The measurement and control of trace moisture is an important procedure to maintain and secure the quality and safety of pipeline systems for natural gas. The natural gas mix typically comprises methane, ethane, propane, some higher hydrocarbons, carbon dioxide and nitrogen. It is too impractical to perform additional calibrations of trace moisture measurement every time the combination of gas components in the natural gas mix is changed. This paper proposes a method to separate and compensate for the effect of the background gas composition from the trace moisture measurement by using two different frequencies in a trace moisture analyzer utilizing a ball surface acoustic wave (SAW) sensor. The experiment was performed by connecting the ball SAW trace moisture sensor to supplies of trace moisture in multiple background gases from the National Physical Laboratory (NPL) Multi-gas, Multi-pressure Standard Humidity Generator, and changing the composition of the background gases including N2, air, CH4, 80% CH4/20% C2H6, and 50% CH4/50% C2H6. At the nominal humidity frost-point values of −70 °C, −50 °C, −40 °C and −30 °C, the deviations due to the change of the background gases were within ± 1 °C in frost point.
Meteorological measurements of air humidity in ground-based weather stations worldwide are increasingly being used in studies of climate change. However, electronic humidity sensors often suffer gradual drift in sensor readings, particularly at the high end of the relative humidity range. This phenomenon is well known, but there is currently limited quantitative information available about the drift characteristics, and hence about the consequent measurement bias or uncertainty that should be attributed to historical humidity data sets.In order to quantify weather-station hygrometer drift, a dataset has been studied from UK weather-station hygrometer records supplied by the UK Met Office calibration laboratory. As well as documenting the calibrations and adjustments, the records include 'as-found' checks of the hygrometers on return from field use. This allows average in-service error and drift to be evaluated for the population of instruments.The approach of the study is presented, together with illustrative initial results quantifying mean sensor drift of up to 5%rh. The implications of this for estimating bias in observations are discussed, along with discussion of associated uncertainty. This includes consideration of the distribution of the data, including the end-limited range where readings are capped at 100%rh. The study results justify the Met Office practice of adjusting hygrometers to minimise the errors in use. Preliminary conclusions and recommendations are made, and further steps are identified for developing the methodology.
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