Fuel Spill Identification Using Solid-Phase Extraction and Solid-Phase Microextraction. 1. Aviation Turbine Fuels (Postprint)

Abstract

The water-soluble fraction of aviation jet fuels is examined using solid-phase extraction and solid-phase microextraction. Gas chromatographic profiles of solid-phase extracts and solid-phase microextracts of the water-soluble fraction of kerosene- and nonkerosene-based jet fuels reveal that each jet fuel possesses a unique profile. Pattern recognition analysis reveals fingerprint patterns within the data characteristic of fuel type. By using a novel genetic algorithm (GA) that emulates human pattern recognition through machine learning, it is possible to identify features characteristic of the chromatographic profile of each fuel class. The pattern recognition GA identifies a set of features that optimize the separation of the fuel classes in a plot of the two largest principal components of the data. Because principal components maximize variance, the bulk of the information encoded by the selected features is primarily about the differences between the fuel classes.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2001
Accession Number
ADA595971

Entities

People

  • A. J. Moores
  • B. K. Lavine
  • D. M. Brzozwski
  • H. T. Mayfield
  • J. Ritter

Organizations

  • Armstrong Laboratory

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Aviation Fuels
  • Aviation Gasoline
  • Chemistry
  • Data Sets
  • Dimensionality Reduction
  • Fuels
  • Jet Engine Fuels
  • Machine Learning
  • Materials Laboratories
  • Materials Processing
  • Materials Science
  • Materials Testing
  • Operating Systems
  • Pattern Recognition
  • Recognition

Readers

  • Analytical Chemistry
  • Neural Network Machine Learning.
  • Petroleum Engineering

Technology Areas

  • AI & ML
  • Biotechnology