This category concentrates all the blasting realted themes that support our studies and researchs.

Blast KPI Control System – O-Pitblast Analytics

One of the greatest, and old, truth in the mining world is the one present on Wipware’s slogan:

You can’t manage what you don’t measure.


Peter Drucker

fragmentation is the finger print on a blast crime scene

In fact, this is so real that in the majority of the reputable mines there’s a specific department for Continuous Improvements which is responsible to analyze all the KPI’s gathering process. This department has the responsibility to identify, in each mine sector – from house keeping up to top decision positions – ways to improve the entire mining chain.

To explain Wipware’s slogan, fragmentation is the finger print on a crime scene performed by a blast engineer, a geologist, a mine planner, a blaster, a foreman, a driller, a drill supervisor, …

But usually, geology is chosen as the guilty one. Shame!

This happens because gather information right after the drilling until the loading and haulage is a nightmare.

You know… the rush of blast process, the client that wants a fast job, the safety, the rain, the sun, the dusts, the unpredictive events.. everything serves as an excuse.

Well, O-Pitblast understands all of that and also understand that something needed to be changed! For it was launched

the FIRST blasting process KPI control System

and it is called: O-Pitblast Analytics.

With O-Pitblast Analytics, follow your operations anywhere in the world.

O-Pitblast Analytics concentrates all the information from blast plans and real data from field. It is linked with O-Pitblast App (iOS/Android) that allows the collection of field data such as:

  • Kg’s/hole
  • Hole’s length
  • Kg’s loaded
  • Load issues
  • Stemming length
  • Safety alerts
  • Watered holes
  • Re-drills

In O-Pitblast Analytics you can match different types of KPI’s, control if you’re operation if following the plan or not – and if not, why that is happening.

If you think this is amazing just watch the following video and you’ll understand much better all the idea behind this system.

For any question or trial please contact us through [email protected] or www.o-pitblast.com

Blast Pattern Expansion Process with – O-Pitblast

You can download O-Pitblast article entitled “Blast Pattern Expansion” here.
You’ll find other mining/blasting related scientific papers here.

A pattern expansion process demands several steps in order to determine the best blast parameters (to be changed) and avoid production and safety issues. With O-Pitblast this process is very easy and generated automatically. If you’re interested, in this post we’ll explain you how we do it and to get a trial and see by yourself just contact us directly on our website or through [email protected]

So, let’s start from the beginning…

Geology Gathering

Naturally, mining and explosives engineers need to understand what they are blasting. The first step of a blast process is to try to understand the rock that is meant to be blasted. Identify the exact parameter, in terms of geology and rock structures, that affect the blast and determine the easiness of a rock to break when submitted to an explosive stress, was always a complex process. The practice field experience still plays the major role when the discussion is about the future blast results (Persson, Holmberg, & Lee, 1993). In the next chapters the authors will present a new methodology to, statistically identify this rock factor or rock influence in the process of fragmentation prediction.

Pattern Planning

The second step on blast planning is the definition of its volume and general dimensions. This should be limited by operation characteristics like blasted volume needed, drilling and explosive supplier capacity Load&Haul availability and production. The general planning department generates a blast polygon with certain characteristics. Holes are distributed inside the polygon in order to provide the best energy or powder factor (kg of explosives per m3/t of rock) distribution. The sequence of diagrams shows the overall process.

blast design process

Blast Design Process

Fragmentation Prediction

As mentioned before, based on primordial geology analysis and blast pattern characteristic it’s possible to infer (with a determinate degree of confidence) the size distribution of blast fragments. This first approach allows engineers to assess if their blast will achieve operation needs. Since it depends on a rock factor or rock constant, and the knowing that the crust can be very heterogenic, the prediction model needs to be constantly calibrated in order to provide reliable results. It will be explained afterwards.

Fragmentation prediction using kuz-ram

Fragmentation Analysis

The way authors found to calibrate the fragmentation curve was by comparing the predicted fragmentation with the actual one. The last one can be obtained by photo analysis. There are several tools in the market that provide the needed technology to estimate the block size in a muckpile. In this research was used the iPad and iPhone WipWare’s application, which turned to be a very useful and accurate tool.

