A novel computational strategy to estimate CO2 solubility in brine solutions for CCUS applications

Ratnakar, Ram R.; Chaubey, Vivek; Dindoruk, Birol

doi:10.1016/j.apenergy.2023.121134

Cited by 20 publications

(2 citation statements)

References 31 publications

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“…They employed the group method of data handling and gene expression programming for this purpose. Ratnakar et al 55 introduced several machine learning models to estimate CO 2 solubility in both pure water, utilizing 137 instances, and brine solutions. Khoshraftar and Ghemei 56 collected 240 data points related to CO 2 solubility in pure water and employed response surface methodology and deep learning to develop a model for this solubility phenomenon.…”

Section: Introductionmentioning

confidence: 99%

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

Zou,

Zhu,

et al. 2024

ACS Omega

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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 solubility databank, cascade forward neural network (CFNN) and generalized regression neural network (GRNN) were employed. Furthermore, three optimization algorithms, namely, Bayesian Regularization (BR), Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton, and Levenberg–Marquardt (LM), were applied to enhance the performance of the CFNN models. The CFNN-LM model showcased average absolute percent relative error (AAPRE) values of 5.37% for the overall data set, 5.26% for the training subset, and 5.85% for the testing subset. Overall, the CFNN-LM model stands out as the most accurate among the models crafted in this study, boasting the highest overall R 2 value of 0.9949 among the other models. Based on sensitivity analysis, pressure exerts the most significant influence and stands as the sole parameter with a positive impact on CO2 solubility in brine. Conversely, temperature and the concentration of all six salts considered in the model exhibited a negative impact. All salts exert a negative impact on CO2 solubility due to their salting-out effect, with varying degrees of influence. The salting-out effects of the salts can be ranked as follows: from the most pronounced to the least: MgCl2 > CaCl2 > NaCl > KCl > NaHCO3 > Na2SO4. By employing the leverage approach, only a few instances of potential suspected and out-of-leverage data were found. The relatively low count of identified potential suspected and out-of-leverage data, given the expansive solubility database, underscores the reliability and accuracy of both the data set and the CFNN-LM model’s performance in this survey.

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Section: Introductionmentioning

confidence: 99%

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

Zou,

Zhu,

et al. 2024

ACS Omega

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“…In water-mechanical-chemical-coupled simulations, simplified flow mechanisms can lead to significant deviations in predicted throughput and storage performance [30]. Ratnakar and Omosebi et al developed a machine learning-based workflow to inject single-phase supercritical carbon dioxide into deep saline aquifers to assess leakage risks [31][32][33][34][35][36]. The shortcoming is that these studies did not conduct sufficient and effective analysis and research on formation pressure changes.…”

Section: Introductionmentioning

confidence: 99%

A Case Study on the CO2 Sequestration in Shenhua Block Reservoir: The Impacts of Injection Rates and Modes

Tang,

Ding,

Song

et al. 2023

Energies

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Carbon capture and storage (CCS) is the most promising method of curbing atmospheric carbon dioxide levels from 2020 to 2050. Accurate predictions of geology and sealing capabilities play a key role in the safe execution of CCS projects. However, popular forecasting methods often oversimplify the process and fail to guide actual CCS projects in the right direction. This study takes a specific block in Shenhua, China as an example. The relative permeability of CO2 and brine is measured experimentally, and a multi-field coupling CO2 storage prediction model is constructed, focusing on analyzing the sealing ability of the block from the perspective of injection modes. The results show that when injected at a constant speed, the average formation pressure and wellbore pressure are positively correlated with the CO2 injection rate and time; when the injection rate is 0.5 kg/s for 50 years, the average formation pressure increases by 38% and the wellbore pressure increases by 68%. For different injection modes, the average formation pressures of various injection methods are similar during injection. Among them, the pressure increases around the well in the decreasing injection mode is the smallest. The CO2 concentration around the wellbore is the largest, and the CO2 diffusion range continues to expand with injection time. In summary, formation pressure increases with the increase in injection rate and injection time, and the decreasing injection mode has the least impact on the increase in formation pressure. The CO2 concentration is the largest around the well, and the CO2 concentration gradually decreases. The conclusion helps determine the geological carrying capacity of injection volumes and provides insights into the selection of more appropriate injection modes. Accurate predictions of CO2 storage capacity are critical to ensuring project safety and monitoring potentially hazardous sites based on reservoir characteristics.

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Electric field-enhanced CO2 dissolution and adsorption for geological carbon sequestration in saline aquifers

Li,

Zhao,

Kou

et al. 2025

Applied Surface Science

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A novel computational strategy to estimate CO2 solubility in brine solutions for CCUS applications

Cited by 20 publications

References 31 publications

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

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

A Case Study on the CO2 Sequestration in Shenhua Block Reservoir: The Impacts of Injection Rates and Modes

Electric field-enhanced CO2 dissolution and adsorption for geological carbon sequestration in saline aquifers

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A novel computational strategy to estimate CO2 solubility in brine solutions for CCUS applications

Cited by 20 publications

References 31 publications

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

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

A Case Study on the CO2 Sequestration in Shenhua Block Reservoir: The Impacts of Injection Rates and Modes

Electric field-enhanced CO2 dissolution and adsorption for geological carbon sequestration in saline aquifers

Contact Info

Product

Resources

About

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

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