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<p>Accurate estimation of carbon dioxide (CO₂) equilibrium solubility in absorbent solutions is essential for designing and optimizing chemical absorption-based CO₂ capture systems. In this study, in order to simulate the CO<sub>2</sub> loading capacity of Diethanolamine (DEA) aqueous solutions, 3 renowned white box approaches, including the Group Method of Data Handling (GMDH), Genetic Programming (GP), and Gene Expression Programming (GEP), were used. To achieve the aim, a large data bank, including 496 experimental samples, was compiled from published articles. Temperature (273.15 to 413.15&#xa0;K), partial pressure of CO<sub>2</sub> (0.0026 to 6894.76&#xa0;kPa), and concentration of amine (0.5 to 8&#xa0;mol/L) were utilized as input features for training models. The results demonstrated that the GEP technique, having a determination coefficient (R<sup>2</sup>) of 0.9506 as well as a root mean square error (RMSE) of 0.0831, outperforms other techniques in terms of the CO<sub>2</sub> loading capacity prediction of DEA solutions. The R<sup>2</sup> estimates of 0.9415 and 0.9301 for the GP as well as GMDH, accordingly, proved that these two models also produced accurate findings. In addition, it was found that the developed GEP model can accurately follow the patterns of CO<sub>2</sub> loading capacity in response to changes in input variables. The finding of the last research part, the Leverage technique, revealed that the data used for modeling are valid, and the GEP approach is statistically robust. The primary contribution of this research is how interpretable machine learning techniques are combined with chemical process modeling to provide a framework for enhancing CO<sub>2</sub> absorption system.</p>

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Toward smart correlations for predicting CO2 loading capacity of diethanolamine (DEA) aqueous solutions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月24日

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  • Fahimeh Hadavimoghaddam,
  • Alexei Rozhenko,
  • Saeid Atashrouz,
  • Ali Abedi,
  • Elnaz Nasirzadeh,
  • Qichao Lv,
  • Ahmad Mohaddespour,
  • Mehdi Ostadhassan,
  • Abdolhossein Hemmati-Sarapardeh

摘要

Accurate estimation of carbon dioxide (CO₂) equilibrium solubility in absorbent solutions is essential for designing and optimizing chemical absorption-based CO₂ capture systems. In this study, in order to simulate the CO2 loading capacity of Diethanolamine (DEA) aqueous solutions, 3 renowned white box approaches, including the Group Method of Data Handling (GMDH), Genetic Programming (GP), and Gene Expression Programming (GEP), were used. To achieve the aim, a large data bank, including 496 experimental samples, was compiled from published articles. Temperature (273.15 to 413.15 K), partial pressure of CO2 (0.0026 to 6894.76 kPa), and concentration of amine (0.5 to 8 mol/L) were utilized as input features for training models. The results demonstrated that the GEP technique, having a determination coefficient (R2) of 0.9506 as well as a root mean square error (RMSE) of 0.0831, outperforms other techniques in terms of the CO2 loading capacity prediction of DEA solutions. The R2 estimates of 0.9415 and 0.9301 for the GP as well as GMDH, accordingly, proved that these two models also produced accurate findings. In addition, it was found that the developed GEP model can accurately follow the patterns of CO2 loading capacity in response to changes in input variables. The finding of the last research part, the Leverage technique, revealed that the data used for modeling are valid, and the GEP approach is statistically robust. The primary contribution of this research is how interpretable machine learning techniques are combined with chemical process modeling to provide a framework for enhancing CO2 absorption system.