Efficiency of Orange Yellow II Degradation by Synergistic Hydroxylamine with Fe²⁺ to Activate Peroxymonosulfate Oxidation: Machine Learning Prediction and Performance Optimization

Abstract

A back-propagation neural network (BPNN) was used to model and optimize the process of hydroxylamine (HA)-enhanced Fe2+ activating peroxymonosulfate (PMS). Using HA-enhanced Fe2+ to activate PMS is a cost-effective method to degrade orange II (AO7). We investigated the individual and interactive effects of the concentrations of Fe2+, HA, and PMS on the degradation of AO7. The R2 of the BPNN model was 0.99852, and the data were distributed around y = x. Sensitivity analysis showed that the relative importance of each factor was as follows: HA > Fe2+ > PMS. The optimized results obtained by the genetic algorithm were as follows: the concentration of Fe2+ was 35.33 μmol·L−1, HA was 0.46 mmol·L−1, and PMS was 0.93 mmol·L−1. Experiments verified that the AO7 degradation effect within 5 min was 95.7%, whereas the predicted value by the BPNN was 96.2%. The difference between predicted and experimental values is 0.5%. This study provides a new tool (machine learning) to accurately predict the concentrations of HA, Fe2+, and PMS to degrade AO7 under various conditions.

Keywords

• advanced oxidation process
• machine learning
• BPNN
• sensitivity analysis
• genetic algorithm