Reconstruction of high-frequency methane atmospheric concentration peaks from measurements using metal oxide low-cost sensors

Abstract

Detecting and quantifying CH4 gas emissions at industrial facilities is an important goal for being able to reduce these emissions. The nature of CH4 emissions through “leaks” is episodic and spatially variable, making their monitoring a complex task; this is partly being addressed by atmospheric surveys with various types of instruments. Continuous records are preferable to snapshot surveys for monitoring a site, and one solution would be to deploy a permanent network of sensors. Deploying such a network with research-level instruments is expensive, so low-cost and low-power sensors could be a good alternative. However, low cost usually entails lower accuracy and the existence of sensor drifts and cross-sensitivity to other gases and environmental parameters. Here we present four tests conducted with two types of Figaro® Taguchi gas sensors (TGSs) in a laboratory experiment. The sensors were exposed to ambient air and peaks of CH4 concentrations. We assembled four chambers, each containing one TGS sensor of each type. The first test consisted in comparing parametric and non-parametric models to reconstruct the CH4 peak signal from observations of the voltage variations of TGS sensors. The obtained relative accuracy is better than 10 % to reconstruct the maximum amplitude of peaks (RMSE ≤2 ppm). Polynomial regression and multilayer perceptron (MLP) models gave the highest performances for one type of sensor (TGS 2611C, RMSE =0.9 ppm) and for the combination of two sensors (TGS 2611C + TGS 2611E, RMSE =0.8 ppm), with a training set size of 70 % of the total observations. In the second test, we compared the performance of the same models with a reduced training set. To reduce the size of the training set, we employed a stratification of the data into clusters of peaks that allowed us to keep the same model performances with only 25 % of the data to train the models. The third test consisted of detecting the effects of age in the sensors after 6 months of continuous measurements. We observed performance degradation through our models of between 0.6 and 0.8 ppm. In the final test, we assessed the capability of a model to be transferred between chambers in the same type of sensor and found that it is only possible to transfer models if the target range of variation of CH4 is similar to the one on which the model was trained.