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Sensor Data Fusion as an Alternative for Monitoring Chlorate in Electrochlorination Applications

Author

Listed:
  • Edwin Ross

    (Wetsus, European Centre of Excellence for Sustainable Water Technology, Oostergoweg 9, 8911 MA Leeuwarden, The Netherlands
    Mathematical and Statistical Methods-Biometris, Wageningen University and Research, 6700 AA Wageningen, The Netherlands)

  • Martijn Wagterveld

    (Wetsus, European Centre of Excellence for Sustainable Water Technology, Oostergoweg 9, 8911 MA Leeuwarden, The Netherlands)

  • Mateo Mayer

    (EasyMeasure B.V., Breestraat 22, 3811 BJ Amersfoort, The Netherlands)

  • Hans Stigter

    (Mathematical and Statistical Methods-Biometris, Wageningen University and Research, 6700 AA Wageningen, The Netherlands)

  • Bo Højris

    (Grundfos Holding A/S, Poul Due Jensens Vej 7, 8850 Bjerringbro, Denmark)

  • Yang Li

    (Mathematical and Statistical Methods-Biometris, Wageningen University and Research, 6700 AA Wageningen, The Netherlands)

  • Karel Keesman

    (Wetsus, European Centre of Excellence for Sustainable Water Technology, Oostergoweg 9, 8911 MA Leeuwarden, The Netherlands
    Mathematical and Statistical Methods-Biometris, Wageningen University and Research, 6700 AA Wageningen, The Netherlands)

Abstract

As chlorate concentrations have been found to be harmful to human and animal health, governments are increasingly demanding strict control of the chlorate concentration in drinking water. Since there are no chlorate sensors available, the current solution is sampling and laboratory analysis. This is costly and time consuming. The aim of this work was to investigate Sensor Data Fusion (SDF) as an alternative approach, with a focus on chlorate formation in the electrochlorination process, and design an observer for the real-time estimation of chlorate. The pH, temperature and UV-a absorption were measured in real time. A reduced-order nonlinear model was derived, and it was found to be detectable. An Extended Kalman Filter (EKF), based on this model, was then used to estimate the chlorate formation. The EKF algorithm was verified experimentally and was found to be capable of accurately estimating chlorate concentrations in real time. Electrochlorination is an emerging and efficient method of disinfecting drinking water. Soft sensing of chlorate concentrations, as proposed in this paper, may help to better control and manage the process of electrochlorination.

Suggested Citation

  • Edwin Ross & Martijn Wagterveld & Mateo Mayer & Hans Stigter & Bo Højris & Yang Li & Karel Keesman, 2022. "Sensor Data Fusion as an Alternative for Monitoring Chlorate in Electrochlorination Applications," Sustainability, MDPI, vol. 14(10), pages 1-15, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:6119-:d:818251
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