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
- 1Chemical 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
- 2Chemical 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
- 3Chemical Engineering Faculty, Sahand University of Technology, P.O.Box 51335-1996, Sahand New Town, Tabriz, Iran
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.
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BibTeX
@article{taheri2026integrated,
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}},
author = {Mehdi Taheri and Jafarsadegh Moghaddas and Borhan Pourtalebi and Mehdi Mohammadzadeh},
journal = {Ecotoxicology and Environmental Safety},
year = {2026},
month = oct,
doi = {10.1016/j.ecoenv.2026.120981},
url = {https://doi.org/10.1016/j.ecoenv.2026.120981},
note = {CC BY},
}
RIS
TY - JOUR
TI - 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
AU - Mehdi Taheri
AU - Jafarsadegh Moghaddas
AU - Borhan Pourtalebi
AU - Mehdi Mohammadzadeh
PY - 2026
DA - 2026/10/31
JO - Ecotoxicology and Environmental Safety
DO - 10.1016/j.ecoenv.2026.120981
UR - https://doi.org/10.1016/j.ecoenv.2026.120981
AB - 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.
SN - 0147-6513
SN - 1090-2414
ER -