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Modeling CO2 loading capacity of triethanolamine (TEA) aqueous solutions via a deep learning approach

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  • Hadavimoghaddam, Fahimeh
  • Amiri-Ramsheh, Behnam
  • Atashrouz, Saeid
  • Abedi, Ali
  • Mohaddespour, Ahmad
  • Ostadhassan, Mehdi
  • Hemmati-Sarapardeh, Abdolhossein

Abstract

Capturing carbon dioxide (CO2) from natural gas is essential for reducing CO2 emissions as a greenhouse gas as well as increasing the gas heating value. Absorption of CO2 by amine solutions is a method that has seen a widespread use as a CO2 collection technique. In this research, advanced ML approaches are utilized to simulate the CO2 loading capacity of triethanolamine (TEA) aqueous solutions as a function of system temperature, CO2 partial pressure, and amine concentration in the aqueous phase. Deep neural network (DNN), Gaussian process regressor (GPR), deep belief network (DBN), and bagging regressor (BR) are the models that have been developed. The DNN model with the coefficient of determination (R2) of 0.9990 as well as the root mean square error (RMSE) of 0.0073 outperformed other models in terms of accuracy and validity. The R2 values of 0.9911, 0.9861, and 0.9745 for DBN, BR, and GPR, accordingly, showed that other models could likewise perform with high accuracy. Moreover, trend analysis of the results confirmed that the DNN method can correctly estimate the behavior of CO2 loading with variations in the input parameters. Furthermore, sensitivity analysis showed that temperature has a decreasing effect on the CO2 loading, while amine concentration and CO2 partial pressure exhibited to have an incremental effect. Herein, the temperature had the greatest influence on the amount of CO2 loading. The final stage of the research was the leverage approach, which stated that about 99 % of the data are statistically valid and the DNN model is reliable to be replaced by experimental approaches in the prediction of CO2 absorption into certain type of solutions.

Suggested Citation

  • Hadavimoghaddam, Fahimeh & Amiri-Ramsheh, Behnam & Atashrouz, Saeid & Abedi, Ali & Mohaddespour, Ahmad & Ostadhassan, Mehdi & Hemmati-Sarapardeh, Abdolhossein, 2024. "Modeling CO2 loading capacity of triethanolamine (TEA) aqueous solutions via a deep learning approach," Energy, Elsevier, vol. 313(C).
    • Handle: RePEc:eee:energy:v:313:y:2024:i:c:s0360544224032523
    DOI: 10.1016/j.energy.2024.133476
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    References listed on IDEAS

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    1. Haris, Muhammad & Hasan, Muhammad Noman & Qin, Shiyin, 2021. "Early and robust remaining useful life prediction of supercapacitors using BOHB optimized Deep Belief Network," Applied Energy, Elsevier, vol. 286(C).
    2. Fu, Guoyin, 2018. "Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system," Energy, Elsevier, vol. 148(C), pages 269-282.
    3. Yang, Weifei & Xiao, Changlai & Zhang, Zhihao & Liang, Xiujuan, 2022. "Identification of the formation temperature field of the southern Songliao Basin, China based on a deep belief network," Renewable Energy, Elsevier, vol. 182(C), pages 32-42.
    4. Hemmati-Sarapardeh, Abdolhossein & Varamesh, Amir & Husein, Maen M. & Karan, Kunal, 2018. "On the evaluation of the viscosity of nanofluid systems: Modeling and data assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 313-329.
    5. Andrew Gordon Wilson & David A. Knowles & Zoubin Ghahramani, 2011. "Gaussian Process Regression Networks," Papers 1110.4411, arXiv.org.
    6. Sun, Xianke & Wang, Gaoliang & Xu, Liuyang & Yuan, Honglei & Yousefi, Nasser, 2021. "Optimal estimation of the PEM fuel cells applying deep belief network optimized by improved archimedes optimization algorithm," Energy, Elsevier, vol. 237(C).
    Full references (including those not matched with items on IDEAS)

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