Performance study of basic violet 16 degradation by glow discharge electrolysis plasma based on machine learning
This study aimed to quantitatively investigate the factors influencing the degradation efficiency of Basic Violet 16(BV16)by Glow Discharge Electrolysis Plasma(GDEP)and to enhance its degradation performance.A dataset was constructed from 462 experimental data points,and 9 regression models were trained and evaluated.The integrated learning models based on the Gradient Boosting Decision Tree(GBDT)demonstrated superior predictive performance,with the model trained using the Categorical Boosting(CatBoost)algorithm exhibiting the highest performance(R2=0.988,MAE=2.050％).The SHapley Additive exPlanation(SHAP)interpretation method was employed to quantitatively analyze the impact of parameters in the optimal model.The quantitative weight ranking results indicated that reaction time(43.74％),initial pollutant concentration(23.00％),KC1 concentration(15.65％),and average current(12.63％)were the most significant factors influencing BV16 degradation.Furthermore,Partial Dependence Plot(PDP)analysis was utilized to propose an optimization scheme for parameter interactions.The CatBoost-SHAP-PDP model facilitated the simulation and prediction of BV16 degradation by GDEP and provided an effective method for optimizing the variables in the GDEP process.This research offers a scientific foundation and technical support for modeling and application in the field of complex dye wastewater treatment by GDEP.
万方数据
维普
