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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:

Minimum Ignition Energy (MIE) prediction models for ignition sensitive fuels using machine learning methods
Purvali Chaudhari, Nilesh Ade, Lisa M. Pérez, et al.
Journal of Loss Prevention in the Process Industries (2020) Vol. 69, pp. 104343-104343
Closed Access | Times Cited: 14

Showing 14 citing articles:

Machine learning modeling using XGBoost and LightGBM for predicting the minimum ignition temperature of rice husk dust based on the synergistic effect of dispersion pressure and crushed brown rice
Jinglin Zhang, Gang Li, Zhenguo Du, et al.
Powder Technology (2025), pp. 120682-120682
Closed Access | Times Cited: 1
SVM, ANN, and PSF modelling approaches for prediction of iron dust minimum ignition temperature (MIT) based on the synergistic effect of dispersion pressure and concentration
Ushtar Arshad, Syed Ali Ammar Taqvi, Azizul Buang, et al.
Process Safety and Environmental Protection (2021) Vol. 152, pp. 375-390
Closed Access | Times Cited: 33
Synthesis of fluorescent chalcones, photophysical properties, quantitative structure-activity relationship and their biological application
Marco Mellado, Rafaela Sariego-Kluge, Franco Valdés-Navarro, et al.
Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (2023) Vol. 291, pp. 122332-122332
Closed Access | Times Cited: 15
Experimental and theoretical investigation of dry-water containing phosphoric acid for new fire suppressant
Rujia Fan, Yong Jiang, Haoran Jiang
Journal of Loss Prevention in the Process Industries (2021) Vol. 70, pp. 104399-104399
Closed Access | Times Cited: 32
Predicting flammability-leading properties for liquid aerosol safety via machine learning
Chenxi Ji, Shuai Yuan, Zeren Jiao, et al.
Process Safety and Environmental Protection (2021) Vol. 148, pp. 1357-1366
Closed Access | Times Cited: 30
Theoretical prediction model for minimum ignition energy of combustible gas mixtures
Zhongkang Su, Lijuan Liu, Kaiyuan Li, et al.
International Journal of Hydrogen Energy (2024) Vol. 69, pp. 103-112
Closed Access | Times Cited: 4
Yield and Properties Prediction Based on the Multicondition LSTM Model for the Solvent Deasphalting Process
Jian Long, Yifan Chen, Deng‐Ke Cao, et al.
ACS Omega (2023) Vol. 8, Iss. 6, pp. 5437-5450
Open Access | Times Cited: 6
Minimum ignition energy theoretical model for flammable gas based on flame propagation layer by layer
Dafang Li, Weifu Sun, Yangchaoyue Chen, et al.
Journal of Loss Prevention in the Process Industries (2023) Vol. 83, pp. 105086-105086
Closed Access | Times Cited: 6
A model for minimum ignition energy prediction of sugar dust clouds based on interactive orthogonal experiments and machine learning
Yuankun Zhong, Xiaoquan Li, Zhiwen Yang, et al.
Fire Safety Journal (2024) Vol. 144, pp. 104111-104111
Closed Access | Times Cited: 2
Minimum ignition energy of amino acids and their Fmocs
Vishaal Gopalakrishnan, Colson Johnson, Stanley P. Kolis, et al.
Journal of Loss Prevention in the Process Industries (2022) Vol. 77, pp. 104763-104763
Closed Access | Times Cited: 5
Functional group analysis and machine learning techniques for MIE prediction
Jhanvi Kevadiya, Colson Johnson, Purvali Chaudhari, et al.
Journal of Loss Prevention in the Process Industries (2024) Vol. 89, pp. 105289-105289
Closed Access
Prediction of minimum ignition energy for combustible dust using graph neural networks and SMILES data augmentation
Xiaobo Shen, Zhiwei Zhang, Yunsheng Ma, et al.
Powder Technology (2023) Vol. 429, pp. 118876-118876
Closed Access | Times Cited: 1
Modeling and assessment of accidental subsea gas leakage using a coupled computational fluid dynamics and machine learning approaches
Ahmed M. Ellethy, Ahmed S. Shehata, Ali I. Shehata, et al.
Proceedings of the Institution of Mechanical Engineers Part M Journal of Engineering for the Maritime Environment (2022) Vol. 237, Iss. 3, pp. 764-787
Closed Access | Times Cited: 2
Predicting the Fire and Explosion Properties of Early-Phase Active Pharmaceutical Ingredients
Antony Janes, Roy C. Flanagan, Andrew D. Payne, et al.
Organic Process Research & Development (2021) Vol. 25, Iss. 10, pp. 2277-2287
Closed Access | Times Cited: 2
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