{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.",
    "Center 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."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Almuhannad S Alorfi"
    },
    {
      "affs": [
        1
      ],
      "name": "Nidal H Abu-Hamdeh"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7ftbve7hk59q0.bib",
  "cited_by": 0,
  "doi": "10.3389/fmed.2026.1973985",
  "fulltext_url": "https://europepmc.org/articles/PMC13713485",
  "id": "7ftbve7hk59q0",
  "issns": [
    "2296-858X"
  ],
  "landing_url": "https://europepmc.org/article/MED/42952623",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Original Research"
    ],
    [
      "openalex",
      "2204|Phase Equilibria and Thermodynamics"
    ]
  ],
  "openalex": "W7221797979",
  "pdf_url": "https://europepmc.org/articles/PMC13713485?pdf=render",
  "pmcid": "PMC13713485",
  "pmid": "42952623",
  "published": "2026-10-14",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/7ftbve7hk59q0.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T00:20:41Z",
      "native_id": "MED:42952623",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42952623"
    },
    {
      "first_seen": "2026-10-31T12:24:01Z",
      "native_id": "PMC13713485",
      "source": "pmc",
      "url": "https://doi.org/10.3389/fmed.2026.1973985"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "journal-openalex:F27",
      "subfield": "general"
    }
  ],
  "title": "AI-driven predictive modeling of supercritical CO₂ solubility using cheetah-optimized machine learning.",
  "title_text": "AI-driven predictive modeling of supercritical CO₂ solubility using cheetah-optimized machine learning.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T12:21:09Z",
  "url": "https://research.draconitic.com/paper/7ftbve7hk59q0",
  "venue": "Frontiers in medicine",
  "versions": []
}