The measurement of aromaticity in biochars is generally conducted using solid state 13C nuclear magnetic resonance spectroscopy,which is expensive,time-consuming,and only accessible in a small number of researchintens...The measurement of aromaticity in biochars is generally conducted using solid state 13C nuclear magnetic resonance spectroscopy,which is expensive,time-consuming,and only accessible in a small number of researchintensive universities.Mathematical modelling could be a viable alternative to predict biochar aromaticity from other much easier accessible parameters(e.g.elemental composition).In this research,Genetic Programming(GP),an advancedmachine learning method,is used to develop newpredictionmodels.In order to identify and evaluate the performance of prediction models,an experimental data set with 98 biochar samples collected from the literature was utilized.Due to the benefits of the intelligence iteration and learning of GP algorithm,a kind of underlying exponential relationship between the elemental compositions and the aromaticity of biochars is disclosed clearly.The exponential relationship is clearer and simpler than the polynomial mapping relationships implicated by Maroto-Valer,Mazumdar,and Mazumdar-Wang models.In this case,a novel exponential model is proposed for the prediction of biochar aromaticity.The proposed exponential model appears better prediction accuracy and generalization ability than existing polynomial models during the statistical parameter evaluation.展开更多
文摘The measurement of aromaticity in biochars is generally conducted using solid state 13C nuclear magnetic resonance spectroscopy,which is expensive,time-consuming,and only accessible in a small number of researchintensive universities.Mathematical modelling could be a viable alternative to predict biochar aromaticity from other much easier accessible parameters(e.g.elemental composition).In this research,Genetic Programming(GP),an advancedmachine learning method,is used to develop newpredictionmodels.In order to identify and evaluate the performance of prediction models,an experimental data set with 98 biochar samples collected from the literature was utilized.Due to the benefits of the intelligence iteration and learning of GP algorithm,a kind of underlying exponential relationship between the elemental compositions and the aromaticity of biochars is disclosed clearly.The exponential relationship is clearer and simpler than the polynomial mapping relationships implicated by Maroto-Valer,Mazumdar,and Mazumdar-Wang models.In this case,a novel exponential model is proposed for the prediction of biochar aromaticity.The proposed exponential model appears better prediction accuracy and generalization ability than existing polynomial models during the statistical parameter evaluation.
