Volume change of CO2 + long-chain liquid n-alkane (with n ≥ 14) mixtures under geological storage conditions: An observed abrupt change near the critical point of CO2

This study used a high pressure optical cell (HPOC) combined with a heating/cooling stage, a pressure device, and a laser Raman spectrometer to investigate the volume expansion (VE) of CO 2  + tetradecane and CO 2  + hexadecane mixtures under geological storage conditions. Abrupt changes in VE were observed near the CO 2 critical point (CP), which is assumed to be ubiquitous in CO 2  + long-chain alkane mixtures with ≥14 carbon alkanes. This is due to the critical anomaly of the thermodynamic behavior of the mixture near the CP of CO 2 , which inhibits the VE of CO 2  + long-chain alkanes at lower temperatures. In addition, four machine learning (ML) algorithms, including backpropagation neural network (BPNN), K-nearest neighbors (KNN), random forest (RF), and support vector regression (SVR), were applied to predict the volume expansion factor (VEF). The BPNN model provided much higher robustness and accuracy with correlation coefficient ( R 2 ) >0.98 and mean square error ( MSE ) <0.03, which indicates a strong agreement between the predicted result and matching experimental data. We concluded that a predictive framework built using ML could provide rapid predictions of the volume change curve or curved surface of CO 2  + organic mixtures under various temperature and pressure conditions.