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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:

A comprehensive study on estimating higher heating value of biomass from proximate and ultimate analysis with machine learning approaches
Jiangkuan Xing, Kun Luo, Haiou Wang, et al.
Energy (2019) Vol. 188, pp. 116077-116077
Closed Access | Times Cited: 152

Showing 1-25 of 152 citing articles:

Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage
Daniel Rangel-Martínez, K.D.P. Nigam, Luis Ricardez‐Sandoval
Process Safety and Environmental Protection (2021) Vol. 174, pp. 414-441
Closed Access | Times Cited: 176
A Review of Thermochemical Conversion of Waste Biomass to Biofuels
Shivangi Jha, Sonil Nanda, Bishnu Acharya, et al.
Energies (2022) Vol. 15, Iss. 17, pp. 6352-6352
Open Access | Times Cited: 138
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: 137
Machine learning aided bio-oil production with high energy recovery and low nitrogen content from hydrothermal liquefaction of biomass with experiment verification
Jie Li, Weijin Zhang, Tonggui Liu, et al.
Chemical Engineering Journal (2021) Vol. 425, pp. 130649-130649
Closed Access | Times Cited: 134
Machine learning prediction of pyrolytic gas yield and compositions with feature reduction methods: Effects of pyrolysis conditions and biomass characteristics
Qinghui Tang, Yingquan Chen, Haiping Yang, et al.
Bioresource Technology (2021) Vol. 339, pp. 125581-125581
Closed Access | Times Cited: 126
The role of machine learning to boost the bioenergy and biofuels conversion
Zheng‐Xin Wang, Xinggan Peng, Ao Xia, et al.
Bioresource Technology (2021) Vol. 343, pp. 126099-126099
Closed Access | Times Cited: 125
Interpretable machine learning for predicting and evaluating hydrogen production via supercritical water gasification of biomass
Sheng Zhao, Jian Li, Chao Chen, et al.
Journal of Cleaner Production (2021) Vol. 316, pp. 128244-128244
Closed Access | Times Cited: 114
Applications of artificial intelligence‐based modeling for bioenergy systems: A review
Mochen Liao, Yuan Yao
GCB Bioenergy (2021) Vol. 13, Iss. 5, pp. 774-802
Open Access | Times Cited: 103
An overview of biomass solid fuels: Biomass sources, processing methods, and morphological and microstructural properties
Segun E. Ibitoye, Rasheedat M. Mahamood, Tien‐Chien Jen, et al.
Journal of Bioresources and Bioproducts (2023) Vol. 8, Iss. 4, pp. 333-360
Open Access | Times Cited: 61
An initial analysis of the market potential of a wet ash waste stream from a pulp and paper mill in Newfoundland and Labrador
Ana Larissa Santiago Hansted, William James Newell, Kelly Anne Hawboldt
Industrial Crops and Products (2025) Vol. 225, pp. 120557-120557
Open Access | Times Cited: 3
Co-pyrolysis of biomass and plastic wastes and application of machine learning for modelling of the process: A comprehensive review
Deepak Bhushan, Sanjeevani Hooda, Prasenjit Mondal
Journal of the Energy Institute (2025) Vol. 119, pp. 101973-101973
Closed Access | Times Cited: 2
Prediction of Bio-oil Yield and Hydrogen Contents Based on Machine Learning Method: Effect of Biomass Compositions and Pyrolysis Conditions
Qinghui Tang, Yingquan Chen, Haiping Yang, et al.
Energy & Fuels (2020) Vol. 34, Iss. 9, pp. 11050-11060
Closed Access | Times Cited: 124
Critical review on technological advancements for effective waste management of municipal solid waste — Updates and way forward
Priya Prajapati, Sunita Varjani, Reeta Rani Singhania, et al.
Environmental Technology & Innovation (2021) Vol. 23, pp. 101749-101749
Closed Access | Times Cited: 85
Predicting co-pyrolysis of coal and biomass using machine learning approaches
Hao Wei, Kun Luo, Jiangkuan Xing, et al.
Fuel (2021) Vol. 310, pp. 122248-122248
Closed Access | Times Cited: 71
Kinetic and thermodynamic comparative study of quince bio-waste slow pyrolysis before and after sustainable recovery of pectin compounds
Rodrigo Torres-Sciancalepore, Anabel Fernandez, Daniela Asensio, et al.
Energy Conversion and Management (2021) Vol. 252, pp. 115076-115076
Closed Access | Times Cited: 69
Machine learning prediction of the yield and oxygen content of bio-oil via biomass characteristics and pyrolysis conditions
Ke Yang, Kai Wu, Huiyan Zhang
Energy (2022) Vol. 254, pp. 124320-124320
Closed Access | Times Cited: 64
Interpretable machine-learning model with a collaborative game approach to predict yields and higher heating value of torrefied biomass
Thossaporn Onsree, Nakorn Tippayawong, Santi Phithakkitnukoon, et al.
Energy (2022) Vol. 249, pp. 123676-123676
Closed Access | Times Cited: 54
Machine learning and circular bioeconomy: Building new resource efficiency from diverse waste streams
To‐Hung Tsui, Mark C.M. van Loosdrecht, Yanjun Dai, et al.
Bioresource Technology (2022) Vol. 369, pp. 128445-128445
Open Access | Times Cited: 53
A comparison of machine learning algorithms for estimation of higher heating values of biomass and fossil fuels from ultimate analysis
Havva Yaka, Mert Akın İnsel, Özgün Yücel, et al.
Fuel (2022) Vol. 320, pp. 123971-123971
Closed Access | Times Cited: 52
Predictability of higher heating value of biomass feedstocks via proximate and ultimate analyses – A comprehensive study of artificial neural network applications
Fatih Güleç, Direnc Pekaslan, Orla Williams, et al.
Fuel (2022) Vol. 320, pp. 123944-123944
Open Access | Times Cited: 48
Predicting and optimizing syngas production from fluidized bed biomass gasifiers: A machine learning approach
Jun Young Kim, Dongjae Kim, Zezhong John Li, et al.
Energy (2022) Vol. 263, pp. 125900-125900
Closed Access | Times Cited: 45
Characterization of lignocellulose biomass based on proximate, ultimate, structural composition, and thermal analysis
Anthony O. Onokwai, Emmanuel Ajisegiri, Imhade P. Okokpujie, et al.
Materials Today Proceedings (2022) Vol. 65, pp. 2156-2162
Open Access | Times Cited: 44
Process optimization of biomass gasification with a Monte Carlo approach and random forest algorithm
Yi Fang, Li Ma, Zhiyi Yao, et al.
Energy Conversion and Management (2022) Vol. 264, pp. 115734-115734
Open Access | Times Cited: 41
Influence of Biomass Composition and Microwave Pyrolysis Conditions on Biochar Yield and its Properties: a Machine Learning Approach
Mari Selvam S., P. Balasubramanian
BioEnergy Research (2022) Vol. 16, Iss. 1, pp. 138-150
Closed Access | Times Cited: 40
Machine learning approach for the prediction of biomass pyrolysis kinetics from preliminary analysis
Hemant Kumar Balsora, S. Kartik, Vivek Dua, et al.
Journal of environmental chemical engineering (2022) Vol. 10, Iss. 3, pp. 108025-108025
Open Access | Times Cited: 40
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