Eslam Ibrahim, Ahmed Ragab, Mostafa Amer, Olumoye Ajao, Marzouk Benali, Daria Camilla Boffito
, Hanane Dagdougui and Mouloud Amazouz
Article (2025)
 Open Acess document in PolyPublie and at official publisher |
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| Department: |
Department of Chemical Engineering Department of Electrical Engineering Department of Mathematics and Industrial Engineering |
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| Funders: | Natural Resources Canada - Office of Energy Research and Development, Natural Resources Canada - Forest Innovation Program, NSERC / CRSNG |
| PolyPublie URL: | https://publications.polymtl.ca/61946/ |
| Journal Title: | Digital Chemical Engineering (vol. 14) |
| Publisher: | Elsevier |
| DOI: | 10.1016/j.dche.2024.100207 |
| Official URL: | https://doi.org/10.1016/j.dche.2024.100207 |
| Date Deposited: | 16 Jan 2025 14:22 |
| Last Modified: | 14 Jun 2026 19:56 |
| Cite in APA 7: | Ibrahim, E., Ragab, A., Amer, M., Ajao, O., Benali, M., Boffito, D. C., Dagdougui, H., & Amazouz, M. (2025). Ensemble machine learning to accelerate industrial decarbonization: Prediction of Hansen solubility parameters for streamlined chemical solvent selection. Digital Chemical Engineering, 14, 100207 (26 pages). https://doi.org/10.1016/j.dche.2024.100207 |
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