Abstract:
Objective In the context of the development of carbon capture and storage technology, in order to more efficiently and accurately calculate the solubility of carbon dioxide (CO2) in water, both mechanism analysis-based and data-driven methods for calculating the solubility of CO2 in water are explored, respectively.
Method Firstly, based on public literature, 2 029 sets of solubility data of CO2 in water under different temperature and pressure conditions were collected and organized, and a relevant database was established. Subsequently, the classic Henry's law was revised using the database, and the influence of pressure on the formation of hydrates in the hydrate formation region was considered during the revision. Finally, a data-driven model was established based on BP neural network to calculate the solubility of CO2 in water. In the process of establishing the data-driven model, relative error was used as the model evaluation index, and temperature, pressure, and mineralization were selected as input variables.
Result The average relative error of the calculated results when the pressure is below 15 MPa after the correction of Henry's Law is 25.29%, a decrease of 27.11 percentage points compared to before the correction; the average relative error of the calculated results when the pressure is higher than 15 MPa is 29.49%, which has decreased by 91.86 percentage points compared to before correction. The average relative error of the data-driven model has been reduced to 20.64%, and the calculated results are more accurate than the modified Henry's Law.
Conclusion A simple, effective, and more accurate calculation method for the solubility of CO2 in water has been proposed, which can provide an important reference for calculating the solubility of CO2 in water in engineering.
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