Ensemble machine learning to accelerate industrial decarbonization: Prediction of Hansen solubility parameters for streamlined chemical solvent selection

Eslam G. Al-Sakkari, Ahmed Ragab, Mostafa Amer, Olumoye Ajao, Marzouk Benali, Daria Camilla Boffito, Hanane Dagdougui and Mouloud Amazouz

Article (2024)

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Department:	Department of Chemical Engineering Department of Electrical Engineering Department of Mathematics and Industrial Engineering
PolyPublie URL:	https://publications.polymtl.ca/61946/
Journal Title:	Digital Chemical Engineering (vol. 14)
Publisher:	Elsevier BV
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:	16 Jan 2025 14:22

Cite in APA 7:	Al-Sakkari, E. G., Ragab, A., Amer, M., Ajao, O., Benali, M., Boffito, D. C., Dagdougui, H., & Amazouz, M. (2024). 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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