Embedded Files
Research Topics
The IMOM Lab develops artificial intelligence (AI) and data-driven methods to improve mine transport efficiency, reduce carbon emissions, and enable safe and intelligent mining operations.
Intelligent Mine Transport Systems
This research develops data-driven, interpretable, and engineer-oriented tools to improve mine haulage and related bulk material handling systems.
Sustainable and Low-Carbon Mining
This research develops intelligent solutions to quantify and reduce carbon emissions and improve the sustainability of mining and mineral processing.
Intelligent Monitoring and Safe Mining
This research develops intelligent monitoring tools and decision-support systems to enhance the safety and reliability of mining infrastructure.
Geologic CO2 Storage Risk Management
This research integrates monitoring and AI-based modeling to assess storage integrity, predict leakage risks, and support risk management for GCS systems.
Research Funding and Project Support
2026-2028: FRQ-Établissement de la relève professorale, Modélisation efficace en données pour la prévision et l'optimisation de la productivité des camions miniers, PI: Chengkai Fan; $73,660
2026-2031: NSERC/RGPIN, Data-Driven and Transferable Modeling for Mine Truck Haulage: From Manned to Unmanned, PI: Chengkai Fan; NSERC funding: $202,500 including Discovery Launch Supplement; institutional support: $37,500; total: $240,000
2026: Mitacs Accélération, Optimiser la charge dans les camions miniers, Co-Investigator: Chengkai Fan, with Christian Gagné, $30,000
2026: Mitacs Globalink, Apply Machine Learning to Sustainable Mining, PI: Chengkai Fan, $6,000
2025-2026: NRC CBMI, Selective Precipitation Development for Next Generation Battery Materials, PI: Chengkai Fan (PI for the Université Laval portion by formal amendment; Université Laval portion completed in Apr. 2026), $159,610
2024-2028: Université Laval, Faculté des sciences et de génie, Start-up Grant, PI: Chengkai Fan; $80,000
Page updated
Google Sites
Report abuse