环境工程学报2026，Vol.20Issue(2)：453-461,9.DOI:10.12030/j.cjee.202506061
基于机器学习的抗生素对厌氧氨氧化脱氮抑制效应分析
Analysis of the inhibitory effect of antibiotics on anammox nitrogen removal based on machine learning
摘要
Abstract
Anaerobic ammonium oxidation(anammox),as a novel nitrogen removal process,is widely used for treating high-ammonia nitrogen wastewater.However,antibiotics present in wastewater can inhibit microbial activity and reduce nitrogen removal efficiency.Investigating the mechanism by which antibiotics inhibit nitrogen removal efficiency has become key to improving the process.Therefore,this study developed predictive models for nitrogen removal efficiency under antibiotic inhibition using four nonlinear algorithms:Categorical Boosting(CatBoost),Extreme Gradient Boosting(XGB),Random Forest(RF),and K-Nearest Neighbors(KNN).All four machine learning models accurately predicted nitrogen removal efficiency(R2>0.95),with the XGB model showing better goodness-of-fit(adjusted R2adj=0.992)and the CatBoost model demonstrating superior predictive ability(external Q2ext=0.947).In addition,SHAP(SHapley Additive exPlanations)values were used to interpret the models,revealing that hydraulic retention time,influent nitrogen concentration,and antibiotic concentration are the main factors influencing nitrogen removal efficiency.This study also analyzed the mechanisms through which these factors affect nitrogen removal efficiency,providing a theoretical basis for improving nitrogen removal efficiency and optimizing the operational parameters of the anammox process.关键词
抗生素/厌氧氨氧化/脱氮效率/机器学习/模型预测性能Key words
antibiotics/anaerobic ammonium oxidation/nitrogen removal efficiency/machine learning/model prediction performance分类
资源环境引用本文复制引用
朱腾义,杨井路,赵维嘉,李书音,古黎明,李懿,陶翠翠,鄢碧鹏..基于机器学习的抗生素对厌氧氨氧化脱氮抑制效应分析[J].环境工程学报,2026,20(2):453-461,9.基金项目
国家自然科学基金资助项目(42077331) （42077331）
