Conference PaperPDF Available

The effects of Drilling and Blasting Performance on Fragmentation in a Quarry and Time for Loading, Secondary Breaking and Crushing

Authors:

Abstract

Small-scale mining operations are characterised by a large variety of equipment availability and a high level of operational flexibility. Mine planning is usually scarce or absent in small quarries in Brazil, and mine management generally focuses on daily operations. As a result, analysis of the effects of unit operations, such as blasting, over the whole mining process is often neglected. The goal of the following project was to study the effect of production blasting on loading and hauling times, secondary breaking times and primary crushing times at the Experimental Mine of the Research Center for Responsible Mining of the University of São Paulo, Brazil. The operations analysed included blasts that performed: •• under the average operational standard, defined in this paper as Case 1 •• according to quarry standards, defined in this paper as Case 2. In order to establish the effects of blasting on the downstream process, three key performance indictors were used: 1. The time taken to load the truck, when there is the need for material selection. Due to the fact that we do not have an operational baseline, the increase in time cannot be established, meaning that we must content ourselves with comparing the mean time in each scenario. 2. The average time used for secondary breaking machinery. 3. The time required for the primary crusher to fully process the load of a single truck. The results obtained indicate correlations between the coarseness of the blasted material and these operational times. Solutions are suggested and evaluated for the improvement of the smallscale mining process.
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et al
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




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

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

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 

 

 
 
 
 
 
 


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constrains of cost?


self-defeating in terms of drilling accuracy and results in an
drilling is supervised in order to grant correct collar

   
  


 




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 

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




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

   
  


   

  




















1st Quartile 248.00
Median 271.50
3rd Quartile 446.25
Maximum 941.00
301.94 385.44
258.14 282.95
134.23 194.40
A-Squared 6.22
P-Value <0.005
Mean 343.69
StDev 158.78
Variance 25210.74
Skewness 1.63526
Kurtosis 2.37171
N 58
Minimum 185.00
Anderson-Darling Normality Test
95% Confidence Interval for Mean
95% Confidence Interval for Median
95% Confidence Interval for StDev
8006004002000
40
30
20
10
0
seconds
Median
Mean
400375350325300275250
95% Confidence Intervals
tt case 1
1st Quartile 245.00
Median 268.00
3rd Quartile 311.50
Maximum 1182.00
266.46 345.16
258.80 288.10
119.83 176.64
A-Squared 7.56
P-Value <0.005
Mean 305.81
StDev 142.77
Variance 20382.31
Skewness 4.8299
Kurtosis 27.9528
N 53
Minimum 198.00
Anderson-Darling Normality Test
95% Confidence Interval for Mean
95% Confidence Interval for Median
95% Confidence Interval for StDev
12009607204802400
40
30
20
10
0
seconds
Median
Mean
350325300275250
95% Confidence Intervals
tt case 2



1st Quartile 195.00
Median 233.00
3rd Quartile 341.00
Maximum 1862.00
199.17 515.20
199.91 279.95
314.57 547.41
A-Squared 5.52
P-Value <0.005
Mean 357.19
StDev 399.44
Variance 159553.77
Skewness 3.2710
Kurtosis 10.2223
N 27
Minimum 148.00
Anderson-Darling Normality Test
95% Confidence Interval for Mean
95% Confidence Interval for Median
95% Confidence Interval for StDev
180015001200900600300
16
12
8
4
0
seconds
Median
Mean
500450400350300250200
95% Confidence Intervals
tc case 1
1st Quartile 276.50
Median 314.00
3rd Quartile 389.00
Maximum 734.00
316.23 359.83
290.00 356.86
80.36 111.65
A-Squared 1.44
P-Value <0.005
Mean 338.03
StDev 93.44
Variance 8731.14
Skewness 1.39141
Kurtosis 3.40287
N 73
Minimum 186.00
Anderson-Darling Normality Test
95% Confidence Interval for Mean
95% Confidence Interval for Median
95% Confidence Interval for StDev
7506004503001500
20
15
10
5
0
seconds
Median
Mean
360345330315300
95% Confidence Intervals
tc case 2





