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Murthy, V. M. S. R.
- Flyrock in Surface Mine Blasting: Understanding the Basics to Develop a Predictive Regime
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PDF Views:98
Authors
Affiliations
1 CSIR-Central Institute of Mining and Fuel Research, Regional Centre Unit-I, 3rd Floor, MECL Complex, Seminary Hills, Nagpur 440 006, IN
2 Department of Mining Engineering, Indian School of Mines, Dhanbad 826 004, IN
1 CSIR-Central Institute of Mining and Fuel Research, Regional Centre Unit-I, 3rd Floor, MECL Complex, Seminary Hills, Nagpur 440 006, IN
2 Department of Mining Engineering, Indian School of Mines, Dhanbad 826 004, IN
Source
Current Science, Vol 108, No 4 (2015), Pagination: 660-665Abstract
Flyrock is one of the most contentious issues in bench blasting. Unlike ground vibrations, flyrock has the propensity to cause fatality and severe injuries. Although the kinematic equations present a basis for the estimation of flyrock distance, these suffer from the drawback of ignoring the post-release effects of trajectory motion in air. Predictive models that are based on such equations not only suffer from this anomaly, but also fail in flyrock distance prediction due to the gross approximations of initial velocity calculations and shape of the fragments.
This article discusses the flyrock phenomenon, causative factors and their use in developing prediction models. Different predictive models, namely empirical and semi-empirical are reviewed and the drawbacks highlighted. The principal causative factors of flyrock namely blast-hole pressure, time of blasting impact and post-release corrections are discussed with their relevance. The study culminates into a futuristic comprehensive flyrock distance prediction methodology to predict the blast danger zone along with the probability and risk associated with flyrock.
Keywords
Basics, Blast Danger Zone, Flyrock Prediction, Surface Blasting.Full Text
- Chip Size Characterization for Selecting Optimum Production Parameters of Surface Miner Operating in a Coal Mine
Abstract Views :250 |
PDF Views:116
Authors
Affiliations
1 Central Institute of Mining and Fuel Research, Dhanbad 826 015, IN
2 Department of Mining Engineering, Indian School of Mines, Dhanbad 826 004, IN
1 Central Institute of Mining and Fuel Research, Dhanbad 826 015, IN
2 Department of Mining Engineering, Indian School of Mines, Dhanbad 826 004, IN
Source
Current Science, Vol 108, No 3 (2015), Pagination: 422-426Abstract
Coal production using surface miner technology is a well-accepted method today in Indian coal mines contributing a sizeable proportion to the overall production. Production of coal chips of desired size is an important parameter in surface miner performance evaluation in terms of tonnes per hour as well as fulfilment of the need of the consumers. The demand for an average chip size in the range 100-150 mm thermal power plants is growing and this size also fetches a premium price compared to blasted lumpy coal. A field study was conducted at Sonepur Bazari opencast mine, Eastern coalfields (ECL), West Bengal, India for evaluating the cutting operation and performance of a 2200 SM surface miner under varied operational and rock mass conditions. An imaging technique coupled with Fragalyst software was used for grabbing and analysing the sizes of chips produced by surface miner. The wide variation in chip size formation observed in the field was due to the fluctuations in cutting speed and also the presence of joints. This communications reports the study carried out on surface miner to develop a new methodology for characterizing chip size, fixing the optimum machine operating parameters for a desired chip size and also the production potential.Keywords
Surface Miner Technology, Chip Size, Coal Production, Rock Mass.Full Text
- Importance and Sensitivity of Variables Defining Throw and Flyrock in Surface Blasting by Artificial Neural Network Method
Abstract Views :229 |
PDF Views:93
Authors
Affiliations
1 CSIR-Central Institute of Mining and Fuel Research, Regional Centre Unit-I, MECL Complex, Seminary Hills, Nagpur 440 006, IN
2 Department of Mining Engineering, Indian Institute of Technology (ISM), Dhanbad 826 015, IN
1 CSIR-Central Institute of Mining and Fuel Research, Regional Centre Unit-I, MECL Complex, Seminary Hills, Nagpur 440 006, IN
2 Department of Mining Engineering, Indian Institute of Technology (ISM), Dhanbad 826 015, IN
Source
Current Science, Vol 111, No 9 (2016), Pagination: 1524-1531Abstract
Rock breakage by explosives is followed by throw or heaving the broken material and occasional flyrock. Heaving is a desired feature of blasting for efficient mucking. However, flyrock is a rock fragment that travels beyond the designated distance from a blast in surface mines, and poses a threat to adjacent habitats. Here, we decipher the importance and sensitivity of the variables and factors used to establish the predictive regime of throw with more emphasis on flyrock. The data collected were modelled using artificial neural network approach. The importance and sensitivity of variables and factors were delineated so that they are in tune with the rationale of the outcome of the blast. A combinatory approach was devised to arrive at minimal variables and factors to reduce the statistical redundancy, and to propose a rational predictive regime for throw and flyrock in surface mines.Keywords
Artificial Neural Network, Blasting, Flyrock, Throw, Surface Mines.Full Text
References
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- Raina, A. K., Murthy, V. M. S. R. and Soni, A. K., Flyrock in surface mine blasting: understanding the basics to develop a predictive regime. Curr. Sci., 2015, 108(4), 660–665.
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- Mishra, A. K. and Mallick, D. K., Analysis of blasting related accidents with emphasis on flyrock and its mitigation in surface mines. Rock Fragmentation by Blasting (eds Singh, P. K. and Sinha, A.), Taylor and Francis, London, 2013, pp. 555–561.
