Data-driven prediction and thermodynamic performance assessment of industrial cooling towers using advanced machine learning algorithms.
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| Title: | Data-driven prediction and thermodynamic performance assessment of industrial cooling towers using advanced machine learning algorithms. |
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| Authors: | Jamil SR; Department of Mechanical Engineering, University of Engineering and Technology, Lahore, Pakistan., Shehzad A; Automotive Engineering Centre, University of Engineering and Technology, Lahore, Pakistan., Usman M; Department of Mechanical Engineering, University of Engineering and Technology, Lahore, Pakistan., Abbas MM; Department of Mechanical Engineering, University of Engineering & Technology, New Campus, Lahore, Pakistan., Qaisrani OZ; Department of Mechanical Engineering, University of Engineering and Technology, Lahore, Pakistan., Saleem MW; College of Engineering and Energy, Abdullah Al Salem University, Khaldiya, Kuwait., Musharaf HM; Queensland Quantum and Advanced Technologies Research Institute, Griffith University, Brisbane, Australia., Petrů J; Department of Machining, Assembly and Engineering Metrology, Faculty of Mechanical Engineering, VŠB Technical University of Ostrava, Ostrava, Czech Republic., Bashir MN; Department of Machining, Assembly and Engineering Metrology, Faculty of Mechanical Engineering, VŠB Technical University of Ostrava, Ostrava, Czech Republic.; Multi-Scale Fluid Dynamics Lab, Department of Mechanical Engineering, Yonsei University, Seoul, Republic of Korea., Fouad Y; Department of Applied Mechanical Engineering, College of Applied Engineering, Muzahimiyah Branch, King Saud University, Riyadh, Saudi Arabia. |
| Source: | PloS one [PLoS One] 2026 Jul 02; Vol. 21 (7), pp. e0351944. Date of Electronic Publication: 2026 Jul 02 (Print Publication: 2026). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1932-6203 |
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| DOI: | 10.1371/journal.pone.0351944 |
