Quantification of gas concentrations in NO/NO2/C3H8/NH3 mixtures using machine learning

We employ machine learning to decode the composition of unknown gas mixtures from the output of an array of four electrochemical sensors. The sensors use metal oxide electrodes paired with a ceramic electrolyte, yttria-stabilized zirconia (YSZ), to produce voltage responses to the presence of gases in complex mixtures. The voltages from the sensor array serve as inputs to a machine learning pipeline which first carries out multi-class classification of mixtures into types based on which gases are present at non-zero concentrations, and subsequently predicts gas concentrations given the mixture type. Thus, our model is able to take a single reading from the sensor array in response to gas mixtures involving NO, NO 2 , C 3 H 8 , and NH 3 , and output a highly accurate prediction of which gases are present in the mixture, along with the concentrations of each constituent gas. Our computational framework can be easily expanded to include additional gases and additional mixture types, allowing it to be used in numerous automotive, industrial and environmental monitoring settings. • An array of four mixed-potential sensors was used to study gas mixtures representing diesel engine-related emissions. • The voltage output of the sensor array was modeled using a classification/regression machine learning approach. • The model yielded accurate predictions of absolute gas concentrations in an extensive dataset of multi-component mixtures.