TY - JOUR
T1 - Machine learning application for predicting key properties of activated carbon produced from lignocellulosic biomass waste with chemical activation
AU - Zou, Rongge
AU - Yang, Zhibin
AU - Zhang, Jiahui
AU - Lei, Ryan
AU - Zhang, William
AU - Fnu, Fitria
AU - Tsang, Daniel C.W.
AU - Heyne, Joshua
AU - Zhang, Xiao
AU - Ruan, Roger
AU - Lei, Hanwu
N1 - Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2024/5
Y1 - 2024/5
N2 - The successful application of gradient boosting regression (GBR) in machine learning to forecast surface area, pore volume, and yield in biomass-derived activated carbon (AC) production underscores its potential for enhancing manufacturing processes. The GBR model, collecting 17 independent variables for two-step activation (2-SA) and 14 for one-step activation (1-SA), demonstrates effectiveness across three datasets—1-SA, 2-SA, and a combined dataset. Notably, in 1-SA, the GBR model yields R2 values of 0.76, 0.90, and 0.83 for TPV, yield, and SSA respectively, and records R2 of 0.90 and 0.91 for yield in 2-SA and combined datasets. The model highlights the significance of the soaking procedure alongside activation temperature in shaping AC properties with 1-SA or 2-SA, illustrating machine learning's potential in optimizing AC production processes.
AB - The successful application of gradient boosting regression (GBR) in machine learning to forecast surface area, pore volume, and yield in biomass-derived activated carbon (AC) production underscores its potential for enhancing manufacturing processes. The GBR model, collecting 17 independent variables for two-step activation (2-SA) and 14 for one-step activation (1-SA), demonstrates effectiveness across three datasets—1-SA, 2-SA, and a combined dataset. Notably, in 1-SA, the GBR model yields R2 values of 0.76, 0.90, and 0.83 for TPV, yield, and SSA respectively, and records R2 of 0.90 and 0.91 for yield in 2-SA and combined datasets. The model highlights the significance of the soaking procedure alongside activation temperature in shaping AC properties with 1-SA or 2-SA, illustrating machine learning's potential in optimizing AC production processes.
KW - Activated biochar
KW - Environmental remediation
KW - Machine learning
KW - Surface area
KW - Sustainable waste management
KW - Total pore volume
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U2 - 10.1016/j.biortech.2024.130624
DO - 10.1016/j.biortech.2024.130624
M3 - Article
C2 - 38521172
AN - SCOPUS:85189558909
SN - 0960-8524
VL - 399
JO - Bioresource Technology
JF - Bioresource Technology
M1 - 130624
ER -