In-line characterisation of continuous phase conductivity in slurry flows using artificial intelligence tomography

Thomas D. Machin, Kent Wei, Richard W. Greenwood, Mark J.h. Simmons

Research output: Contribution to journalArticlepeer-review

Abstract

Electrical Impedance Tomography (EIT) can be applied to monitor a variety of mineral and chemical processes including: velocity measurements in drilling cuttings and hydrocyclone operations. Hydraulic conveying systems rely upon the knowledge of slurry density to ensure efficient transportation of the solids. Typically, density measurements exploit the attenuation of gamma ray photons which poses complex safety, operational and regulatory concerns with Electrical Impedance Tomography affording a non-nuclear alternative to traditional approaches. To optimise the accuracy of this non-nuclear density measurement, the electrical conductivity of the aqueous phase in a multi-component slurry, is required. Whilst conductivity probes are sufficiently accurate, there are often drawbacks and limitations due to installation restrictions, as it is difficult to separate aqueous and solid phases in real-time. Electrical Impedance Fingerprinting (EIF), is a novel measurement technique which characterises formulation properties, in-situ, based upon electrical impedance sensing and artificial intelligence algorithms. This paper outlines the development of EIF and its application to monitor aqueous phase conductivity in multi-component slurries, containing sands and clays. EIF accurately predicts this conductivity with high accuracy and a root-mean squared error of 0.055 mS cm−1. This development ensures accurate non-nuclear density measurements (<5%) are obtained across an extended aqueous electrical conductivity range of 1.5–70 mS cm−1. This encompasses the majority of target hydraulic conveying systems in mining operations. EIF also enhances the functionality of ‘traditional’ electrical tomography as not only are mineral processes able to be visualised, but the process materials are simultaneously characterised, to improve process understanding, optimisation and control.
Original languageEnglish
Article number107203
Number of pages13
JournalMinerals Engineering
Volume173
Early online date21 Sep 2021
DOIs
Publication statusPublished - 1 Nov 2021

Keywords

  • Slurries
  • Density
  • Hydraulic conveying
  • In-line measurement
  • Electrical tomography
  • Artificial Intelligence
  • Non-nuclear
  • Digital Manufacturing
  • Sensing

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