A Multiple Objective Hybrid Algorithm for Daily Ore Blend in Oil Sands Mines

Abstract

ABSTRACT Oil sands mining contributes to the Canadian daily oil production by producing 1.617 million barrels per day. Processing oil sands is a complex operation with a critical sensitivity to the properties of the blended ore at the crusher that must follow the slurry pipeline and separation tank requirements. The blend optimisation in oil sands mines is a tedious work performed mostly manually by the mining engineers at the mine sites and requires fine-tuning as shovels move from one block to another in the same mining face. Miscalculations leading to deviation from the target properties cause inevitable economically and operationally expensive issues to the value chain including but not limited to sanding the pipeline, separation tank hick-ups, etc. Herein, we present a hybrid multi-objective algorithm addressing abovementioned issues in daily blending process and providing the operation crew with a clear practical production target at each mining face. The algorithm takes the processing targets as inputs and minimises deviations from each desired target by considering material properties at mining faces, the capacity of trucks, and production rates of active shovels.