Analysis of natural gas by gas chromatography

PAS-based analysis of natural gas samples

Photoacoustic spectroscopy (PAS) is well known for the detection of short-chain hydrocarbons, such as methane, ethane and propane, in the ppm (parts per million) or ppb (parts per billion) range. However, in the production process of natural gas and its combustion in gas-fired devices the composition, especially the concentrations of the main alkanes, plays a decisive role. Gas chromatography (GC) is considered the gold standard for natural gas analysis. We present a method to analyze natural gas samples by PAS. Furthermore, we describe a method to prepare storage gas samples, which are usually under atmospheric pressure, for PAS analysis. All measurements are validated by means of GC. The investigation allows conclusions to be drawn to what extent PAS is suitable for the investigation of natural gas samples.

Hydrocarbon ladder polymers with ultrahigh permselectivity for membrane gas separations

Membranes have the potential to substantially reduce energy consumption of industrial chemical separations, but their implementation has been limited owing to a performance upper bound-the trade-off between permeability and selectivity. Although recent developments of highly permeable polymer membranes have advanced the upper bounds for various gas pairs, these polymers typically exhibit limited selectivity. We report a class of hydrocarbon ladder polymers that can achieve both high selectivity and high permeability in membrane separations for many industrially relevant gas mixtures. Additionally, their corresponding films exhibit desirable mechanical and thermal properties. Tuning of the ladder polymer backbone configuration was found to have a profound effect on separation performance and aging behavior.

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.

Design and Application of Mixed Natural Gas Monitoring System Using Artificial Neural Networks

Natural gas component analysis is one of the significant technologies in the exploitation and utilization of natural gas. A stable and accurate online natural gas monitoring system is necessary for the gas extracting industry. We have developed an online monitoring system of natural gas with a novel hardware architecture. It improves the dependability and maintainability of the system. A specific instruction set is designed to facilitate the coordination of software and hardware. To reduce the sample noise, the exponentially weighted moving average (EWMA) method is used to preprocess the real-time raw data of the sensor array. A tailored neural network is designed for calibration. And the relationship between the performance and the structure of the gas neural network is demonstrated to find the optimal solution for accuracy and hardware scale. The design not only focuses on the optimization of individual components but also focuses on system-level improvement. The system has been running stably for several months in the gas fields. It meets the requirements of stability, ease of use, maintainability, and online monitoring in industrial applications.

Portable gas chromatograph calibration with gases of varying viscosities

The quantitative analysis of gas mixtures with gas chromatography is based on the calibration with certified standards and the determination of a response relationship for each species by regression analysis. The conventional assumption of a constant amount-of-substance injected onto the column, the sample size, for all standards analyzed represents one of the largest sources of uncertainies in this analysis technique. For systems using time-based microinjectors, the sample size injected onto the column is determined by the opening time of a pneumatically actuated microvalve and therefore depends upon the gas velocity developed in the injector's microchannel. For this reason the sample size is not necessarily constant for a given opening time, but strongly correlates - according to fluid mechanic theory - with the gas mixture's dynamic viscosity. Neglecting these sample size variations leads to errors in the analysis up to 30%, depending on the diversity of the standards' viscosities, especially in processes with strongly changing hydrogen contents. A mathematical correction exploiting the inverse proportionality of the sample size and the sample's dynamic viscosity normalizes the injection amounts and minimizes the influence of the sample variations on the calibration regression. The data analysis based on the corrected normalized calibration is no longer viscosity dependent and can be applied to gas mixtures of any composition.

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.

Multisensory Gas Chromatography for Field Analysis of Complex Gaseous Mixtures

A novel approach to analysis of complex gaseous mixtures is presented. The approach is based on the utilization of a compact gas chromatograph in combination with an array of highly integrated and selective metal oxide (MOX) sensors. Thanks to the implementation of a multisensory detector, the device collects multiple chromatograms in a single run. The sensors in the integrated MEMS platform are very distinct in their catalytic properties. Hence, the time separation by chromatographic column is complemented by catalytic separation by a multisensory detector. Furthermore, the device can perform the analysis in a broad range of concentrations, from ppb to hundreds of ppm. Low ppb and even sub-ppb levels of detection for some analytes were achieved. As a part of this effort, nanocomposite gas sensors were synthesized for selective detection of hydrogen sulfide, mercaptans, alcohols, ketones, and heavy hydrocarbons.

Harmonisation of coupled calibration curves to reduce correlated effects in the analysis of natural gas by gas chromatography

Quantitative analysis of natural gas depends on the calibration of a gas chromatograph with certified gas mixtures and the determination of a response relationship for each species by regression analysis. The uncertainty in this calibration is dominated by variations in the amount of the sample used for each analysis that are strongly correlated for all species measured in the same run. The "harmonisation" method described here minimises the influence of these correlations on the calculated calibration curves and leads to a reduction in the root-mean-square residual deviations from the fitted curve of a factor between 2 and 5. Consequently, it removes the requirement for each run in the calibration procedure to be carried out under the same external conditions, and opens the possibility that new data, measured under different environmental or instrumental conditions, can be appended to an existing calibration database.