Mining Technology: Transactions of the Institutions of Mining and Metallurgy

Broken ore density (BOD) is an important parameter in planning a block cave mine. However, its assessment is complicated by the heterogeneous nature of its distribution within a draw column, varying from a denser central plug-flow zone and decreasing outwards towards outer perimeter shear bands. The BOD further decreases immediately above the drawpoint due to the development of a loosening zone that develops in response to mucking. This makes determining BOD a challenging task, hindered by the inability to measure it in situ. To address this, several key factors influencing BOD are investigated including the influence of primary and secondary fragmentation, air gap thickness, draw rate and column height. Data is used to link primary and secondary fragmentation to broken ore size distributions. From these, a conceptual framework and empirical procedures are presented for evaluating BOD within draw columns during block caving for feasibility and early stage mine planning and design.

The success of a cement production project depends on the supply of raw materials. Long-term quarry production scheduling (LTQPS) based on resource models is essential to maintain a consistent supply to cement plants. Geological uncertainty is inherent due to sparse exploration data in resource models and significant risk factors for not achieving production targets. This research proposes a stochastic framework for LTQPS that considers the impact of geological uncertainty on raw material supply. A clustering algorithm uses multiple simulated deposit models to aggregate blocks into mining cuts. A new stochastic mixed-integer programming model is formulated with two objectives: to minimise the cost for developing the raw mix and the risk of not meeting production targets. The proposed framework is implemented successfully in a limestone deposit in Southern Vietnam, resulting in an increase of 5 million tons (Mt) and a 30% reduction in unit cost over the deterministic mixed-integer programming model.

The present study aims to propose new strategies based on mixed-integer non-linear programming (MINLP) models for the equipment sizing (ES) problem to verify the overall efficiency of the fleet. The developed models estimate the optimal size of trucks concerning the match factor value with two different strategies. The first strategy deals with each loader type, and the second one is applied simultaneously with all types of loaders. The proposed approaches are compared to a simulation strategy to assess the models. Implementing models with a copper mine case study provides a more efficient haul fleet size than the decisions offered by the simulation method. Moreover, the presented strategies provide an effective way to improve equipment performance where the current mine strategy does not adapt well. A key contribution of this research is the development, implementation, and verification of new optimization and simulation methods to address the ES problem in open-pit mines.

Haul roads play a significant role in mining, with rolling resistance being one of the main aspects that can affect road quality and consequently the performance of mining trucks. The aim of this study is to estimate the rolling resistance of haul road segments considering longitudinal grade crossed with dispatch system data using back-calculation from Rimpull curves and estimated performance equations developed by Cooper. The use of this method demonstrated that it could assist in quickly identifying issues on mine haul roads and assess road quality locally through rolling resistance estimated maps, which will help to improve maintenance routines and truck performance.

Complex load and haul cycles in mining are composed of individual steps, whose times could be better described by a statistical distribution than by the average value. In order to evaluate how loading times and dumping times behave, this paper tested a large dataset of loading and dumping times measured at an open pit limestone mine in Brazil against the distributions most commonly used to model these variables, Log-normal and Normal; as well as Gamma, Logistic, Weibull and Exponential distributions. None of the tested distributions provided statistically significant adherence to the data, but it was possible to identify that for most equipment, Logistic and Normal distributions would produce less error on stochastic modelling then the other tested distributions.