A novel elemental composition based prediction model for biochar aromaticity derived from machine learning

A novel elemental composition based prediction model for biochar aromaticity derived from machine learning

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 research-intensive universities. Mathematical modelling could be a viable alternative to predi...

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Bibliographic Details
Main Authors: Hongliang Cao, Yaime Jefferson Milan, Sohrab Haghighi Mood, Michael Ayiania, Shu Zhang, Xuzhong Gong, Electo Eduardo Silva Lora, Qiaoxia Yuan, Manuel Garcia-Perez
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2021-01-01
Series:Artificial Intelligence in Agriculture
Subjects:
Biochar
C aromaticity
Prediction model
Machine learning
Genetic programming
Online Access:http://www.sciencedirect.com/science/article/pii/S2589721721000210

Internet

http://www.sciencedirect.com/science/article/pii/S2589721721000210

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