Sedat ESEN
Esen Mining Consulting (EMC), Sydney, Australia
Abstract
This paper presents the effective fragmentation strategies that could be applied at quarries to improve the blast fragmentation and productivity. Fragmentation measurement and modelling as well as a comprehensive drill and blast audit are essential for improving the fragmentation. Face profiling and boretracking are good tools to manage the “as-designed” and “as-drilled” conditions to get reasonable fragmentation from face burden zone and minimise the flyrock risk. In general, a large scatter in fragmentation data was observed at quarries and mine sites and the causes should be analysed by a detailed root-cause analysis technique. Two case studies were presented in this paper showing some of the effective fragmentation strategies.
1. Introductıon
Drill and blast is understood to be the first stage of the comminution and known to affect the downstream processes (load and haul, crushing and grinding). Investigations by several researchers to date have shown that all the processes in the mine to mill value chain are inter-dependent and the results of the upstream mining processes (especially blast results such as fragmentation, muckpile shape and movement, rock damage) have a significant impact on the efficiency of downstream processes, especially crushing and grinding (Eloranta 1995, McKee et al. 1995, Kojovic et al. 1998, Kanchibotla et al. 1998, Simkus and Dance 1998, Scott et al. 1999, Kanchibotla et al. 1999, Valery et al. 1999, Valery et al. 2004, Dance et al. 2006, Esen et al. 2007, Valery et al. 2007, Kanchibotla and Valery, 2010).
The aggregates industry appears to be a perfect candidate for Mine-to-Mill optimization since its main focus is particle size reduction through blasting and crushing. Since a typical crushed stone quarry can consume between 1.7 – 2.2 kWh/t, over 2.5 billion kWh of electrical energy are consumed per year by crushed stone production in the US. Clearly, if there is a segment of the mining industry where energy saving research can have an impact, it is the aggregates industry (Adel et al., 2006).
As shown in Table 1, energy used in crushing is approximately 9 times more than drill and blast which indicates that more efficient size reduction should occur at earlier step (blasting) to minimize the total energy consumption.
| Specific energy, kWh/t | Energy factor | |
| Blasting | 0.1-0.3 | 1 |
| Load and haul | 0.2-0.5 | 2 |
| Crushing | 1.0-2.0 | 9 |
| Cement grinding | 40-60 | 286 |
Table 1. Energy consumption at an aggregate quarry
Fragmentation size distribution affects the crushing circuit in many ways. Coarse fragments and oversize present in the muckpile will reduce the primary crusher throughput and will lead to downtime to clear crusher bridging. The maximum feed size should be no greater than 80% of the crusher feed opening. Poor fragmentation will also increase the load to the secondary and tertiary crushing stages, because there will be less undersize to bypass these stages. This will affect productivity and energy consumption. The other effect of blasting is the production of the fractures that are produced within the rock fragments. There is substantial evidence that such cracking is produced (Nielsen and Kristiansen 1996, Katsabanis et al. 2003). The effect of internal fractures is to soften the fragments, making them easier to break. Thus, the use of greater energy input in the blasting operation can often be less costly than expending energy downstream. This has benefits to productivity, energy expenditure and to the wear of crusher liners.
With the help of the improved blast outcomes at the aggregate quarries, the sites have reduced rock breaker hours due to less oversize, improved loader productivity, increased crusher throughput, less power draw and downtime at the crushers. A few examples were given below:
• A drill and optimization study at Linwood Quarry by Martin (2012) showed that a) 26% reduction in oversize material; b) 28% reduction in fines material; c) drill and blast savings of up to 22%, including oversize costs.
• Lawrance et al. (2009) carried out a mine-to-mill type project at a quarry in the US and they achieved impressive cost savings and increases in plant tonnage throughput: Crusher throughput resulting from all the validation blasts was increased by at least 28%. In spite of a 28% increase in drilling and blasting cost, the standard cost model for the project showed: a 10% to 27% increase in crusher plant capacity over a baseline of 373 tons per hour (TPH) to an average of 475 TPH. (A 102 TPH positive shift in capacity); a 17% to 31% reduction in net total cost per ton when scalping; and even without scalping an 8.8% reduction in the net cost per ton.
• Chavez et al. (2007) recorded about 30% increase in the primary crusher throughput and improvements in total cycle time in load and haul.
• JKMRC’s Mine-to-Mill project at Pittsboro quarry (Adel et al., 2006) in the USA achieved a) throughput at the primary crushing stage increase: The ramp-up was 9.5% (to 1035 tonnes perhour) for material Luck Stone was producing for itself from dacite tuffs and basalt, and 14.6% (to 965 tph) for andesite material the company was crushing for the quarry owner, 3M.
