A compact and portable gas sensor based on quartz-enhanced photoacoustic spectroscopy (QEPAS) for the detection of methane (C1), ethane (C2), and propane (C3) in natural gas (NG)-like mixtures is reported. An interband cascade laser (ICL) emitting at 3367 nm is employed to target absorption features of the three alkanes, and partial least-squares regression analysis is employed to filter out spectral interferences and matrix effects characterizing the examined gas mixtures. Spectra of methane, ethane, and propane mixtures diluted in nitrogen are employed to train and test the regression algorithm, achieving a prediction accuracy of ∼98%, ∼96%, and ∼93% on C1, C2, and C3, respectively. With respect to previously reported QEPAS sensors for natural gas analysis, the high prediction accuracy as well as the capability to discriminate and detect C3 within natural gas-like complex mixtures provided by the employment of partial least-squares regression mark significant improvements. Furthermore, these results enable an improved performance of the sensor for in situ, real-time, and online natural gas composition analysis.
Methane, Ethane, and Propane Detection Using a Quartz-Enhanced Photoacoustic Sensor for Natural Gas Composition Analysis / Cantatore, Aldo F. P.; Menduni, Giansergio; Zifarelli, Andrea; Patimisco, Pietro; Giglio, Marilena; Gonzalez, Miguel; Seren, Huseyin R.; Luo, Pan; Spagnolo, Vincenzo; Sampaolo, Angelo. - In: ENERGY & FUELS. - ISSN 0887-0624. - ELETTRONICO. - (2024). [10.1021/acs.energyfuels.4c03726]
Methane, Ethane, and Propane Detection Using a Quartz-Enhanced Photoacoustic Sensor for Natural Gas Composition Analysis
Menduni, Giansergio;Zifarelli, Andrea;Patimisco, Pietro;Giglio, Marilena;Spagnolo, Vincenzo;Sampaolo, Angelo
2024-01-01
Abstract
A compact and portable gas sensor based on quartz-enhanced photoacoustic spectroscopy (QEPAS) for the detection of methane (C1), ethane (C2), and propane (C3) in natural gas (NG)-like mixtures is reported. An interband cascade laser (ICL) emitting at 3367 nm is employed to target absorption features of the three alkanes, and partial least-squares regression analysis is employed to filter out spectral interferences and matrix effects characterizing the examined gas mixtures. Spectra of methane, ethane, and propane mixtures diluted in nitrogen are employed to train and test the regression algorithm, achieving a prediction accuracy of ∼98%, ∼96%, and ∼93% on C1, C2, and C3, respectively. With respect to previously reported QEPAS sensors for natural gas analysis, the high prediction accuracy as well as the capability to discriminate and detect C3 within natural gas-like complex mixtures provided by the employment of partial least-squares regression mark significant improvements. Furthermore, these results enable an improved performance of the sensor for in situ, real-time, and online natural gas composition analysis.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.