Accurate correlations to estimate refinery fuel gas, natural gas, and fuel oil CO₂ emission factors and its uncertainty

Abstract

The quantification of Greenhouse Gas (GHG) inventories and its associated uncertainty is a relevant activity often requested by authorities. Accurate methods to calculate both inventories and the involved uncertainty are convenient for close monitoring purposes. Using Monte Carlo simulations, correlations of high accuracy between emission factors (EFs), lower heating value (LHV), and density were built for refinery fuel gas, natural gas and fuel/residual oil. In all cases, the data generated by the simulations also served the purpose of building correlations for upper and lower bounds of the EF that can be readily used to estimate the EF estimation uncertainty. The correlations were tested against actual refinery data and the results show that more accurate estimations were obtained compared with EF obtained from laboratory composition methods and from methods that estimate EF as proportional to LHV only. In the case of fuel and residual oils, the correlations developed are a function of LHV only but were improved by using a cubic polynomial. The calculation of upper and lower bounds for EF offer a convenient method to estimate EF uncertainties that are required in official GHG emissions inventory calculations. In conclusion, in addition to LHV, the use of one additional readily available fuel property, namely fuel density is sufficient to reduce uncertainty of estimation of GHG (in this case CO 2 ) from combustion to acceptable levels.

Comparison of Asian Aquaculture Products by Use of Statistically Supported Life Cycle Assessment

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