Hypotheses on optimization of mining systems operating parameters using predictive analytics

Abstract

The article assesses possible approaches to enhancing the efficiency of data collection to manage mining systems and it proposes an option to formulate hypotheses on optimizing their operating parameters in dynamically changing mining and geological, mining engineering and external conditions. The method of formulating and verifying hypotheses on implicit relationships between the parameters of mining and related systems appears to be an efficient tool for targeted data collection from digital sources and their storage for use in predictive analytics. The second approach is creation of artificial intelligence systems to work with data that aim to identify deviations and adjust the operating parameters of the mining system based on retrospective analysis of the collected arrays of historical information, without having hypotheses formulated in advance when managing the mining system. In addition to hypotheses on the regularities and relationships between the mining system parameters, the article emphasizes the importance of forecasting and accounting for the extent of changes in the parameters of the related systems. The related systems mean such systems as the environment and society, which interact with the mining systems in time and space. Moreover, the functioning of the latter, due to the global scale of man-caused transformation of the lithosphere, results in inevitable changes in the state of the related systems. Only the big data technologies can make it possible to reveal implicit regularities in changes in the parameters of each adjacent system, including identification of rational indicators of the mining systems operation. The paper emphasizes the data collection based on the principle of capturing all changes of the information from the digital sources. Based on this principle, we propose to standardize the approach to data collection for mining systems management