Rock Fragmentation Evaluation towards Blast-To-Mill Concept of Blast Optimization in Hard Rock Mines

Traditional blast optimisation studies ensure efficient mining operation but ignore potential impact of blasting on primary crushing. The performance of the primary crusher is key to the ore beneficiation process. Optimisation studies conducted through the mining operations to the comminution circuit is vital to the mine-to-mill concepts in the mining industry. In this approach, an innovative approach to the assessment of in-situ blasting is proposed and evaluated. This approach focuses on the acceptability of rock fragments on the Run-of-Mine (ROM) pad as opposed to the pits. Fragmentation analysis was conducted in the pit and on the ROM pad. A correlation efficiency of 0.92 was realized between the measured rock fragments in the pit and that on the ROM pad. About 10% of the rock fragments in the pit were classified as boulders while about 30% of the same rock fragments deposited on the ROM pad were classified as boulders. However, about 30% of the rockpile on the ROM pad was estimated to be lower than the Close Side Setting (CSS) of the primary crusher. It is recommended that future research evaluates the energy consumption and its related cost at the primary crusher in comparison to in-pit fragmentation and mucking cost performance.

PlatMine pillar strength formula for the UG2 Reef

The Upper Group 2 (UG2) chromitite reef is a shallow-dipping stratiform tabular orebody in the South African Bushveld Complex, which strikes for hundreds of kilometres. Mining is extensive, with depths ranging from close-to-surface to 2 500 m. Pillars are widely used to support the open stopes and bords. Little work has been done in the past to determine the strength of pillars on the UG2 Reef and design was done using formulae developed for other hard-rock mines. This has led to oversized pillars with consequent sterilization of ore. In this paper we describe a back-analysis of stable and failed UG2 pillars on the Bushveld platinum mines, and provides a strength formula for UG2 pillars. The formula may be used cautiously on all Bushveld platinum mines with similar geotechnical, geometrical, and geomechanical conditions to the pillars in the database.

Pre-incident planning of fires in underground hard rock mines: old and new risks

Fires in underground mines may pose a challenge to fire and rescue personnel where the complex environment and multiple influences of a fire are poorly considered during pre-incident planning. A better knowledge of pre-incident planning in underground mines would improve the safety of personnel. This study on pre-incident planning in underground mines applied data from experiments, inventories and design fire studies. A number of questions were considered related to information sources, fire modelling, capturing complexity and using fire scenarios. When performing fire modelling, empirical models could be used to complement other modelling tools. The study found that for modelling of spatially extensive mine sections, the use of ventilation network-based mine fire simulations could be a better option. Using an analytical toolbox, an iterative testing of plans and an ongoing planning process, the pre-planning challenges for a mine can be mitigated. The purpose of this study was to examine existing pre-incident planning and propose information sources, tools and specific actions for future plans.

Consolidation of hard rock tailings under positive and negative pore-water pressures: testing procedures and experimental results

This paper presents laboratory testing procedures and key results on the consolidation of tailings from hard rock mines under positive or negative pore-water pressures (PWP). Specific experimental protocols have been developed and applied to assess the behaviour of low-density tailings (slurry) using compression tests in instrumented columns. The testing results on saturated specimens with positive PWP are used to determine the primary and secondary compression (consolidation) parameters of the tailings, based on excess PWP and displacement measurements. The compression tests with controlled negative PWP were conducted using two stress paths: vertical loading with a constant (imposed) suction and with a progressively increasing suction. The results from these tests illustrate specifically, for the first time, the combined effects of the net vertical stress and suction on tailings compressibility parameters, and on the evolution of PWP. The experimental procedures and related experimental results presented here can be quite useful for the analysis of tailings consolidation in the field, where both positive and negative PWP can occur.

Predicting Hard Rock Pillar Stability Using GBDT, XGBoost, and LightGBM Algorithms

Predicting pillar stability is a vital task in hard rock mines as pillar instability can cause large-scale collapse hazards. However, it is challenging because the pillar stability is affected by many factors. With the accumulation of pillar stability cases, machine learning (ML) has shown great potential to predict pillar stability. This study aims to predict hard rock pillar stability using gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) algorithms. First, 236 cases with five indicators were collected from seven hard rock mines. Afterwards, the hyperparameters of each model were tuned using a five-fold cross validation (CV) approach. Based on the optimal hyperparameters configuration, prediction models were constructed using training set (70% of the data). Finally, the test set (30% of the data) was adopted to evaluate the performance of each model. The precision, recall, and F1 indexes were utilized to analyze prediction results of each level, and the accuracy and their macro average values were used to assess the overall prediction performance. Based on the sensitivity analysis of indicators, the relative importance of each indicator was obtained. In addition, the safety factor approach and other ML algorithms were adopted as comparisons. The results showed that GBDT, XGBoost, and LightGBM algorithms achieved a better comprehensive performance, and their prediction accuracies were 0.8310, 0.8310, and 0.8169, respectively. The average pillar stress and ratio of pillar width to pillar height had the most important influences on prediction results. The proposed methodology can provide a reliable reference for pillar design and stability risk management.