AI RESEARCH
Enhancing the interpretability of spatially variable N2O model predictions with soft sensors during wastewater treatment
arXiv CS.LG
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ArXi:2605.04082v1 Announce Type: new Model-based solutions for nitrous oxide (N2O) emissions from wastewater treatment plants (WWTP) are informed by operational datasets designed to control nutrient levels in liquid waste, coupled with dedicated campaigns for N2O measurements. We analysed how machine learning (ML) models predict disturbances to WWT operation and spatially variable N2O emissions. A real dataset was investigated to validate the modelling framework from N2O emissions predicted by four ML models (R2 = 0.79 - 0.89.