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Record #66667

Neural network-based optimization of hydrogen fuel production energy system with proton exchange electrolyzer supported nanomaterial

Journal
Fuel
Publisher
Elsevier
Original paper date
Oct 3, 2022
Institution
School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, Guizhou 558000, China · Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou, Duyun, Guizhou 558000, China · Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti Teknologi MARA, Shah Alam, Selangor 40450, Malaysia · Department of Chemistry, College of Science, University of Sulaimani, Qlyasan Street, Kurdistan Region 46001, Iraq · Department of Medical Laboratory of Science, College of Health Sciences, University of Human Development, Sulaimaniyah, Iraq · School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, Guizhou 558000, China · Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou, Duyun, Guizhou 558000, China · Mechanical Engineering Department, University of Technology, Baghdad, Iraq · Institute of Engineering and Technology, GLA University, Mathura, Uttar Pradesh 281406, India · Department of Civil Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia · Department of Computer Engineering, College of Engineering and Computer Science, Lebanese French University, Kurdistan Region, Iraq · Department of Mechanical Engineering, College of Engineering at Al Kharj, Prince Sattam bin Abdulaziz University, 16273, Saudi Arabia · Department of Mechanical Engineering, University of Tunis El Manar, ENIT, BP 37, Le Belv’ed‘ere, Tunis 1002 Tunisia · Department of Chemical Engineering, Faculty of Engineering & Technology, Future University in Egypt, New Cairo 11500, Tunisia · Key Laboratory of Complex Systems and Intelligent Optimization of Qiannan, Duyun 558000, China
Open access
No
Article type
Research Article
Broad categories
(B/T) Data Science(PHY) Energy(PHY) Engineering - Electrical(PHY) Nanotechnology
Notes from Retraction Watch

see also: https://pubpeer.com/publications/0B339D324BE1B700D3A45BA07AFBB1;

Citation lifecycle

How this paper is being cited

Pre-retraction
34
Same day
0
Post-retraction
1
% post-retraction
2.9%
~13.9 citations/yr before retraction · ~0.7 citations/yr after
— citing papers

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