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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 novel effluent quality predicting model based on genetic-deep belief network algorithm for cleaner production in a full-scale paper-making wastewater treatment
Guoqiang Niu, Xiaohui Yi, Chen Chen, et al.
Journal of Cleaner Production (2020) Vol. 265, pp. 121787-121787
Closed Access | Times Cited: 92

Showing 1-25 of 92 citing articles:

Application of machine learning in anaerobic digestion: Perspectives and challenges
Ianny Andrade Cruz, Wachiranon Chuenchart, Fei Long, et al.
Bioresource Technology (2021) Vol. 345, pp. 126433-126433
Open Access | Times Cited: 177
Heterogeneous electro–Fenton using three–dimension NZVI–BC electrodes for degradation of neonicotinoid wastewater
Chao Zhang, Feng Li, Rubing Wen, et al.
Water Research (2020) Vol. 182, pp. 115975-115975
Closed Access | Times Cited: 142
Data to intelligence: The role of data-driven models in wastewater treatment
Majid Bahramian, Recep Kaan Dereli, Wanqing Zhao, et al.
Expert Systems with Applications (2022) Vol. 217, pp. 119453-119453
Open Access | Times Cited: 98
Exploring potential machine learning application based on big data for prediction of wastewater quality from different full-scale wastewater treatment plants
Quang Viet Ly, Viet Hung Truong, Bingxuan Ji, et al.
The Science of The Total Environment (2022) Vol. 832, pp. 154930-154930
Closed Access | Times Cited: 77
Deep learning in wastewater treatment: a critical review
Maira Alvi, Damien J. Batstone, Christian Kazadi Mbamba, et al.
Water Research (2023) Vol. 245, pp. 120518-120518
Open Access | Times Cited: 72
Applications of machine learning to water resources management: A review of present status and future opportunities
Ashraf Ahmed, Sakina Sayed, Antoifi Abdoulhalik, et al.
Journal of Cleaner Production (2024) Vol. 441, pp. 140715-140715
Open Access | Times Cited: 63
Revolutionizing wastewater treatment toward circular economy and carbon neutrality goals: Pioneering sustainable and efficient solutions for automation and advanced process control with smart and cutting-edge technologies
Stefano Cairone, Shadi W. Hasan, Kwang‐Ho Choo, et al.
Journal of Water Process Engineering (2024) Vol. 63, pp. 105486-105486
Open Access | Times Cited: 18
Use, Potential, Needs, and Limits of AI in Wastewater Treatment Applications
Andrea G. Capodaglio, Arianna Callegari
Water (2025) Vol. 17, Iss. 2, pp. 170-170
Open Access | Times Cited: 2
Artificial intelligence as a sustainable tool in wastewater treatment using membrane bioreactors
Mohammadreza Kamali, Lise Appels, Xiaobin Yu, et al.
Chemical Engineering Journal (2020) Vol. 417, pp. 128070-128070
Closed Access | Times Cited: 104
A multi-stage predicting methodology based on data decomposition and error correction for ultra-short-term wind energy prediction
Yagang Zhang, Jingyi Han, Guifang Pan, et al.
Journal of Cleaner Production (2021) Vol. 292, pp. 125981-125981
Closed Access | Times Cited: 83
Prediction of effluent quality in a wastewater treatment plant by dynamic neural network modeling
Yongkui Yang, Kyong-Ryong Kim, Rongrong Kou, et al.
Process Safety and Environmental Protection (2021) Vol. 158, pp. 515-524
Closed Access | Times Cited: 73
Applications of deep learning in water quality management: A state-of-the-art review
Kok Poh Wai, Min Yan Chia, Chai Hoon Koo, et al.
Journal of Hydrology (2022) Vol. 613, pp. 128332-128332
Closed Access | Times Cited: 68
Water quality forecasting based on data decomposition, fuzzy clustering and deep learning neural network
Jin‐Won Yu, Ju-Song Kim, Xia Li, et al.
