Biochar produced from pyrolysis of biomass has been developed as a platform carbonaceous material that can be used in various applications.The specific surface area(SSA)and functionalities such as N-containing functio...Biochar produced from pyrolysis of biomass has been developed as a platform carbonaceous material that can be used in various applications.The specific surface area(SSA)and functionalities such as N-containing functional groups of biochar are the most significant properties determining the application performance of biochar as a carbon material in various areas,such as removal of pollutants,adsorption of CO_(2)and H2,catalysis,and energy storage.Producing biochar with preferable SSA and N functional groups is among the frontiers to engineer biochar materials.This study attempted to build machine learning models to predict and optimize specific surface area of biochar(SSA-char),N content of biochar(N-char),and yield of biochar(Yield-char)individually or simultaneously,by using elemental,proximate,and biochemical compositions of biomass and pyrolysis conditions as input variables.The predictions of Yield-char,N-char,and SSA-char were compared by using random forest(RF)and gradient boosting regression(GBR)models.GBR outperformed RF for most predictions.When input parameters included elemental and proximate compositions as well as pyrolysis conditions,the test R^(2) values for the single-target and multi-target GBR models were 0.90-0.95 except for the two-target prediction of Yield-char and SSA-char which had a test R^(2) of 0.84 and the three-target prediction model which had a test R^(2) of 0.81.As indicated by the Pearson correlation coefficient between variables and the feature importance of these GBR models,the top influencing factors toward predicting three targets were specified as follows:pyrolysis temperature,residence time,and fixed carbon for Yield-char;N and ash for N-char;ash and pyrolysis temperature for SSA-char.The effects of these parameters on three targets were different,but the trade-offs of these three were balanced during multi-target ML prediction and optimization.The optimum solutions were then experimentally verified,which opens a new way for designing smart biochar with target properties and oriented application potential.展开更多
基金the National Key Research and Development Program of China(2021YFE0104900)the National Natural Science Foundation of China(51906247)+1 种基金Hunan Provincial Natural Science Foundation of China(2022JJ20064)the Science and Technology Innovation Program of Hunan Province(2021RC4005).
文摘Biochar produced from pyrolysis of biomass has been developed as a platform carbonaceous material that can be used in various applications.The specific surface area(SSA)and functionalities such as N-containing functional groups of biochar are the most significant properties determining the application performance of biochar as a carbon material in various areas,such as removal of pollutants,adsorption of CO_(2)and H2,catalysis,and energy storage.Producing biochar with preferable SSA and N functional groups is among the frontiers to engineer biochar materials.This study attempted to build machine learning models to predict and optimize specific surface area of biochar(SSA-char),N content of biochar(N-char),and yield of biochar(Yield-char)individually or simultaneously,by using elemental,proximate,and biochemical compositions of biomass and pyrolysis conditions as input variables.The predictions of Yield-char,N-char,and SSA-char were compared by using random forest(RF)and gradient boosting regression(GBR)models.GBR outperformed RF for most predictions.When input parameters included elemental and proximate compositions as well as pyrolysis conditions,the test R^(2) values for the single-target and multi-target GBR models were 0.90-0.95 except for the two-target prediction of Yield-char and SSA-char which had a test R^(2) of 0.84 and the three-target prediction model which had a test R^(2) of 0.81.As indicated by the Pearson correlation coefficient between variables and the feature importance of these GBR models,the top influencing factors toward predicting three targets were specified as follows:pyrolysis temperature,residence time,and fixed carbon for Yield-char;N and ash for N-char;ash and pyrolysis temperature for SSA-char.The effects of these parameters on three targets were different,but the trade-offs of these three were balanced during multi-target ML prediction and optimization.The optimum solutions were then experimentally verified,which opens a new way for designing smart biochar with target properties and oriented application potential.
