Providing wastewater treatment plants with predictive knowledge based on transition networks

J. M. Gimeno, J. Bejar, M. Sanchez-Marre, U. Cortes, I. R. Roda, M. Poch, J. Lafuente

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    4 Citations (Scopus)

    Abstract

    Presents a progress report on integrating predictive skills into an integrated AI system for wastewater treatment plant (WWTP) supervision and control. Although the embedded approaches within the previously developed architecture, called DAI-DEPUR, such as numerical control knowledge, rule-based reasoning and case-based reasoning, are able to cope with the overall supervision task of a plant, one feature is missing: Predictive knowledge. With the previous approaches, the supervisory system works reasonably well, but the actuation process always restores the normal operation of a WWTP tardily. Thus, the supervision is implemented in an a posteriori fashion, which can be very dangerous for the environment. The integration of a new kind of knowledge can overcome this problem of control systems.

    Original languageAmerican English
    Title of host publicationProceedings - Intelligent Information Systems, IIS 1997
    EditorsHojjat Adeli
    Pages355-359
    Number of pages5
    ISBN (Electronic)0818682183, 9780818682186
    DOIs
    Publication statusPublished - 1 Jan 1997

    Publication series

    NameProceedings - Intelligent Information Systems, IIS 1997

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