Machine learning models for predicting biochar properties from lignocellulosic biomass torrefaction.
Bioresour Technol
; 399: 130519, 2024 May.
Article
in En
| MEDLINE
| ID: mdl-38437964
ABSTRACT
This study developed six machine learning models to predict the biochar properties from the dry torrefaction of lignocellulosic biomass by using biomass characteristics and torrefaction conditions as input variables. After optimization, gradient boosting machines were the optimal model, with the highest coefficient of determination ranging from 0.89 to 0.94. Torrefaction conditions exhibited a higher relative contribution to the yield and higher heating value (HHV) of biochar than biomass characteristics. Temperature was the dominant contributor to the elemental and proximate composition and the yield and HHV of biochar. Feature importance and SHapley Additive exPlanations revealed the effect of each influential factor on the target variables and the interactions between these factors in torrefaction. Software that can accurately predict the element, yield, and HHV of biochar was developed. These findings provide a comprehensive understanding of the key factors and their interactions influencing the torrefaction process and biochar properties.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Charcoal
/
Machine Learning
Language:
En
Journal:
Bioresour Technol
Journal subject:
ENGENHARIA BIOMEDICA
Year:
2024
Document type:
Article