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ArticleName Use of multifunctional crust breaker and machine vision system for acquisition and processing of aluminium reduction cell data
DOI 10.17580/tsm.2023.04.06
ArticleAuthor Petrov P. A., Shestakov A. K., Nikolaev M. Yu.

Saint Petersburg Mining University, Saint Petersburg, Russia:

P. A. Petrov, Dean of the Minerals Processing Faculty, Candidate of Technical Sciences, e-mail:
A. K. Shestakov, Postgraduate Student at the Department of Process and Plant Automation, e-mail:
M. Yu. Nikolaev, Master’s Student at the Department of Process and Plant Automation, e-mail:


The aluminium output control systems that are most commonly used in practice fail to ensure timely monitoring and adjustment of the key process parameters, such as electrolyte temperature, alumina concentration, cryolite ratio, metal and electrolyte levels. These parameters are measured manually, at a large interval (once a day). The difficulty of introducing automatic control systems comes down to the fact that most instruments and solutions are not practicable due to harsh process environment (i.e. high temperature, harmful emissions, alumina dusting, varying magnetic field). This paper describes a solution that enables to automatically collect electrolyte level values without compromising the tightness of the cell during measurement. The measurements are taken with a laser distance meter installed inside a crust breaker cylinder of the point feeding control system. Knowing the level of electrolyte in each feed cycle, one can define the smallest portion of alumina (the feeding interval) and add crushed bath automatically (if there is a hopper with a crushed bath feeding device). A neural network-based machine vision system developed for detecting visible emissions helps to quickly restore the cell cover in case of cryolite-alumina crust breakage or loss of cell tightness.

keywords Electrowinning of aluminium, electrolyte level monitoring, laser distance meter, automatic alumina feeding, TensorFlow, machine learning, convolutional neural network, object recognition, machine vision, emissions

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