Estimation of bulk electrical conductivity in saline medium with contaminated lead solution through TDR coupled with machine learning

Won Taek Hong, Jong Sub Lee, Dongsoo Lee, Hyung Koo Yoon

Research output: Contribution to journalArticlepeer-review

Abstract

Time-domain reflectometry (TDR) has been used for the characterization of media; however, the results of TDR tests significantly differ according to the types of solutions. The objective of this study is to suggest a new relationship between TDR output values and bulk electrical conductivity based on a machine learning algorithm for enhancing the reliability of TDR measurement. Various salinities (0%, 1%, 2%, and 3%) and lead concentrations (0, 0.5, 1, 2, 5, and 10 mg/L) are applied along with silica sand, classified as SP in USCS, to create media. A laboratory test is performed to measure the TDR waveform at the bottom of the cylindrical cell, and a resistance probe is also installed to obtain the true bulk electrical conductivity in the cell. A deep neural network machine learning algorithm is applied to establish the relationship between the TDR output value and the bulk electrical conductivity at each frequency of 0.1, 0.12, 1, 10, and 100 kHz. The highly important variables are also defined through random forest. This study demonstrates that the TDR can be reliably converted into bulk electrical conductivity when two different solutions are mixed.

Original languageEnglish
Pages (from-to)58-66
Number of pages9
JournalProcess Safety and Environmental Protection
Volume161
DOIs
Publication statusPublished - 2022 May

Keywords

  • Bulk electrical conductivity
  • Deep neural network
  • Lead solution
  • Salinity
  • Time-domain reflectometry (TDR)

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • Chemical Engineering(all)
  • Safety, Risk, Reliability and Quality

Fingerprint

Dive into the research topics of 'Estimation of bulk electrical conductivity in saline medium with contaminated lead solution through TDR coupled with machine learning'. Together they form a unique fingerprint.

Cite this