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- Monjezi, M., Bahrami, A. and Varjani, A. Y., Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks. Int. J. Rock Mech. Min. Sci., 2010, 47, 476–480.
- Stojadinovic, S., Pantovic, R. and Zikic, M., Prediction of flyrock trajectories for forensic applications using ballistic flight equations. Int. J. Rock Mech. Min. Sci., 2011, 48, 1086–1094.
- Rezaei, M., Monjezi, M. and Varjani, Y. A., Development of a fuzzy model to predict flyrock in surface mining. Saf. Sci., 2011, 49, 298–305.
- Ghasemi, E., Amini, H., Ataei, M. and Khalokakaei, R., Application of artificial intelligence techniques for predicting the flyrock distance caused by blasting operation. Arab J. Geosci., 2012; doi:10.1007/s12517-012-0703-6,10.
- Monjezi, M., Dehghani, H., Singh, T. N., Sayadi, A. R. and Gholinejad, A., Application of TOPSIS method for selecting the most appropriate blast design. Arab J. Geosci., 2012, 5, 95–101.
- Khandelwal, M. and Monjezi, M., Prediction of flyrock in open pit blasting operation using machine learning method. Int. J. Min. Sci. Technol., 2013, 23(3), 313–316.
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- Hierarchy of Parameters Influencing Cutting Performance of Surface Miner through Artificial Intelligence and Statistical Methods
Abstract Views :207 |
PDF Views:85
Authors
Affiliations
1 CSIR-Central Institute of Mining and Fuel Research, Dhanbad 826 015, IN
2 Department of Mining Engineering, Indian School of Mines, Dhanbad 826 004, IN
1 CSIR-Central Institute of Mining and Fuel Research, Dhanbad 826 015, IN
2 Department of Mining Engineering, Indian School of Mines, Dhanbad 826 004, IN
Source
Current Science, Vol 112, No 06 (2017), Pagination: 1242-1249Abstract
Applicability of a surface miner (SM) must be based on a careful assessment of intact rock and rock mass properties. A detailed literature review was made to identify different parameters influencing the performance of various types of cutting machines deployed in different parts of the world. The critical parameters influencing the production, diesel consumption and pick consumption of SM in Indian coal and limestone mines, were identified through artificial neural network (ANN) technique and screened by correlation coefficient analysis. Parameters that were common in both ANN and correlation analysis were grouped under critical category and others in semi-critical category.Keywords
Artificial Neural Network, Intact Rock, Rock Mass, Surface Miner.Full Text
References
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- Semi-Empirical Model for Predicting Pot-Hole Depth in Underground Coal Mining
Abstract Views :195 |
PDF Views:86
Authors
Affiliations
1 Visvesvaraya National Institute of Technology, Nagpur 440 010, IN
2 Indian Institute of Technology (ISM), Dhanbad 826 004, IN
3 Central Institute of Mining and Fuel Research, Dhanbad 826 001, IN
1 Visvesvaraya National Institute of Technology, Nagpur 440 010, IN
2 Indian Institute of Technology (ISM), Dhanbad 826 004, IN
3 Central Institute of Mining and Fuel Research, Dhanbad 826 001, IN
Source
Current Science, Vol 115, No 9 (2018), Pagination: 1761-1769Abstract
Pot-hole subsidence can be induced by extracting underground coal seam at shallow depth and is a matter of great concern. This has been the case in some of the coal mines of South Eastern Coalfields Limited, a subsidiary of Coal India Limited. Many of the old underground coal mines developed by bord and pillar method of mining lying at shallow depth are posing stability concerns to the habitat due to pillar collapse and gallery widening under the creep loading and weathering. This requires a systematic study for developing an in-depth analysis on various parameters which influence pot-hole occurrence and also for formulating suitable predictive models. A study was conducted to analyse the pot-hole subsidence data related to 34 pot-hole cases and develop a semi-empirical model for simulating pot-hole depth. This study was carried out in some of the Indian coal mines during different stages of coal extraction, i.e. development and depillaring. Data analysis indicates that height and width of extraction, thickness of soil and rock layers, weighted density and compressive strength are key contributing parameters for the occurrence of pot-hole subsidence. The predicted results match with the actual pot-hole depth measured in the field, validating the model.Keywords
Bord and Pillar Method, Depillaring, Pothole Subsidence, Pot-Hole Depth, Underground Coal Mining.Full Text
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- Drillability predictions in Aravalli and Himalayan rocks – a petro-physico-mechanical approach
Abstract Views :954 |
PDF Views:120
Authors
Affiliations
1 Numerical Modelling Department, National Institute of Rock Mechanics, Bengaluru 560 070, IN
2 Department of Mining Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826 004, IN
1 Numerical Modelling Department, National Institute of Rock Mechanics, Bengaluru 560 070, IN
2 Department of Mining Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826 004, IN
Source
Current Science, Vol 122, No 8 (2022), Pagination: 907-917Abstract
Dolomite, siliceous dolomite, phyllite, schist, leucogranite, pegmatite and gneissic rocks from the Indian Aravalli Hills and Bhutan Himalayan mountains were studied to examine the influence of petrographic and physico-mechanical properties on rock drillability. From petrographic assessments, a measure of grain size distribution, i.e. ‘granularity index’ and a ‘modified saturation index’ are proposed. Extensive rock mechanics and drilling experiments were also performed to correlate physico-mechanical properties with intact rock drillability. Statistical analysis revealed that no single petrographic parameter could completely explain the variance in drill penetration rate (DPR). The proposed indices and the petro-physico-mechanical approach helped in the rapid assessment of DPR in hard rocks.Keywords
Granularity index, hard rock drillability, modified saturation index, petrography, physico-mechanical approach.Full Text
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