Optimizing of In Situ Bioremediation Technology to Manage Perchlorate-Contaminated Groundwater

Abstract

Combining horizontal flow treatment wells (HFTWs) with in situ biodegradation is an innovative approach with the potential to remediate perchlorate-contaminated groundwater. A technology model was recently developed that combines the groundwater flow induced by HFTWs with in situ biodegradation processes that result from using the HFTWs to mix electron donor into perchlorate-contaminated groundwater. A field demonstration of this approach is planned to begin this year. In order to apply the technology in the field, project managers need to understand how contaminated site conditions and technology design parameters impact technology performance. One way to gain this understanding is to use the technology model to select engineering design parameters that optimize performance under given site conditions. In particular, a project manager desires to design a system that: 1) maximizes perchlorate destruction; 2) minimizes treatment expense; and 3) attains regulatory limits on down gradient contaminant concentrations. Unfortunately, for a relatively complex technology with a number of engineering design parameters to determine, as well as multiple objectives, system optimization is not straightforward. In this study, a multi-objective genetic algorithm (MOGA) is used to determine design parameter values (flow rate, well spacing, concentration of injected electron donor, and injection schedule) that optimize the first two objectives noted; to maximize perchlorate destruction while minimizing cost. Four optimization runs are performed, using two different remediation time spans (300 and 600 days) for two different sets of site conditions. Results from all four optimization runs indicate that the relationship between perchlorate mass removal and operating cost is positively correlated and nonlinear.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2003
Accession Number
ADA415320

Entities

People

  • Mark R. Knarr

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Chemical Synthesis
  • Chemistry
  • Computational Complexity
  • Differential Equations
  • Environment
  • Environmental Protection
  • Evolutionary Algorithms
  • Genetic Algorithms
  • Genetics
  • Groundwater
  • Health Services
  • Multiobjective Optimization
  • Three Dimensional
  • United States
  • Water Resources

Fields of Study

  • Environmental science

Readers

  • Groundwater Contamination Remediation.
  • Operations Research

Technology Areas

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