Analyzing the trends and hotspots of biochar’s applications in agriculture, environment, and energy: a bibliometrics study for 2022 and 2023 DOI Creative Commons
Ping Wu,

Yingdong Fu,

Tony Vancov

et al.

Biochar, Journal Year: 2024, Volume and Issue: 6(1)

Published: Sept. 19, 2024

Language: Английский

Conversion of organic solid waste into energy and functional materials using biochar catalyst: Bibliometric analysis, research progress, and directions DOI Open Access
Honghong Lyu, Juin Yau Lim, Qianru Zhang

et al.

Applied Catalysis B Environment and Energy, Journal Year: 2023, Volume and Issue: 340, P. 123223 - 123223

Published: Sept. 1, 2023

Language: Английский

Citations

48
Machine learning applications for biochar studies: A mini-review DOI
Wei Wang, Jo‐Shu Chang, Duu‐Jong Lee

et al.

Bioresource Technology, Journal Year: 2024, Volume and Issue: 394, P. 130291 - 130291

Published: Jan. 4, 2024

Language: Английский

Citations

20
A machine learning assisted prediction of potential biochar and its applications in anaerobic digestion for valuable chemicals and energy recovery from organic waste DOI Creative Commons
Pengshuai Zhang, Tengyu Zhang, Jingxin Zhang

et al.

Carbon Neutrality, Journal Year: 2024, Volume and Issue: 3(1)

Published: Jan. 8, 2024

Abstract The utilization of biochar derived from biomass residue to enhance anaerobic digestion (AD) for bioenergy recovery offers a sustainable approach advance energy and mitigate climate change. However, conducting comprehensive research on the optimal conditions AD experiments with addition poses challenge due diverse experimental objectives. Machine learning (ML) has demonstrated its effectiveness in addressing this issue. Therefore, it is essential provide an overview current ML-optimized processes biochar-enhanced order facilitate more systematic ML tools. This review comprehensively examines material flow preparation impact comprehension reviewed optimize production process perspective. Specifically, summarizes application techniques, based artificial intelligence, predicting yield properties residues, as well their AD. Overall, analysis address challenges recovery. In future research, crucial tackle that hinder implementation pilot-scale reactors. It recommended further investigate correlation between physicochemical process. Additionally, enhancing role throughout entire holds promise achieving economically environmentally optimized efficiency. Graphical

Language: Английский

Citations

19
Machine learning in clarifying complex relationships: Biochar preparation procedures and capacitance characteristics DOI
Yuxuan Sun, Peihao Sun,

Jixiu Jia

et al.

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 485, P. 149975 - 149975

Published: Feb. 24, 2024

Language: Английский

Citations

18
Biochar production and its environmental applications: Recent developments and machine learning insights DOI

Kolli Venkata Supraja,

Himanshu Kachroo, Gayatri Viswanathan

et al.

Bioresource Technology, Journal Year: 2023, Volume and Issue: 387, P. 129634 - 129634

Published: Aug. 21, 2023

Language: Английский

Citations

29
Using automated machine learning techniques to explore key factors in anaerobic digestion: At the environmental factor, microorganisms and system levels DOI
Yi Zhang,

Zhangmu Jing,

Yijing Feng

et al.

Chemical Engineering Journal, Journal Year: 2023, Volume and Issue: 475, P. 146069 - 146069

Published: Sept. 15, 2023

Language: Английский

Citations

27
Explicable Machine Learning for Predicting High-Efficiency Lignocellulose Pretreatment Solvents Based on Kamlet–Taft and Polarity Parameters DOI
Hanwen Ge,

Yuekun Bai,

Rui Zhou

et al.

ACS Sustainable Chemistry & Engineering, Journal Year: 2024, Volume and Issue: 12(19), P. 7578 - 7590

Published: April 29, 2024

Incorporating density functional theory (DFT) and machine learning (ML) methodologies, an intrinsic relationship model was developed utilizing the Kamlet–Taft parameters polarity values of 104 deep eutectic solvents (DES). DES with high lignocellulosic pretreatment efficiency were expected to be screened through synergistic combination hydrogen bond acidity (α), basicity (β), polarization (Π*) molecular index (MPI). Partial least-squares (PLS) models a variety ML used predict cellulose retention delignification. The XGBoost has highest predictive performance R2 0.97 0.91, respectively. Feature importance analysis partial dependence explain variables based on model. showed that α, β, Π* MPI donor determined efficiency. among 4 is nonlinear, there are multiple extreme in different intervals. gave parameter range corresponding Based given this study, composition ratio can selected ensure at least 80% retained 50% lignin removed. Molecular simulation results these highly efficient often contain large number bonds polar groups.

Language: Английский

Citations

13
Machine learning application for predicting key properties of activated carbon produced from lignocellulosic biomass waste with chemical activation DOI Creative Commons
Rongge Zou, Zhibin Yang, Jiahui Zhang

et al.

Bioresource Technology, Journal Year: 2024, Volume and Issue: 399, P. 130624 - 130624

Published: March 21, 2024

Language: Английский

Citations

10
Rethinking the biochar impact on the anaerobic digestion of food waste in bench-scale digester: Spatial distribution and biogas production DOI
Jilei Zhang, He Liu, Jun-Yi Wu

et al.

Bioresource Technology, Journal Year: 2025, Volume and Issue: unknown, P. 132115 - 132115

Published: Jan. 1, 2025

Language: Английский

Citations

2
Machine learning-based analysis of microplastic-induced changes in anaerobic digestion parameters influencing methane yield DOI Creative Commons
Zhenghui Gao, Zongqiang Ren, Tianyi Cui

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 377, P. 124627 - 124627

Published: Feb. 23, 2025

Language: Английский

Citations

1