Classification and Regression of Binary Hydrocarbon Mixtures using Single Metal Oxide Semiconductor Sensor With Application to Natural Gas Detection

Abstract

To detect natural gas pipeline leaks, ethane in the natural gas can be used to discriminate it from background methane emissions. Our approach classifies samples of methane and simulated natural gas based on time-series data collected from a low-cost microfluidic detector. This detector houses a single metal oxide semiconducting (MOS) gas sensor in a coated microchannel, which provides selectivity based on diffusion times of different gases. A data generation apparatus was designed and built, allowing us to create customized gas mixtures and collect data from our sensing apparatus in an automated fashion. We present a comparison of machine learning models and data representation methods, and demonstrate the feasibility of both discriminating methane from simulated natural gas mixtures, and also obtaining accurate concentration estimates in random binary mixtures of methane and ethane. We achieve a 98.75% classification rate between methane, ethane, and binary mixtures, and a 12.0% mean relative error in regression estimates for arbitrary mixtures. In addition, we discriminate samples of pure methane from simulated natural gas mixtures containing 1% and 3% ethane with 86.7% and 93.3% accuracy, respectively, and regress the concentrations of both the methane and ethane components with a maximum of a 19.3% mean relative error.