Grant: CCI Applied Research and Development Grants

The oil sands industry faces huge pressures to reclaim landscapes dedicated to fluid fine tailings (FFT) impoundment. Suncor has over 363 Mm³ of fluid tailings and uses a treatment strategy called Permanent Aquatic Storage Structure (PASS), which stores 100% of its tailings. PASS aims to minimize liabilities from tailings waste by creating a substrate suitable for reclamation into a man-made freshwater lake. However, despite numerous laboratory scale studies, fully implementing findings at field scale has been difficult due to challenges in collecting data on controlling variables at a commercial scale. This project is a collaboration between Suncor and the Northern Alberta Institute of Technology (NAIT). The overarching goal of the project is to improve PASS performance, which will reduce the volume of tailings stored in impoundments and significantly reduce the potential for containment failure. The project will be divided into three themes. Theme 1 aims to improve the quantification of uncertainty in measurements used for dosing decisions on-site, employing propagation of uncertainty methods to calculate uncertainties in crucial parameters for decision-making. Theme 2 seeks to comprehend the differences between results obtained in controlled lab environments and pilot-scale field tests versus those achieved at full scale operation. Finally, theme 3 emphasizes knowledge exchange among researchers, process designers, and operations experts, so NAIT researchers will travel to directly observe the PASS operations. Suncor engineers and technicians will also travel to NAIT to learn more about laboratory testing and discuss recommendations for improvements to field scale PASS operations derived from Themes 1 and 2. In summary, the objectives of this study are: 1. Enhance our understanding of error sources in variables affecting tailings flocculation under the PASS protocol. 2. Identify tailings treatment factors directly controlled, indirectly measured, and those requiring further understanding through an annual pond survey. 3. Utilize numerical and stochastic modeling to predict alterations in tailings treatment metrics with changes in process input parameters. 4. Foster collaboration among researchers, process engineers, operations engineers, and field technologists to improve PASS performance by understanding operational limitations, observations at different scales, and identifying enhancement opportunities.