An Evaluation of Iron Ore Characteristics Through Machine Learning and 2-D LiDAR Technology

Conveyor belts are the most effective way to transport ore in a mining complex. The ore that comes from the mining areas can be heterogeneous in size and type. As the ore has to pass through several processing stages, online information about the ore type and degree of fragmentation can help improve mineral processing for both safety and efficiency. Current instrumentation systems are expensive and require frequent calibration and maintenance. This article presents a novel intelligent instrument for online recognition of type and degree of fragmentation. A 2-D light detection and ranging (LiDAR) sensor along with machine learning (ML) techniques was used to estimate the characteristics of iron ore particles on conveyor belts. An experiment was conducted using several types of ore and granulometry. Five ML models were compared by means of statistical methods, including average accuracy and normality and hypothesis tests. Among them, the random forest (RF) models achieved the highest rate of accuracy, 93.81% for ore type and 85.52% for degree of fragmentation. These models were improved by a voting mechanism that resulted in a reduction of classification errors of 93.3% for ore type and 99.2% for degree of fragmentation. These findings demonstrate that the system has the potential for improving mineral processing controls and heightening operational safety within the mining sector.