Fragmentation Analysis with Wipware iphone app
Fragmentation Analysis with Wipware – Wipfrag

Model Calibration

Based on a linear optimization method (download full artice here), the process to calibrate the rock factor/rock influence constant, analyses the predicted and measured X20, X50, X80 and X90 to obtain a perfect match between the two fragmentation curves.

Rock factor calibration for blast pattern expansion

Rock Factor Calibration Process

Results demands and application

On the changes application stage, there is the need to define the fragmentation restrictions. The model will find the best design parameter (optimum global points), such as burden, spacing, stemming, subdrilling, taking into account the restriction defined, to reduce the blast cost (objective function) and this last one based on the fragmentation restrictions calculated by the Kuz-Ram model. The design parameters restrictions, are based on empirical ranges that can be inspired by the investigation results of the researchers mentioned on the background chapter.

Optimization process for blast pattern expansion

Optimized Design Parameters

CASE STUDY AND RESULTS ANALYSIS

The next points detail each step of the optimization process (based on the methodology described before) and presents some of the achieved results.

Initial stage (IS)

The original situation’s benchmarking is a very important point to record. Every field change must be gradual and studied individually to identify potential issues or deep improvements on the process. The initial stage of the blast designs and results were recorded.

IS Design and Results

In terms of design, the analysed operation parameters and fragmentation results are presented on the following tables.

Blast pattern expansion

Rock factor calibration

Rock factor (rock blastability influence) parameters are present in the 3rd table (of the previous image). These values were used to predict further designs and pattern expansion plans.

It is possible to observe that the obtained fragmentation from photo analysis is slightly smaller than the prediction. Since Kuz-Ram models retrieve higher values of fragmentation when rock factor is higher (meaning the higher the rock factor the hardest is to break that rock) is understandable that the best fit factor must be smaller.

Application

With the calibrated rock factor, applied on the described on the non-lineal optimization model process, the design parameters, that best fulfils the empirical restrictions and match the fragmentation demands (X90 ≤ 400,00mm), were determined (following table)

Blast pattern expansion

This first approach must be treated as any other non-linear problem, considering that this solution can be an optimum local and not the global one. Knowing this, the practical methodology is presented below.

Results

The authors defined a plan to achieve the obtained results in order to avoid too much changes in the terrain and manage the results at every stage. Small changes were applied on each stage and fragmentation results were evaluated. The pattern was expanded until the limits of the desired fragmentation were acceptable. On the next table is possible to analyse the evolution on each stage.

Blast pattern expansion

Conclusion

Analysing previous table the authors incremented 10 cm on burden and spacing on each stage. Up to Stage 4 no fragmentation issue, however when the Stage 5 was applied some oversizes were observed (X90 = 481,53mm). The authors took the decision to select the Stage 4 as the “optimum global”.

This blast pattern was used to blast 5 020 000 m3 and, on next the first graphic, is presented the Drill and Blast improvements in terms of holes reduction (were estimated a reduction of 2779 holes applying this methodology). On the second one, the savings for drilling, explosives and accessories represents an overall saving of 826 019,59€.

The cost benefits and the quality of blast results prove by themselves the utility of this kind of numerical approaches on blast pattern definition. With this research is proved that it’s possible to build mathematical models that simulate results for a blast geometric variables. This methodology proved to be very useful in setting strategies for cost reduction and blast optimization. It’s always important to combine mathematic models with field experience to avoid excessive changes and end up with productivity and safety issues.

This kind of approaches can be used not only for pattern expansion but also for patter adjustments (sometime closing the pattern) to fulfill mine to mill demands in terms of blast results.

Authors

Vinicius Miranda, Francisco Leite, Gean Frank

O-Pitblast & Faculdade de Engenharia da Universidade do Porto

Rock Fragmentation – Kuz-Ram Model

You can download O-Pitblast and Wipware article entitled “Pattern Expansion Optimization Model Based on Fragmentation Analysis With Drone Technology” here.

