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Improved Solubility Model for Pure Gas and Binary Mixture of CO 2 -H 2 S in Water: A Geothermal Case Study with Total Reinjection

Author

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  • Pouriya H. Niknam

    (Department of Industrial Engineering, Università degli Studi di Firenze, Viale Morgagni 40, 50135 Florence, Italy)

  • Lorenzo Talluri

    (Department of Industrial Engineering, Università degli Studi di Firenze, Viale Morgagni 40, 50135 Florence, Italy)

  • Daniele Fiaschi

    (Department of Industrial Engineering, Università degli Studi di Firenze, Viale Morgagni 40, 50135 Florence, Italy)

  • Giampaolo Manfrida

    (Department of Industrial Engineering, Università degli Studi di Firenze, Viale Morgagni 40, 50135 Florence, Italy)

Abstract

Geothermal energy is acknowledged globally as a renewable resource, which, unlike solar, wind or wave energy, can be continuously exploited. The geothermal fluids usually have some acid gas content, which needs to be precisely taken into account when predicting the actual potential of a power plant in dealing with an effective reinjection. One of the key parameters to assess is the solubility of the acid gas, as it influences the thermodynamic conditions (saturation pressure and temperature) of the fluid. Therefore, an enhanced solubility model for the CO 2 -H 2 S-water system is developed in this study, based on the mutual solubility of gases. The model covers a wide range of pressures and temperatures. The genetic algorithm is employed to calculate the correlation constants and corresponding solubility values of both CO 2 and H 2 S as functions of pressure, temperature and the balance of the gas. The results are validated against previously published models and experimental data available in the literature. The proposed model estimates the pure gas solubility, which is also a feature of other models. The more innovative feature of the model is the solubility estimation of each CO 2 or H 2 S in simultaneous presence, such as when the binary gas is injected into the pure water of the geothermal reinjection well. The proposed solubility model fits well with the available experimental data, with a mean deviation lower than 0.2%.

Suggested Citation

  • Pouriya H. Niknam & Lorenzo Talluri & Daniele Fiaschi & Giampaolo Manfrida, 2020. "Improved Solubility Model for Pure Gas and Binary Mixture of CO 2 -H 2 S in Water: A Geothermal Case Study with Total Reinjection," Energies, MDPI, vol. 13(11), pages 1-14, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2883-:d:367556
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    References listed on IDEAS

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    1. Shafaei, Mohammad Javad & Abedi, Jalal & Hassanzadeh, Hassan & Chen, Zhangxin, 2012. "Reverse gas-lift technology for CO2 storage into deep saline aquifers," Energy, Elsevier, vol. 45(1), pages 840-849.
    2. Rivera Diaz, Alexandre & Kaya, Eylem & Zarrouk, Sadiq J., 2016. "Reinjection in geothermal fields − A worldwide review update," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 105-162.
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    Cited by:

    1. Zhen Zhao & Guangxiong Qin & Yinfei Luo & Songhe Geng & Linchao Yang & Ronghua Wen & Jiahao Chao & Liang Zhang, 2021. "Experimental Study on Reservoir Physical Properties and Formation Blockage Risk in Geothermal Water Reinjection in Xining Basin: Taking Well DR2018 as an Example," Energies, MDPI, vol. 14(9), pages 1-19, May.
    2. Moein Shamoushaki & Pouriya H. Niknam & Lorenzo Talluri & Giampaolo Manfrida & Daniele Fiaschi, 2021. "Development of Cost Correlations for the Economic Assessment of Power Plant Equipment," Energies, MDPI, vol. 14(9), pages 1-19, May.
    3. Moein Shamoushaki & Giampaolo Manfrida & Lorenzo Talluri & Pouriya H. Niknam & Daniele Fiaschi, 2021. "Different Geothermal Power Cycle Configurations Cost Estimation Models," Sustainability, MDPI, vol. 13(20), pages 1-19, October.
    4. Ruben Zieba Falama & Felix Ngangoum Welaji & Abdouramani Dadjé & Virgil Dumbrava & Noël Djongyang & Chokri Ben Salah & Serge Yamigno Doka, 2021. "A Solution to the Problem of Electrical Load Shedding Using Hybrid PV/Battery/Grid-Connected System: The Case of Households’ Energy Supply of the Northern Part of Cameroon," Energies, MDPI, vol. 14(10), pages 1-23, May.
    5. Niknam, Pouriya H. & Talluri, Lorenzo & Fiaschi, Daniele & Manfrida, Giampaolo, 2021. "Sensitivity analysis and dynamic modelling of the reinjection process in a binary cycle geothermal power plant of Larderello area," Energy, Elsevier, vol. 214(C).

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