Let’s start reducing nitrous oxide emissions today!

In our white paper, written by Product Manager Wastewater Otto Icke MSc. Eng and Process Engineer and Consultant Ellen van Voorthuizen, we examine the research which we have been undertaking for more than 12 years, and the direction we need to take towards the mitigation of greenhouse gas emission.

Wastewater utilities around the world have set ambitious goals to tackling greenhouse gas emissions, to become carbon neutral by 2030. That being said, the contribution of nitrous oxide (N2O) emissions, also known as laughing gas, is becoming a more pronounced part of the carbon footprint of wastewater treatment works (WwTW). 

While carbon emissions from grid electricity will decrease as more electricity is produced from renewable sources, the contribution of nitrous oxide and methane emission will become a more pronounced part of their carbon footprint. And, as N2O is 265 times as potent as carbon for heating the atmosphere and causing climate change, the need to reduce emissions is higher than ever.

What is nitrous oxide (N2O)?

It is a chemical compound, which at room temperature is a colourless non-flammable gas, with a slightly sweet scent and taste. At elevated temperatures, nitrous oxide is a powerful oxidiser similar to molecular oxygen.

Our white paper

In this white paper, written by our Product Manager Wastewater Otto Icke MSc. Eng and process engineer and consultant Ellen van Voorthuizen, we examine the research which we have been undertaking for more than 12 years, and the direction we need to take towards the mitigation of greenhouse gas emission.

Throughout the white paper, Otto and Ellen look at the major challenges, including climate change, facing water utilities in the journey towards reducing emissions and ultimately achieving zero carbon. How not only stable processes and operations can be achieved, but also how the balance between effluent quality, emission reduction and energy savings can be established.

Otto and Ellen examine the role that a predictive control strategy, using artificial intelligence techniques and domain knowledge of the processes can enable patterns to be recognised and predicted. Alongside this, how the relationships between process parameters determined with machine learning and applied for advanced process control and anomaly detection can make the difference.