You’ll find other mining/blasting related scientific papers here.

The basics

In the mining world, rock blasting is one of the main procedures of ore winning process (Hustrulid, 1999). The use of explosives, to break and fragment rock, is the fastest and efficient procedure to make it transportable has become a world-wide used technique. The majority of mines and many civil works recurs to the use of explosives and, since 1627 (the first time explosives were used for rock blasting), lots of blasting techniques were developed (Konya & Walter, 1990).

Overview Mining Process

On one hand, these techniques were established in order to optimize the use of explosive energy and in the other hand, more recently, reduce the overall cost of the operation maintaining blast results’ quality.

Nowadays, with the cost optimization pressure in the majority of mining companies, is compulsory to analyse each mine-to-mill operation and get the best results from it.

Rock Blasting

The three main factors affecting the blast results, depends on explosive selection (and its quality), blast design and the procedures implemented to replicate this design. It’s important to understand the rock characteristics, structures and behaviour when submitted to a certain kind of stress generated by explosives (Bhandari, 1997).

Empirical research and evidences on blasting operations helped to develop a series of blast design formulae in order to propose guidelines for the design process. Is believed, that these important “rules” are meant to be applied with the objective to achieve the desired blast results in an initial stage of any operation (Jimeno, Jimeno, & Carcedo, 1995; p. 200). The results, ground conditions, operation details and geology will be the real decisive kpi’s to define the blast design.

Blast design using drone photogrammetry technology. O-Pitblast Software. Blast Design Software
O-Pitblast – Blast Desing Software

As mentioned by Jimeno, Jimeno, & Carcedo, 1995, there are a series of authors, mining engineers and researchers that developed empirical formulas, for pattern design, involving relations between:

  • Diameter;
  • Bench high;
  • Hole length;
  • Stemming;
  • Charge length;
  • Rock density;
  • Rock resistance;
  • Rock constants;
  • Rock seismic velocity;
  • Explosive density;
  • Detonation pressure;
  • Burden/Spacing ratio;
  • Explosive energy.

Some of the researchers are Andersen (1952), Pearse (1955), Hino (1959), Allsman (1960), Ash (1963), Langefors (1963), Hansen (1967), Konya (1972) and Lopez Jimeno, E(1980). In Figure 1 are presented some of these parameters on a bench blasting model.

Fragmentation Prediction – Kuz-Ram Model

Humans always tried to understand the future. The same happens with mining engineer trying to predict their blast results. In this case, for fragmentation results and prediction, a world-wide well-known model is presented by Cunningham, 2005– Kuz-Ram Fragmentation model.

Despite several models were developed along the years, the simplicity offered by Kuz-Ram model makes it one of the most used prediction models (Cunningham, 2005). This Model is based in three main equations:

Kuznetsov Equation, presented by Kuznetsov, determines the blast fragments mean particle size based on explosives quantities, blasted volumes, explosive strength and a Rock Factor.

Where = Medium size of fragments (cm); A= Rock factor; K = Powder factor (kg/m3); Q= Explosive per hole (kg); 115 = Relative Weight Strength (RWS) of TNT compared to ANFO; = Relative Weight Strength (RWS) of the used explosive compared to ANFO.

Rosin-Ramler Equation, represents the size distributions of fragmented rock. It is precise on representing particles between 10 and 1000mm (Catasús, 2004; p80).

Where = Mass fraction passed on a screen opening x, n = Uniformity Index

Uniformity index equation, determines a constant representing the uniformity of blasted fragments based on the design parameters indicated as

Where B = Burden (m), S= Spacing (m), d = Drill diameter (mm), W = Standard deviation of drilling precision (m),  = Bottom charge length (m),  = Column charge length (m), L = Charge Length (m), H = Bench height (m).

Authors

Vinicius Gouveia, Francisco Leite, Thomas Palangio
O-Pitblast & Wipware