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Applying a sophisticated approach to predict CO2 solubility in brines: application to CO2 sequestration

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  • Mohammad Ali Ahmadi
  • Alireza Ahmadi

Abstract

Specification of CO2 and brine phase behaviour plays a vital role in CO2 sequestration and CO2 reduction from atmosphere to deep saline aquifers. Because CO2 solubility in brines determine how much carbon can be stored in deep saline aquifers. To tackle the referred issue, high precise model with low uncertainty parameters called ‘least square support vector machine (LS-SVM)’ was executed to predict CO2–brine solubility. The proposed intelligent-based approach is examined by using extensive experimental data reported in open literature. Results obtained from the proposed numerical solution model were compared with the relevant experimental CO2–brine solubility data. The average relative absolute deviation between the model predictions and the relevant experimental data was found to be <0.1% for LS-SVM model.

Suggested Citation

  • Mohammad Ali Ahmadi & Alireza Ahmadi, 2016. "Applying a sophisticated approach to predict CO2 solubility in brines: application to CO2 sequestration," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 11(3), pages 325-332.
    • Handle: RePEc:oup:ijlctc:v:11:y:2016:i:3:p:325-332.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctu034
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    Cited by:

    1. Ghorbani, Bahram & Salehi, Gholamreza & Ebrahimi, Armin & Taghavi, Masoud, 2021. "Energy, exergy and pinch analyses of a novel energy storage structure using post-combustion CO2 separation unit, dual pressure Linde-Hampson liquefaction system, two-stage organic Rankine cycle and ge," Energy, Elsevier, vol. 233(C).
    2. Ramezanizadeh, Mahdi & Ahmadi, Mohammad Hossein & Nazari, Mohammad Alhuyi & Sadeghzadeh, Milad & Chen, Lingen, 2019. "A review on the utilized machine learning approaches for modeling the dynamic viscosity of nanofluids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    3. Wang, Xiao & van ’t Veld, Klaas & Marcy, Peter & Huzurbazar, Snehalata & Alvarado, Vladimir, 2018. "Economic co-optimization of oil recovery and CO2 sequestration," Applied Energy, Elsevier, vol. 222(C), pages 132-147.
    4. Vo Thanh, Hung & Lee, Kang-Kun, 2022. "Application of machine learning to predict CO2 trapping performance in deep saline aquifers," Energy, Elsevier, vol. 239(PE).
    5. Si Le Van & Bo Hyun Chon, 2017. "Applicability of an Artificial Neural Network for Predicting Water-Alternating-CO 2 Performance," Energies, MDPI, vol. 10(7), pages 1-20, June.

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