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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:

The state of art on the prediction of efficiency and modeling of the processes of pollutants removal based on machine learning
Nawal Taoufik, W. Boumya, Mounia Achak, et al.
The Science of The Total Environment (2021) Vol. 807, pp. 150554-150554
Closed Access | Times Cited: 105

Showing 1-25 of 105 citing articles:

A review of the application of machine learning in water quality evaluation
Mengyuan Zhu, Jiawei Wang, Yang Xiao, et al.
Eco-Environment & Health (2022) Vol. 1, Iss. 2, pp. 107-116
Open Access | Times Cited: 329
Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems
Nitin Kumar Singh, Manish Yadav, Vijai Singh, et al.
Bioresource Technology (2022) Vol. 369, pp. 128486-128486
Closed Access | Times Cited: 89
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: 81
Surface Modification of Biochar for Dye Removal from Wastewater
Lalit Goswami, Anamika Kushwaha, Saroj Raj Kafle, et al.
Catalysts (2022) Vol. 12, Iss. 8, pp. 817-817
Open Access | Times Cited: 70
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: 38
A Comprehensive Survey of Machine Learning Methodologies with Emphasis in Water Resources Management
Maria Drogkoula, Konstantinos Kokkinos, Nicholas Samaras
Applied Sciences (2023) Vol. 13, Iss. 22, pp. 12147-12147
Open Access | Times Cited: 36
Nanocomposite ceramic membranes as novel tools for remediation of textile dye waste water – A review of current applications, machine learning based modeling and future perspectives
Joynab Mohammed Solaiman, Natarajan Rajamohan, Mohammad Yusuf, et al.
Journal of environmental chemical engineering (2024) Vol. 12, Iss. 2, pp. 112353-112353
Closed Access | Times Cited: 14
A novel method integrating response surface method with artificial neural network to optimize membrane fabrication for wastewater treatment
Bowen Li, Yue Rong, Liguo Shen, et al.
Journal of Cleaner Production (2022) Vol. 376, pp. 134236-134236
Closed Access | Times Cited: 60
Comprehensive review on machine learning methodologies for modeling dye removal processes in wastewater
Suraj Kumar Bhagat, Karl Ezra Pilario, Olusola Emmanuel Babalola, et al.
Journal of Cleaner Production (2022) Vol. 385, pp. 135522-135522
Closed Access | Times Cited: 56
Removal and recovery of aqueous U(VI) by heterogeneous photocatalysis: Progress and challenges
Yin Ye, Jian Jin, Fan Chen, et al.
Chemical Engineering Journal (2022) Vol. 450, pp. 138317-138317
Closed Access | Times Cited: 51
Advanced insights on removal of antibiotics by microalgae-bacteria consortia: A state-of-the-art review and emerging prospects
Zeyuan Wang, Yuhao Chu, Haixing Chang, et al.
Chemosphere (2022) Vol. 307, pp. 136117-136117
Closed Access | Times Cited: 41
Recent trends of machine learning applied to multi-source data of medicinal plants
Yanying Zhang, Yuanzhong Wang
Journal of Pharmaceutical Analysis (2023) Vol. 13, Iss. 12, pp. 1388-1407
Open Access | Times Cited: 29
Machine Learning to Predict the Adsorption Capacity of Microplastics
Gonzalo Astray, Anton Soria-López, Enrique Barreiro, et al.
Nanomaterials (2023) Vol. 13, Iss. 6, pp. 1061-1061
Open Access | Times Cited: 22
MachIne learning for nutrient recovery in the smart city circular economy – A review
Allan Soo, Li Wang, Chen Wang, et al.
Process Safety and Environmental Protection (2023) Vol. 173, pp. 529-557
Closed Access | Times Cited: 21
Photocatalytic degradation of drugs and dyes using a maching learning approach
Ganesan Anandhi, M. Iyapparaja
RSC Advances (2024) Vol. 14, Iss. 13, pp. 9003-9019
Open Access | Times Cited: 10
Artificial intelligence methods for modeling gasification of waste biomass: a review
Fatma Alfarra, Hasan Özcan, Pınar Cihan, et al.
Environmental Monitoring and Assessment (2024) Vol. 196, Iss. 3
Closed Access | Times Cited: 7
Artificial intelligence interventions in 2D MXenes-based photocatalytic applications
Durga Madhab Mahapatra, Ashish Kumar, Rajesh Kumar, et al.
Coordination Chemistry Reviews (2025) Vol. 529, pp. 216460-216460
Closed Access
Forecasting energy consumption and enhancing sustainability in microbreweries: Integrating ANN-based models with thermal storage solutions
J.E. Conduah, K. Kusakana, O.Y. Odufuwa, et al.
Journal of Energy Storage (2025) Vol. 112, pp. 115508-115508
Open Access
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: 33
Assessing the potential of machine learning methods to study the removal of pharmaceuticals from wastewater using biochar or activated carbon
Jude A. Okolie, Shauna Savage, Chukwuma C. Ogbaga, et al.
Total Environment Research Themes (2022) Vol. 1-2, pp. 100001-100001
Open Access | Times Cited: 31
A systematic and critical review on development of machine learning based-ensemble models for prediction of adsorption process efficiency
Elahe Abbasi, Mohammad Reza Alavi Moghaddam, Elaheh Kowsari
Journal of Cleaner Production (2022) Vol. 379, pp. 134588-134588
Closed Access | Times Cited: 29
Utilization of random forest classifier and artificial neural network for predicting the acceptance of reopening decommissioned nuclear power plant
Ardvin Kester S. Ong, Yogi Tri Prasetyo, Kenzo Emmanuel C. Velasco, et al.
Annals of Nuclear Energy (2022) Vol. 175, pp. 109188-109188
Closed Access | Times Cited: 28
Application of machine learning methods for lignocellulose biomass pyrolysis: Activation energy prediction from preliminary analysis and conversion degree
Jingxin Liu, Hang Jia, Kashif Mairaj Deen, et al.
Fuel (2023) Vol. 343, pp. 128005-128005
Closed Access | Times Cited: 17
Machine learning toward improving the performance of membrane-based wastewater treatment: A review
Panchan Dansawad, Yanxiang Li, Yize Li, et al.
Advanced Membranes (2023) Vol. 3, pp. 100072-100072
Open Access | Times Cited: 15
Unraveling the role of Fe in As(III & V) removal by biochar via machine learning exploration
Jingxin Liu, Zelin Xu, Wenjuan Zhang
Separation and Purification Technology (2023) Vol. 311, pp. 123245-123245
Closed Access | Times Cited: 14
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