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

被引:11
作者
Sadeghi, Mohammadamin [1 ]
Salami, Hossein [1 ]
Taghikhani, Vahid [1 ]
Robert, Marc A. [2 ]
机构
[1] Sharif Univ Technol, Dept Chem & Petr Engn, Tehran, Iran
[2] Rice Univ, Dept Chem & Biomol Engn, Houston, TX 77251 USA
关键词
Vapor liquid equilibria; Artificial neural networks; Carbon dioxide; Brine; IONIC LIQUID; GEOLOGICAL SEQUESTRATION; CARBON-DIOXIDE; CO2-H2O MIXTURES; PREDICTION; TEMPERATURES; PRESSURES; STORAGE; SYSTEM; WATER;
D O I
10.1016/j.fluid.2015.06.021
中图分类号
O414.1 [热力学];
学科分类号
摘要
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.
引用
收藏
页码:153 / 159
页数:7
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