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

Sadeghi, M ; Sharif University of Technology | 2015

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  1. Type of Document: Article
  2. DOI: 10.1016/j.fluid.2015.06.021
  3. Publisher: Elsevier , 2015
  4. Abstract:
  5. 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 283-383K, and for pressure is 0-600bar, 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 R2=0.975. In addition, the neural network model showed lower mean absolute percentage error (3.41%) compared to the thermodynamic model (3.55%)
  6. Keywords:
  7. Artificial neural networks ; Vapor liquid equilibria ; Brines ; Carbon ; Carbon dioxide ; Equations of state ; Genetic algorithms ; Geology ; Neural networks ; Phase equilibria ; Solubility ; Thermodynamic properties ; Thermodynamics ; Genetic-algorithm optimizations ; Geological formation ; Geological sequestration ; Mean absolute percentage error ; Phase equilibrium data ; Redlich-kwong equation of state ; Regression coefficient ; Temperature and pressures ; Network layers
  8. Source: Fluid Phase Equilibria ; Volume 403 , October , 2015 , Pages 153-159 ; 03783812 (ISSN)
  9. URL: http://www.sciencedirect.com/science/article/pii/S0378381215003404