• Elliott et al. (1999) conducted a fragmentation study at Lafarge Exshaw operation which resulted in 15.6% increase in the crusher throughput and 30% reduction in the power draw.
• Rock breakage is more cost effective using explosives than a hydraulic breaker, and explosive consumption costs have been increased at the quarry to effect an overall reduction in the costs of quarrying. Although powder factor was increased approximately 23%, overall cost (drill and blast and rock breaking) dropped by approximately 7% (Cox and Cotton, 1995). This study did not quantify the benefits in crushing.
Cement production is an energy intensive process. It consumes 2% of the global primary energy and 5% of the total global industrial energy. Grinding is a high-cost operation consuming approximately 60% of the total electrical energy expenditure in a typical cement plant and 40% of this energy is for raw material grinding (Fujimoto 1993, Benzer 2005). Therefore, fragmentation of raw materials (limestone, etc) fed into the mills is crucial for reducing the energy consumed in raw material grinding stage. Table 1 shows that specific energy consumption is very low at blasting and crushing stages. As discussed earlier, cement grinding is a very energy intensive process and size reduction should occur as much as possible prior to the raw material grinding.
This paper presents the tools and methodologies followed by the author used in a drill and blast study to control the fragmentation. Case studies are presented to demonstrate the application.
2. Fragmentation Measurement
There are numerous image processing softwares (Split-Desktop, WipFrag, FragScan, PortaMetrics etc) which are commercially available. For manual systems, usually 10-20 pictures should be sufficient to adequaltely describe the fragmentation size distribution from a blast. Pictures should be taken at different shift breaks whilst blasted material is being excavated to accurately represent the fragmentation from inner and outer parts of the blast. Figure 1 shows an example of a muckpile image and the delineated picture used by the image analysis software to determine the particle size distribution.
Figure 1. Original (top) and processed (bottom) image for fragmentation analysis
3. Fragmentation Modeling
Blasting community has been widely using the fragmentation model developed by Cunningham (1983) which was later revised a few times (see Cunningham 1987, 2005). Due to the Kuz-Ram model’s poor ability to describe the fines, the Two Component Model (Djordjevic, 1999), the Crush Zone Model (Kanchibotla et al., 1999) and Onederra and Esen’s (2004) model were developed at the JKMRC in Australia. All combine two Rosin-Rammler distributions or components, one for the coarse part of the curve and one for the fines. Onederra and Esen (2004) showed that the Kuz-Ram model is not able to satisfactorily predict the complete size distribution of fragments, particularly in the fine and intermediate size fractions. The model was later updated (Esen, 2013) using Swebrec function (Ouchterlony, 2005). Figure 2 shows a calibrated model at a gold mine where a partial sieving data is available for a muckpile.
Figure 2. Comparison of the sieve data at 10 and 30mm with the fragmentation model
Having carried out the image analysis to determine the size distribution of the blasted muckpile, the fragmentation model was calibrated using the measured fragmentation data. Sieving was carried out onsite and the sieve sizes were 10mm and 30mm (Figure 2). It is shown that results of the fragmentation model compare well with measured data (Esen, 2013).
Figure 3 shows another example of the validation which shows a good agreement between sieved data and model prediction at Bararp Quarry in Sweden.
Figure 3. Bararp Quarry fragmentation data – experimental vs model fit
4. Quality Control At Bench
A good on-bench drill and blast audit can show how well the blast is implemented and show the detailed analysis of the hole depth (backfill/re-drills), hole collar deviations (deviations in burden and spacing), stemming (material type, size, length), priming quality, bulk explosive performance, initiation control (selection of delay times and burden relief), bench preparation and a general overview of the drill and blast process. The audit process can help understand the issues in the implementation. Figures 4-5 show two sites with poor and good drill control. Drill tolerance is 0.5m for both sites. Example 1 has got almost half of the blastholes out of tolerance limit whereas Example 2 is a much better site (approximately 20% out of tolerance). These analyses should be extended for hole length and stemming length.
For quarries, face profiling and boretracking are key tools to manage the face burdens and hole deviations. Their use also minimizes the airblast and flyrock risks.
Figure 4. Hole collar accuracy for Example 1
Figure 5. Hole collar accuracy for Example 2
5. Root Cause Analysis For The Variability in the Fragmentation Data
Figure 6 shows an example of fragmentation data obtained from a mine site. 80% passing size (F80) was chosen in this figure. It is shown that there is a significant variability in the data and coarse sizes are clearly seen (>300mm). So, what causes such a large scatter? The answer lies in the root cause analysis which should be carried out by evaluating the QA/QC data, rock data (strength and structure) and blast design parameters using the fragmentation model. Two case studies were presented as examples.