Cheatham Jr,
drilling, Engineering Geology,
Chi,
mining, International Journal of Mineral Processing,
Kanchibotla,
Proceedings Explo ’99,
Kojovic,
Minerals Engineering,
Konya, Surface Blast Design, pp 20–34, 118
Lopez Jimeno,
Drilling and Blasting of Rocks,
Michaux,
Minerals Engineering,
Nielsen,
optimisation of an integrated comminution system, in Proceedings
Fifth International Symposium on Rock Fragmentation by Blasting
,
Nielsen,
International Journal of
Rock Mechanics and Mining Sciences,
Oriard, Explosives Engineering, Construction Vibrations
and Geotechnology,
Ouchterlony,
©
Mining Technology,
Peres, Theory and Practice of Mineral
Roy,
Proceedings 14th Annual Symposium on
Explosives and Blasting Research,
Seccatore,
Materials Science Forum,
Workman,
Proceedings 29th
Annual Conference on Explosives and Blasting Technique, pp 131–140
Workman,
Proceedings 35th
Annual Conference on Explosives and Blasting Technique,
 







   

   

   

   



... Studied the effect of drilling and blasting performance on fragmentation, loading time and crushing. It is reported that smaller fragment size leads to minimum cycle time, and improved productivity[8]. Choudhary conducted trial blasts at two different surface coal mines namely A and B of India, to assess excavator's performance with varying rock fragmentation and angle of muckpile. ...
Research Proposal
In this research work we will quantify the impact of fragmentation on fleet performance
... Mining Mineração Considering the comminution system as a whole (Da Gama, 1983;Da Gama and Jimeno, 1993;McCarter, 1996;Morrell, 1998;Nielsen, 1998;Bergman, 2005), every size reduction phase contributes to the final result, and this has consequences on the global energy consumption. Investigations by several researchers (Kanchibotla, 1994;Eloranta, 1995, Kojovic et al., 1995, Kanchibotla et al 1998, Simkus and Dance 1998, Scott, 1996, Kanchibotla et al 1999, Kanchibotla 2000 have shown that all the processes in the "mine to mill" chain are inter-dependent and the results of the upstream mining processes, especially blast results such as fragmentation, muckpile shape and movement, have a relevant impact on crushing and grinding (Mohanty and Chung, 1990;Man-cini et al., 1991;Chakraborty et al., 2002;Ouchterlony et al., 2006;Marin et al., 2015). This review focused on understanding the relationships between the energy provided for size reduction and the resistances to size reduction. ...
Article
Full-text available
This article deals with a study performed at the Experimental Mine of the Research Center of Responsible Mining of the University of São Paulo, to examine the correlations between geological environment, blasting parameters and energy consumption in the primary crushing phase. The research is designed to appreciate the relationships between the energy provided for size reduction and the resistances to size reduction. For this purpose, Key Performance Indicators (KPIs) are used to describe the possible improvements on the energy consumption due to crushing. Four blast tests were performed: for each blast, KPIs were recorded regarding the blast design, the particle size distribution, the real power energy consumption at the primary crushing unit and its rate of utilization. The results show that energy consumption at the primary crusher is a sum of two components: energy directly involved in crushing the rock, and additional energy used for winning the inertial resistances of the moving parts of the crusher. We show how explosive energy and delay times influence the production of coarse fragments that jam the crusher, therefore influencing machinery stops and inertia loads related to putting the jaws back into movement.
Article
Producing more fragmented rock by blasting requires increased costs in drilling and blasting operations. On the other hand, it leads to a reduction of loading, haulage operation and no need for secondary blasting. In this study, the effective parameters in machinery efficiency of Sarcheshmeh Copper Mine was explored. For this purpose, the ratio parameter of length to width of the blast block (RLW) and the Loading parameters including Operation(O1), Failure(F1), Ready (R1), Movement of loading machine (M1), were introduced, measured and used. The blasting blocks' ratio of length to width (RLW) parameter is presented in most of the proposed models. This parameter has been effective in efficiency of loading machinery. The high correlation coefficient of a loading cycle of machine’s bucket (C1) with the independent variables shows that this variable is more affected by the fragmentation and the dimensions of blasted blocks. The best and the worst of a loading cycle of machine’s bucket (C1) are equal to 14.38 and 51.15 seconds in Sarcheshmeh Copper mine for which (D50) corresponds to 3.02 and 10.39, cm respectively and (D80) equals to 6.69 and 24.49 cm, respectively. The Correlation coefficient of 0.8 for specific loading (S1) indicates the high influence of fragmentation caused by blasting operations on specific loading (S1).
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