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Monitoring chlorate through sensor data fusion; A digital twin for electrochlorinators

  • 26 January, 2024
  • Wageningen University, Mathematical and Statistical Methods
  • K.J. Keesman
  • J.D. R.M. Wagterveld

Electrochlorination is an attractive method of disinfecting water that is growing in popularity. However, the chlorate by-product has been found to be harmful for human health and the environment, and is strictly regulated in the EU since 2020.
To ensure that the chlorate concentration in drinking water and food remains at a safe level, monitoring is required. Current monitoring techniques are sample-based and therefore costly, intermittent and with delay.
In this work, soft sensors based on Sensor Data Fusion (SDF) have been developed for electrochlorinators that enable the monitoring of chlorate in real-time. Several SDF algorithms were developed, including two that integrate machine learning and first-principles models, and one based on a model of the electrochemistry that includes mass transport limitations. The resulting soft sensors were validated experimentally, and were shown to accurately predict chlorate concentrations.
The resulting soft sensor enables a higher standard of safety when electrochlorinators are applied.


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