Enhanced Prediction of CO-Brine Interfacial Tension at Varying Temperature Using a Multibranch-Structure-Based Neural Network Approach.

Interfacial tension () between CO and brine depends on chemical components in multiphase systems, intricately evolving with a change in temperature. In this study, we developed a convolutional neural network with a multibranch structure (MBCNN), which, in combination with a compiled data set containing measurement data of 1716 samples from 13 available literature sources at wide temperature and pressure ranges (273.15-473.15 K and 0-70 MPa), was used to quantitatively explore the correlation of various chemical components with at varying temperature, aiming to achieve accurate predictions of under complex conditions. Our multibranch neural network analysis yielded some important insights: (1) Leveraging the convolutional and multibranch structure, MBCNN effectively mitigates the adverse effects of sparse matrices resulting from the absence of certain basic components, exhibiting higher prediction accuracy particularly for low scenarios (MAE = 0.47, and R = 0.9921) than other AI models. (2) The multibranch structure allows MBCNN to additionally capture the interattribute relationship between temperature and each chemical component. Such interattribute relationships are quantitatively correlated with , demonstrating that varying temperature significantly influences the dependence of on chemical components in gas and brine by causing the variation in their solubility. Specifically, the ratio of to the molality of monovalent cations (Na and K) and bivalent cations (Ca and Mg) in brine, as well as to the mole fraction of non-CO components (CH and N) in the gas phase, varies with increasing temperature, approximately following a quadratic function. (3) By formulating the effect of each attribute on and quantifying their respective weight, we derived a new piecewise function for predicting at three temperature intervals ( ≤ 293.15 K, 293.15 K < ≤ 324.4 K, and > 324.4 K), with high prediction performance (MAE = 2.3672, R = 0.9263) across a wide temperature range in saline aquifers.