Validating and improving predictive models using spectral reflectance measurements for the estimation of Methylene Blue Index of soft tailings

Annually, large number of tailings samples are collected by operators and sent to laboratories for measurement of Methylene Blue Index (MBI). This procedure is costly, time-consuming, and results are a function of the methods used and personnel expertise. In prior research we developed predictive models for the quick and consistent estimation of tailings MBI from hyperspectral measurements using a limited number of dry samples. The proposed research focuses on assessment of the robustness of the established models to tailings composition and adapting the spectral models to be applicable on saturated tailings. This would enable the industrial partners to quickly estimate MBI in a wide range of tailings observation conditions including on-site and in-situ on saturated samples and could significantly reduce the costs and inconsistencies associated with laboratory measurements.

Faculty Supervisor:

Benoit Rivard


Iman Entezari


Suncor Energy Inc.


Geography / Geology / Earth science


Oil and gas




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