Machine learning prediction of biochar yield and carbon contents in biochar based on biomass characteristics and pyrolysis conditions. (September 2019) - Zhu, Xinzhe
Graphical abstract: Highlights: The pyrolysis process of lignocellulosic biomass was modeled by machine learning. Random forest showed good prediction ability for biochar yield and carbon contents. Comprehensive and appropriate inputs were critical to predict the target outputs. Pyrolysis temperature was most significant for the change of both yield and carbon contents. Analysis of Partial Dependence Plot provided inside information for pyrolysis process. Abstract: In the study, machine learning was used to develop prediction models for yield and carbon contents of biochar (C-char) based on the pyrolysis data of lignocellulosic biomass, and explore inside information underlying the models. The results suggested that random forest could accurately predict biochar yield and C-char according to biomass characteristics and pyrolysis conditions. Furthermore, the relative contribution of pyrolysis conditions was higher than that of biomass characteristics for both yield (65%) and C-char (53%). For biomass characteristics, structural information was more important than elements compositions for accurately predicting biochar yield and it was inverse for C-char. The partial dependence plot analysis showed the impact way of each influential factor on the target variable and the interactions among these factors in the pyrolysis process. The present work provided new insights for understanding pyrolysis process of biomass and improving biochar yield and C-char.