RATNESH TRIVEDI

T. N. SINGH

NEEL GUPTA

SUSHIL BHANDARI

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FLYROCK

• Flyrock is the propelling rock from the blast area.

• A leading cause of fatalities and equipment damage

Use of down the hole delays and accurate electronic delays have given some control.

• Use of GPS controlled drills, bore hole

deviation tracker and appropriate design tools

have reduced incidence of flyrock accidents.

• In Indian mines 60 % fatal accidents in open

• pit coal mines and 54 % in non coal mines are

• caused by flyrock.

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FLYROCK PREDICTION

Aim of this study is to predict throw of flyrock.

In past several empirical models have been proposed

In present case, two soft computing techniques have been used for flyrock prediction and compared with flyrock measurements carried out for 120 blasts. In these blasts high resolution videography was used. The techniques used are artificial neuro fuzzy inference system (ANFIS) and Flyrock Predictor software of Terrock.

ANFIS is fuzzy logic based artificial intelligence technique that is framed in adaptive systems for ease in learning. It can also be looked as combination of neural network and fuzzy logic for more precise results.

Flyrock Predictor software is based on the empirical studies made by Richards and Moore.

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FIELD STUDY 120 blasts in 4 Indian limestone mines have been studied. All 4 mines have working benches of 9m to 10m in height.

Drilling pattern geometry is staggered where blastholes are stemmed using drill cuttings. Explosive used is ANFO and Excel or Raydet signal tube is used for initiation.

Blast design parameters like burden, spacing, stemming, borehole diameter, bench height, linear charge concentration, specific charge and flyrock distance have been recorded at sites.

Out of innumerable blasted fragments only those having size more than 10 cm has been selected for flyrock study purpose whose throw was estimated using hand held GPS.

Value of RQD for the rock study was estimated using volumetric count method5 and UCS was determined.

Blasting events videos recorded at site have also been analyzed using ProAnalyst 1.5.6.7 software to determine flyrock launch angle and launch velocity.

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BLASTING OPERATIONS AT STUDY SITES

Study site Borehole Explosive Initiation Velocity of

Diameter (mm)

System

Detonation (m/s)

1 Mehagaon-Bamangaon 115 ANFO Excel

3000- 4000

2 Sheopura- Kesarpura 165 ANFO Excel

2500- 3500

3 Aditya Limestone 115 ANFO Excel

2500- 3500

4 Kotputli 115 ANFO Excel 2500- 3500

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BLAST DESIGN AND GEOTECHNICAL PARAMETERS AT MINE SITE

Symbol Unit Description Range

Mine1 Mine 2 Mine3 Mine4

B m Input 3-3.5 4-4.5 4.3-4.6 3-3.4

ls m Input 2.9-3.9 3.8-5.0 2.4-3.5 3.8-4.3

ql kg/m Input 8.5-8.8 16.3-16.7 8.5-8.7 8.7-9

q kg/ Ton Input 0.09-0.15 0.10-0.16 0.06-0.08 0.15-0.18

ơc MPa Input 60-68 60-66 58-62 56-63

RQD % Input 58-77 55-75 55-76 60-71

Rf m Output 28-49 28-54 20-36 28-50

Mine1 Mine 2 Mine3 Mine4

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BLASTING SITE

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MEASUREMENT OF LAUNCH VELOCITY AND LAUNCH ANGLE OF FLYROCK PROJECTILE USING ‘PROANALYST’ SOFTWARE

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ANFIS ANFIS, Adaptive Neuro-Fuzzy Inference System a branch of

artificial intelligence technique, being currently used to solve complicated blast induced problems like flyrock, back break etc.

It’s common name is Tagaki-Sugeno fuzzy model that is stochastic in nature and widely used in information technology, decision making, data analysis and predictive models etc.

It is a fuzzy technique that uses given input and output dataset to describe system behavior using membership functions.

Membership functions is a curve that defines how each point in the input space is mapped to a membership value between 0 and 1. It’s purpose is to transform the discrete input value into continuous one e.g. gaussmf, gbellmf, trimf etc.

In ANFIS first the discrete input are converted into continuous one through membership function.

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PERFORMANCE OF ANFIS

•Adaptive neuro fuzzy inference system (ANFIS) maps input with output through membership functions.

• Its method of generating ANFIS model named ‘subtractive clustering’ divide complete dataset into a number of clusters.

•The accuracy of the ANFIS model is determined by RMSE value. This value can be further reduced by tuning model using Hybrid algorithm.

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Burden conditions usually control flyrock distances in front of the face and maximum throw Lmax can be determined

6.22

maxB

m

g

kL

B= Burden, m m = Charge mass /m k = a constant Lmax = Maximum throw (m)

Fly Rock Prediction and Reduction

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Apply safety (uncertainty) factors to the calculated maximum throw

The clearance distance for plant and equipment is double the maximum throw. The clearance distance for personnel is four times the maximum throw.

FLYROCK PREDICTOR It’s a empirical studies based software whose working

principle lies with the relationship between throw of flyrock, face velocity and scaled burden.

For performing analysis using this software charge mass, burden and/or stemming height and site constant (k) that is determined based on site calibration are required.

Site calibrated values of k for 4 different mines

Mine Mehagaon-Bamangaon

Sheopura- Kesarpura

Aditya Kotputli

K 21.2 15.41 19.93 21.90

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DATA FOR FLYROCK PREDICTOR

FLYROCK PREDICTOR

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OBSERVED AND PREDICTED FLYROCK DISTANCE FOR VARIOUS BLASTS IN MINES

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RESULTS OF ANFIS & FLYROCK PREDICTOR

Predictive efficiency of these two techniques have been compared based on the values of performance indices like root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2).

Performance indices of ANFIS & Flyrock

Predictor

Conclusive graphs demonstrating the blast wise deviation in flyrock prediction and performance of ANFIS and Flyrock Predictor

S. No. Performance index

ANFIS Flyrock Predictor

1 RMSE 1.75 2.48

2 MAE 1.54 1.96

3 R2 0.966 0.932

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F CORRELATION BETWEEN OBSERVED AND ANFIS PREDICTED FLYROCK DISTANCE

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CORRELATION BETWEEN OBSERVED AND FLYROCK PREDICTOR VALUE

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CONCLUSION

This research concludes first the level of accuracy that can be achieved with the application of these 2 excellent soft computing techniques in predicting throw of flyrock.

Further, the working efficiency and simplicity of application of Flyrock Predictor software in mines for predicting flyrock throw compare to artificial intelligence techniques e.g. ANFIS that is quite complicated and needs special expertise in its use.

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