CO 2 –N 2 Mixture Viscosity Modelling Using Machine Learning: Towards Sustainable Carbon Capture and Energy Efficiency
機械学習を用いたCO2-N2混合気体の粘度モデリング：持続可能な炭素回収とエネルギー効率に向けて (AI 翻訳)
M. Alwerfali, F. Faraji, Mardin Abdalqadir, Jagar A. Ali
🤖 gxceed AI 要約
日本語
CO2-N2混合気体の粘度はCCUSプロセスに重要だが、実験や従来モデルでは不正確。本研究は約3036のデータポイントを用いて勾配ブースティング、XGBoost、ランダムフォレスト、ニューラルネットワークなど9種類の機械学習モデルを開発・比較。勾配ブースティングが最高精度（R²=0.9933）を達成し、温度・圧力・組成から粘度を高速で予測可能にした。さらに、GMDHに基づく実用的な明示式も提案した。
English
Accurate viscosity prediction of CO2-N2 mixtures is vital for CCUS processes but costly experiments and inaccurate conventional models hinder rapid decision-making. This study developed and benchmarked nine ML models (GB, XGBoost, LightGBM, CatBoost, RF, three MLP-ANNs, stacking ensemble, GMDH) using 3036 data points. Gradient boosting achieved best performance (R²=0.9933, RMSE=4.83 μPa·s). Two explicit equations from GMDH were provided for practical use.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本政府はGX実現に向けてCCUSを重要な柱と位置づけており、コスト低減と効率化が急務。本研究成果は、CO2回収・貯留の設計・運用における粘度予測を迅速化し、CCUSの社会実装を後押しする。
In the global GX context
Globally, CCUS is recognized as essential for net-zero targets. This paper offers a cost-effective, data-driven approach to predict mixture viscosity, which can accelerate CCUS project planning and reduce reliance on empirical correlations. It contributes to the growing literature on ML applications in carbon management.
👥 読者別の含意
🔬研究者:Provides a comprehensive ML benchmark for CO2-N2 viscosity, offering insights into model selection and feature importance.
🏢実務担当者:The explicit equations from GMDH enable quick viscosity estimates for CCUS process design without complex simulations.
🏛政策担当者:Demonstrates that ML can reduce CCUS operational uncertainty, supporting policy incentives for technology deployment.
📄 Abstract（原文）
Accurate determination of the viscosity of carbon dioxide (CO 2 ) mixed with nitrogen (N 2 ) is vital for enhanced oil recovery (EOR) and carbon capture, utilisation and storage (CCUS/CCS). The determination of this important thermophysical property is usually through costly and time‐consuming experiments, which is not ideal for field recovery planning and rapid decision‐making. On the other hand, the conventional modelling relies largely on equations of state (EoS) and empirical correlations, which can be inaccurate for CO 2 –N 2 viscosity, particularly near supercritical conditions due to simplifying assumptions and limited transferability. Consequently, machine‐learning (ML) methods have gained popularity for fast and accurate prediction. Hence, in this study, ~3036 literature data points spanning pressures of 0.00127–160.99 MPa and temperature of 66.55–575.15 K were collected, cleaned and pre‐processed. Then, using pre‐processed data, several ML models, including gradient boosting (GB), extreme gradient boosting (XGBoost), LightGBM, CatBoost, random forest, three multilayer perceptron artificial neural networks (MLP‐ANNs), a stacking ensemble and a group method of data handling (GMDH) were developed. The developed models were benchmarked to predict CO 2 –N 2 viscosity as a function of temperature, pressure and the mole fractions of CO 2 –N 2 in the mixture. The analysis of the results indicate that the GB achieved the best performance with a correlation coefficient ( R 2 ) of 0.9933 ± 0.0011, root mean square error (RMSE) of 4.83 ± 0.39 μPa·s and mean absolute error (MAE) of 2.34 ± 0.10 μPa·s (mean ± 95% CI) for the test dataset, outperforming all other ML models and the utilised literature correlations. In addition, based on GMDH, two practical explicit equations within temperature ranges of T < 300 K and T > 300 K that predict the experimental viscosity with high accuracy were proposed. The sensitivity analysis also shows that the pressure has the highest positive impact, while temperature exhibited a comparably strong negative effect on viscosity.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1155/er/2452722first seen 2026-05-05 23:35:12
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