STP1102

    Expert Systems for Classification and Identification of Waterborne Petroleum Oils

    Published: Jan 1991


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    Abstract

    Expert systems are among the most fascinating application of computers which attempt to emulate human cognition and perceptual abilities. In the environmental field, expert systems have been used for spectral pattern recognition and have proven useful for classification and identification of waterborne petroleum oils.

    This paper, after reviewing expert systems in general, describes a few successful attempts by the authors for classification of synchronous fluorescence spectra of petroleum oils. A typical scheme for developing an expert system for the classification and identification of petroleum oils is also presented. The scheme suggested uses pattern recognition and artificial intelligence (AI) for modeling expert knowledge and spectral processing. Using this model, an expert system called Spectral Expert system (SES) was designed. SES comprised of five primary components, fact gathering, knowledge/rule base, knowledge organization/learning, inference engine and expert/user interface. All these components were developed and integrated into a working system. The system is successfully used to classify several oil samples.

    Keywords:

    expert systems, petroleum oils, ultravioletvisible fluorescence, spectral pattern recognition, oil identification, oil classification, synchronous fluorescence


    Author Information:

    Siddiqui, KJ
    assistant professor, Creighton University, Omaha, NE

    Lidberg, RL
    senior staff scientist and research scientists, Lockheed Engineering and Sciences Company, Las Vegas, NV

    Eastwood, D
    senior staff scientist and research scientists, Lockheed Engineering and Sciences Company, Las Vegas, NV

    Gibson, G
    senior staff scientist and research scientists, Lockheed Engineering and Sciences Company, Las Vegas, NV


    Paper ID: STP17626S

    Committee/Subcommittee: D19.31

    DOI: 10.1520/STP17626S


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