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Quantitative Acoustic Relaxational Spectroscopy for real-time monitoring of natural gas: A perspective on its potential

机译:用于实时监测天然气的定量声弛豫光谱法:关于其潜力的观点

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摘要

Although there are several methods for high sensitivity gas composition detection, typically off-line and laboratory-based, approaches for real-time, moderate-sensitivity detection are few. An approach based on Quantitative Acoustic Relaxation Spectroscopy (QARS) is proposed here. The methodology is based on the frequency-dependent acoustic propagation in a gas mixture, which depends on the concentration of contaminant gases in a known base gas. After describing the molecular acoustics model and QARS algorithm, we provide examples related to the identification of "mine gas" contamination in air, a mining safety application, and detecting impurities in low quality natural gas, an important issue in the natural gas processing industry. In both of these cases where moderate-sensitivity, continuous gas composition sensing would be useful, the QARS approach shows significant promise, at least theoretically, for realistic contaminant levels. Of course, further work is needed to refine several aspects of the molecular relaxation model that is necessary for the approach to be successful, not to mention challenges related to the development of a sensor based on the technique. Nevertheless, QARS holds potential to be a simple, robust, and inexpensive approach for moderate-sensitivity, continuous, real-time gas composition sensing.
机译:尽管有几种用于高灵敏度气体成分检测的方法,通常是离线的和基于实验室的方法,但是用于实时,中等灵敏度检测的方法却很少。本文提出了一种基于定量声学弛豫光谱法(QARS)的方法。该方法基于气体混合物中与频率相关的声传播,该传播取决于已知基础气体中污染物气体的浓度。在描述了分子声学模型和QARS算法之后,我们提供了与空气中“矿井瓦斯”污染的识别,采矿安全应用以及检测低质量天然气中的杂质有关的示例,低质量天然气是天然气加工行业中的一个重要问题。在这两种情况下,中等灵敏度,连续的气体成分感测将是有用的,至少在理论上,QARS方法显示出对实际污染物水平的巨大希望。当然,需要进一步的工作来完善分子松弛模型的几个方面,这些方面对于该方法的成功是必要的,更不用说与基于该技术的传感器的开发有关的挑战。尽管如此,QARS仍有望成为一种简单,可靠且廉价的方法,用于中等灵敏度,连续,实时的气体成分感测。

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