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1.
Blast design is a critical factor dominating fragmentation and cost of actual bench blasts. However, due to the varying nature of rock properties and geology as well as free surface conditions, reliable theoretic formulae are still unavailable at present and in most cases blast design is carried out by personal experience. As an effort to find a more scientific and reliable tool for blast design, a computer-aided bench blast design and simulation system, the BLAST-CODE model, is developed for Shuichang surface mine, Mining Industry Company of the Capital Iron and Steel Corporation Beijing. The BLAST-CODE model consists of a database representing geological and topographical conditions of the mine and the modules Frag + and Disp + for blast design and prediction of resultant fragmentation and displacement of rock mass. The two modules are established in accordance with cratering theory qualitatively and modified quantitatively by regression of the data collected from 85 bench blasting practices conducted in 3 mines of the Shuichang surface mine. Blasting parameters are selected based upon quantitative and comprehensive evaluation on the effect of the factors such as rock properties, geology, free surface conditions and detonation characteristics of the explosive products in use. In order to ensure practicality and reliability of the system, the BLAST-CODE model allows automatic adjustment to the selected parameters such as burden B and spacing S as well as explosive charge amount Q of any blasthole under irregular topographic and/or varying blastability conditions of the rock mass to be blasted. Simulation of the BLAST-CODE model includes prediction of fragmentation and displacement that are demonstrated in terms of swell factor, characteristic rock size x c and size distribution coefficient n by Rossin-Ramler's equation, and 3-dimentional muck pile profile. The BLAST-CODE model also permits interactive parameter selection based on comparison of the predicted fragmentation and displacement as well as the cost for drilling, explosives, and accessories until the most effective option can be selected.  相似文献   

2.
New Prediction Models for Mean Particle Size in Rock Blast Fragmentation   总被引：2，自引：1，他引：1  
The paper refers the reader to a blast data base developed in a previous study. The data base consists of blast design parameters, explosive parameters, modulus of elasticity and in situ block size. A hierarchical cluster analysis was used to separate the blast data into two different groups of similarity based on the intact rock stiffness. The group memberships were confirmed by the discriminant analysis. A part of this blast data was used to train a single-hidden layer back propagation neural network model to predict mean particle size resulting from blast fragmentation for each of the obtained similarity groups. The mean particle size was considered to be a function of seven independent parameters. An extensive analysis was performed to estimate the optimum value for the number of units for the hidden layer for each of the obtained similarity groups. The blast data that were not used for training were used to validate the trained neural network models. For the same two similarity groups, multivariate regression models were also developed to predict mean particle size. Capability of the developed neural network models as well as multivariate regression models was determined by comparing predictions with measured mean particle size values and predictions based on one of the most applied fragmentation prediction models appearing in the blasting literature. Prediction capability of the trained neural network models as well as multivariate regression models was found to be strong and better than the existing most applied fragmentation prediction model. Diversity of the blasts data used is one of the most important aspects of the developed models.  相似文献   

3.
One of the fundamental requirements for being able to optimise blasting is the ability to predict fragmentation. An accurate blast fragmentation model allows a mine to adjust the fragmentation size for different downstream processes (mill processing versus leach, for instance), and to make real time adjustments in blasting parameters to account for changes in rock mass characteristics (hardness, fracture density, fracture orientation, etc). A number of blast fragmentation models have been developed in the past 40 years such as the Kuz-Ram model [1]. Fragmentation models have a limited usefulness at the present time because: 1. The input parameters are not the most useful for the engineer to determine and data for these parameters are not available throughout the rock mass. 2. Even if the input parameters are known, the models still do not consistently predict the correct fragmentation. This is because the models capture some but not all of the important rock and blast phenomena. 3. The models do not allow for 'tuning' at a specific mine site. This paper describes studies that are being conducted to improve blast fragmentation models. The Split image processing software is used for these studies [2, 3].  相似文献   

4.
This paper is an application of artificial neural networks (ANNs) in the prediction of the geometry of surface blast patterns in limestone quarries. The built model uses 11 input parameters which affect the design of the pattern. These parameters are: formation dip, blasthole diameter, blasthole inclination, bench height, initiation system, specific gravity of the rock, compressive and tensile strength, Young's modulus, specific energy of the explosive and the average resulting fragmentation size. Detailed data from a previous investigation were used to train and verify the network and predict burden and spacing of a blast. The built model was used to conduct parametric studies to show the effect of blasthole diameter and bench height on pattern geometry.  相似文献   

