Due to its complex composition and high concentration of persistent organic pollutants, petroleum refinery wastewater (PRW) is challenging to treat with a single traditional approach. Combining many cutting-edge treatment techniques is often required to meet national discharge or reuse regulations. The challenge lies in effectively removing recalcitrant hydrocarbons while maintaining operational feasibility and environmental compliance. In this work, the treatment of PRW was performed by the photo Fenton oxidation process through a uniquely designed pilot plant using a digital baffled photo batch reactor (DBPBR), focusing on the removal of organic pollutants in the wastewater of south Iraqi refineries. The Box–Behnken design (BBD) was applied to assess both the individual and interactive effects of four vital operating variables oxidation time, pH, hydrogen peroxide concentration, and ferrous ion concentration. The findings showed that removal efficiencies ranged from 65.9 to 98.6% for photo Fenton oxidation and from 55.9 to 88.5% for Fenton oxidation. These results suggest that the DBPBR significantly enhanced pollutant degradation efficiency, primarily due to UV-assisted acceleration of hydroxyl radical generation and improved mixing and light distribution enabled by the reactor’s baffle design. Of all the factors that were evaluated, the concentration of H2O2 had the greatest effect on the degradation efficiency, followed by pH, Fe2+ concentration, and oxidation time. For the kinetic analysis, both pseudo-first-order and pseudo-second-order models gave strong correlation coefficients. This implies that the saturation-like behavior that mimics site-limited reactions or the dynamics of the interaction between the reactive species and hydrocarbons may have an impact on the reaction rate. The neural network model developed in this study demonstrated excellent correlation coefficients (R2) of 0.9998 and minimal differences between the predicted and actual rates of hydrocarbon degradation supported by a low mean squared error (MSE) of <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$2.4\times {10}^{-2}$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>2.4</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>-</mo> <mn>2</mn> </mrow> </msup> </mrow> </math></EquationSource> </InlineEquation> (based on actual, not normalized data).

Hydrocarbon removal from refinery wastewater by photo-Fenton oxidation process: a new digital baffled photo batch reactor

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发表日期：

2025年11月22日

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M. A. Shihab,
O. I. Farhan,
M. I. Fathi,
A. A. Hassan,
A. T. Nawaf,
J. I. Humadi

摘要

Due to its complex composition and high concentration of persistent organic pollutants, petroleum refinery wastewater (PRW) is challenging to treat with a single traditional approach. Combining many cutting-edge treatment techniques is often required to meet national discharge or reuse regulations. The challenge lies in effectively removing recalcitrant hydrocarbons while maintaining operational feasibility and environmental compliance. In this work, the treatment of PRW was performed by the photo Fenton oxidation process through a uniquely designed pilot plant using a digital baffled photo batch reactor (DBPBR), focusing on the removal of organic pollutants in the wastewater of south Iraqi refineries. The Box–Behnken design (BBD) was applied to assess both the individual and interactive effects of four vital operating variables oxidation time, pH, hydrogen peroxide concentration, and ferrous ion concentration. The findings showed that removal efficiencies ranged from 65.9 to 98.6% for photo Fenton oxidation and from 55.9 to 88.5% for Fenton oxidation. These results suggest that the DBPBR significantly enhanced pollutant degradation efficiency, primarily due to UV-assisted acceleration of hydroxyl radical generation and improved mixing and light distribution enabled by the reactor’s baffle design. Of all the factors that were evaluated, the concentration of H₂O₂ had the greatest effect on the degradation efficiency, followed by pH, Fe²⁺ concentration, and oxidation time. For the kinetic analysis, both pseudo-first-order and pseudo-second-order models gave strong correlation coefficients. This implies that the saturation-like behavior that mimics site-limited reactions or the dynamics of the interaction between the reactive species and hydrocarbons may have an impact on the reaction rate. The neural network model developed in this study demonstrated excellent correlation coefficients (R²) of 0.9998 and minimal differences between the predicted and actual rates of hydrocarbon degradation supported by a low mean squared error (MSE) of $2.4\times {10}^{-2}$ $2.4 \times 10^{- 2}$ (based on actual, not normalized data).