EVALUATION OF PROSPECTS FOR APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNOLOGY FOR BREAKING ROCKS BY A HYDROMONITOR WITH FINE PARTICLES IN ITS JET

Relevance. Hydraulic monitors are widely used in the development of alluvial deposits in technological schemes with circulating water supply. Currently, deposits with more complex mining and geological conditions of occurrence are involved in mining. For placers, this is, first of all, a very significant content of clay in the host rocks and sands. When using the hydraulic method of field development, in particu-lar, during hydromonitor breaking of rocks, during circulating water supply, process water is taken from special settling ponds. However, due to the high content of clay in eroded rocks, fine particles of which have a very long period of settling in the water of the sump, this technology is experiencing significant difficulties. There are known solutions for the development of deposits in similar conditions with a controled content of fine soil particles in the pressurized water of the hydromonitor, where the calculation of the parameters of rock break-ing by the hydromonitor is proposed to carry out using the developed mathematical model. Further development of the proposed solutions is possible through the implementation of automation elements into the technological process, in particular, artificial neural networks.Purpose: to assess the prospects for using artificial intelligence technology for breaking rocks with a hydromonitor with fine particles in its jetMethods: implementation of artificial intelligence technologies based on artificial neural networks in the technological process of develop-ing aluvial deposits with clay content in host rocks and sands.Results. This article assesses the prospects for using artificial intelligence technology to control the breaking process using a multilayer feed-forward neural network.Conclusions. The proposed technology alows quickly managing the productivity of rock breaking with a hydraulic monitor, regulating power consumption, and improving as welthe quality of gravitational enrichment of a mineral by supplying water with an acceptable co n -tent of fine particles to the locks, which ultimately leads to reduction in the loss of the mineral and decrease in the cost of rock breaking with a hydromonitor and the entire technology as a whole, as welas to improvement of useful component recovery.

Automated summary

This study evaluates the prospects of using artificial intelligence technology to break rocks with fine particles in water jets. By implementing a multilayer feed-forward neural network, this technology enables quick management of the productivity of hydraulic monitors and improves the quality of gravitational enrichment of minerals, ultimately reducing mineral loss, decreasing the cost of rock breaking, and improving useful component recovery.