5.
In the blasting operation, risk of facing with undesirable environmental phenomena such as ground vibration, air blast, and flyrock is very high. Blasting pattern should properly be designed to achieve better fragmentation to guarantee the successfulness of the process. A good fragmentation means that the explosive energy has been applied in a right direction. However, many studies indicate that only 20–30 % of the available energy is actually utilized for rock fragmentation. Involvement of various effective parameters has made the problem complicated, advocating application of new approaches such as artificial intelligence-based techniques. In this paper, artificial neural network (ANN) method is used to predict rock fragmentation in the blasting operation of the Sungun copper mine, Iran. The predictive model is developed using eight and three input and output parameters, respectively. Trying various types of the networks, it was found that a trained model with back-propagation algorithm having architecture 8-15-8-3 is the optimum network. Also, performance comparison of the ANN modeling with that of the statistical method was confirmed robustness of the neural networks to predict rock fragmentation in the blasting operation. Finally, sensitivity analysis showed that the most influential parameters on fragmentation are powder factor, burden, and bench height.  相似文献   

6.
Excavation of coal, overburden, and mineral deposits by blasting is dominant over the globe to date, although there are certain undesirable effects of blasting which need to be controlled. Blast-induced vibration is one of the major concerns for blast designers as it may lead to structural damage. The empirical method for prediction of blast-induced vibration has been adopted by many researchers in the form of predictor equations. Predictor equations are site specific and indirectly related to physicomechanical and geological properties of rock mass as blast-induced ground vibration is a function of various controllable and uncontrollable parameters. Rock parameters for blasting face and propagation media for blast vibration waves are uncontrollable parameters, whereas blast design parameters like hole diameter, hole depth, column length of explosive charge, total number of blast holes, burden, spacing, explosive charge per delay, total explosive charge in a blasting round, and initiation system are controllable parameters. Optimization of blast design parameters is based on site condition and availability of equipment. Most of the smaller mines have predesigned blasting parameters except explosive charge per delay, total explosive charge, and distance of blast face from surface structures. However, larger opencast mines have variations in blast design parameters for different benches based on strata condition: Multivariate predictor equation is necessary in such case. This paper deals with a case study to establish multivariate predictor equation for Moher and Moher Amlohri Extension opencast mine of India. The multivariate statistical regression approach to establish linear and logarithmic scale relation between variables to predict peak particle velocity (PPV) has been used for this purpose. Blast design has been proposed based on established multivariate regression equation to optimize blast design parameters keeping PPV within legislative limits.  相似文献   

7.
Blasting is the primary comminution process in most mining operations. This process involves the highly complex and dynamic interaction between two main components. The first is the detonating explosive and the second is the rock mass into which the explosive is loaded. The mechanical properties of the rock material (such as dynamic strength, tensile strength, dynamic modulus and fracture toughness) are important considerations in understanding the blasting process. However, it is the characteristics of the geological defects (joints, foliation planes, bedding planes) within the rock mass that ultimately determine how effectively a blast performs in terms of fragmentation, all else being equal. The defect characteristics include, but are not limited to, their orientation, spacing, and mechanical properties. During the blasting process, some of the geotechnical characteristics of the rock mass are substantially changed. From the blasting outcome point of view, the most notable and important is the change in fragment size distribution that the rock mass undergoes. The pre-blast in situ defect-bounded block size distribution is transformed into the post-blast muckpile fragment size distribution. Consequently, it is fundamental to our understanding of and ability to predict the blasting process that both the blastability of a rock mass and its transformation into the fragment size distribution can be appropriately quantified.  相似文献   

8.
Blast Design Using Measurement While Drilling Parameters   总被引：1，自引：0，他引：1  
Measurement while drilling (MWD) techniques can provide a useful tool to aid drill and blast engineers in open cut mining. By avoiding time consuming tasks such as scan-lines and rock sample collection for laboratory tests, MWD techniques can not only save time but also improve the reliability of the blast design by providing the drill and blast engineer with the information specially tailored for use. While most mines use a standard blast pattern and charge per blasthole, based on a single rock factor for the entire bench or blast region, information derived from the MWD parameters can improve the blast design by providing more accurate rock properties for each individual blasthole. From this, decisions can be made on the most appropriate type and amount of explosive charge to place in a per blasthole or to optimise the inter-hole timing detonation time of different decks and blastholes. Where real-time calculations are feasible, the system could extend the present blast design even be used to determine the placement of subsequent holes towards a more appropriate blasthole pattern design like asymmetrical blasting.  相似文献   

