Abstract

This is the second part of a study of predictive models of oil sand ore and froth characteristics using infrared hyperspectral data as a potential new means for process control. In Alberta, Canada, bitumen in shallow oil sands deposits is accessed by surface mining and then extracted from ore using flotation processes. The ore displays variability in the clay, bitumen, and fines content and this variability affects the separability and product quality in flotation units. Flotation experiments were performed on a set of ore samples of different types to generate froth and determine the ore processability (e.g., separation performance) and froth characteristics (bitumen and solids content, fines distribution). We show that point spectra and spectral imagery of good quality can be acquired rapidly (<1 s and <15 s, respectively) and these capture spectral features diagnostic of bitumen and solids. Ensuing models can predict the solids/bitumen (r2 = 0.88) and the %fines and ultrafines (particle passing at 3.9 and 0.5 µm) content of froth (r2 = 0.8 and 0.9, respectively). The latter model could be used to reject froth with a high solids content. Alternately, the strength of the illite-smectite absorption observed in froth could be used to retain all the samples above a pre-defined processability. Given that point spectrometers can currently be acquired for less than half the cost of an imaging system, we recommend the use of the former for future trials in operating environments.

Highlights

  • IntroductionFlotation is one of the most common methods used to separate valuable minable material (e.g., oil, minerals, metals) from waste rock [1]

  • Flotation is one of the most common methods used to separate valuable minable material from waste rock [1]

  • In this study models were pursued for the prediction of froth solids to bitumen ratio, pp3.9, pp0.5, and ore processability

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Summary

Introduction

Flotation is one of the most common methods used to separate valuable minable material (e.g., oil, minerals, metals) from waste rock [1]. Machine vision analysis of froths in flotation cells has been explored to assess the general state of the flotation cells. Froth color information is exploited across mining sectors and related to grades [2,3]. In a mineral setting [4], visible near-infrared reflectance spectroscopy (0.4–1.0 μm) was combined with X-ray fluorescence analysis to assess the mineral slurry contents in copper and zinc. In a subsequent study [5], data in the same spectral range were used to establish quantitative mineral information in a slurry at an iron ore mine which was combined with laser-induced breakdown spectroscopy (LIBS) and X-ray fluorescence (XRF) information to improve the estimation of the onstream mineral content.

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