Riboflavin is a redox mediator that promotes volatile fatty acids(VFAs)production from waste activated sludge(WAS)and is a promising method for WAS reuse.However,time-and labor-consuming experiments challenge obtainin...Riboflavin is a redox mediator that promotes volatile fatty acids(VFAs)production from waste activated sludge(WAS)and is a promising method for WAS reuse.However,time-and labor-consuming experiments challenge obtaining optimal operating conditions for maximal VFA production.In this study,three machine learning(ML)models were developed to predict the VFAs production from riboflavin-mediated WAS fermentation systems.Among the three tested ML algorithms,eXtreme Gradient Boosting(XGBoost)presented the best prediction performance and excellent generalization ability,with the highest testing coefficient of determination(R^(2)of 0.93)and lowest root mean square error(RMSE of 0.070).Feature importance analysis and their interactions using the Shepley Additive Explanations(SHAP)method indicated that pH and soluble protein were the top two input features for the modeling.The intrinsic correlations between input features and microbial communities corroborated this deduction.On the optimized ML model,genetic algorithm(GA)and particle swarm optimization(PSO)solved the optimal solution of VFA output,predicting the maximum VFA output as 650 mg COD/g VSS.This study provided a data-driven approach to predict and optimize VFA production from riboflavin-mediated WAS fermentation.展开更多
基金supported by the National Key R&D Program of China(No.2022YFE0210700)the National College Students Innovation and Entrepreneurship Training Program in China(No.202210336059)+1 种基金the Key Research and Development Program of Zhejiang Province,China(No.2023C03134)the Zhejiang Provincial Ecological&Environmental Research Project and Application(No.2021HT0028).
文摘Riboflavin is a redox mediator that promotes volatile fatty acids(VFAs)production from waste activated sludge(WAS)and is a promising method for WAS reuse.However,time-and labor-consuming experiments challenge obtaining optimal operating conditions for maximal VFA production.In this study,three machine learning(ML)models were developed to predict the VFAs production from riboflavin-mediated WAS fermentation systems.Among the three tested ML algorithms,eXtreme Gradient Boosting(XGBoost)presented the best prediction performance and excellent generalization ability,with the highest testing coefficient of determination(R^(2)of 0.93)and lowest root mean square error(RMSE of 0.070).Feature importance analysis and their interactions using the Shepley Additive Explanations(SHAP)method indicated that pH and soluble protein were the top two input features for the modeling.The intrinsic correlations between input features and microbial communities corroborated this deduction.On the optimized ML model,genetic algorithm(GA)and particle swarm optimization(PSO)solved the optimal solution of VFA output,predicting the maximum VFA output as 650 mg COD/g VSS.This study provided a data-driven approach to predict and optimize VFA production from riboflavin-mediated WAS fermentation.
