Machine learning based prediction the photocatalytic degradation rate of organic water pollutants
To predict the photocatalytic degradation rate of organic pollutants,and investigate the structure-activity relationship between pollutant molecular structures and degradation rate,a ma-chine learning model based on molecular fingerprints was developed.The model utilized a dataset comprising 523 records of 81 organic pollutants,with MACCS molecular fingerprints of pollu-tants and five experimental conditions(irradiance,temperature,catalyst dosage,initial pollutant concentration,and pH value)serving as input features.Ten machine learning algorithms were employed for modeling purposes.The results demonstrated that the LightGBM algorithm exhibi-ted the highest performance(R2=0.909 4).Additionally,the contribution of each input feature to the degradation rate was evaluated using the SHAP framework,to elucidate the specific factors influencing the degradation rate.The analysis revealed that the inherent structural characteristics of the pollutants played a pivotal role in determining the rate.Moreover,the pollutants contai-ning halogen atoms,nitrogen atoms,and unsaturated carbon within their molecular structures exhibited the fastest degradation rate,whereas those containing ether bonds or carbonyl groups exhibited the slowest degradation rate.
万方数据
维普
