Aprendizado de máquina aplicado ao planejamento de lavra de curto prazo para o aumento do desempenho operacional de equipamentos de mina.

Abstract

The complex combination of controls, systems and human aspects presented in the mine value chain are today responsible for an increasing amount of digital data in the mining industry. In this scenario, it is imperative the use of reliable and intelligent systems that can store and process the data to predict mine equipment performance. The objective of this study is to improve the prediction of operational performance in mine equipment for the short-term planning. For this purpose, it is proposed a machine learning (ML) methodology to map the production process from data collection until planning, with replication of the generated routines for subsequent short-term period analysis. The methodology was applied to predict the operational performance of excavators during working shifts in an open pit copper mine located in Northern Brazil, considering a series of variables such as operational, geological, geographic, maintenance. 175 predictive models were generated during the study, which were tested through cross-validation to improve the model adjustment to the collected data. The results obtained using this methodology confirmed that the use of ML predictive models provides a better understanding of the operation and allocation of mine loading fleet through the use of fast and realistic predictive routines.

Aplicação Da Inteligência Artificial Na Mineração

“…Alan Turing, em seu artigo publicado no ano de 1950 -"Computing Machinery and Intelligency", formulou a visão de inteligência artificial atual assim como temas fundamentais na área, aprendizagem de reforço e o teste de turing (Silva, 2021). (Russel e Norving, 2004).…”