Heterogeneous Fenton process using iron-containing waste (ICW) for methyl orange degradation: process performance and modeling

Abstract

The feasibility of using iron-containing waste (ICW) as new low-cost heterogeneous Fenton catalyst for methyl orange (MO) degradation has been studied. Process modeling and simulation has been conducted using artificial neural network (ANN). Complete degradation of MO was achieved at 0.2 g/L ICW concentration, 24 mM H₂O₂ dose, and pH 2 in 30 min. A three-layered back-propagation neural network with tangent sigmoid transfer function (tansig) at hidden layer and linear transfer function (purelin) at output layer was used for modeling the process performance. ANN-predicted results are very close to the experimental results with a correlation coefficient (R²) of 0.961 and a mean squared error of 0.039. The sensitivity analysis showed that all studied variables (reaction time, ICW concentration, H₂O₂ dose, pH, and MO concentration) have strong effect on MO degradation. Among all studied variables, pH appeared to be the most influential input variable followed by ICW concentration.

Keywords:

• Artificial neural network (ANN)
• Modeling
• Iron-containing waste
• Heterogeneous Fenton
• Degradation
• Methyl orange