A comprehensive study on CO2 solubility in brine: Thermodynamic-based and neural network modeling

Phase equilibrium data are required to estimate the capacity of a geological formation to sequester CO2. In this paper, a comprehensive study, including both thermodynamic and neural network modeling, is performed on CO2 solubility in brine. Brine is approximated by a NaCl solution. The Redlich-Kwong equation of state and Pitzer expansion are used to develop the thermodynamic model. The equation of state constants are adjusted by genetic algorithm optimization. A novel approach based on a neural network model is utilized as well. The temperature range in which the presented model is valid is 283-383 K, and for pressure is 0-600 bar, covering the temperature and pressure conditions for geological sequestration. A two-layer network consisting 5 neurons in its hidden layer, was chosen as the optimum topology. The regression coefficient for the neural network model was calculated R-2=0.975. In addition, the neural network model showed lower mean absolute percentage error (3.41%) compared to the thermodynamic model (3.55%). (C) 2015 Elsevier B.V. All rights reserved.