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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.
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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  Data: Data-driven prediction and thermodynamic performance assessment of industrial cooling towers using advanced machine learning algorithms.
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  Data: <searchLink fieldCode="AU" term="%22Jamil+SR%22">Jamil SR</searchLink>; Department of Mechanical Engineering, University of Engineering and Technology, Lahore, Pakistan.<br /><searchLink fieldCode="AU" term="%22Shehzad+A%22">Shehzad A</searchLink>; Automotive Engineering Centre, University of Engineering and Technology, Lahore, Pakistan.<br /><searchLink fieldCode="AU" term="%22Usman+M%22">Usman M</searchLink>; Department of Mechanical Engineering, University of Engineering and Technology, Lahore, Pakistan.<br /><searchLink fieldCode="AU" term="%22Abbas+MM%22">Abbas MM</searchLink>; Department of Mechanical Engineering, University of Engineering & Technology, New Campus, Lahore, Pakistan.<br /><searchLink fieldCode="AU" term="%22Qaisrani+OZ%22">Qaisrani OZ</searchLink>; Department of Mechanical Engineering, University of Engineering and Technology, Lahore, Pakistan.<br /><searchLink fieldCode="AU" term="%22Saleem+MW%22">Saleem MW</searchLink>; College of Engineering and Energy, Abdullah Al Salem University, Khaldiya, Kuwait.<br /><searchLink fieldCode="AU" term="%22Musharaf+HM%22">Musharaf HM</searchLink>; Queensland Quantum and Advanced Technologies Research Institute, Griffith University, Brisbane, Australia.<br /><searchLink fieldCode="AU" term="%22Petrů+J%22">Petrů J</searchLink>; Department of Machining, Assembly and Engineering Metrology, Faculty of Mechanical Engineering, VŠB Technical University of Ostrava, Ostrava, Czech Republic.<br /><searchLink fieldCode="AU" term="%22Bashir+MN%22">Bashir MN</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Fouad+Y%22">Fouad Y</searchLink>; Department of Applied Mechanical Engineering, College of Applied Engineering, Muzahimiyah Branch, King Saud University, Riyadh, Saudi Arabia.
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  Data: <searchLink fieldCode="JN" term="%22101285081%22">PloS one</searchLink> [PLoS One] 2026 Jul 02; Vol. 21 (7), pp. e0351944. <i>Date of Electronic Publication: </i>2026 Jul 02 (<i>Print Publication: </i>2026).
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Public+Library+of+Science%22">Public Library of Science </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>101285081 <i>Publication Model: </i>eCollection <i>Cited Medium: </i>Internet <i>ISSN: </i>1932-6203 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2219326203%22">19326203 </searchLink><i>NLM ISO Abbreviation: </i>PLoS One <i>Subsets: </i>MEDLINE
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              Text: 2026 Jul 02
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