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Related Experiment Videos

Software sensor design for COD estimation in an anaerobic fluidized bed reactor.

C Aubrun1, D Theilliol, J Harmand

  • 1CRAN-ESA CNRS 7039, Université Henri Poincaré-Nancy 1, BP 239, 54506 Vandúuvre, France.

Water Science and Technology : a Journal of the International Association on Water Pollution Research
|June 2, 2001
PubMed
Summary

This study introduces a software sensor using an unknown input Kalman filter to estimate unmeasured influent components in bioprocess systems. This method enhances real-time monitoring for stochastic systems like anaerobic digesters.

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Area of Science:

  • Bioprocess Engineering
  • Control Systems
  • Stochastic Systems

Background:

  • Bioprocess systems often lack reliable on-line measurements for real-time monitoring.
  • Accurate monitoring is crucial for optimizing process performance and control.
  • Unmeasured influent components pose a significant challenge in bioprocess analysis.

Purpose of the Study:

  • To present a method for unknown input estimation in stochastic bioprocess systems.
  • To develop a software sensor for estimating unmeasured influent components.
  • To apply and validate the method in an anaerobic digester.

Main Methods:

  • Utilized an unknown input Kalman filter for state and unknown input estimation.
  • Developed a software sensor to estimate unmeasured influent characteristics.

Related Experiment Videos

  • Applied the observer to an anaerobic fluidized bed reactor.

    • Main Results:

    • Successfully estimated unmeasured influent components using available on-line measurements.
    • Demonstrated the effectiveness of the unknown input Kalman filter in a stochastic system.
    • Presented experimental results validating the software sensor's performance in an anaerobic fluidized bed reactor.

    Conclusions:

    • The proposed method effectively estimates unknown inputs and unmeasured components in stochastic bioprocesses.
    • Software sensors based on unknown input observers can overcome limitations of unreliable on-line measurements.
    • This approach enhances real-time monitoring and control capabilities for anaerobic digesters.