AI-driven predictive modeling of supercritical CO₂ solubility using cheetah-optimized machine learning.
- 1Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
- 2Center of Research Excellence in Renewable Energy and Power Systems/Energy Efficiency Group, Department of Mechanical Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia.
Abstract
Artificial intelligence (AI) and machine-learning models provide powerful data-driven tools for modeling complex nonlinear systems and predicting process behavior from limited experimental data. In this study, an AI-driven predictive framework is developed for modeling paracetamol solubility and solvent density in supercritical carbon dioxide (SC-CO2). A dataset comprising 40 experimental observations was employed, with pressure and temperature used as input variables and paracetamol mole fraction and SC-CO2 density considered as target outputs. Three machine-learning regression algorithms-Gaussian Process Regression (GPR), Tweedie Regression (TDR), and K-Nearest Neighbors (KNN)-were developed and comparatively evaluated. To enhance predictive performance and automate model configuration, the Cheetah Optimizer (CO), a nature-inspired metaheuristic optimization algorithm, was integrated into the modeling framework for hyperparameter optimization. Model performance was assessed using the coefficient of determination (R2), mean squared error (MSE), and mean absolute error (MAE). The results demonstrated that TDR provided the highest predictive accuracy for paracetamol mole fraction, achieving an R2 of 0.985, whereas GPR exhibited the best performance for SC-CO2 density prediction, with an R2 of 0.991. The developed framework successfully captured the nonlinear relationships between operating conditions and the predicted properties while demonstrating that the optimal learning algorithm depends on the target variable. These findings highlight the potential of AI-assisted optimization and machine-learning approaches for data-driven prediction and intelligent modeling of complex supercritical-fluid processes.
Related
- Computational modeling and optimized machine learning for multidrug solubility prediction in supercritical carbon dioxide
- Symbolic and domain-generalized machine learning for interpretable solubility modeling in supercritical CO₂
- Physics-consistent equation discovery for drug solubility in supercritical CO₂ through symbolic regression and domain generalization
- HSPiP, computational, and thermodynamic models-based solubility validation of tofacitinib in PEG400 – water system, shelf-life estimation, and GastroPlus-enabled predictive in vivo performance
- Evaluation of Perioperative Cardiovascular Event Risk in Gastrointestinal Surgery ― Predictive Modeling and Risk Stratification Using Machine Learning ―
BibTeX
@article{alorfi2026aidriven,
title = {{AI-driven predictive modeling of supercritical CO₂ solubility using cheetah-optimized machine learning.}},
author = {Almuhannad S Alorfi and Nidal H Abu-Hamdeh},
journal = {Frontiers in medicine},
year = {2026},
month = oct,
doi = {10.3389/fmed.2026.1973985},
url = {https://europepmc.org/article/MED/42952623},
note = {CC BY},
}
RIS
TY - JOUR
TI - AI-driven predictive modeling of supercritical CO₂ solubility using cheetah-optimized machine learning.
AU - Almuhannad S Alorfi
AU - Nidal H Abu-Hamdeh
PY - 2026
DA - 2026/10/14
JO - Frontiers in medicine
DO - 10.3389/fmed.2026.1973985
UR - https://europepmc.org/article/MED/42952623
AB - Artificial intelligence (AI) and machine-learning models provide powerful data-driven tools for modeling complex nonlinear systems and predicting process behavior from limited experimental data. In this study, an AI-driven predictive framework is developed for modeling paracetamol solubility and solvent density in supercritical carbon dioxide (SC-CO2). A dataset comprising 40 experimental observations was employed, with pressure and temperature used as input variables and paracetamol mole fraction and SC-CO2 density considered as target outputs. Three machine-learning regression algorithms-Gaussian Process Regression (GPR), Tweedie Regression (TDR), and K-Nearest Neighbors (KNN)-were developed and comparatively evaluated. To enhance predictive performance and automate model configuration, the Cheetah Optimizer (CO), a nature-inspired metaheuristic optimization algorithm, was integrated into the modeling framework for hyperparameter optimization. Model performance was assessed using the coefficient of determination (R2), mean squared error (MSE), and mean absolute error (MAE). The results demonstrated that TDR provided the highest predictive accuracy for paracetamol mole fraction, achieving an R2 of 0.985, whereas GPR exhibited the best performance for SC-CO2 density prediction, with an R2 of 0.991. The developed framework successfully captured the nonlinear relationships between operating conditions and the predicted properties while demonstrating that the optimal learning algorithm depends on the target variable. These findings highlight the potential of AI-assisted optimization and machine-learning approaches for data-driven prediction and intelligent modeling of complex supercritical-fluid processes.
SN - 2296-858X
ER -