{
  "abstract": "Phenol is a highly toxic pollutant frequently detected in industrial wastewater. This study presents the first investigation of a hydrophilic silica aerogel/ZSM-5 nanocomposite for phenol adsorption, integrated with advanced machine-learning modeling and multi-objective optimization. Silica aerogel was synthesized via an ambient-pressure sol–gel route and combined with hydrothermally prepared ZSM-5. The 75:25 aerogel/ZSM-5 composite showed the highest surface area and adsorption performance after hydrophilic conversion. Batch experiments evaluated adsorbent dosage (0.1–0.5 g), initial concentration (5–50 mg L⁻¹), contact time (5–180 min), and temperature (25–55 °C). Under optimized conditions (0.3 g, 10 mg L⁻¹, 90 min, 55 °C), 96.1% removal was achieved. Equilibrium data followed the Freundlich isotherm, indicating multilayer adsorption on a heterogeneous surface, while kinetics obeyed the pseudo-second-order model. Thermodynamic parameters confirmed a spontaneous, endothermic process; the low ΔH° (30.27 kJ mol⁻¹) and activation energy (18.54 kJ mol⁻¹) indicate weak interactions between the adsorbate and adsorbent surface. Ethanol regeneration retained the highest capacity over three cycles. Four machine-learning models trained on 150 data points showed that the NSGA-II-optimized ANN and XGBoost delivered superior accuracy (test R² > 0.995). Multi-objective particle swarm optimization identified a validated Pareto-optimal solution (0.139 g, 11.44 mg L⁻¹, 180 min, 54.2 °C) yielding 90.95% removal and 4.86 mg g⁻¹ capacity. SHAP and partial-dependence analyses ranked contact time and initial concentration as the dominant variables. The integrated experimental–ML framework offers a robust pathway for designing high-performance adsorbents and optimizing phenol-removal processes under conflicting objectives.",
  "abstract_shown": true,
  "affiliations": [
    "Chemical Engineering Faculty, Sahand University of Technology, P.O.Box 51335-1996, Sahand New Town, Tabriz, Iran; Transport phenomenon and Laser Research Center, Chemical Engineering Faculty, Tabriz University of Technology, P. O. Box 51335-1996, Sahand New Town, Tabriz, Iran",
    "Chemical Engineering Faculty, Sahand University of Technology, P.O.Box 51335-1996, Sahand New Town, Tabriz, Iran; Environmental Engineering Research Center (EERC), Sahand University of Technology, P.O.Box 51335-1996, Sahand New Town, Tabriz, Iran",
    "Chemical Engineering Faculty, Sahand University of Technology, P.O.Box 51335-1996, Sahand New Town, Tabriz, Iran"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Mehdi Taheri"
    },
    {
      "name": "Jafarsadegh Moghaddas"
    },
    {
      "affs": [
        1
      ],
      "name": "Borhan Pourtalebi"
    },
    {
      "affs": [
        2
      ],
      "name": "Mehdi Mohammadzadeh"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e8c6kaftdr6wy.bib",
  "cited_by": 0,
  "doi": "10.1016/j.ecoenv.2026.120981",
  "fulltext_url": "http://www.sciencedirect.com/science/article/pii/S0147651326013114",
  "id": "e8c6kaftdr6wy",
  "issns": [
    "0147-6513",
    "1090-2414"
  ],
  "landing_url": "https://doi.org/10.1016/j.ecoenv.2026.120981",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "GE1-350"
    ],
    [
      "lcc",
      "TD172-193.5"
    ],
    [
      "openalex",
      "2312|Adsorption and biosorption for pollutant removal"
    ]
  ],
  "openalex": "W7224102687",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-31",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/e8c6kaftdr6wy.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T14:22:44Z",
      "native_id": "0d7346ea608646a7a1d5b3390c0f8f79",
      "source": "doaj",
      "url": "https://doi.org/10.1016/j.ecoenv.2026.120981"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2312|Adsorption and biosorption for pollutant removal",
      "subfield": "hydrology-soils"
    }
  ],
  "title": "Integrated experimental and machine learning-driven multi-objective optimization for phenol removal via silica aerogel/ZSM-5 nanocomposite: Isotherm and kinetic analysis, thermodynamic insights and pareto-based process design",
  "title_text": "Integrated experimental and machine learning-driven multi-objective optimization for phenol removal via silica aerogel/ZSM-5 nanocomposite: Isotherm and kinetic analysis, thermodynamic insights and pareto-based process design",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T14:20:43Z",
  "url": "https://research.draconitic.com/paper/e8c6kaftdr6wy",
  "venue": "Ecotoxicology and Environmental Safety",
  "versions": []
}