This PPT refelcets on the procedure to Predict PPV

1. Prediction of PPV in South
kaliapani Chromite mines
Debasish Nath
114MN0528

2. Literature review
 In 2006 Manoj Khandelwal, T.N. Singh concluded that few important and widelyused predictors have
been used to predict the peak particle velocity (PPV) and computed results are compared with actual
field data. The same input–output data sets have been also used for the prediction by artificial neural
network (ANN). The basic idea is to find the scope and suitability of the ANN for prediction of PPV over
the widely used vibration predictors.
 In 2015 S.R. Dindarloo proposed the support vector machine (SMV) algorithm for prediction of the
peak particle velocity (PPV) induced by blasting at a surface mine. Twelve input variables in three
categories of rock mass, blast pattern, and explosives were used for prediction of the PPV at different
distances from the blast face. The results of 100 experiments were used for model-building, and 20 for
testing. A high coefficient of determination with low mean absolute percentage error (MAPE) was
achieved, which demonstrates the suitability of the algorithm in this case. The very high accuracy of
prediction and fast computation are the two major advantages of the method.
Where v=ppv, fc =UCS

3. OBJECTIVES
 Study and analysis of blasting and its effects
 Collection of information for blasting parameter of the mine.
 Study of different ppv prediction models
 Data analysis of collected data by prediction models.
 Comparison of Predicted PPV value with measured value
 Proposition of suitable model for the mine.

4. Blasting
 Rock blasting is the process which consists of several operations such as drilling blast holes,
charging explosives into the holes, connecting the holes by suitable blasting pattern with surface
delay and igniting by safety fuse or exploder. Rock is affected when explosives are detonated.
Total charge is converted into hot gas and intense shock pressure. The rock is crushed and
fractured by intense shock pressure and separated from each fracture by gas pressure. The shock
energy creates fracture in the rock mass. Gas pressure expands the fracture and also helps in
move the rock from original position.

5. Effects of blasting
 Air overpressure : One of the principal disturbances created by surface blasting is air blast. In the
surface blast, a part of the total blast energy escapes in to the atmosphere which is usually above
speed of the sound, typically at 300m/sec in normal air . This over pressure wave is atmospheric.
It is transmitted away from blast sites in the form of wave that travel at the called Air Blast or Air
Over Pressure (AOP) . Noise is the audible and infrasonic part of this wave spectrum from 20 Hz
to 20 KHz.
 Fly Rock: Flyrock is defined as the rock propelled by the force of explosion generated from
explosive in confined form in blast hole beyond the blast hole . When explosive is detonated in
blast hole, high pressure energy with gas energy is generated. The pressure is the cause of
fragmentation while high pressure is responsible for bursting of rock masses from the bench. Fly
rock is generated due to mismatch of the distribution of explosive energy, type of confinement of
explosive charge and mechanical strength of explosive energy.

6. Parameters affecting blasting
 Controllable parameters : It includes explosives , effective energy, strength, detonation velocity,
types of explosives, diameter, density ,blasting design , detonating pressure etc.
 Uncontrollable parameters: It deals in density , moisture content , thermal properties , joints
,tensile strength, ultrasonic velocity.

7. Standards
Name Equation References
USBM Duvall and petkof
Langfors-kihlstrom Langfors and kihlstrom
Ambraseys-hendron Ambrseys and hendron
Bureau of Indian
standards
Indian standard institute
CMRI predictor Pal Roy
PPV is peak particle velocity USBM US bureau of mines CMRI cardiac magnetic
resonance imaging, v ppv in mm/s Q is the maximum charge per delay, D /R is
the distance between the blasting face to vibration monitoring point(m) K,A,B,
n are site constants,

8. Blast site description
 Kaliapani Chromite Mine (ML area – 64.463 Ha.) gives Production of Capacity of 1.692 MTPA At
Village Kaliapani, Tehsil Sukinda, District Jajpur, Odisha State . The area lies in the South-Western
quadrant of the survey of India topo sheet No. 73 G/16. The Tomka-Mangalpur all weather road
passes almost along the northern lease boundary. This road connects the area with Jajpur-
Keonjhar Road, the nearest rail head on the South- Eastern Railways via-Mangalpur as well as
Tomka. At Tomka it joins with the Express Highway (Daitari -Paradeep) and via-Duburi to Jajpur
Road the total distance is 57 Km. The distance via- Mangalpur where it joins NH 53 to Jajpur
Road is 53 Km.

10. Instrumentation and data collection
 The blasting operation was monitored by seismograph(make: Instatel inc,Canada –minimate plus
with two geophones and two microphones). Total nine blast events were monitored. The
diameter of blasthole was 102m and with spacing and burden of 3 and 2.5 respectively. The PPV
data was recorded at different distances with in the mine premises with respect to
maximumexplosive per delay

11. observation
SL No. Radial dist.(m) Sacaled distance
(m/√kg)
Cuberoot of
scaled distance
PPV(mm/s)
1 420 41.98321 90.60114 13.8
2 320 31.98721 69.02944 18.5
3 380 36.03555 79.14632 6.55
4 536 50.8291 111.638 6.24
5 640 78.35234 158.0166 0.984
6 400 26.70229 65.9431 1.76
7 460 30.70764 75.83457 1.38
8 420 72.42456 130.2714 0.996
9 480 82.77092 148.8817 0.509

12. Data analysis
 Multivariable linear regression model attempts to the relationship between two or more
explanatory variables and a response variable by fitting a linear equation to observed data. Every
value of the independent variable x is associated with a value of the dependent variable y.
 Formally, the model for multiple linear regression, given observation, is Yi = B +B 1X i1 +
B 2Xi2 +……………+B pX ip + ei For i = 1, 2……, n .
 In the least square model the best fitting line for the observed data is calculated by minimizing the
sum of the squares of the vertical deviations from each point to the line. The deviations are firstly
squared then summed, there are no cancellations between positive and negative values.
PPV prediction by multiple regression analysis

13. Output of regression analysis

14.  The equation developed from Multiple regression analysis is
PPV= 39.81484 -0.14122(Qmax /delay)+0.022069(D)-0.56476(S.D)

15. Work to be done
 Study and understanding of ANN model
 Analysis of data using ANN model
 Comparison of results from Multiple regression model and ANN model.
 Proposition of suitable model for the mine.