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

Machine Learning-Based Screening of Plant-Derived Saponins and Their Derivatives for Lipase Inhibitory Activity Using R-Group Contribution Values DOI
Jianjun Ding, Yulun Chen, Tao Jiang

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
Targeted conversion of cellulose and hemicellulose macromolecules in the phosphoric acid/acetone/water system: An exploration of machine learning evaluation and product prediction DOI

Yuhang Sun,

Qiong Wang, Zhitong Yao

et al.

International Journal of Biological Macromolecules, Journal Year: 2025, Volume and Issue: unknown, P. 141912 - 141912

Published: March 1, 2025

Language: Английский

Citations

0
Machine Learning-Assisted Prediction and Exploration of the Homogeneous Oxidation of Mercury in Coal Combustion Flue Gas DOI
Weijin Zhang,

Jiefeng Chen,

Guohai Huang

et al.

Environmental Science & Technology, Journal Year: 2025, Volume and Issue: unknown

Published: March 12, 2025

Mercury emission from coal combustion flue gas is a significant environmental concern due to its detrimental effects on ecosystems and human health. Elemental mercury (Hg0) the dominant species in hard immobilize. Therefore, it necessary comprehend reaction mechanisms of Hg0 oxidation, namely, oxidized (Hg2+), for immobilization. In spite extensive research homogeneous universal accurate prediction models unified explanations are lacking. this study, first time, quantitative were developed oxidation percentage with machine learning (ML) using compositions operating conditions as inputs. Gradient boosting regression showed optimal performance (test R2 ≥ 0.85). ML-aided feature analysis results exhibited that Cl2, HCl, Hg0, temperature, HBr top five critical factors affecting oxidation. Halogen promoted at temperatures around 250 °C, while SO2, quench rates not conducive High rate coefficients Hg/Cl Hg/Br reactions verified ML interpretive revealed major mechanisms. Models here may play important roles understanding optimizing Hg immobilization technologies.

Language: Английский

Citations

0
Machine Learning-Assisted Multi-Property Prediction and Sintering Mechanism Exploration of Mullite–Corundum Ceramics DOI Open Access
Qingyue Chen, Weijin Zhang, Xiaocheng Liang

et al.

Materials, Journal Year: 2025, Volume and Issue: 18(6), P. 1384 - 1384

Published: March 20, 2025

Mullite–corundum ceramics are pivotal in heat transfer pipelines and thermal energy storage systems due to their excellent mechanical properties, stability, chemical resistance. Establishing relationships mechanisms through traditional experiments is time-consuming labor-intensive. In this study, gradient boosting regression (GBR), random forest (RF), artificial neural network (ANN) models were developed predict essential properties such as apparent porosity, bulk density, water absorption, flexural strength of mullite–corundum ceramics. The GBR model (R2 0.91–0.95) outperformed the RF ANN 0.83–0.89 0.88–0.91, respectively) accuracy. Feature importance partial dependence analyses revealed that sintering temperature K2O (~0.25%) positively affected density while negatively influencing porosity absorption. Additionally, temperature, additives, Fe2O3 (optimal content ~5% 1%, related strength. This approach provided new insight into between feedstock compositions process parameters ceramic it explored possible involved.

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