Machine learning to guide the use of plasma technology for antibiotic degradation

Antibiotics are misused and discharged into environmental water, posing a constant potential threat to the ecosystem. Utilising plasma's physical and chemical effects to remove antibiotics has emerged as a promising wastewater treatment technology. However, the complexity and high cost of reactor configurations represent significant limitations to the practical application of this technology. Furthermore, evaluating the degradation efficiency of antibiotics necessitates using costly and sophisticated testing instruments, coupled with time-consuming and labour-intensive experiments. The present study developed a generalised model using machine learning algorithms to predict the removal efficiency of antibiotics by a plasma system. Of the eight machine learning algorithms constructed, the ensemble model XGBoost exhibited the highest prediction accuracy, as indicated by a Pearson correlation coefficient of 0.943. This correlation indicates a strong relationship between the predicted removal rates and the experimental values. Moreover, the accuracy of the prediction was enhanced through the utilisation of a multi-model stacking approach. A further quantitative assessment of the key factors affecting the efficiency of the plasma process, and their synergistic effects, is provided by the interpretable analysis of the model's behaviour. It is anticipated that the results will facilitate the design of efficient plasma systems, reduce the need for extensive experimental screening, and improve practical applications in the removal of antibiotic contamination. This provides an informative view of the applications of plasma technology, opening the way for new environmental research questions.