Biochar
(Nov 2022)
Machine learning predicting and engineering the yield, N content, and specific surface area of biochar derived from pyrolysis of biomass
Lijian Leng,
Lihong Yang,
Xinni Lei,
Weijin Zhang,
Zejian Ai,
Zequn Yang,
Hao Zhan,
Jianping Yang,
Xingzhong Yuan,
Haoyi Peng,
Hailong Li
Affiliations
Lijian Leng
School of Energy Science and Engineering, Central South University
Lihong Yang
School of Energy Science and Engineering, Central South University
Xinni Lei
School of Energy Science and Engineering, Central South University
Weijin Zhang
School of Energy Science and Engineering, Central South University
Zejian Ai
School of Energy Science and Engineering, Central South University
Zequn Yang
School of Energy Science and Engineering, Central South University
Hao Zhan
School of Energy Science and Engineering, Central South University
Jianping Yang
School of Energy Science and Engineering, Central South University
Xingzhong Yuan
College of Environmental Science and Engineering, Hunan University
Haoyi Peng
School of Energy Science and Engineering, Central South University
Hailong Li
School of Energy Science and Engineering, Central South University
DOI
https://doi.org/10.1007/s42773-022-00183-w
Journal volume & issue
Vol. 4,
no. 1
Abstract
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Highlights Yield, N content, and specific surface area of biochar predicted by machine learning Gradient boosting regression outperformed random forest, with test R2 of 0.81–0.95 Temperature, nitrogen, and ash were top features for predicting the three targets The yield and properties of biochar were engineered and experimentally verified
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