic enhanced bioremediation of chlorinated solvents in low-permeability porous media, which is an in situ remediation technology entailing a suite of complex and coupled physical, chemical and biological processes. A process-based, multicomponent reactive transport model, capable of describing the key mechanisms of electrokinetic flow and transport, is setup in a two-dimensional domain. The model accounts for electromigration and electroosmosis, the electrostatic interactions between charged species, the chemistry of the pore water solution, the microbially-mediated degradation of the organic compounds, and the dynamics of different degraders. We develop a response surface surrogate framework using an artificial neural network as approximation function and we show that the surrogate model has the capability and the flexibility to capture the complex dynamics of electrokinetic remediation in subsurface porous media and allows computationally efficient model exploration, sensitivity analysis and uncertainty quantification. This article is protected by copyright. All rights reserved. |
领域 | 资源环境 |
URL | 查看原文 |
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文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/335440 |
专题 | 资源环境科学 |
推荐引用方式 GB/T 7714 | R. Sprocati,M. Rolle. Integrating process-based reactive transport modeling and machine learning for electrokinetic remediation of contaminated groundwater[J]. Water Resources Research,2021. |
APA | R. Sprocati,&M. Rolle.(2021).Integrating process-based reactive transport modeling and machine learning for electrokinetic remediation of contaminated groundwater.Water Resources Research. |
MLA | R. Sprocati,et al."Integrating process-based reactive transport modeling and machine learning for electrokinetic remediation of contaminated groundwater".Water Resources Research (2021). |
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