Using AI to reduce process emissions from water utilities

In 2015, the vast majority of countries committed to limiting global warming during a summit in Paris, France. Since then, a growing number of countries have put in place legally binding dates by which to reach net zero carbon. Given this commitment to mitigate against climate change, reducing greenhouse gas (GHG) emissions is key to its success.

The wastewater treatment sector is responsible for approximately 2% of globally emitted GHGs. The most significant contributor is nitrous oxide (N2O), given that it has a 300-fold effect compared to carbon dioxide (CO2). Many biological wastewater treatment plants undertake nitrogen removal in a non-optimal way and are prone to emitting N2O.

As a result, together with several partners, I have contributed to the development of the N2O Risk Model, which has now evolved into the N2ORisk Decision Support System (DSS). The N2ORisk DSS uses artificial intelligence (AI) to combine expert knowledge on N2O and machine learning (ML) to quickly diagnose the process and N2O emissions, propose mitigation actions, and ultimately eliminate N2O. This tool demonstrates how digitalization can be used to solve problems faced by utilities in an automated fashion, yet still based on process knowledge. Furthermore, putting this knowledge in a scalable platform allows us to have a wider impact on society and the planet.

Following the N2ORisk DSS approach and focusing on N2O, we have been able to reduce the overall GHG emissions of water resource recovery facilities by up to 70%. This has occurred by simply optimizing the process with the help of AI and expert bioprocess knowledge of N2O emissions reduction. We are convinced that comparable gains can be made in many plants globally. This is a relatively inexpensive, low-hanging fruit in the battle against climate change and can help quickly bring the water sector one step closer to carbon neutrality.

This is just one example of the impact AI and ML can have on sustainable water management and how they can be used to tackle many more problems. Soft sensor solutions are going to be an important cornerstone in the digitalization of utilities. The fact that data from direct measurements, ML and process knowledge can be incorporated into a soft sensor improves efficiency. This is because it helps the operator make faster and more consistent decisions based on predictions made by combining direct measurements and process knowledge.

AI solutions are vastly underused, often due to lack of awareness of how they can be applied and how they work. Hence, there is a need for transparent, tailored solutions to find their way into the market. But alongside “solutions”, people with a unique combined skillset of both data analytics/AI and water process knowledge are needed to respond to the challenges facing utilities. To advance this, utilities could set up teams having this skillset, or dedicated companies could offer it as a tech service. In fact, in the very near future, digital tools will become an important virtual asset, as well as the teams developing them. The most progressive utilities already have these teams.

Another game changer will be that site-specific operator knowledge can be embedded in these solutions. The responsibilities of an operator will evolve into a process supervisor role, or a facilitator for further embedding their knowledge and experience into solutions for other water facility problems, which can be solved more effectively and faster with the power of AI.

In the same way in which machines revolutionized the automotive industry and banking, we can anticipate smart digitalization (incorporating expert knowledge) having a similar impact on the water sector. As water professionals, we will be able to do more and faster by scaling our expertise with digital solutions. Doing so will not only lead to increased efficiency and productivity, but will also help to tackle problems such as climate change – one of the biggest challenges facing us today.

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Jose Porro

Founder/CEO of Cobalt Water Global