Predicting remediation efficiency of LNAPLs using surrogate polynomial chaos expansion model and global sensitivity analysis

Taehoon Kim, Weon Shik Han, Jize Piao, Peter K. Kang, Jehyun Shin

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

A multi-phase and multi-component numerical simulator assessed the removal efficiencies of light non-aqueous phase liquid (LNAPL) consisting of benzene, toluene, ethylbenzene, and xylene-p. Scenarios of the LNAPL-spilling, natural distribution, and remediation stages were designed in the full-physics numerical modeling. For LNAPLs remediation, a multi-phase extraction (MPE) and a steam injection technique were employed. The removal efficiencies of LNAPLs were computed by systematically varying 6 factors that determine the configuration of the remediation wells. Then, surrogate polynomial chaos expansion (PCE) models mathematically predicting the removal efficiencies were developed through 600 training datasets representing 4 scenario cases; different permeability and the location of SI well were considered in the scenario cases. The PCE models were utilized for Sobol global sensitivity analysis and stochastic Monte Carlo prediction. As a result, the depth of the MPE well was identified as the most significant factor in determining the removal efficiency of the LNAPLs. The removal efficiency was maximized when the MPE well was positioned 1.5 m below the groundwater table. Additionally, the contributions of influencing factors were significantly changed by the field permeability. This study proposed a general framework that efficiently predicts LNAPLs remediation efficiency and identifies key influencing factors by combining advanced numerical modeling, PCE-based surrogate modeling, and sensitivity analyses.

Original languageEnglish (US)
Article number104179
JournalAdvances in Water Resources
Volume163
DOIs
StatePublished - May 2022

Bibliographical note

Funding Information:
This research was supported by the Subsurface Environmental Management (SEM) project through the Korea Environmental Industry and Technology Institute (KEITI) funded by the Ministry of Environment (Grant Number: 2018002440003) and by Energy & Mineral Resources Development Association of Korea (EMRD) grant funded by the Korea government (MOTIE) (Data science based oil/gas exploration consortium). Peter K. Kang acknowledges the support by the National Science Foundation under Grant No. EAR-2046015. In addition, the authors would like to thank the useful discussion with Dowan Koo from Yonsei University, Republic of Korea.

Funding Information:
This research was supported by the Subsurface Environmental Management (SEM) project through the Korea Environmental Industry and Technology Institute (KEITI) funded by the Ministry of Environment (Grant Number: 2018002440003 ) and by Energy & Mineral Resources Development Association of Korea (EMRD) grant funded by the Korea government (MOTIE) (Data science based oil/gas exploration consortium). Peter K. Kang acknowledges the support by the National Science Foundation under Grant No. EAR-2046015 . In addition, the authors would like to thank the useful discussion with Dowan Koo from Yonsei University, Republic of Korea.

Publisher Copyright:
© 2022 Elsevier Ltd

Keywords

  • LNAPLs remediation
  • Monte Carlo prediction
  • Multi-phase modeling
  • Polynomial chaos expansion
  • Removal efficiency
  • Sobol sensitivity analysis

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