9.
Summary The increasing range of explosive types and methods of initiation available to the blasting design engineer, and the possibilities of obtaining more detailed rock property data, require improvements in the precision of blasting design methods. Average design values, such as powder factor and specific charge, have little significance where rock properties vary in any lithological section of the blast. Application of the concept of incremental explosive energy distribution will increase the design sensitivity and control over blastability variations. In this paper the use of this concept is described for different levels of complexity. These range from the simple allocation of explosive energy for large rock sections, to the use of more complex energy attentuation functions to allocate incremental specific energy levels. Procedures to develop rock fragmentation predictions from such data are also outlined.  相似文献   

10.
In the last decade, fragmentation prediction has been attempted by many researchers in the field of blasting. Kuznetsov developed an equation for the estimation of average fragment size, x 50 , based on explosive energy and powder factors. Cunningham introduced a uniformity index n as a function of drilling accuracy, blast geometry and a rock factor A associated with a “blastability index”, which can be calculated from the jointing, density and hardness of the blasted rock mass. Knowing the mean size and the uniformity index, a Rosin-Rammler distribution equation can then be derived for calculating the fragment size distribution in a blasted muckpile. Analysis of existing data has revealed serious discrepancies between actual and calculated uniformity indices. The current integrated approach combines the Kuznetsov or similar equation and a comminution concept like the Bond Index equation to enable the estimation of both the 50% and 80% passing sizes ( k 50 and k 80 ). By substituting these two passing sizes into the Rosin-Rammler equation, the characteristic size x c and the uniformity index n can be obtained to allow the calculation of various fragment sizes in a given blast. The effectiveness of this new fragmentation prediction approach has been tested using sieved data from small-scale bench blasts, available in the literature. This paper will cover all tested results and a discussion on the discrepancy between measurement and prediction due to possible energy loss during blasting.  相似文献   

11.
The purpose of this article is to evaluate and predict the blast induced ground vibration using different conventional vibration predictors and artificial neural network (ANN) at a surface coal mine of India. Ground Vibration is a seismic wave that spread out from the blast hole when detonated in a confined manner. 128 blast vibrations were recorded and monitored in and around the surface coal mine at different strategic and vulnerable locations. Among these, 103 blast vibrations data sets were used for the training of the ANN network as well as to determine site constants of various conventional vibration predictors, whereas rest 25 blast vibration data sets were used for the validation and comparison by ANN and empirical formulas. Two types of ANN model based on two parameters (maximum charge per delay and distance between blast face to monitoring point) and multiple parameters (burden, spacing, charge length, maximum charge per delay and distance between blast face to monitoring point) were used in the present study to predict the peak particle velocity. Finally, it is found that the ANN model based on multiple input parameters have better prediction capability over two input parameters ANN model and conventional vibration predictors.  相似文献   

12.
Shallow buried explosives pose a significant threat to lightweight vehicles and their onboard personnel. To date, designers of lightweight vehicles are limited in their knowledge of what occurs during the blast. The high intensity, short term loading imparted by the explosion is enormously complex and can be significantly affected by a number of parameters including the size, shape, type, detonation point and depth of burial (DOB) of the explosive and the type, density and water content of the soil. Recent advancements in numerical simulations have enabled the complex blast event to be accurately modelled by coupling Eulerian and Lagrangian analyses: the former is well suited to modelling the blast and while the latter, the structural response. Further validation of the modelling technique is considered in the current paper, which details simulations performed utilising the coupled Eulerian-Lagrangian analysis to study the blast output of explosives buried in saturated sand. These experiments varied explosive charge size, its depth of burial, the target stand-off (SO) distance and the dimensions of the target plate. The investigation concludes with a discussion of the accuracy of the numerical simulations when compared with the experimental observations.  相似文献   

13.
Summary Formulation and case studies of a three dimensional kinematic model are presented. Thein situ overburden geometry can be simulated accurately and various initiation patterns of blasts can be modelled. The overburden geometry, hole patterns and explosive distribution are all explicit model inputs. Because the effect of explosive properties, rock mass condition and inter-row delay are very difficult to measure in terms of blast performance, these are represented in the model by control parameters which are left for calibration using field data. The output of the model is a three dimensional muckpile shape of any cross section and a contour map of grade distribution within the muckpile. Two case studies are presented which have shown that the model is a valuable tool for optimizing production blasting as well as for controlling grade dilution during blasting.  相似文献   

