Documents publiés en "2025"

Département de génie chimique

Al-Sakkari, E. G., 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). Disponible

Département de génie électrique

Al-Sakkari, E. G., 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). Disponible

Département de mathématiques et de génie industriel

Abdellaoui, A., Aubé, F., Benabbou, L., El Hallaoui, I., & Amazouz, M. (2025). A Decomposition-Based Framework for Large-Scale Multi-Period Log-Truck Routing and Scheduling: A Case Study in Canadian Forestry. IFAC-PapersOnLine, 59(10), 208-213. Lien externe

Al-Sakkari, E. G., 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). Disponible