Figure 6. Histogram of the fragmentation data (F80) from an audited mine site
5.1. Case Study 1
Figure 7a shows the results the hole depth compliance from a site. Figure 7b shows the energy distribution which is an output given by JKSimBlast software. It shows the hot and cold spots (high and low explosive energy, respectively as represented by MJ/m3). This figure is a critical one as it identifies a few key issues on-site:
Figure 7. a)histogram of hole depth (actual – design) b)Explosive energy distribution showing the variability in the energy levels
• Overdrilling and backfilling issues;
• Inadequate bench preparation and re-drill issues (some large areas represented by blue color indicating no blasthole);
• Large variations in burden and spacing causing non-uniform energy distribution;
• Poor blast shape.
The site implemented the recommendations to address above issues. In addtion, they improved the size distribution of the stemming material (from 15-40mm to 5-20mm for 165mm blastholes). Table 2 indicates the major changes and the fragmentation results. The site experienced reduced crusher downtime and increased crusher throughput after this study.
| better blast shapes | ||||
| re-drills and backfills were carried out | ||||
| improved stemming size | ||||
| better hole collar location accuracy | ||||
| Base
case |
Modified case | |||
| Diameter, mm | 165 | 165 | ||
| Drill pattern | 3.8×4.4 | 3.5×4.1 | ||
| Powder factor, kg/m3 | 1.2 | 1.4 | ||
| Stemming, m | 3 | 3 | ||
| Measured F80, mm | 380 | 255 | ||
Table 2. Changes in the drill and blast
5.2. Case Study 2
A fragmentation study was carried out at an Australian Quarry due to the coarse fragmentation complaints at site. Figure 8 shows an image from an oversize pile. Rock type was basalt with in-situ block size of 0.5m. Table 3 summarizes the blast design parameters for the base case. Emulsion explosive with 30% ANFO at a density of 1.20 g/cm3 is used. Face profiling is carried out to manage the blasthole’s locations and their angles to achieve face burden of 3.3-3.8m. Boretracker is used to manage the issues caused by drill deviations.
Figure 8. Oversize piled at a separate stockpile. Scale is 1m.
| Dia,mm | 89 |
| Hole length, m | 10.3 |
| Bench height, m | 9.3 |
| Hole angle | 10 |
| BxS, m | 2.7*3
(rectangular pattern) |
| Face burden, m | 3.6 |
| Face burden range, m | 3.3-3.8 |
| Stemming length, m | 2.2 |
| Number of rows | 4 |
| Control row, ms | 42 |
| Echelon row, ms | 25 |
| Face row powder factor, kg/m3 | 0.61 |
| Inner row powder factor, kg/m3 | 0.81 |
Table 3. Blast design parameters
Numerous pictures were acquired from an oversize pile and from the blasted muckpile. Measured F80 (80% passing size) and top sizes were 293mm and 580mm, respectively for the production blasts. Top size was 960mm for the oversize pile (Figure 9). The site was happy with the fragmentation obtained from the inner rows; however, the causes of oversize which were present in the stockpile needed to be identified and minimized.
Figure 9. Measured fragmentation data and fragmentation model
Fragmentation model was calibrated with the coarse size matching to the measured oversize data. Two cases were run with the model as shown in Table 4. Alternative cases had different front row burden, spacing and stemming length values. As shown in Table 5, Case 2 had a top size around 700mm and had a similar fragmentation when compared to the inner rows. The site adopted Case 2 with improved fragmentation outcomes. Staggered drill pattern was chosen for better energy distribution.
6. Conclusions
Effective control strategies for fragmentation are presented in this paper. Some of the key conclusions are as follows:
• Mine to mill type optimization studies can help improve the productivity of the quarries and reduce the total cost per ton significantly:
• Some researchers showed that total drill and blast and oversize cost dropped by 7-22% with the drill and blast optimization study;
• 10 to 30% increase in the primary crusher throughput;
• Up to 30% decrease in power draw;
• 17% to 31% reduction in net total cost per ton.
• Fragmentation measurements should be carried out on-site to understand the variability of the data and minimise the variability to provide a consistent feed to the crusher without oversize.
• Fragmentation model calibrated to the measured data offers significant benefits to the quarries as it presents alternative designs for better fragmentation.
• Blast auditing is crucial at any site as it identifies the issues with QA/QC, pit planning, drill and blast process as well as safety. It should be conducted at every site regularly.
| Front burden, m | Spacing, m | Stemming, m | |
| Base Case | 3.6 | 3 | 2.2 |
| Case 1 | 3 | 3 | 2 |
| Case 2 | 2.7 | 2.8 | 1.8 |
Table 4. Base case and two alternative cases
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