14.
The efficiency of a blast depends very widely on three groups of parameters: rock mass, blast geometrical parameters and energy distribution in space (borehole bottom, column energy) and time (delays between holes and rows). According to the expected results from a blast, there are several definitions for the term efficiency. The criterion for the block size reduction in the muck pile is often considered as important, because generally it has a great influence on the efficiency of all the operations after the blast. In this paper, a new parameter for the assessment of the blast efficiency is proposed, based on the relative comparison of area delimited by the Rosin-Rammler curves of the in situ rock mass and of the muck pile. This parameter is then compared to others previously established, namely the fragmentation index (Aler et al. 1996) and the surface energy ratio (Hamdi et al. 2001).  相似文献   

15.
Blasting is well-known as an effective method for fragmenting or moving rock in open-pit mines. To evaluate the quality of blasting, the size of rock distribution is used as a critical criterion in blasting operations. A high percentage of oversized rocks generated by blasting operations can lead to economic and environmental damage. Therefore, this study proposed four novel intelligent models to predict the size of rock distribution in mine blasting in order to optimize blasting parameters, as well as the efficiency of blasting operation in open mines. Accordingly, a nature-inspired algorithm (i.e., firefly algorithm – FFA) and different machine learning algorithms (i.e., gradient boosting machine (GBM), support vector machine (SVM), Gaussian process (GP), and artificial neural network (ANN)) were combined for this aim, abbreviated as FFA-GBM, FFA-SVM, FFA-GP, and FFA-ANN, respectively. Subsequently, predicted results from the abovementioned models were compared with each other using three statistical indicators (e.g., mean absolute error, root-mean-squared error, and correlation coefficient) and color intensity method. For developing and simulating the size of rock in blasting operations, 136 blasting events with their images were collected and analyzed by the Split-Desktop software. In which, 111 events were randomly selected for the development and optimization of the models. Subsequently, the remaining 25 blasting events were applied to confirm the accuracy of the proposed models. Herein, blast design parameters were regarded as input variables to predict the size of rock in blasting operations. Finally, the obtained results revealed that the FFA is a robust optimization algorithm for estimating rock fragmentation in bench blasting. Among the models developed in this study, FFA-GBM provided the highest accuracy in predicting the size of fragmented rocks. The other techniques (i.e., FFA-SVM, FFA-GP, and FFA-ANN) yielded lower computational stability and efficiency. Hence, the FFA-GBM model can be used as a powerful and precise soft computing tool that can be applied to practical engineering cases aiming to improve the quality of blasting and rock fragmentation.  相似文献   

16.
In blasting with air decks, repeated oscillation of shock waves within the air gap increases the time over which it acts on the surrounding rock mass by a factor at between 2 and 5. The ultimate effect lies in increasing the crack network in the surrounding rock and reducing the burden movement. Trials of air deck blasting in the structurally unfavourable footwall side of an open pit manganese mine has resulted in substantial improvements in fragmentation and blast economics. Better fragmentation resulted in improved shovel loading efficiency by 50–60%. Secondary blasting was almost eliminated. Use of ANFO explosive with this technique reduced explosive cost by 31.6%. Other benefits included reductions in overbreak, throw and ground vibration of the order of 60–70, 65–85 and 44% respectively. This paper reviews the theory of air deck blasting and describes in detail the air deck blast trials conducted in a manganese open pit mine in India. The blast performance data have been analysed to evaluate the benefits of air decking over conventional blasting.  相似文献   

