Mathieu Lamotte

As the wastewater industry faces a growing need to address climate change, nitrous oxide (N2O) emissions from treatment plants have emerged as a critical concern. N2O, a potent greenhouse gas with a global warming potential 273 times that of CO2, poses a significant challenge to the sector's climate mitigation efforts as it could represent even more than 50% of the entire GHGs of a WWTP.

This presentation focuses on one of Veolia’s most recent cases, where we present a long period without mitigation (2023) and more than a full year of measure with mitigation (2024). Veolia has developed a 4-steps methodology to mitigate N2O emissions with a holistic approach combining real-time data management, process expertise, and digital twin technology. By providing the successful example of Bjergmarken's approach, other WWTPs will be able to replicate their success.

This methodology consists of four key steps: Measure, Understand, Strategize, and Empower.
⇒ Measure emissions: First, we employ advanced monitoring systems to accurately measure N2O emissions across various treatment processes. This continuous, real-time data collection provides a comprehensive picture of emission patterns and hotspots within the plant.
⇒ Understand the process: Next, we understand the complex mechanisms behind N2O formation and emission through in-depth analysis of the collected data. This step involves identifying the key factors influencing N2O production, such as dissolved oxygen levels, nitrite concentrations, and even microbial community dynamics.
⇒ Develop a strategy: The “strategize" phase leverages Veolia's extensive process expertise to develop effective mitigation scenarios. Our team of experienced engineers and scientists analyses the plant-specific data and applies industry best practices to formulate tailored, cost-effective mitigation plans. These strategies balance emission reduction with other operational priorities, ensuring optimal overall plant performance.
⇒ Empower operators: Finally, we empower plant operators by integrating digital twin technology into their daily operations. It provides real-time insights and control, enabling operators to make informed decisions that optimize plant performance while minimizing N2O emissions. The digital twin acts as a powerful tool for continuous improvement, adapting to changing conditions and evolving regulatory requirements.

This 4-steps approach represents a significant advancement in sustainable wastewater management, addressing the critical need for climate change mitigation in the sector.

By sharing the experiences and insights from the Bjergmarken WWTP case study, we aim to contribute to the industry's collective knowledge and inspire innovative solutions across the field. This methodology offers a practical framework for tackling the complex challenge of N2O emissions. We believe that by working together and openly sharing successes and lessons learned, the wastewater treatment community can make significant strides in reducing our environmental impact, improving operational efficiency, and building a more sustainable water industry for the future.

The article "Sustainable real-time optimization of energy and chemical consumption in a COD & Phosphorous removing MBBR plant in Norway" details a case study at the Skreia Wastewater Treatment Plant (WWTP) in Østre Toten Municipality, Norway. The WWTP, designed for 18,350 population equivalents, employs a Moving Bed Biofilm Reactor (MBBR) system followed by high-rate clarification using Actiflo technology for the removal of COD (Chemical Oxygen Demand) and Phosphorus.

The primary goal of the project was to optimize operations for sustainability by implementing real-time control strategies. Traditionally, WWTPs operate with fixed high levels of aeration and chemical dosage to ensure compliance even during peak load periods, leading to high operational costs and greenhouse gas emissions. The Skreia WWTP aimed to move away from this standard practice by introducing a more dynamic and responsive control system.

The original instrumentation setup was expanded with additional sensors to monitor parameters like turbidity, nitrate, Total Organic Carbon (TOC), and Total Phosphorus (TP) in the effluent. This data was integrated into the Hubgrade Wastewater Plant Performance (HWP) platform, a digital tool that leverages live data and artificial intelligence for real-time optimization.

A key innovation was the implementation of a new aeration control strategy that moved away from continuous aeration at a fixed Dissolved Oxygen (DO) setpoint. Instead, the system calculated the Oxygen Uptake Rate (OUR) in real-time based on DO, water temperature, and airflow measurements. This allowed for a dynamic DO setpoint and the introduction of intermittent aeration during low load periods. The system also included compensation components based on TOC and nitrate levels to prevent insufficient aeration and nitrification, respectively, further optimizing energy use and reducing greenhouse gas emissions like nitrous oxide (N2O).

Similarly, the chemical dosage control strategy was upgraded from a fixed rate to a cascade PID controller system. This system used TP levels to calculate a turbidity target, which then controlled the dosage of coagulant and polymer in the Actiflo units. A pH compensation component was also added to prevent excessive chemical dosage and ensure the effectiveness of the chemicals.

The results were significant. Compared to the design expectations and the initial operation with standard controls, the new real-time control strategy led to a 51% reduction in energy consumption, a 26% reduction in coagulant dosage, and a 20% reduction in polymer dosage. These savings also translated to a 50-tonne reduction in CO2-eq emissions per year. The effluent quality remained compliant throughout the implementation, demonstrating the effectiveness of the new strategies.

The article highlights the importance of real-time data and advanced control systems in optimizing wastewater treatment operations. It also acknowledges the challenges related to sensor reliability, which will be further discussed in the full paper. Overall, the case study demonstrates a successful approach to making wastewater treatment plants smarter, safer, and more sustainable.