Efficacy of advanced Fenton-photo systems for the degradation of petroleum hydrocarbons using complex neural networks

Abstract

Environmental contamination from petroleum hydrocarbons poses significant challenges, particularly in regions affected by oil industry activities and conflict-related environmental disasters. This study investigated AI-optimized photo-Fenton systems for remediation of oil-contaminated desert soils through three key objectives: (1) systematically evaluating multiple catalysts, particularly iron(III) sulfate pentahydrate, and EDTA as a chelating agent for TPH removal, determining optimal conditions for specific reduction thresholds; (2) assessing four advanced photo-Fenton systems incorporating EDTA and Fe₂(SO₄)₃·5H₂O across multiple performance metrics, using statistical validation and machine learning to identify key performance factors; and (3) employing neural network modeling and hierarchical clustering to evaluate predictive accuracy, identify influential factors, and discover natural PAH classifications based on degradation patterns. Soil samples from Kuwait's Great Burgan Field were treated using various combinations of Fe₂(SO₄)₃·5H₂O (5–10 g L⁻¹), Fe₂O₃ (5–35 g L⁻¹), EDTA (15–20 g L⁻¹), and H₂SO₄ (5–25 mL L⁻¹). Fe₂(SO₄)₃·5H₂O demonstrated superior catalytic activity, achieving 99% TPH removal at 10 g L⁻¹, compared to Fe₂O₃'s 92% at 35 g L⁻¹. Advanced photo-Fenton system 3, combining both catalysts, showed exceptional performance with >98% removal of high-molecular-weight PAHs. The synergistic effect is proposed to arise from enhanced radical generation through both hydroxyl (˙OH) and sulfate radical (SO₄˙⁻) pathways, based on an established mechanistic precedent for Fe(III)-sulfate photochemistry. Neural network models successfully predicted PAH removal with R² > 0.91, while hierarchical clustering revealed distinct contaminant groupings. Treatment efficiency was primarily governed by EDTA concentration and Fe²⁺/H₂O₂ ratio, with degradation mechanisms varying based on PAH structure. This AI-driven optimization provides an efficient framework for soil remediation in petroleum-contaminated desert environments where traditional methods face significant challenges.