Rapid method for the determination of the composition of natural gas by gas chromatography

Baseline correction method for dynamic pressure gradient modulated comprehensive two-dimensional gas chromatography with flame ionization detection

A baseline correction method is developed for comprehensive two-dimensional (2D) chromatography (GC × GC) with flame-ionization detection (FID) using dynamic pressure gradient modulation (DPGM). The DPGM-GC × GC-FID utilized porous layer open tubular (PLOT) columns in both dimensions to focus on light hydrocarbon separations. Since DPGM is nominally a stop-flow modulation technique, a rhythmic baseline disturbance is observed in the FID signal that cycles with the modulation period (P_M). This baseline disturbance needs to be corrected to optimize trace analysis. The baseline correction method has three steps: collection of a background "blank" chromatogram and multiplying it by an optimized normalization factor, subtraction of the normalization-optimized background chromatogram from a sample chromatogram, and application of Savitzky-Golay smoothing. An alkane standard solution, containing pentane, hexane and heptane was used for method development, producing linear calibration curves (r² > 0.991) over a broad concentration range (7.8 ppm - 4000 ppm). Further, the limit-of-detection (LOD) and limit-of-quantification (LOQ) were determined for pentane (LOD = 2.5 ppm, LOQ = 8.2 ppm), hexane (LOD = 0.9 ppm, LOQ = 3.0 ppm), and heptane (LOD = 1.9 ppm, LOQ = 6.4 ppm). A natural gas sample separation illustrated method applicability, whereby the DPGM produced a signal enhancement (SE) of 30 for isopentane, where SE is defined as the height of the tallest ²D peak in the modulated chromatogram for the analyte divided by the height of the unmodulated ¹D peak. The 30-fold SE resulted in about a 10-fold improvement in the signal-to-noise ratio (S/N) for isopentane. Additional versatility of the baseline correction method for more complicated samples was demonstrated for an unleaded gasoline sample, which enabled the detection (and visual appearance) of trace components.

Mixed Natural Gas Online Recognition Device Based on a Neural Network Algorithm Implemented by a FPGA

It is a daunting challenge to measure the concentration of each component in natural gas, because different components in mixed gas have cross-sensitivity for a single sensor. We have developed a mixed gas identification device based on a neural network algorithm, which can be used for the online detection of natural gas. The neural network technology is used to eliminate the cross-sensitivity of mixed gases to each sensor, in order to accurately recognize the concentrations of methane, ethane and propane, respectively. The neural network algorithm is implemented by a Field-Programmable Gate Array (FPGA) in the device, which has the advantages of small size and fast response. FPGAs take advantage of parallel computing and greatly speed up the computational process of neural networks. Within the range of 0–100% of methane, the test error for methane and heavy alkanes such as ethane and propane is less than 0.5%, and the response speed is several seconds.

Analysis of natural gas by gas chromatography

The results of gas chromatographic analysis of natural gas mixtures reveal strong correlations (Pearson correlation coefficient of >0.96) between the uncertainty of each component and variations in the ambient pressure. Although correction for ambient pressure variations can reduce this variability, normalisation of the results of each analysis using the assumption that the sum of all component amount fractions is unity provides significantly greater reductions in the uncertainty of each measured component. We show that the uncertainty in normalised components can be estimated approximately using the correlation coefficient as a measure of the correlation present in the measurements, or exactly using a full calculation of the variance/covariance (V/C) structure of the data.

Analysis of natural gas: the necessity of multiple standards for calibration

The importance of natural gas as an international trading commodity and the cost to consumers has made the accuracy of determinations for the components of natural gas very important. Pricing of natural gas is based on the heating value of the gas determined from either calorimetry measurements or calculations based on individual component concentrations determined by gas chromatography (GC). Due to the expense of accurate calibration standards, many analysts and laboratories will use a single calibration standard to perform natural gas determinations. Therefore, the purpose of this study was to determine whether an analyst could accurately measure the components of natural gas, in particular methane, using a single standard, or whether a suite of standards is necessary to calibrate the analytical instrument. A suite of eight gravimetric primary standards was prepared covering a concentration range for methane of 64-94 mol%, with uncertainties of +/-0.05% relative (95% confidence interval). These natural gas primary standards also contained nitrogen, carbon dioxide, ethane, propane, iso-butane, n-butane, iso-pentane, n-pentane, and n-hexane with varying concentrations from 0.02 to 14%. A single analytical method was used in which only the amount of sample injected onto the column was altered. The results show that when injecting a 0.5 ml sample volume a second-order regression through the standards is necessary for the determination of methane. The results for nitrogen, ethane and propane also show the same trend. Only those individual standards whose methane concentration is within 1% of the test mixture predicted a concentration within 0.05% of the regression value. Those individual primary standards whose methane concentration is different by more than +/-1% of the test mixture predicted values differing by +/-0.5 to +/-2.0% from the regression value. These differences lie well outside the predicted concentration uncertainty interval of +/-0.20%. A smaller sample volume, 0.1 ml, resulted in a set of data that could be fit using linear regression. Each of the eight primary standards individually predicted the methane in the test mixture to be within +/-0.11% of the predicted value from linear regression. The data confirm that it is imperative to fully characterize the analytical system before proceeding with an analysis.