Predicting the solubility of hydrogen in hydrocarbon fractions: Advanced data-driven machine learning approach and equation of state

Abstract

BackgroundHydrogen is a free carbon source for energy that attracts massive interest to be utilized during energy transition period in the near future. Molecular hydrogen soon will be a key component in fuels productions. It can be produced through catalytic steam and natural gas. For such processes the accurate estimation of solubility of hydrogen in many alkanes’ solution can have a major impact on the design and optimization of such petrochemical processes. MethodsIn this study, advanced data-driven scheme consisting in a committee machine intelligent system (CMIS) model was proposed for predicting the solubility of hydrogen in many hydrocarbon liquids raging from light n-alkanes C1 to heavy n-alkanes of C46. The CMIS was gained by combining multilayer perceptron (MLP) and cascaded forward neural network (CFNN) beneath a sole model using gene expression programming (GEP). A wide range database of hydrogen solubility in a binary system of hydrogen and various n-alkanes types was considered for developing the intelligent model. Besides, the reliability of the physics-based models, namely the Peng–Robinson equations of state (PR EOS) was checked for predicting the solubility of H2 in n-alkanes. Significant findingsThe main results demonstrated that the developed CMIS scheme can estimate the solubility of hydrogen in n-alkanes with a quite low computational cost and excellent performance, where the yielded overall RMSE and R2 values were 0.0033 and 0.9972, respectively. Besides, the established machine learning based model is capable of providing a good match against a wide range of experiment data. Furthermore, it is found that the implemented CMIS paradigm is superior to the existing intelligent and physics-based models in terms of accuracy. Lastly, the result of this work is vital for increasing the accuracy of hydrogen solubility in various hydrocarbon solvents required for process optimization of the hydrogen production units.