17.
Air gap in an explosive column has long been applied in open-pit blasting as a way of reducing explosive charge, vibration, fly rock and improve fragment size. In conventional blasting a greater amount of explosive energy is lost in the generation of oversize fragments. Oversize fragments reduces loading and hauling efficiencies of equipment which requires secondary blasting. Recurring oscillation of shock waves in the air gap increases the time over which it acts on the adjacent rock mass by factor of 2–5. Top air deck blasting technique trial conducted with an application of gas bags at Chimiwungo pit resulted in an improved fragmentation of about 94 % less than 950 mm. Results obtained from the analysis of muckpile images using split-desktop exhibited that the mean fragment size was 264.81 mm and F20, F80 and top-size were 41.99, 683.18 and 1454.69 mm respectively. Optimum crusher feed size was as large as 1200 mm and crushed down to the 40 mm and only a small percent of the material was above 1200 mm. Gas bag application resulted in a significant reduction in explosives load in production holes without loss in fragmentation or movement of the collar zone. This reduced total cost of charging as compared to conventional blasts with a variance of $20, powder factor was dropped to an average of 0.86 kg/bcm. The technique reduced the cost of bulk blend explosive by 15 %, reduced overall cost of charging per hole by 12 %, enhanced premature ejections. The overall blast results were satisfactory, 443,624 tonnes of blasted material from the block which represented 90 % of the total muckpile material was within 900 mm size. The overall muckpile blasted was well fragmented.  相似文献   

18.
This paper addresses the effects of randomness of initial damage in a rock mass and the critical tensile strain of the rock material on its dynamic responses and damage under explosive loads. A fuzzy definition is proposed to describe the fuzzy nature of failure phenomenon in a rock mass. The initial damage of the rock mass is estimated using the longitudinal and transverse elastic wave velocities. By using statistical analysis, the initial damage of the rock mass is found having the Beta distribution. The statistical estimation of a damage state and properties of randomly damaged rock mass are evaluated by the Rosenbluth's point estimate method. In numerical calculation, an isotropic continuum damage model with the initial damage and the cumulative damage dependent on an equivalent tensile strain is suggested to model the rock mass behavior under blast loads. A Beta distribution is proposed to represent the probabilistic distribution of the damage variable of the rock mass under explosive loads. Several types of membership functions are suggested to represent the fuzziness of material failure. Based on the fuzzy–random probabilistic theory, a model including both the effects of randomness and fuzziness is proposed for the failure analysis of rock mass under explosive loads. The suggested models are coded and linked with an available computer program AUTODYN2D through its user's subroutine capacity. The fuzzy failure probability and dynamic responses of the rock mass are calculated. Numerical results are compared with those obtained from independent field tests.  相似文献   

19.
Tests to determine the complete stress–strain curve of rocks indicate whether the rocks can be classified a Class I or Class II. Class II rocks exhibits the potential for self-sustained failure in the post-peak region. The purpose of the research described in this paper was to investigate whether or not this self-sustained failure characteristic is related to the fragmentation of the rock. The aim of the research was, therefore, to determine possible relationships between fragmentation and various properties of several rocks types, including the influence of the Class II characteristic. Fragmentation of rock depends on its self-sustaining failure behaviour and the energy available in the post-peak region to shatter the rock. The correlation of static and dynamic rock properties with size of fragments resulting from compression tests demonstrate clear relationships of Class II rocks, but the same cannot be said for Class I rocks. Analyses of test results show that fragmentation increases with an increase in rock strength, and is explosive for Class II rocks. Probability density distributions were constructed to show the overall comparison of fragment sizes produced during failure of Class II and Class rocks. The calculated probability of passing at X50 and X10 sieve sizes show that Class II rocks as a group are more finely fragmented. It can therefore be concluded that, when breaking rocks under the same steady loading conditions, Class II rocks will show greater fragmentation than Class I rocks.  相似文献   

20.
Summary The purpose of this study is to statistically correlate the fragmentation gradient () and average fragment size () with the blasting test parameters for rock masses having different characteristics. Blasting tests were conducted in limestone exposed during the highway construction between Tarsus and Pozanti (Turkey). Three test sites were classified as poor rock, good rock, and very good rock according to their RMR ratings. The selected blasting test parameters that affect the degree of fragmentation were burden, bench height and ANFO charge. After each blast, the muckpiles were screened and fragment size distribution graphs were plotted. Yates' method was applied for experimental design and analysis of variance. The single and combined effects of blasting test parameters were analyzed through the Yates' tables and significant and non-significant treatment combinations were determined for different rock masses. Some conclusions drawn from this research are: 1. The increase of RMR ratings promotes fragmentation, hence, increases blasting efficiency. 2. In rock masses of low RMR ratings, the volume of broken material is large, but fragmentation into small sizes is low. The opposite is true for rock masses of high RMR ratings. 3. The length of charge column is the significant factor affecting the average fragment size regardless the type of rock mass and is more significant in very good quality rock mass.  相似文献   

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