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Thermal conductivities of n-octane, isooctane, 1-octene, and 1-octanol were measured with a transient hot-wire apparatus. The measurements for these fluids covered a temperature range from 253 to 373 K with pressures up to 30 MPa, except for 1-octanol which covered a temperature range from 273 to 393 K. The relative expanded uncertainty with a level of confidence of 0.95 (k = 2) of the data was estimated to be better than 2.0%. The experimental values were correlated by a polynomial thermal conductivity model for each of the fluids. The average absolute percentage deviations were all within 0.3%. Comparisons between the data obtained by this work and published literature studies are also presented here.

In this work, thermal conductivities of a homologous series of liquids n-pentane, isopentane, 1-pentene, and 1-pentanol were measured from 253 to 373 K at pressures up to 30 MPa with a transient hot-wire apparatus. The relative expanded uncertainty of the reported data was estimated to be 2.0% with a confidence level of 0.95 (k = 2). The polynomial thermal conductivity model was used to correlate the experimental values for each of the fluids. The average absolute percentage deviations of the experimental results from fitting were 0.3, 0.3, 0.3, and 0.1%. Comparisons were provided between the data obtained in this work and published literature.

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With the rapid growth of natural gas consumption in China, the monthly peak shaving and security issues of the gas supply have become increasingly prominent. In view of this situation, the monthly fluctuation law of natural gas consumption in China must be studied to guide gas storage peak shaving and gas supply planning. In this study, the concepts of the gas year (statistical method of breaking through the calendar year to gas year and reflecting the complete gas consumption cycle), typical year (the year that had the representative load curve of China), and some fluctuation characteristics parameters were applied to study the monthly fluctuation law of natural gas consumption in China. Furthermore, according to the monthly statistical data of China, including natural gas demand, power generation, crude steel output, refined copper output, the monthly average temperature in the typical city, the relationship between influencing factors and natural gas demand was analyzed by the gray relative correlation method. Based on the stepwise regression Cobb–Douglas (C–D) production function, the natural gas demand of China in the gas year of 2030/31 was predicted. The research results can be used for energy planning, statistics, peak shaving of gas storage, liquefied natural gas trade, and can also be used as a reference for energy big data analysis and refined management.