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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 hybrid data-driven and mechanistic modelling approach for hydrothermal gasification
Jie Li, Manu Suvarna, Lanjia Pan, et al.
Applied Energy (2021) Vol. 304, pp. 117674-117674
Open Access | Times Cited: 73

Showing 1-25 of 73 citing articles:

Artificial intelligence for waste management in smart cities: a review
Bingbing Fang, Jiacheng Yu, Zhonghao Chen, et al.
Environmental Chemistry Letters (2023) Vol. 21, Iss. 4, pp. 1959-1989
Open Access | Times Cited: 210
Machine learning predicts and optimizes hydrothermal liquefaction of biomass
Alireza Shafizadeh, Hossein Shahbeig, Mohammad Hossein Nadian, et al.
Chemical Engineering Journal (2022) Vol. 445, pp. 136579-136579
Closed Access | Times Cited: 142
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: 141
Machine learning prediction and optimization of bio-oil production from hydrothermal liquefaction of algae
Weijin Zhang, Jie Li, Tonggui Liu, et al.
Bioresource Technology (2021) Vol. 342, pp. 126011-126011
Closed Access | Times Cited: 129
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
Wet wastes to bioenergy and biochar: A critical review with future perspectives
Jie Li, Lanyu Li, Manu Suvarna, et al.
The Science of The Total Environment (2022) Vol. 817, pp. 152921-152921
Open Access | Times Cited: 87
Machine learning methods for modeling conventional and hydrothermal gasification of waste biomass: A review
Great C. Umenweke, Inioluwa Christianah Afolabi, Emmanuel I. Epelle, et al.
Bioresource Technology Reports (2022) Vol. 17, pp. 100976-100976
Closed Access | Times Cited: 86
Understanding and optimizing the gasification of biomass waste with machine learning
Jie Li, Lanyu Li, Yen Wah Tong, et al.
Green Chemical Engineering (2022) Vol. 4, Iss. 1, pp. 123-133
Open Access | Times Cited: 74
Recent advances and future prospects of thermochemical biofuel conversion processes with machine learning
Pil Rip Jeon, Jong-Ho Moon, Nafiu Olanrewaju Ogunsola, et al.
Chemical Engineering Journal (2023) Vol. 471, pp. 144503-144503
Open Access | Times Cited: 42
Waste tire valorization: Advanced technologies, process simulation, system optimization, and sustainability
Yusha Hu, Xiaoping Yu, Jingzheng Ren, et al.
The Science of The Total Environment (2024) Vol. 942, pp. 173561-173561
Closed Access | Times Cited: 20
Active Learning-Based Guided Synthesis of Engineered Biochar for CO2 Capture
Xiangzhou Yuan, Manu Suvarna, Juin Yau Lim, et al.
Environmental Science & Technology (2024) Vol. 58, Iss. 15, pp. 6628-6636
Open Access | Times Cited: 18
Enhancing hydrogen production prediction from biomass gasification via data augmentation and explainable AI: A comparative analysis
Chiagoziem C. Ukwuoma, Dongsheng Cai, Anto Leoba Jonathan, et al.
International Journal of Hydrogen Energy (2024) Vol. 68, pp. 755-776
Closed Access | Times Cited: 16
A comprehensive artificial neural network model for gasification process prediction
Simon Ascher, William T. Sloan, Ian Watson, et al.
Applied Energy (2022) Vol. 320, pp. 119289-119289
Open Access | Times Cited: 66
Interpretable machine learning to model biomass and waste gasification
Simon Ascher, Xiaonan Wang, Ian Watson, et al.
Bioresource Technology (2022) Vol. 364, pp. 128062-128062
Open Access | Times Cited: 64
Predicting biodiesel properties and its optimal fatty acid profile via explainable machine learning
Manu Suvarna, M.I. Jahirul, Wai Hung Aaron-Yeap, et al.
Renewable Energy (2022) Vol. 189, pp. 245-258
Closed Access | Times Cited: 44
A review of computational modeling techniques for wet waste valorization: Research trends and future perspectives
Jie Li, Manu Suvarna, Lanyu Li, et al.
Journal of Cleaner Production (2022) Vol. 367, pp. 133025-133025
Closed Access | Times Cited: 44
An integrated framework of data-driven, metaheuristic, and mechanistic modeling approach for biomass pyrolysis
Zahid Ullah, Muzammil Khan, Salman Raza Naqvi, et al.
Process Safety and Environmental Protection (2022) Vol. 162, pp. 337-345
Closed Access | Times Cited: 42
Hydrogen yield prediction for supercritical water gasification based on generative adversarial network data augmentation
Zherui Ma, Jiangjiang Wang, Yingsong Feng, et al.
Applied Energy (2023) Vol. 336, pp. 120814-120814
Closed Access | Times Cited: 31
A hybrid data-driven and metaheuristic optimization approach for the compressive strength prediction of high-performance concrete
Muhammad Imran, Rao Arsalan Khushnood, Muhammad Fawad
Case Studies in Construction Materials (2023) Vol. 18, pp. e01890-e01890
Open Access | Times Cited: 28
Co-valorisation of sewage sludge and poultry litter waste for hydrogen production: Gasification process design, sustainability-oriented optimization, and systematic assessment
Tao Shi, Jianzhao Zhou, Jingzheng Ren, et al.
Energy (2023) Vol. 272, pp. 127131-127131
Closed Access | Times Cited: 28
Recent advances in plastic waste pyrolysis for liquid fuel production: Critical factors and machine learning applications
Jie Li, Di Yu, Lanjia Pan, et al.
Applied Energy (2023) Vol. 346, pp. 121350-121350
Closed Access | Times Cited: 28
A hybrid data-driven machine learning framework for predicting the performance of coal and biomass gasification processes
Qingchun Yang, Jinliang Zhang, Jianlong Zhou, et al.
Fuel (2023) Vol. 346, pp. 128338-128338
Closed Access | Times Cited: 26
Applications of machine learning in supercritical fluids research
Lucien Roach, Gian‐Marco Rignanese, Arnaud Erriguible, et al.
The Journal of Supercritical Fluids (2023) Vol. 202, pp. 106051-106051
Open Access | Times Cited: 24
Optimization and prediction of catalysts for precise synthesis of methyl glycolate from dimethyl oxalate using machine learning coupled with particle swarm optimization algorithm
Qingchun Yang, Zhou Jianlong, Runjie Bao, et al.
Chemical Engineering Science (2024) Vol. 297, pp. 120295-120295
Closed Access | Times Cited: 14
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