TY - JOUR
T1 - Multi-task prediction and optimization of hydrochar properties from high-moisture municipal solid waste
T2 - Application of machine learning on waste-to-resource
AU - Li, Jie
AU - Zhu, Xinzhe
AU - Li, Yinan
AU - Tong, Yen Wah
AU - Ok, Yong Sik
AU - Wang, Xiaonan
N1 - Funding Information:
This work was supported by the National Research Foundation , Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) program (Grant Number R-706-000-103-281 and R-706-001-102-281 ). The authors acknowledge the Singapore RIE2020 Advanced Manufacturing and Engineering (AME) Programmatic grant “Accelerated Materials Development for Manufacturing” by the Agency for Science, Technology and Research under Grant No. A1898b0043 and the IAF-PP grant “Cyber-physical production system (CPPS) towards contextual and intelligent response” by the Agency for Science, Technology and Research under Grant No. A19C1a0018 .
Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2021/1/1
Y1 - 2021/1/1
N2 - Hydrothermal carbonization (HTC) is a promising technology for valuable resources recovery from high-moisture wastes without pre-drying, while optimization of operational conditions for desired products preparation through experiments is always energy and time consuming. To accelerate the experiments in an efficient, sustainable, and economic way, machine learning (ML) tools were employed for bridging the inputs and outputs, which can realize the prediction of hydrochar properties, and development of ML-based optimization for achieving desired hydrochar. The results showed that deep neural network (DNN) model was the best one for joint prediction of both fuel properties (FP) and carbon capture and storage (CCS) stability of hydrochar with an average R2 and root mean squared error (RMSE) of 0.91 and 3.29. The average testing prediction errors for all the targets were below 20%, furtherly within 10% for HHV, carbon content and H/C predictions. ML-based feature analysis unveiled that both elementary composition and temperature were crucial to FP and CCS. Furthermore, a ML-based software interface was provided for practitioners and researchers to freely access. The insights and Pareto solution provided from ML-based multi-objective optimization benefitted desired hydrochar preparation for the potential application of fuel substitution or carbon sequestration in soil.
AB - Hydrothermal carbonization (HTC) is a promising technology for valuable resources recovery from high-moisture wastes without pre-drying, while optimization of operational conditions for desired products preparation through experiments is always energy and time consuming. To accelerate the experiments in an efficient, sustainable, and economic way, machine learning (ML) tools were employed for bridging the inputs and outputs, which can realize the prediction of hydrochar properties, and development of ML-based optimization for achieving desired hydrochar. The results showed that deep neural network (DNN) model was the best one for joint prediction of both fuel properties (FP) and carbon capture and storage (CCS) stability of hydrochar with an average R2 and root mean squared error (RMSE) of 0.91 and 3.29. The average testing prediction errors for all the targets were below 20%, furtherly within 10% for HHV, carbon content and H/C predictions. ML-based feature analysis unveiled that both elementary composition and temperature were crucial to FP and CCS. Furthermore, a ML-based software interface was provided for practitioners and researchers to freely access. The insights and Pareto solution provided from ML-based multi-objective optimization benefitted desired hydrochar preparation for the potential application of fuel substitution or carbon sequestration in soil.
KW - Biochar
KW - Carbon sequestration
KW - Hydrothermal carbonization
KW - Multi-objective optimization
KW - Renewable energy
KW - Waste-to-energy
UR - http://www.scopus.com/inward/record.url?scp=85090346771&partnerID=8YFLogxK
U2 - 10.1016/j.jclepro.2020.123928
DO - 10.1016/j.jclepro.2020.123928
M3 - Article
AN - SCOPUS:85090346771
SN - 0959-6526
VL - 278
JO - Journal of Cleaner Production
JF - Journal of Cleaner Production
M1 - 123928
ER -