Solubility study of hydrogen in direct coal liquefaction solvent based on quantitative structure–property relationships model

Direct coal liquefaction (DCL) is an important and effective method of converting coal into high-value-added chemicals and fuel oil. In DCL, heating the direct coal liquefaction solvent (DCLS) from low to high temperature and pre-hydrogenation of the DCLS are critical steps. Therefore, studying the dissolution of hydrogen in DCLS under liquefaction conditions gains importance. However, it is difficult to precisely determine hydrogen solubility only by experiments, especially under the actual DCL conditions. To address this issue, we developed a prediction model of hydrogen solubility in a single solvent based on the machine–learning quantitative structure-property relationship (ML-QSPR) methods. The results showed that the squared correlation coefficient R2 = 0.92 and root mean square error RMSE = 0.095, indicating the model’s good statistical performance. The external validation of the model also reveals excellent accuracy and predictive ability. Molecular polarization (α) is the main factor affecting the dissolution of hydrogen in DCLS. The hydrogen solubility in acyclic alkanes increases with increasing carbon number. Whereas in polycyclic aromatics, it decreases with increasing ring number, and in hydrogenated aromatics, it increases with hydrogenation degree. This work provides a new reference for the selection and proportioning of DCLS, i.e., a solvent with higher hydrogen solubility can be added to provide active hydrogen for the reaction and thus reduce the hydrogen pressure. Besides, it brings important insight into the theoretical significance and practical value of the DCL.