Application of advanced techniques and machine learning(ML)for designing and predicting the properties of engineered hydrochar/biochar is of great agro-environmental concern.Carbon(C)stability and phosphorus(P)availab...Application of advanced techniques and machine learning(ML)for designing and predicting the properties of engineered hydrochar/biochar is of great agro-environmental concern.Carbon(C)stability and phosphorus(P)availability in hydrochar(HC)are among the key limitations as they cannot be accurately predicted by traditional one-factor tests and might be overcome by engineering the pristine HC.Therefore,the aims of this study were(1)to determine the optimal production conditions of engineered swine manure HC with high C stability and P availability,and(2)to develop the best ML models to predict the properties of HC derived from different feedstocks.Pristine-(HC)and FeCl_(3)impregnated swine manure-derived HC(HC-Fe)were produced by hydrothermal carbonization under different pH(4,7,and 10),reaction temperature(180,220,and 260℃),and residence time(60,120,and 180 min)and characterized using thermo-gravimetric,microscopic,and spectroscopic analyses.Also,different ML algorithms were used to model and predict the hydrochar solid yield,properties,and nutrients content.FeCl_(3)impregnation increased Fe-phosphate content,while it reduced H/C and O/C ratios and hydroxyapatite P content,and therefore improved C stability and P availability in the HC-Fe as compared to HC,particularly under lower pH(4),temperature of 220℃,and at 120 min.The generalized additive ML model outperformed the other models for predicting the HC properties with a correlation coefficient of 0.86.The ML analysis showed that the most influential features on the hydrochar C stability were the H and O contents in the biomass,while P availability in HC was more dependent on the C,N and O contents in biomass.These results provided optimal production conditions for Fe-engineered manure hydrochar and identified the best performing ML model for predicting hydrochar properties.The main implication of this study is that it offers a high potential to improve the utilization of biowastes and produce biowastederived engineered hydrochar with high C stability and P availability on a large scale.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]The author Esmat F.Ali extends his appreciation to Taif University,Saudi Arabia for supporting this work through project number(TU-DSPP-2024-27).
文摘Application of advanced techniques and machine learning(ML)for designing and predicting the properties of engineered hydrochar/biochar is of great agro-environmental concern.Carbon(C)stability and phosphorus(P)availability in hydrochar(HC)are among the key limitations as they cannot be accurately predicted by traditional one-factor tests and might be overcome by engineering the pristine HC.Therefore,the aims of this study were(1)to determine the optimal production conditions of engineered swine manure HC with high C stability and P availability,and(2)to develop the best ML models to predict the properties of HC derived from different feedstocks.Pristine-(HC)and FeCl_(3)impregnated swine manure-derived HC(HC-Fe)were produced by hydrothermal carbonization under different pH(4,7,and 10),reaction temperature(180,220,and 260℃),and residence time(60,120,and 180 min)and characterized using thermo-gravimetric,microscopic,and spectroscopic analyses.Also,different ML algorithms were used to model and predict the hydrochar solid yield,properties,and nutrients content.FeCl_(3)impregnation increased Fe-phosphate content,while it reduced H/C and O/C ratios and hydroxyapatite P content,and therefore improved C stability and P availability in the HC-Fe as compared to HC,particularly under lower pH(4),temperature of 220℃,and at 120 min.The generalized additive ML model outperformed the other models for predicting the HC properties with a correlation coefficient of 0.86.The ML analysis showed that the most influential features on the hydrochar C stability were the H and O contents in the biomass,while P availability in HC was more dependent on the C,N and O contents in biomass.These results provided optimal production conditions for Fe-engineered manure hydrochar and identified the best performing ML model for predicting hydrochar properties.The main implication of this study is that it offers a high potential to improve the utilization of biowastes and produce biowastederived engineered hydrochar with high C stability and P availability on a large scale.