Environmental Pollution (2022) Vol. 303, pp. 119136-119136
Closed Access | Times Cited: 67
Water quality prediction model using Gaussian process regression based on deep learning for carbon neutrality in papermaking wastewater treatment system
Xin Wan, Xiaoyong Li, Xinzhi Wang, et al.
Environmental Research (2022) Vol. 211, pp. 112942-112942
Closed Access | Times Cited: 60
Application of novel hybrid deep leaning model for cleaner production in a paper industrial wastewater treatment system
Xiaoyong Li, Xiaohui Yi, Zhenghui Liu, et al.
Journal of Cleaner Production (2021) Vol. 294, pp. 126343-126343
Closed Access | Times Cited: 58
Deep learning applications in manufacturing operations: a review of trends and ways forward
Saumyaranjan Sahoo, Satish Kumar, Mohammad Zoynul Abedin, et al.
Journal of Enterprise Information Management (2022) Vol. 36, Iss. 1, pp. 221-251
Closed Access | Times Cited: 54
Dynamic optimization of wastewater treatment process based on novel multi-objective ant lion optimization and deep learning algorithm
Guoqiang Niu, Xiaoyong Li, Xin Wan, et al.
Journal of Cleaner Production (2022) Vol. 345, pp. 131140-131140
Closed Access | Times Cited: 48
Hybrid modelling of water resource recovery facilities: status and opportunities
Mariane Yvonne Schneider, Ward Quaghebeur, Sina Borzooei, et al.
Water Science & Technology (2022)
Open Access | Times Cited: 46
Predicting CO2 trapping in deep saline aquifers using optimized long short-term memory
Mohammed A. A. Al‐qaness, Ahmed A. Ewees, Hung Vo Thanh, et al.
Environmental Science and Pollution Research (2022) Vol. 30, Iss. 12, pp. 33780-33794
Closed Access | Times Cited: 42
Predicting Cu(II) Adsorption from Aqueous Solutions onto Nano Zero-Valent Aluminum (nZVAl) by Machine Learning and Artificial Intelligence Techniques
Ahmed H. Sadek, Omar M. Fahmy, Mahmoud Nasr, et al.
Sustainability (2023) Vol. 15, Iss. 3, pp. 2081-2081
Open Access | Times Cited: 37
Fatigue life prediction based on a deep learning method for Ti-6Al-4V fabricated by laser powder bed fusion up to very-high-cycle fatigue regime
Yinfeng Jia, Rui Fu, Chao Ling, et al.
International Journal of Fatigue (2023) Vol. 172, pp. 107645-107645
Closed Access | Times Cited: 32
Water quality prediction of copper-molybdenum mining-beneficiation wastewater based on the PSO-SVR model
Xiaohua Fu, Qingxing Zheng, Guomin Jiang, et al.
Frontiers of Environmental Science & Engineering (2023) Vol. 17, Iss. 8
Closed Access | Times Cited: 27
Enhancing wastewater treatment efficiency through machine learning-driven effluent quality prediction: A plant-level analysis
Maria Alice Prado Cechinel, Juliana Neves, João Vitor Rios Fuck, et al.
Journal of Water Process Engineering (2024) Vol. 58, pp. 104758-104758
Closed Access | Times Cited: 16
The coagulation-precipitation turbidity prediction model for precision drug delivery system based on deep learning and machine vision
Shuo Peng, Yong Guo, Jihang Wang, et al.
Journal of environmental chemical engineering (2024) Vol. 12, Iss. 2, pp. 112211-112211
Closed Access | Times Cited: 14
Prediction of purified water quality in industrial hydrocarbon wastewater treatment using an artificial neural network and response surface methodology
Nour El Houda Mellal, Wafa Tahar, Messaouda Boumaaza, et al.
Journal of Water Process Engineering (2024) Vol. 58, pp. 104757-104757
Closed Access | Times Cited: 12
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