Use of Genetic Algorithms to Characterize Groundwater Contamination Source Areas

Abstract

In this work, genetic algorithms (GAs) were used to help interpret tracer breakthrough curves from partitioning interwell tracer tests (PITTs) conducted at Hill AFB, Utah by researchers from the University of Florida. Two transport models were developed to simulate tracer transport in the test cells. One model assumed the cell consisted of multiple layers, and that transport in each layer could he described by the one-dimensional advective/dispersive equation. The second model also assumed multiple layers, and modeled transport in the individual layers as advective transport through 100 tubes. Transport times were represented by a stochastic (lognormal) distribution. The model solutions were coded into Microsoft Excel. Model parameters were optimized using Evolutionary Solver, a GA developed by Froutline Systems. The optimized parameters were used to estimate pre-and post-flushing NAPL saturations, as well as cleanup efficiency. Results were compared to estimates obtained through moment analysis of me PITT data. Results demonstrated that GAS are a tool that may be useful in interpreting PITT data for the characterization of NAPL source areas. In particular, using the GAs to interpret PITT data provided more information than could he obtained from moment analysis.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2000
Accession Number
ADA377108

Entities

People

  • Chaz M. Williamson

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Alkanes
  • Civil Engineering
  • Computational Science
  • Core Sampling
  • Ecology
  • Efficiency
  • Equations
  • Experimental Data
  • Genetic Algorithms
  • Groundwater
  • Hydrophobic Properties
  • Linear Programming
  • Mathematical Models
  • Organic Compounds
  • Spreadsheet Software
  • Water Resources

Readers

  • Computational Modeling and Simulation
  • Groundwater Contamination Remediation.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Biotechnology
  • Biotechnology - Bioremediation