Applications of artificial intelligence(AI)-and machine learning(ML)-based methodologies for predicting optimal conditions in sustainable and effective management of biowastes and natural resources are of great concer...Applications of artificial intelligence(AI)-and machine learning(ML)-based methodologies for predicting optimal conditions in sustainable and effective management of biowastes and natural resources are of great concern.However,the AI-applications for optimizing the hydrothermal treatment(HT)of organic solid biowastes and prediction of nutrients fate during the HT process have not yet been investigated.Therefore,this study explores the application of different ML models(e.g.,XGBoost,Decision Tree,and Random Forest)for optimizing HT of swine manure,focusing on the role of calcium(Ca)and iron(Fe)ions in phosphorus(P)distribution in the produced liquid and solid phases(hydrochar).Specifically,we investigated the fate of total P(TPS)in the hydrochar and inorganic P(IPL)in the liquid phase during HT.Experimental validation was conducted alongside the ML predictions,with XGBoost outperforming the other models,showing strong predictive accuracy for TPS(R^(2)=0.77)and IPL(R^(2)=1.0).Key factors influencing model accuracy included feedstock composition,reaction temperature,duration,solid–liquid ratio,and Ca and Fe concentrations.We found that the impact of time on TPS and IPL was minimal when the reaction time was less than 200 min,while pH showed a positive correlation with TPS and IPL.NMR and XRD analyses indicated that as the reaction severity increased,the organic P content in the hydrochar became more uniform.These findings highlight the potential of AI-based methodologies for optimizing HT processes,contributing to more sustainable and effective solutions for safe recycling,management,and development of bioresources.展开更多
基金sustained by a grant from the National Key Research and Development Program of China"Intergovernmental Cooperation in International Science and Technology Innovation"[Grant number 2023YFE0104700]the National Natural Science Foundation of China[Grant Number 31401944]funded by Taif University,Saudi Arabia through project number(TU-DSPP-2024-27),which is appreciated by the author Esmat F.Ali.
文摘Applications of artificial intelligence(AI)-and machine learning(ML)-based methodologies for predicting optimal conditions in sustainable and effective management of biowastes and natural resources are of great concern.However,the AI-applications for optimizing the hydrothermal treatment(HT)of organic solid biowastes and prediction of nutrients fate during the HT process have not yet been investigated.Therefore,this study explores the application of different ML models(e.g.,XGBoost,Decision Tree,and Random Forest)for optimizing HT of swine manure,focusing on the role of calcium(Ca)and iron(Fe)ions in phosphorus(P)distribution in the produced liquid and solid phases(hydrochar).Specifically,we investigated the fate of total P(TPS)in the hydrochar and inorganic P(IPL)in the liquid phase during HT.Experimental validation was conducted alongside the ML predictions,with XGBoost outperforming the other models,showing strong predictive accuracy for TPS(R^(2)=0.77)and IPL(R^(2)=1.0).Key factors influencing model accuracy included feedstock composition,reaction temperature,duration,solid–liquid ratio,and Ca and Fe concentrations.We found that the impact of time on TPS and IPL was minimal when the reaction time was less than 200 min,while pH showed a positive correlation with TPS and IPL.NMR and XRD analyses indicated that as the reaction severity increased,the organic P content in the hydrochar became more uniform.These findings highlight the potential of AI-based methodologies for optimizing HT processes,contributing to more sustainable and effective solutions for safe recycling,management,and development of bioresources.
