Cobalt Water NL
About us
We are a climate tech company with the all-in-one solution for planning, monitoring, and achieving nitrous oxide emissions reduction from wastewater treatment.
Products
The All-in-one AI/ML Platform for assessing, monitoring and mitigating nitrous oxide (N₂O) emissions from wastewater treatment plants
Cobalt Water provides Artificial Intelligence and Machine Learning (AI/ML) driven software and analytics through the N2ORisk Decision Support System (N2ORisk DSS), an all in one N₂O solution that transforms how water utilities and industries manage wastewater emissions. The knowledge-based AI for N₂O within the N2ORisk DSS has been peer-reviewed and validated using N₂O data from more than 80 sites. The platform also includes an extensive library of machine learning models trained on over 100 datasets from wastewater treatment plants, covering a wide range of process conditions, configurations, and seasonal variations. This enables utilities to track emissions and performance at sites that are not yet monitoring N₂O, and to prioritize and rank facilities based on where monitoring and mitigation efforts should be implemented next. The all-in-one AI/ML platform supports N₂O planning, AI-driven monitoring, sensing, and mitigation, helping utilities and industries effectively understand, manage, and reduce N₂O emissions.
N₂O Planning, Site Ranking and AI Monitoring
Identifying which wastewater treatment plants should be prioritized for N₂O mitigation can be challenging, especially when measurements are limited and generic emission factors are inaccurate. Cobalt Water developed the N2ORisk Decision Support System (N2ORisk DSS), an AI and machine learning platform that estimates N₂O emissions using available operational data. The system analyzes historical plant data, generates insights and produces a ranked list of sites based on emission risk and mitigation potential. This allows utilities to focus monitoring and mitigation efforts where they will have the greatest impact, accelerating progress toward reducing N₂O emissions.
Predictive Emissions Monitoring Systems (PEMS)
A Predictive Emissions Monitoring System (PEMS) is a software-based approach that estimates and monitors emissions in real time using operational wastewater treatment plant (WWTP) data and advanced analytics. Instead of relying solely on physical monitoring equipment, PEMS uses machine learning and statistical models to predict emissions from process parameters such as temperature, airflow, loading conditions and -concentrations. This enables continuous insight into emissions performance while reducing the need for extensive monitoring hardware. PEMS provides a scalable and cost-effective solution for monitoring nitrous oxide (N₂O) emissions. Measurements from selected aeration lanes are used to train predictive models that estimate emissions across the entire WWTP. These models are continuously improved as new monitoring data becomes available, allowing WWTPs to maintain reliable emissions tracking even when continuous measurements are limited. Model performance is validated using the Relative Accuracy (RA) method defined by the U.S. Environmental Protection Agency (EPA) for PEMS. Maintaining RA values below 20% demonstrates that model predictions closely match measured emissions, ensuring confidence for environmental reporting, regulatory alignment, and operational decision-making.
Real-time hard and soft sensing of N₂O for robust monitoring
Cobalt Water possesses extensive expertise in both process engineering and N₂O emissions monitoring. This combined knowledge enables the development and implementation of optimized physical N₂O sensor placement and soft sensor applications. Cobalt Water can provide, install, calibrate, and maintain these sensors. The N₂O Soft Sensor enables the training and deployment of site-specific machine learning models using measured N₂O data. This allows for accurate quantification and verification of emissions without the need for year-round sensor operation. Advanced monitoring includes a detailed baseline assessment and the identification of specific opportunities to reduce N₂O emissions. These may involve process adjustments such as optimizing dissolved oxygen (DO) concentrations or adjusting control setpoints to minimize N₂O generation while maintaining treatment performance. Together, these tools deliver continuous, data-driven insights to support proactive N₂O management and reduction.
Mitigate N₂O Emissions
Mitigation involves minimizing overall greenhouse gas (GHG) emissions and operational costs while ensuring compliance with environmental regulations and net zero targets. A site-specific machine learning model, trained on real N₂O measurement data, is used to monitor and predict emissions across all reactors. Continuous integration of measured and modeled data enables dynamic tracking of N₂O trends, early detection of process deviations, and proactive optimization of operational conditions to sustain long-term emission reductions.
