Integrating process-based modeling and machine learning to simulate electrokinetic bioremediation of chlorinated solvents

Riccardo Sprocati, John Flyvbjerg, Nina Tuxen, Massimo Rolle

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    Abstract

    Electrokinetic (EK) remediation is one of the few in-situ technologies that can effectively remove contaminants from low-permeability porous media. Process-based modeling, including the complex multiphysics and biogeochemical processes occurring during electrokinetic remediation, is instrumental to describe EK systems and to assist in their design. Multidimensional process-based models require long computational times, which limit their use for optimization and prediction of system performances over wide ranges of conditions. To overcome such limitation, in this work we develop an approach integrating a process-based model and a surrogate model based on machine learning. The approach provides fast predication of system performances and is therefore highly suitable for decision-making processes involving uncertainty analysis, sensitivity analysis and feasibility studies.
    Original languageEnglish
    Publication date2021
    Number of pages3
    Publication statusPublished - 2021
    EventIWA Digital World Water Congress 2021 - Online
    Duration: 24 May 20214 Jun 2021

    Conference

    ConferenceIWA Digital World Water Congress 2021
    LocationOnline
    Period24/05/202104/06/2021

    Keywords

    • groundwater remediation
    • machine learning
    • reactive transport modeling

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