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Related Experiment Videos

An intelligent controller for automated operation of sequencing batch reactors.

A Cohen1, D Hegg, M de Michele

  • 1Waste Solutions Ltd, Dunedin, New Zealand. tico.cohen@wastetechnz.com

Water Science and Technology : a Journal of the International Association on Water Pollution Research
|August 21, 2003
PubMed
Summary

This study introduces intelligent methods for detecting breakpoints in Dissolved Oxygen (DO) profiles from wastewater treatment. The developed system achieves high accuracy, adapting to changing conditions for effective process control.

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Area of Science:

  • Environmental Engineering
  • Water Treatment Technologies
  • Process Control Systems

Background:

  • Dissolved Oxygen (DO) profiles are critical for monitoring wastewater treatment processes.
  • Accurate detection of breakpoints in DO profiles is essential for optimizing operations.
  • Existing methods may struggle with dynamically changing process conditions.

Purpose of the Study:

  • To develop and evaluate automatic and intelligent methods for breakpoint detection in DO profiles.
  • To classify detected breakpoints using soft computing techniques.
  • To create an adaptive control model for robust breakpoint detection.

Main Methods:

  • Analysis of geometric features in DO profiles for automatic breakpoint detection.
  • Classification of breakpoints using Neural Network (NN), Fuzzy Neural Network (FuNN), and Evolving Fuzzy Neural Network (EfuNN).

Related Experiment Videos

  • Development of an intelligent control model combining EfuNN with a logic decision unit for adaptive learning.

    • Main Results:

    • High success rates in breakpoint detection and classification, achieving up to 96% accuracy.
    • The intelligent control model demonstrates "on-the-fly" learning capabilities.
    • Successful embedding of the software system on a programmable controller for integration into larger systems.

    Conclusions:

    • The developed intelligent system effectively detects and classifies breakpoints in DO profiles.
    • The adaptive control model enhances robustness under varying process conditions.
    • The system is suitable for integration into Sequencing Batch Reactor (SBR) plant control systems.