Data-driven prediction and thermodynamic performance assessment of industrial cooling towers using advanced machine learning algorithms.
- DOI
- 10.1371/journal.pone.0351944
- Published
- 2026-07-02
- Container
- PLoS One
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1371/journal.pone.0351944,
title = {Data-driven prediction and thermodynamic performance assessment of industrial cooling towers using advanced machine learning algorithms.},
author = {Jamil SR and Shehzad A and Usman M and Abbas MM and Qaisrani OZ and Saleem MW and Musharaf HM and Petrů J and Bashir MN and Fouad Y.},
year = {2026},
journal = {PLoS One},
doi = {10.1371/journal.pone.0351944},
url = {https://doi.org/10.1371/journal.pone.0351944}
}RIS
TY - JOUR TI - Data-driven prediction and thermodynamic performance assessment of industrial cooling towers using advanced machine learning algorithms. AU - Jamil SR AU - Shehzad A AU - Usman M AU - Abbas MM AU - Qaisrani OZ AU - Saleem MW AU - Musharaf HM AU - Petrů J AU - Bashir MN AU - Fouad Y. PY - 2026 JO - PLoS One DO - 10.1371/journal.pone.0351944 UR - https://doi.org/10.1371/journal.pone.0351944 ER -
APA
SR, J., A, S., M, U., MM, A., OZ, Q., MW, S., HM, M., J, P., MN, B., & Y., F. (2026). Data-driven prediction and thermodynamic performance assessment of industrial cooling towers using advanced machine learning algorithms.. PLoS One. https://doi.org/10.1371/journal.pone.0351944
Source records
- europe-pmc · retrieved 2026-09-25T09:07:58.169Z