Application of advanced correlative approaches to modeling hydrogen solubility in hydrocarbon fuels

Abstract

In petroleum and petrochemical refineries, having precise knowledge regarding H2 solubility in hydrocarbon fuels and feedstocks is critical. In this study, the hydrogen solubility in hydrocarbon fuels was estimated using genetic programming (GP) and group method of data handling (GMDH), two exemplary robust advanced models for generating correlation. To do this, 445 observations derived from labratory findings on hydrogen solubility in 17 different hydrocarbon fuels such as bitumen, atmospheric residue, heavy coking gas oil, heavy virgin gas oil, light virgin gas oil, straight run gas oil, shale fuel oil, dephenolated shale fuel oil, diesel, hydrogenated coal liquid, coal liquid, and coal oil, over a large interval of P- operating pressures and T-temperatures were collected. Temperature, pressure, as well as density at 20 °C, molecular weight, and weight percentage of carbon (C) and hydrogen (H) in hydrocarbon fuels, were used as input parameters in developing robust correlations. The outcomes showed the GMDH approach is more precise compared to the GP, with a root mean square error (RMSE) of 0.053302 and a determination coefficient (R2) of 0.9641. Additionally, sensitivity analysis showed that pressure, followed by temperature and H (wt%) of hydrocarbon fuels, has the greatest impact on hydrogen solubility in hydrocarbon fuels. Ultimately, the Leverage method's results suggested that the GMDH model could be relied on to predict hydrogen solubility in hydrocarbon fuels.