Machine Learning and Computer Vision in the Mining Industry
The article will focus on the use of machine learning and computer vision technologies in the mining industry of our country.
In my humble opinion, IT solutions in the field of mining transport and processing plants froze at the level of automation of the main, key technological processes. Now, in the third decade of the 21st century, the time for optimizations and improvements has come for large enterprises, the time for the introduction of new technologies of machine learning and technical vision. And this work has already begun.
Introduction
According to wikipedia in Russia 26 mining enterprises (there are actually more of them). The largest and most famous of them are: Alrosa, Norilsk Nickel, RusAl, Polyus and others. All of them are united by a similar device and organization of key technological processes: there is a place for the extraction of minerals (quarry), as well as a processing plant (plant).
Redistribution of the State Customs Committee (mining and transport complex) is more or less the same for everyone: ore from the quarry is transported either directly to the factory, or to the ore warehouses, and from there to the factory. Empty overburden is stored in dumps. Transportation can be carried out both by heavy-duty dump trucks and conveyors, or by combined methods. Delivery by rail, it seems to me, is not widespread in our country, in contrast to Australian companies.
Factories vary widely from company to company. However, some branches are similar. For example, the task of the ore preparation department at all factories is to grind the ore to acceptable fractions. This is done with the help of crushers and mills (by the way, this is most often the most expensive operation in factories).
Further, depending on the type of ore and mineral, different approaches to beneficiation are applied. For example, diamond mining uses its unique property – luminescence (glow) in X-rays, which allows simple pneumatics to “shoot” diamonds from a stream of crushed ore. For gold mining, chemical processes are used (flotation, sorption and desorption, electrolysis) and even biotechnology (specially removed bacteria that help the gold to be released from the shell). And for the production of aluminum from alumina, the basis of everything is electrolysis.
Current trends in the State Customs Committee
So, as we understood, at the redistribution of the mining and transport complex, the main task is to overwhelm, blow up and transport the rock mass from the quarry. A fleet of drilling rigs, heavy-duty dump trucks and excavators is engaged in this.
The control over a large number of equipment (sometimes parks can number 400 or more units) are carried out by the dispatcher and operators using the ASUGTK systems (automated control systems for the mining and transport complex). The tasks of the ASUGTK systems are to control the operating parameters and the state of mining machines, control and manage the loading of dump trucks, forecast and monitor the implementation of the plan, provide reporting, and sometimes – in the optimization and dynamic distribution of dump trucks along routes.
But this is all yesterday. Today, point applications of machine learning and technical vision systems are becoming relevant.
Analysis of the condition of the bucket teeth of a mining excavator
Parts of mining equipment, in particular the bucket teeth of a mining excavator, operating in the harsh conditions of rock excavation, are constantly exposed to so-called. shock and abrasive wear. At the same time, the loss of a tooth is fraught with troubles: starting from a decrease in the performance of the excavator during scooping, damage to the bucket itself, and ending with such a tooth getting into the crusher after transportation. As a result, the risk of significant material losses increases: prolonged downtime of equipment, restoration and repair, the need to extract a tooth from a grinder by a person (a rather dangerous event).
To solve these problems, solutions based on technical vision are applied. Here an example of such a system. The system analyzes the frames and constantly determines the condition of the bucket teeth and the degree of their wear.
At night, a spotlight is used. Surveillance cameras provide the operator with a view on all sides of the excavator: all information collected by the system is displayed on a monitor in the operator’s cab and allows you to detect missing teeth in time, assess the bucket payload and the degree of tooth wear.
Control of gran-composition of transported ore
The particle size of the rock mass must be controlled at almost every stage of production: after explosion, during transportation, when ore is supplied to the factory, after crushing, after crushing. This is the most important indicator that is monitored, since the quality and continuity of technological processes depends on it: from the quality of the explosion to the possible backfilling (clogging) of the equipment.
At the GTK redistribution, technical vision systems are used to automatically measure the grain size of ore. They can track the particle size distribution of each truck on the way to the crusher and make recommendations for the removal of oversized material.
It looks like this: