Dataset Information

Toward Estimating CO₂ Solubility in Pure Water and Brine Using Cascade Forward Neural Network and Generalized Regression Neural Network: Application to CO₂ Dissolution Trapping in Saline Aquifers.

ABSTRACT: 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 so

SUBMITTER: Zou X

PROVIDER: S-EPMC10831835 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Dataset Information

Toward Estimating CO₂ Solubility in Pure Water and Brine Using Cascade Forward Neural Network and Generalized Regression Neural Network: Application to CO₂ Dissolution Trapping in Saline Aquifers.

OmicsDI is part of the ELIXIR infrastructure

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Dataset Information

Toward Estimating CO2 Solubility in Pure Water and Brine Using Cascade Forward Neural Network and Generalized Regression Neural Network: Application to CO2 Dissolution Trapping in Saline Aquifers.

OmicsDI is part of the ELIXIR infrastructure

Tweets

Toward Estimating CO₂ Solubility in Pure Water and Brine Using Cascade Forward Neural Network and Generalized Regression Neural Network: Application to CO₂ Dissolution Trapping in Saline Aquifers.