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

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. TheR2of the BPNN model was 0.99852, and the data were distributed aroundy=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