Machine learning-driven optimization and application of bimetallic catalysts in peroxymonosulfate activation for degradation of fluoroquinolone antibiotics

Abstract

In this research, the XGBoost (XGB) and CatBoost (CB) algorithms were utilized to examine the experimental refinement and deployment of bimetallic catalysts for the activation of peroxymonosulfate in the removal of fluoroquinolone antibiotics from aqueous solutions. For the XGB model, shapley additive explanations analysis was utilized to provide model interpretability, identifying the features with significant impact on model prediction. By integrating multiple optimization techniques, such as particle swarm optimization and differential evolution, we successfully determined the optimal feature combination for the XGB model, providing accurate guidance for degradation experiments. Under the designed experimental conditions, the two sets of materials could degrade 93% of ciprofloxacin and 92% of norfloxacin within 30 and 27 min, respectively, with the discrepancies from model-predicted outcomes all within 3%. Concurrently, through electron paramagnetic resonance testing and quenching experiments, we explored the role of reactive oxygen species in the catalytic degradation process, with the experimental evidence demonstrating a 100% prediction accuracy for the CB model. The experimental results underscored the substantial potential of machine learning in the optimization design of environmental engineering.