3D real-time stockpile mapping and modelling with accurate quality calculation using voxels

Abstract

Stockpile blending is widely accepted as an effective method to reduce the short-term quality variations and optimise the homogeneity of bulk materials, such as iron ore. Currently, both industry practice and academic research focus on planning, scheduling and optimisation algorithms to stack a stockpile that meets the predefined quality requirements. Namely, using ‘selective stacking’ algorithms to optimise the quality of a stockpile and improve the operational efficiency. However, it has been identified that stockpiled products are currently being reclaimed at approximately 50% of their potential engineering productive rates after applying such ‘selective stacking’ methods at most iron ore loading ports in Australia. There is an evident lack of solutions to this issue in the literature. This study focuses on stockpile modelling techniques to estimate the quality of a stockpile in both stacking and reclaiming operations for consistent and efficient product quality planning and control. The main objective of this work is to build an up-to-date geometric model of a stockpile using laser scanning data and apply this model to quality calculations throughout the stacking and reclaiming operations. The significant elements of the proposed research are to: (1) upgrade a stockyard machine used to stack or reclaim the stockpile (i.e. a Bucket Wheel Reclaimer) into a mobile scanning device using Kalman filtering to measure the stockpile surface continuously; (2) build a 3D stockpile model from the measurement data in real time using polynomial and B-spline surface modelling techniques and use this model to calculate the quality of a stockpile with a great degree of accuracy when the quality composition is available; (3) associate the 3D model with the reclaiming machine model to achieve autonomous operation and predict the quality of the reclaimed material through voxelization techniques. In order to validate the developed techniques, several experimental tests were conducted using simulation and real scenarios. It was verified that the proposed 3D stockpile modelling algorithms are adequate to represent the real geometric shape with great accuracy. The percentage error in volume is better than 0.2%. Therefore, the combination of stock pile and BWR (Bucket Wheel Reclaimer) models enables the reclaiming to be conducted automatically. To the best of author’s knowledge, this is the first time that a stockpile is modelled automatically in real-time and the integration of the stockpile and BWR model generates a novel stockpile management model allows true reclaiming automation. Thus, the quality of material composition after every stacking/reclaiming operation is calculated from the geometric shape/volume, density and quality assay results. Through accomplishing this project, the quality of a stockpile and its distribution inside the stockpile can be tracked continuously and the stacking/reclaiming trajectory of the machine can be controlled precisely. By making available such information, it is then possible to develop proactive stacking or reclaiming pattern strategies with more accurate product quality grade planning and control. Therefore, the workload of current selectively stacking and reactive reclaiming algorithms can be relieved, and the production rates can be improved with good output product quality control.