Automated Machine Learning-Aided Prediction and Interpretation of Gaseous By-Products from the Hydrothermal Liquefaction of Biomass DOI
Weijin Zhang,

Zejian Ai,

Qingyue Chen

et al.

Published: Jan. 1, 2024

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Language: Английский

Machine learning prediction of biochar physicochemical properties based on biomass characteristics and pyrolysis conditions DOI

Yuanbo Song,

Zipeng Huang,

Mengyu Jin

et al.

Journal of Analytical and Applied Pyrolysis, Journal Year: 2024, Volume and Issue: 181, P. 106596 - 106596

Published: June 13, 2024

Language: Английский

Citations

13
Biochar design for antibiotics adsorption based on a hybrid machine-learning-based optimization framework DOI
Jie Li,

Lanjia Pan,

Yahui Huang

et al.

Separation and Purification Technology, Journal Year: 2024, Volume and Issue: 348, P. 127666 - 127666

Published: April 27, 2024

Language: Английский

Citations

12
Automated machine learning-aided prediction and interpretation of gaseous by-products from the hydrothermal liquefaction of biomass DOI
Weijin Zhang,

Zejian Ai,

Qingyue Chen

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 945, P. 173939 - 173939

Published: June 20, 2024

Language: Английский

Citations

5
Machine learning accelerated catalysts design for CO reduction: An interpretability and transferability analysis DOI
Yuhang Wang, Yaqin Zhang, Ninggui Ma

et al.

Journal of Material Science and Technology, Journal Year: 2024, Volume and Issue: 213, P. 14 - 23

Published: June 27, 2024

Language: Английский

Citations

4
Boosting the optimization strategy for the waste plastics pyrolysis engineering application: a machine learning multi-dimensional evaluation framework DOI
Xin Zhou, Jinqing Zhang, Zhibo Zhang

et al.

Journal of Cleaner Production, Journal Year: 2025, Volume and Issue: unknown, P. 144891 - 144891

Published: Jan. 1, 2025

Language: Английский

Citations

0
Machine learning technologies for digestate-derived hydrochar yields DOI
Wei Wang, Jo‐Shu Chang, Duu‐Jong Lee

et al.

Process Safety and Environmental Protection, Journal Year: 2025, Volume and Issue: unknown, P. 106905 - 106905

Published: Feb. 1, 2025

Language: Английский

Citations

0
Machine-learning-aided life cycle assessment and techno-economic analysis of hydrothermal liquefaction of sewage sludge for bio-oil production DOI

Junhui Zhou,

Jiefeng Chen,

Weijin Zhang

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135026 - 135026

Published: Feb. 1, 2025

Language: Английский

Citations

0
Recent advances in Biomass-Derived hydrochar for photocatalytic and electrocatalytic applications DOI

Xianglong Meng,

Xingqiang Liu, Debin Zeng

et al.

Chemical Engineering Science, Journal Year: 2025, Volume and Issue: unknown, P. 121435 - 121435

Published: Feb. 1, 2025

Language: Английский

Citations

0
Automated machine learning-assisted analysis of biomass catalytic pyrolysis for selective production of benzene, toluene, and xylene DOI
Zihang Zhang, Jinlong Liu, Weiming Yi

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135389 - 135389

Published: March 1, 2025

Language: Английский

Citations

0
A Review on Machine Learning-Aided Hydrothermal Liquefaction Based on Bibliometric Analysis DOI Creative Commons
Lili Qian,

Xu Zhang,

Xianguang Ma

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(21), P. 5254 - 5254

Published: Oct. 22, 2024

Hydrothermal liquefaction (HTL) is an effective biomass thermochemical conversion technology that can convert organic waste into energy products. However, the HTL process influenced by various complex factors such as operating conditions, feedstock properties, and reaction pathways. Machine learning (ML) methods utilize existing data to develop accurate models for predicting product yields which be used optimize operation conditions. This paper presents a bibliometric review on ML applications in from 2020 2024. CiteSpace, VOSviewer, Bibexcel were analyze seven key attributes: annual publication output, author co-authorship networks, country co-citation of references, journals, collaborating institutions, keyword co-occurrence well time zone maps timelines, identify development research. Through detailed analysis co-occurring keywords, this study aims frontiers, research gaps, trends field ML-aided HTL.

Language: Английский

Citations

3