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Hydrocarbon Dew Point Measurement and Model Evaluation of Synthetic and Real Natural Gases.

ABSTRACT: Hydrocarbon dew point (HCDP) is one of the most important quality specifications of natural gas. Measuring and predicting the HCDP accurately are essential for the natural gas industry. However, the comprehensive experimental HCDP curve data are still rare, and knowledge about adopting proper prediction models remains unclear. In view of this, HCDP determination work by use of an improved test system and model evaluation based on more than 1000 dew points data have been done to improve the aforementioned dilemma. HCDP curve data of three gravimetrically prepared synthetic natural gases (SNGs) and one real gas (RG) are determined first. Then, one set of data containing 712 dew points from 28 SNGs and 334 dew points from 14 RGs is used to evaluate the performance of eight different HCDP prediction models including Soave-Redlich-Kwong (SRK), SRK-Twu, Peng-Robinson (PR), Twu-Sim-Tassone (TST), predictive SRK (PSRK), GERG-2008, PSRK, and perturbed-chain statistical associating fluid theory (PC-SAFT) models. Considerable prediction deviation of these models in the high-pressure region (pressure above 6.0 MPa) is observed compared to that in the low-pressure region (under 6.0 MPa), and the reasons for that difference are discussed. Evaluation results reveal that among the eight prediction models, GERG-2008 has the best performance (overall average absolute deviation (AAD): 1.44 °C) for SNGs, and PSRK and SRK-Twu fits the experimental data best for RGs (overall AAD: 2.50 °C). Therefore, GERG-2008 is recommended for HCDP prediction of relatively lean gas, whereas PSRK and SRK-Twu are recommended for calculating the HCDP of relatively heavy natural gases in low-pressure and high-pressure regions, respectively.

SUBMITTER: Zhang P

PROVIDER: S-EPMC7178342 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Hydrocarbon Dew Point Measurement and Model Evaluation of Synthetic and Real Natural Gases.

Zhang Pu P Zhou Li L Zeng Wenping W Xiong Gang G Huang Hongfa H Cai Li L Ye Huiyan H Qu Shanshan S

ACS omega 20200407 15

Hydrocarbon dew point (HCDP) is one of the most important quality specifications of natural gas. Measuring and predicting the HCDP accurately are essential for the natural gas industry. However, the comprehensive experimental HCDP curve data are still rare, and knowledge about adopting proper prediction models remains unclear. In view of this, HCDP determination work by use of an improved test system and model evaluation based on more than 1000 dew points data have been done to improve the afore ...[more]

PMID: 32337407

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Project description:BackgroundPlasmodium falciparum transmission has decreased significantly in Zambia in the last decade. The malaria transmission is influenced by environmental variables. Incorporation of environmental variables in models of malaria transmission likely improves model fit and predicts probable trends in malaria disease. This work is based on the hypothesis that remotely-sensed environmental factors, including nocturnal dew point, are associated with malaria transmission and sustain foci of transmission during the low transmission season in the Southern Province of Zambia.MethodsThirty-eight rural health centres in Southern Province, Zambia were divided into three zones based on transmission patterns. Correlations between weekly malaria cases and remotely-sensed nocturnal dew point, nocturnal land surface temperature as well as vegetation indices and rainfall were evaluated in time-series analyses from 2012 week 19 to 2013 week 36. Zonal as well as clinic-based, multivariate, autoregressive, integrated, moving average (ARIMAX) models implementing environmental variables were developed to model transmission in 2011 week 19 to 2012 week 18 and forecast transmission in 2013 week 37 to week 41.ResultsDuring the dry, low transmission season significantly higher vegetation indices, nocturnal land surface temperature and nocturnal dew point were associated with the areas of higher transmission. Environmental variables improved ARIMAX models. Dew point and normalized differentiated vegetation index were significant predictors and improved all zonal transmission models. In the high-transmission zone, this was also seen for land surface temperature. Clinic models were improved by adding dew point and land surface temperature as well as normalized differentiated vegetation index. The mean average error of prediction for ARIMAX models ranged from 0.7 to 33.5%. Forecasts of malaria incidence were valid for three out of five rural health centres; however, with poor results at the zonal level.ConclusionsIn this study, the fit of ARIMAX models improves when environmental variables are included. There is a significant association of remotely-sensed nocturnal dew point with malaria transmission. Interestingly, dew point might be one of the factors sustaining malaria transmission in areas of general aridity during the dry season.

Dataset Information

Hydrocarbon Dew Point Measurement and Model Evaluation of Synthetic and Real Natural Gases.

Publications

Hydrocarbon Dew Point Measurement and Model Evaluation of Synthetic and Real Natural Gases.

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