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Z. B. Wu, X. D. Zhou, X. Q. Liu, Y. Ni, K. Zhao, F. Peng and L. Z. Yang (2016) Fuel 181 836-842.
文章来源:SKLFS    作者:SKLFS    发布时间:2017-03-17

Z. B. Wu, X. D. Zhou, X. Q. Liu, Y. Ni, K. Zhao, F. Peng and L. Z. Yang (2016) Investigation on the dependence of flash point of diesel on the reduced pressure at high altitudes. Journal/Fuel 181 836-842. [In English]
Web link: http://dx.doi.org/10.1016/j.fuel.2016.05.062
Keywords: Flash point; Diesel; Reduced pressure; High altitude; Prediction method; MISCIBLE MIXTURES; FLAMMABLE LIQUIDS; MODEL; EQUILIBRIUM; PREDICTION; EXPRESSION; ENERGY

Abstract: The high altitudes of the plateau areas lead to a decreasing flash point temperature of liquid fuels due to the reduced ambient pressure. It indicates that liquid fuels suffer greater fire and explosion hazards at high altitudes. To reveal the dependence of flash point on reduced pressure at high altitudes, a series of field flash point determinations are performed at six different altitudes on the Qinghai-Tibet Plateau. The results show that flash point decreases nonlinearly with the reduced pressure, which is inconsistent with the current standards of flash point determination, where adopt a linear correction for the pressure effect on flash point. Taking diesel as an example, two methods, the Clausius-Clapeyron relation method and the LCR method, are proposed to predict the flash point of diesel under different pressures. The results show that the predictive accuracy of the two methods is similar, and both of the two methods give more accurate predictive flash point than the linear relationship. The Clausius-Clapeyron relation is validated to be able to expound the dependence of flash point of liquid fuels on reduced pressure. These two methods can complement each other. The Clausius-Clapeyron relation method is recommended when the accurate the phase-transition enthalpy is known, the LCR model is an available method when the phase-transition enthalpy is unknown or uncertain, especially for the complicated fuel mixtures. (C) 2016 Elsevier Ltd. All rights reserved.

 
 
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Z. B. Wu, X. D. Zhou, X. Q. Liu, Y. Ni, K. Zhao, F. Peng and L. Z. Yang (2016) Fuel 181 836-842.
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