Machine learning enabled prediction and process optimization of VFA production from riboflavin-mediated sludge fermentation  

作　　者：Weishuai Li Jingang Huang Zhuoer Shi Wei Han Ting Lü Yuanyuan Lin Jianfang Meng Xiaobing Xu Pingzhi Hou 

机构地区：[1]College of Materials and Environmental Engineering,Hangzhou Dianzi University,Hangzhou 310018,China [2]China-Austria Belt and Road Joint Laboratory on Artificial Intelligence and Advanced Manufacturing,Hangzhou Dianzi University,Hangzhou 310018,China [3]Zhejiang Province Environmental Engineering Co.Ltd.,Hangzhou 310012,China [4]M-U-T Maschinen-Umwelttechnik-Transportanlagen GmbH,Stockerau 2000,Austria

出　　处：《Frontiers of Environmental Science & Engineering》2023年第11期181-193,共13页环境科学与工程前沿（英文）

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);the Key Research and Development Program of Zhejiang Province,China(No.2023C03134);the Zhejiang Provincial Ecological&Environmental Research Project and Application(No.2021HT0028).

摘　　要：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.

关 键 词：Machine learning Volatile fatty acids RIBOFLAVIN Waste activated sludge eXtreme Gradient Boosting 

分 类 号：X70[环境科学与工程—环境工程]