Predicting carbon dioxide (CO2) solubility
in water
and brine is crucial for understanding carbon capture and storage
(CCS) processes. Accurate solubility predictions inform the feasibility
and effectiveness of CO2 dissolution trapping, a key mechanism
in carbon sequestration in saline aquifers. In this work, a comprehensive
data set comprising 1278 experimental solubility data points for CO2–brine systems was assembled, encompassing diverse
operating conditions. These data encompassed brines containing six
different salts: NaCl, KCl, NaHCO3, CaCl2, MgCl2, and Na2SO4. Also, this databank encompassed
temperature spanning from 273.15 to 453.15 K and a pressure range
spanning 0.06–100 MPa. To model this solubility databank, cascade
forward neural network (CFNN) and generalized regression neural network
(GRNN) were employed. Furthermore, three optimization algorithms,
namely, Bayesian Regularization (BR), Broyden–Fletcher–Goldfarb–Shanno
(BFGS) quasi-Newton, and Levenberg–Marquardt (LM), were applied
to enhance the performance of the CFNN models. The CFNN-LM model showcased
average absolute percent relative error (AAPRE) values of 5.37% for
the overall data set, 5.26% for the training subset, and 5.85% for
the testing subset. Overall, the CFNN-LM model stands out as the most
accurate among the models crafted in this study, boasting the highest
overall R
2 value of 0.9949 among the other
models. Based on sensitivity analysis, pressure exerts the most significant
influence and stands as the sole parameter with a positive impact
on CO2 solubility in brine. Conversely, temperature and
the concentration of all six salts considered in the model exhibited
a negative impact. All salts exert a negative impact on CO2 solubility due to their salting-out effect, with varying degrees
of influence. The salting-out effects of the salts can be ranked as
follows: from the most pronounced to the least: MgCl2 >
CaCl2 > NaCl > KCl > NaHCO3 > Na2SO4. By employing the leverage approach, only a
few instances
of potential suspected and out-of-leverage data were found. The relatively
low count of identified potential suspected and out-of-leverage data,
given the expansive solubility database, underscores the reliability
and accuracy of both the data set and the CFNN-LM model’s performance
in this survey.