Machine Learning-Driven Kinetic Optimization of Hydroxylamine-Modified Transition Metal Oxide/Peroxymonosulfate System for Antibiotic Degradation

Hydroxylamine-modified transition-metal oxides (HA-TMOs) represent a promising class of catalysts for activating peroxymonosulfate (PMS) to degrade antibiotics. However, identifying energy-efficient operational conditions remains challenging. This study established a comprehensive dataset encompassing 600 experimental records from both in-house experiments and literature and systematically compared 12 machine learning algorithms for predicting the antibiotic degradation efficiency (%) in hydroxylamine-modified transition metal oxide/peroxymonosulfate (HA-TMO/PMS) systems. Among these models, CatBoost delivered the best generalization (test-set R2 = 0.8110, RMSE = 8.92, MAE = 6.15) across repeated 80/20 stratified splits with 5-fold cross-validation, outperforming other ensembles as confirmed by cumulative distribution plots and error-metric analyses. Moreover, the permutation importance analysis identified PMS dosage, HA level, pH, initial pollutant concentration, and catalyst loading as the dominant drivers governing the pollutant removal performance. The partial-dependence plots, incorporating two-variable interactions, elucidated the response surfaces and enabled the discovery of operating windows that jointly maximize degradation efficiency and minimize electrical energy per order (EE/O). ML-guided optimization yielded optimal conditions, which were experimentally verified with sulfamethoxazole (SMZ). The HA-Co3O4/PMS system achieved the highest degradation rate constant (k = 0.11 min−1) and the lowest EE/O value (6.84 kWh·m−3·order−1), markedly improving kinetics and reducing energy consumption compared with non-optimized runs. This work establishes an interpretable ML framework that connects catalyst properties and reaction conditions to degradation kinetics and mechanisms, providing a practical strategy for the screening and scale-up of energy-efficient HA-TMOs/PMS-based advanced oxidation processes (AOPs).