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Multivariate Image Analysis in Mineral Processing

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Book cover Advanced Control and Supervision of Mineral Processing Plants

Part of the book series: Advances in Industrial Control ((AIC))

Abstract

In several process industries including mineral processing, where the materials are solids or slurries, some important measurements cannot be obtained using standard instrumentation (e.g., flow, temperature, pressure, pH, power draw, etc.), but can be visually appraised, and could be automatically quantified using machine vision techniques. In general, the information to extract from process images is not well defined and is stochastic in nature compared with those typically encountered in the manufacturing industry for automatic inspection, which have well defined deterministic features. Multivariate image analysis (MIA) as well as Multiresolution analysis (MRA) have been shown to be very efficient methods for spectral/textural analysis of process images. The objective of this chapter is to illustrate these methods using three mineral processing problems: (1) on-line estimation of froth grade in a flotation process; (2) flotation froth health monitoring for appropriate reagent dosage; and (3) on-line estimation of run-of-mine ore lithotype composition on conveyor belts. In all cases, the extracted image information could be used for developing new vision sensors for advanced control of mineral processing plants.

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  1. Department of Chemical Engineering, Université Laval, G1V 0A6, Québec City, Canada

    Carl Duchesne

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  1. Carl Duchesne
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  1. Departamento de Ingeniería Eléctrica, Universidad de Concepción, Casilla 160-C, Correo 3, Concepción, Chile

    Daniel Sbárbaro

  2. Département de Génie des Mines, de la Métallurgie et des Matériaux, Université Laval, Pavillon Adrien-Pouliot 1065, Avenue de la Médecine, G1V 0A6, Québec, QC, Canada

    René del Villar

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Duchesne, C. (2010). Multivariate Image Analysis in Mineral Processing. In: Sbárbaro, D., del Villar, R. (eds) Advanced Control and Supervision of Mineral Processing Plants. Advances in Industrial Control. Springer, London. https://doi.org/10.1007/978-1-84996-106-6_3

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  • DOI: https://doi.org/10.1007/978-1-84996-106-6_3

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