August 2, 2021 Digitalisation

DTHub: modelEAU

DTHub theme: Data Management

The challenge

Conventional biological wastewater treatment is energy-intensive and often relies on extensive chemical additions. Moreover, in the transition from a fossil-based to a bio-based economy, it has become critical to close nutrients cycles and therefore ensuring that nutrients are not lost in the waste disposal systems, but rather taken back into the soils. In order to tackle these and other challenges, process operators and engineers need clever tools to transform raw data into actionable insights.

Pilot summary

modelEAU operates a highly instrumented and controlled pilot wastewater treatment plant for carbon and nitrogen removal. It consists of two identical biological treatment lanes to compare and optimize control strategies. Pilot data and metadata (process, weather, maintenance, etc.) are saved in an in-house developed database structure, with integrated quality checks, which acts as a single source of truth for reliable operational decision-making.

Year started: 2015

Deployment scale: Pilot and full scale

Lessons learned and outcomes

  1. Without efficient storage and rigorous contextualization, the life expectancy of data is often limited to the specific project for which they were collected.
  2. The success of smart water systems requires the creation and maintenance of an integrated data pipeline with a strong emphasis on metadata.
  3. An important area of further investigation, boosted by the ongoing digitalisation efforts, is that of data validation. Including automatic methods for the detection of process outliers, anomalies, faults, etc., which heavily rely on a complete and contextualised dataset.
  4. Because data and metadata play an important role in the decision-making process, it has become even clearer that the workers operating at and interacting with the front end of the data pipeline are essential. Novel roles, such as data stewards and engineers, will almost certainly be required to maintain the automatic flow of high-quality data to the decision-makers of smart water resource recovery facilities (WRRFs) of the future.

End users contact(s)

Jeff, Sparks, JSparks@hrsd.com, Process Engineer, HRSD (VA, USA)

Charles, Bott, cbott@hrsd.com, Director of Water Technology and Research, HRSD (VA, USA)

Jean, Bernier, jean.bernier@siaap.fr, Responsible for operational modelling services, SIAAP (Paris, FR)

Vincent, Rocher, Vincent.rocher@siaap.fr, Innovation director, SIAAP (Paris, FR)

Additional contacts:

Niels Nicolaï, Niels.Nicolai@gci.ulaval.ca, Postdoctoral Researcher, modelEAU – Université Laval (QC, Canada)

Jean-David Therrien, jean-david.therrien.1@ulaval.ca, Research Assistant, modelEAU – Université Laval (QC, Canada) 

Peter Vanrolleghem, Peter.Vanrolleghem@gci.ulaval.ca, Full Professor, modelEAU – Université Laval (QC, Canada)

Additional information:

  • Queralt Plana, Janelcy Alferes, Kevin Fuks, Tobias Kraft, Thibaud Maruéjouls, Elena Torfs, Peter A. Vanrolleghem; Towards a water quality database for raw and validated data with emphasis on structured metadata. Water Quality Research Journal 1 February 2019; 54 (1): 1–9. doi: https://doi.org/10.2166/wqrj.2018.013
  • Jean-David Therrien, Niels Nicolaï, Peter A. Vanrolleghem; A critical review of the data pipeline: how wastewater system operation flows from data to intelligence. Water Sci Technol 15 December 2020; 82 (12): 2613–2634. doi: https://doi.org/10.2166/wst.2020.393
  • To visit the modelEAU webpage, click here
  • To visit the “Virtual visit” page, click here