Introducing a new rock abrasivity index using a scaled down disc cutter

Rock abrasivity influences wear of cutting tools and consequently, performance of mechanized tunneling machines. Several methods have been proposed to evaluate rock abrasivity in recent decades, each one has its own advantages. In this paper, a new method is introduced to estimate wear of disc cutters based on rock cutting tests using scaled down discs (i.e. 54 and 72 mm diameter). The discs are made of H13 steel, which is a common steel type in producing real-scale discs, with hardness of 32 and 54 HRC. The small-scale linear rock cutting machine and a new abrasion test apparatus, namely University of Tehran abrasivity test machine, are utilized to perform the tests. Tip width of the worn discs is monitored and presented as the function of the accumulated test run to classify the rock abrasion. Abrasivity tests show that by increasing the UCS of the rock samples, wear rate is doubled gradually that reveals the sensitivity of the test procedure to the main parameters affecting the abrasivity of hard rocks. For the rocks with the highest UCS, the normal wear stops after performing 5 to 10 rounds of the tests, and then, deformation of the disc tip is detectable. Two abrasivity indices are defined based on the abrasivity tests results and their correlations with CAI and UCS are established. Comparison of the established correlations in this study with previous investigations demonstrates the sensitivity of the indices to the parameters affecting wear of the disc cutters and repeatability of the outputs obtained from abrasivity tests using scaled down discs. Findings of this study can be used to enhance the accuracy of rock abrasivity classifications

Comparison and application of top and bottom air decks to improve blasting operations

The blasting operation is an integral part of mines, and it is still being used as the most economical tool to fragment and displace rock mass. Appropriate blast optimization alleviates undesirable side effects, such as ground vibration, air blasts and flyrock, and it and enhances rock fragmentation. Blast optimization can also be effective in reducing the overall mining cost. One way of reducing blasting side effects is to use deck charges instead of continuous ones. The location of the deck(s) is still considered an unanswered question for many researchers. In this study, an investigation was carried out to find an appropriate air deck position(s) within the blast hole. For this, air decks were placed at three different positions (top, middle and bottom) within a blast hole at Cheshmeh-Parvar gypsum and Chah-Gaz iron ore mines to understand and evaluate air deck location impact on blast fragmentation and blast nuisances. The results were compared based on the existing blasting practices at both mines, as well as the air-deck blasting results. The results obtained from the blasting were very satisfactory; it was found that charging with a top air deck, as compared to current blasting practices, causes a decrement in the specific charge, as well as a decrement of 38% in the back break and 50% in flyrock; the average size of fragments obtained from blasting was increased by 26%. Thus, it can be said that the top air deck is more advantageous than the bottom air deck in terms of reducing undesired blasting consequences

The integrated optimization of underground stope layout designing and production scheduling incorporating a non-dominated sorting genetic algorithm (NSGA-II)

Stope layout designing and production scheduling are main phases to determine the profitability of an underground mining project. These are mainly related to the output of one phase, which has a significant impact on generating of the next phase. Individual optimization of these two phases results only in a local optimal solution. To date, the integrated optimization of these phases has been carried out to maximize net present value (NPV). In this paper, a multi-objective integer programming model (MOIP)was developed to optimize this integrated problem in a sublevel stoping operation. The non-dominated sorting genetic algorithm (NSGA-II)was incorporated to solve the objective functions. The Pareto front generated by NSGA-II showed good convergence and diversity with the solution time decreasing significantly (from 7-8 days to 6–7 h). The most preferred solution point on the Pareto front increased recovery by 18.55% in exchange for a 0.41% reduction in NPV with respect to the single objective optimization to maximize NPV