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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:

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

Showing 1-25 of 30 citing articles:

The effect of pH on stability and thermal performance of graphene oxide and copper oxide hybrid nanofluids for heat transfer applications: Application of novel machine learning technique
Praveen Kumar Kanti, Prabhakar Sharma, K.V. Sharma, et al.
Journal of Energy Chemistry (2023) Vol. 82, pp. 359-374
Closed Access | Times Cited: 76
Experimental analysis of novel ionic liquid-MXene hybrid nanofluid's energy storage properties: Model-prediction using modern ensemble machine learning methods
Zafar Said, Prabhakar Sharma, Navid Aslfattahi, et al.
Journal of Energy Storage (2022) Vol. 52, pp. 104858-104858
Closed Access | Times Cited: 65
Modeling-optimization of performance and emission characteristics of dual-fuel engine powered with pilot diesel and agricultural-food waste-derived biogas
Zafar Said, Prabhakar Sharma, Bhaskor Jyoti Bora, et al.
International Journal of Hydrogen Energy (2022) Vol. 48, Iss. 18, pp. 6761-6777
Closed Access | Times Cited: 54
Prediction of harbour vessel fuel consumption based on machine learning approach
Zhong Shuo Chen, Jasmine Siu Lee Lam, Zengqi Xiao
Ocean Engineering (2023) Vol. 278, pp. 114483-114483
Closed Access | Times Cited: 33
A novel prediction model for ship fuel consumption considering shipping data privacy: An XGBoost-IGWO-LSTM-based personalized federated learning approach
Peixiu Han, Zhongbo Liu, Zhuo Sun, et al.
Ocean Engineering (2024) Vol. 302, pp. 117668-117668
Closed Access | Times Cited: 14
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
Prediction of methane hydrate formation conditions in salt water using machine learning algorithms
Hongfei Xu, Zeren Jiao, Zhuoran Zhang, et al.
Computers & Chemical Engineering (2021) Vol. 151, pp. 107358-107358
Closed Access | Times Cited: 45
A two-step strategy for fuel consumption prediction and optimization of ocean-going ships
Zhihui Hu, Tianrui Zhou, Zhen Rong, et al.
Ocean Engineering (2022) Vol. 249, pp. 110904-110904
Closed Access | Times Cited: 30
Mapping the Flammability Space of Sustainable Refrigerant Mixtures through an Artificial Neural Network Based on Molecular Descriptors
Carlos G. Albà, Ismail I.I. Alkhatib, Lourdes F. Vega, et al.
ACS Sustainable Chemistry & Engineering (2024) Vol. 12, Iss. 31, pp. 11561-11577
Open Access | Times Cited: 7
Post-combustion carbon capture for tank to propeller via process modeling and simulation
Chenxi Ji, Shuai Yuan, Mitchell Huffman, et al.
Journal of CO2 Utilization (2021) Vol. 51, pp. 101655-101655
Closed Access | Times Cited: 40
Can Artificial Intelligence and Machine Learning Be Used to Accelerate Sustainable Chemistry and Engineering?
Kevin C. Leonard, M. M. Faruque Hasan, Helen F. Sneddon, et al.
ACS Sustainable Chemistry & Engineering (2021) Vol. 9, Iss. 18, pp. 6126-6129
Closed Access | Times Cited: 31
Deep learning based quantitative property-consequence relationship (QPCR) models for toxic dispersion prediction
Zeren Jiao, Chenxi Ji, Yue Sun, et al.
Process Safety and Environmental Protection (2021) Vol. 152, pp. 352-360
Closed Access | Times Cited: 29
Machine learning analysis for the rheological mechanism of polysaccharide colloids
Xiaoman Liu, Hu Lei, Shilong Chen, et al.
Journal of Molecular Liquids (2025) Vol. 424, pp. 127093-127093
Closed Access
Carbon capture with pure monoethanolamine aerosols: Method development for assessing safety hazards and identifying a safe operating window using oxygen-containing flue gas streams
Annelot Van den Bogaert, Filip Verplaetsen, Maarten Vanierschot, et al.
Journal of Loss Prevention in the Process Industries (2025), pp. 105587-105587
Closed Access
Machine learning integration in thermodynamics: Predicting CO2 mixture saturation properties for sustainable refrigeration applications
Carlos G. Albà, Ismail I.I. Alkhatib, Lourdes F. Vega, et al.
Journal of CO2 Utilization (2025) Vol. 95, pp. 103072-103072
Open Access
Towards a new standard: procedure proposal for the determination of mist explosion characteristics
Stephanie El-Zahlanieh, Alexis Vignes, Olivier Dufaud
Process Safety and Environmental Protection (2025), pp. 107195-107195
Open Access
CFD simulation of turbulent diffusion and phase transition of methanol spray in a 20L spherical vessel
Feng Xie, Zhenyu Wang, Shucheng Guo, et al.
Journal of Loss Prevention in the Process Industries (2025), pp. 105656-105656
Closed Access
Correlation between concentration, droplet size, turbulent intensity and explosion parameters of gas-liquid two-phase mixture
Y.S. Jia, C. Zhang, Q. Zhang, et al.
Fire Safety Journal (2023) Vol. 136, pp. 103731-103731
Closed Access | Times Cited: 9
Modeling and estimation of water activity for the ionic-liquid-based aqueous ternary systems by smart paradigms
Ehsan Davoudi, Abolhasan Ameri
Journal of the Taiwan Institute of Chemical Engineers (2024) Vol. 157, pp. 105396-105396
Closed Access | Times Cited: 3
Optimizing the Model-prediction of Date Palm Fronds-Derived Producer Gas and Third Generation Biodiesel Powered Dual-fuel engine by Employing Bayesian-Optimized Boosted Regression Trees for Enhanced Prognostics
Mansoor Alruqi
Process Safety and Environmental Protection (2024) Vol. 187, pp. 1338-1353
Closed Access | Times Cited: 2
Estimation of bulk electrical conductivity in saline medium with contaminated lead solution through TDR coupled with machine learning
Won-Taek Hong, Jong‐Sub Lee, Dongsoo Lee, et al.
Process Safety and Environmental Protection (2022) Vol. 161, pp. 58-66
Closed Access | Times Cited: 9
Power Prediction of a 15,000 TEU Containership: Deep-Learning Algorithm Compared to a Physical Model
Alessandro La Ferlita, Yan Qi, Emanuel Di Nardo, et al.
Journal of Marine Science and Engineering (2023) Vol. 11, Iss. 10, pp. 1854-1854
Open Access | Times Cited: 5
Predicting Gas Hydrate Equilibria in Multicomponent Systems with a Machine Learning Approach
Qazi Nasir, Wameath S. Abdul‐Majeed, Humbul Suleman
Chemical Engineering & Technology (2023) Vol. 46, Iss. 9, pp. 1854-1867
Closed Access | Times Cited: 4
Effect of Temperatures on Flame Propagation of Diethyl Ether Spray Explosion
Xuhai Pan, Shucheng Guo, He Wang, et al.
Combustion Science and Technology (2024), pp. 1-26
Closed Access | Times Cited: 1
Flame propagation mechanism of methanol fuel spray explosion in a square closed vessel
Enhua Shi, Xingqing Yan, Liang He, et al.
Journal of Loss Prevention in the Process Industries (2024) Vol. 91, pp. 105408-105408
Closed Access | Times Cited: 1
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