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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Machine learning predicting wastewater properties of the aqueous phase derived from hydrothermal treatment of biomass
Lijian Leng, Weijin Zhang, Tonggui Liu, et al.
Bioresource Technology (2022) Vol. 358, pp. 127348-127348
Closed Access | Times Cited: 54

Showing 1-25 of 54 citing articles:

Applications of machine learning in thermochemical conversion of biomass-A review
Muzammil Khan, Salman Raza Naqvi, Zahid Ullah, et al.
Fuel (2022) Vol. 332, pp. 126055-126055
Closed Access | Times Cited: 138
Machine-learning-aided thermochemical treatment of biomass: a review
Hailong Li, Jiefeng Chen, Weijin Zhang, et al.
Biofuel Research Journal (2023) Vol. 10, Iss. 1, pp. 1786-1809
Open Access | Times Cited: 96
Machine learning for hydrothermal treatment of biomass: A review
Weijin Zhang, Qingyue Chen, Jiefeng Chen, et al.
Bioresource Technology (2022) Vol. 370, pp. 128547-128547
Closed Access | Times Cited: 88
Machine learning assisted predicting and engineering specific surface area and total pore volume of biochar
Hailong Li, Zejian Ai, Lihong Yang, et al.
Bioresource Technology (2022) Vol. 369, pp. 128417-128417
Closed Access | Times Cited: 75
Tree-based machine learning model for visualizing complex relationships between biochar properties and anaerobic digestion
Yi Zhang, Yijing Feng, Zhonghao Ren, et al.
Bioresource Technology (2023) Vol. 374, pp. 128746-128746
Closed Access | Times Cited: 47
Machine learning prediction of nitrogen heterocycles in bio-oil produced from hydrothermal liquefaction of biomass
Lijian Leng, Weijin Zhang, Qingyue Chen, et al.
Bioresource Technology (2022) Vol. 362, pp. 127791-127791
Closed Access | Times Cited: 44
Predicting municipal solid waste gasification using machine learning: A step toward sustainable regional planning
Yadong Yang, Hossein Shahbeik, Alireza Shafizadeh, et al.
Energy (2023) Vol. 278, pp. 127881-127881
Closed Access | Times Cited: 41
State-of-the-art and future directions of machine learning for biomass characterization and for sustainable biorefinery
Aditya Velidandi, Pradeep Kumar Gandam, Madhavi Latha Chinta, et al.
Journal of Energy Chemistry (2023) Vol. 81, pp. 42-63
Closed Access | Times Cited: 37
Machine learning-aided prediction of nitrogen heterocycles in bio-oil from the pyrolysis of biomass
Lijian Leng, Tanghao Li, Hao Zhan, et al.
Energy (2023) Vol. 278, pp. 127967-127967
Closed Access | Times Cited: 33
Machine learning-driven prediction and optimization of monoaromatic oil production from catalytic co-pyrolysis of biomass and plastic wastes
Dan Xu, Zihang Zhang, Zijian He, et al.
Fuel (2023) Vol. 350, pp. 128819-128819
Closed Access | Times Cited: 27
Recent advances in hydrothermal liquefaction of manure wastes into value-added products
Quan Liu, Ge Kong, Guanyu Zhang, et al.
Energy Conversion and Management (2023) Vol. 292, pp. 117392-117392
Closed Access | Times Cited: 26
Machine learning applications for biochar studies: A mini-review
Wei Wang, Jo‐Shu Chang, Duu‐Jong Lee
Bioresource Technology (2024) Vol. 394, pp. 130291-130291
Closed Access | Times Cited: 15
Tree-structured parzen estimator optimized-automated machine learning assisted by meta–analysis for predicting biochar–driven N2O mitigation effect in constructed wetlands
Bi–Ni Jiang, Yingying Zhang, Zhiyong Zhang, et al.
Journal of Environmental Management (2024) Vol. 354, pp. 120335-120335
Closed Access | Times Cited: 13
A novel intelligent system based on machine learning for hydrochar multi-target prediction from the hydrothermal carbonization of biomass
Weijin Zhang, Junhui Zhou, Qian Liu, et al.
Biochar (2024) Vol. 6, Iss. 1
Open Access | Times Cited: 13
Predicting co-liquefaction bio-oil of sewage sludge and algal biomass via machine learning with experimental optimization: Focus on yield, nitrogen content, and energy recovery rate
Tonggui Liu, Weijin Zhang, Donghai Xu, et al.
The Science of The Total Environment (2024) Vol. 920, pp. 170779-170779
Closed Access | Times Cited: 11
Machine learning for sustainable organic waste treatment: a critical review
Rohit Gupta, Zahra Hajabdollahi Ouderji, Uzma Uzma, et al.
npj Materials Sustainability (2024) Vol. 2, Iss. 1
Open Access | Times Cited: 11
Machine learning-assisted exploration for carbon neutrality potential of municipal sludge recycling via hydrothermal carbonization
Xinzhe Zhu, Bingyou Liu, Lianpeng Sun, et al.
Bioresource Technology (2022) Vol. 369, pp. 128454-128454
Closed Access | Times Cited: 35
Using artificial intelligence-based algorithms to identify critical fouling factors and predict fouling behavior in anaerobic membrane bioreactors
Chengxin Niu, Bin Li, Zhiwei Wang
Journal of Membrane Science (2023) Vol. 687, pp. 122076-122076
Closed Access | Times Cited: 17
Machine learning application for predicting key properties of activated carbon produced from lignocellulosic biomass waste with chemical activation
Rongge Zou, Zhibin Yang, Jiahui Zhang, et al.
Bioresource Technology (2024) Vol. 399, pp. 130624-130624
Closed Access | Times Cited: 7
Machine-learning-aided prediction and optimization of struvite recovery from synthetic wastewater
Lijian Leng, Bingyan Kang, Donghai Xu, et al.
Journal of Water Process Engineering (2024) Vol. 58, pp. 104896-104896
Closed Access | Times Cited: 6
The combined machine learning model SMOTER-GA-RF for methane yield prediction during anaerobic digestion of straw lignocellulose based on random forest regression
Zini Wang, Fuxing Wu, Ning Hao, et al.
Journal of Cleaner Production (2024) Vol. 466, pp. 142909-142909
Closed Access | Times Cited: 6
Plant-scale biogas production prediction based on multiple hybrid machine learning technique
Yi Zhang, Linhui Li, Zhonghao Ren, et al.
Bioresource Technology (2022) Vol. 363, pp. 127899-127899
Closed Access | Times Cited: 27
Advances in machine learning for high value-added applications of lignocellulosic biomass
Hanwen Ge, Jun Zheng, Huanfei Xu
Bioresource Technology (2022) Vol. 369, pp. 128481-128481
Closed Access | Times Cited: 26
Mathematical models application in optimization of hydrothermal liquefaction of biomass
Botian Hao, Donghai Xu, Ya Wei, et al.
Fuel Processing Technology (2023) Vol. 243, pp. 107673-107673
Closed Access | Times Cited: 14
Machine-learning-aided hydrochar production through hydrothermal carbonization of biomass by engineering operating parameters and/or biomass mixture recipes
Lijian Leng, Junhui Zhou, Weijin Zhang, et al.
Energy (2023) Vol. 288, pp. 129854-129854
Closed Access | Times Cited: 13
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