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Machine Learning Weather Soft-Sensor for Advanced Control of Wastewater Treatment Plants.

Félix Hernández-Del-Olmo1, Elena Gaudioso2, Natividad Duro3

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This study introduces a novel machine learning soft-sensor to predict weather conditions for wastewater treatment plant (WWTP) control. This approach enhances WWTP management by using influent data to forecast weather impacts.

Keywords:
machine learning techniquessoft-sensorswastewater treatment plants

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Area of Science:

  • Environmental Engineering
  • Process Control
  • Machine Learning

Background:

  • Wastewater treatment plant (WWTP) control is complex due to nonlinearity and external factors like weather.
  • Weather significantly impacts WWTP influent flow rates and pollutant concentrations (e.g., N-ammonia).
  • Operators traditionally use weather as a crucial, yet unmeasurable, signal for WWTP control.

Purpose of the Study:

  • To develop a novel soft-sensor for predicting weather signals relevant to WWTP operation.
  • To enable operators to anticipate weather-induced changes for improved WWTP control.
  • To leverage machine learning for predicting non-observable operational parameters.

Main Methods:

  • Development of a machine learning-based soft-sensor.
  • Utilizing past WWTP influent states measured by standard sensors as input data.
  • Training the model to predict weather conditions from influent data, mimicking operator intuition.

Main Results:

  • The soft-sensor achieved a good level of accuracy in predicting the weather signal.
  • The predicted weather signal is highly relevant for advanced WWTP control systems.
  • Demonstrated the feasibility of using influent data for weather prediction in WWTPs.

Conclusions:

  • The developed soft-sensor provides an accurate and useful prediction of weather conditions for WWTPs.
  • This approach offers a practical solution for incorporating weather impacts into WWTP control strategies.
  • Machine learning soft-sensors can effectively predict non-observable signals crucial for optimizing industrial processes.