Machine learning: technologies and potential application at mining companies

Kryvyi Rih State Pedagogical University, 54 Gagarin Ave., Kryvyi Rih, 50086, Ukraine

^* Corresponding author: zvit-zss@ukr.net

Abstract

Implementation of machine learning systems is currently one of the most sought-after spheres of human activities at the interface of information technologies, mathematical analysis and statistics. Machine learning technologies are penetrating into our life through applied software created with the help of artificial intelligence algorithms. It is obvious that machine learning technologies will be developing fast and becoming part of the human information space both in our everyday life and in professional activities. However, building of machine learning systems requires great labour contribution of specialists in the sphere of artificial intelligence and the subject area where this technology is to be applied. The article considers technologies and potential application of machine learning at mining companies. The article describes basic methods of machine learning: unsupervised learning, action learning, semi-supervised machine learning. The criteria are singled out to assess machine learning: operation speed; assessment time; implemented model accuracy; ease of integration; flexible deployment within the subject area; ease of practical application; result visualization. The article describes practical application of machine learning technologies and considers the dispatch system at a mining enterprise (as exemplified by the dispatch system of the mining and transportation complex “Quarry” used to increase efficiency of operating management of enterprise performance; to increase reliability and agility of mining and transportation complex performance records and monitoring. There is also a list of equipment performance data that can be stored in the database and used as a basis for processing by machine learning algorithms and obtaining new knowledge. Application of machine learning technologies in the mining industry is a promising and necessary condition for increasing mining efficiency and ensuring environmental security. Selection of the optimal process flow sheet of mining operations, selection of the optimal complex of stripping and mining equipment, optimal planning of mining operations and mining equipment performance control are some of the tasks where machine learning technologies can be used. However, despite prospectivity of machine learning technologies, this trend still remains understudied and requires further research.