Machine learning to guide the use of plasma technology for antibiotic degradation,Journal of Hazardous Materials

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Machine learning to guide the use of plasma technology for antibiotic degradation
Journal of Hazardous Materials ( IF 12.2 ) Pub Date : 2024-09-12 , DOI: 10.1016/j.jhazmat.2024.135787
Li Xue ₁ , Runyu Jing ₂ , Nanya Zhong ₃ , Xiaoyu Nie ₄ , Yitong Du ₄ , Jiesi Luo ₄ , Kama Huang ₃

Affiliation

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.

中文翻译：

机器学习指导使用血浆技术进行抗生素降解

抗生素被滥用并排放到环境水中，对生态系统构成持续的潜在威胁。利用等离子体的物理和化学效应去除抗生素已成为一种很有前途的废水处理技术。然而，反应器配置的复杂性和高成本对该技术的实际应用构成了重大限制。此外，评估抗生素的降解效率需要使用昂贵且复杂的测试仪器，以及耗时且劳动密集型的实验。本研究使用机器学习算法开发了一个通用模型来预测血浆系统去除抗生素的效率。在构建的 8 种机器学习算法中，集成模型 XGBoost 的预测精度最高，皮尔逊相关系数为 0.943。这种相关性表明预测的去除率与实验值之间存在很强的关系。此外，通过使用多模型堆叠方法，预测的准确性得到了提高。通过对模型行为的可解释分析，可以进一步定量评估影响等离子体过程效率的关键因素及其协同效应。预计这些结果将有助于设计高效的等离子体系统，减少对广泛实验筛选的需求，并改善去除抗生素污染的实际应用。这为等离子体技术的应用提供了信息丰富的视角，为新的环境研究问题开辟了道路。

更新日期：2024-09-12

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