A low-cost calibration method for the infrared sensor array for quantitative analysis of natural gas

Infrared-based measurement technology is a promising approach to natural gas analysis, and high-precision calibration models for infrared sensor arrays require sufficient calibration data, which is costly. Therefore, we propose a calibration-transfer-based low-cost calibration method for infrared gas sensor arrays. We construct four identical infrared sensor arrays, one as the master array and three as slave arrays. A dedicated multi-layer perceptual neural network undertakes the data space mapping between the slave array and the master array. Thus, the slave sensor arrays can reuse the existing high-precision calibration model of the master array, reducing the calibration cost. The root mean squared errors of methane, ethane, and propane of the calibrated slave array are 2.87%(detection range: 0%–100%), 1.56%(detection range: 0%–24%), and 1.54%(detection range: 0%–12%), respectively, which are close to the accuracy level of the master array. The results show that our approach outperforms related algorithms in infrared sensor array calibration. In addition, experimental results on actual natural gas samples validate the method. The proposed low-cost and high-precision calibration method provides a basis for the generalization of infrared sensor arrays for mixed gas concentration monitoring. Furthermore, the reported scheme can be extended to quantitative analysis of other gas mixtures. [Display omitted] •A low-cost calibration method for infrared sensor array in mixed gas analysis.•Data transfer between sensor arrays via neural networks.•Calibration cost reduction via reusing the existing model.•The scheme outperforms the conventional DS, SBC, and the latest Deep CT.•Experimental results on actual natural gas samples validate the method.