Blasting Operations Research Articles

Blasting of Unstable Rock Elements on Steep Slopes

The improvement of safety conditions on hazardous rock slopes in civil work, mining and quarrying, and urban environments can be achieved through the use of explosives for the removal of unstable rock elements and final profiling. This technique is often applied because, in most cases, drill and blast operations, where they can be used, are cheaper and faster than other techniques and require fewer subsequent maintenance interventions. Blasting represents a suitable and effective solution in terms of different geometries, rock formation types, access to site, safety, and the long-term durability of results. The primary purpose of this approach is the improvement of the safety conditions of sites, depending on their local features, as well as the safety of workers, so that the blasting scheme, geometry, and firing can be carefully adapted, thus imposing relevant limitations on the operating techniques. All these constraints associated with complex logistics make it difficult to standardize the demolition technique, due to different situations in terms of extension, location, fracturing state, and associated traffic risk. Considering the significant number of influencing factors for both the rock mass features and for the topography, the present research has been necessarily validated through the analysis of several case histories, thus on an experiential basis focusing on some simple control parameters to help engineers and practitioners regarding the first design and control of blasting schemes.

Effect of supplying natural gas through flange holes on the operation of blast furnace tuyere with heat-insulating insert in the blast channel

Effect of supplying natural gas through flange holes on the operation of blast furnace tuyere with heat-insulating insert in the blast channel

Numerical Computation-Based Analysis of Blasting Vibration Effects and Slope Stability in an Open-Pit Quarry

With the large-scale exploitation of mineral resources, the safety problems of open-pit slopes and dumps become increasingly prominent. In this work, the safety demonstration of the interaction between the eastside slope of an open-pit quarry and the dump was carried out. Firstly, the influence of blasting vibration on the stability of the eastside slope and the dump was analyzed, and the propagation law of blasting vibration wave and the attenuation law of velocity were obtained. Secondly, the finite element analysis of the seepage field of the slope was carried out, and the basic seepage parameters of the rock and soil were determined. Then, the limit equilibrium analysis method was used to quantitatively calculate the stability of the eastside slope of the stope under the load of the dump to assess landslide risk. The results show that no landslides would occur at the dump or affect the performance of the slope, and that the slope was able to reach the required safety reserve factor. Then, through the finite element analysis of the 3-3 profile, it was found that the deformation caused by the excavation unloading of the eastside slope was mainly concentrated in the lower part of the slope, and the displacement variation caused by the top area of the slope was about 0~10 mm. The research verified the stability of slope and dump, which provides technical support for slope project design and blast operation in open-pit quarries.

Blasting techniques applied to control vibrations, flyrock, and slope damage while ensuring compatibility with fragmentation

Blasting operations in mines near communities are of increasing social concern and scientific investigation due to the potential structural damage and human discomfort caused by blasting induced vibrations. Therefore, a technical-scientific approach is necessary to conduct these activities safely and sustainably compatible with fragmentation requirements of the scale of production. This paper presents a case study of an open-pit mine located in Brazil, where blasts are frequently carried out near a community. Additionally, controlling flyrock is extremely important in this mine due to the proximity of people to the blasting sites, as well as controlling the slope damage. Thus, it became necessary to develop a methodology for the application and combination of techniques and simulation fo blasting-induced vibration and wall control, flyrock and fragmentation such as attenuation law, signature holes, decking charge techniques, pre-split holes. The proposed methodology was effective, validated, and is recommended for open-pit mines operating near communities.

Grounding Resistance Monitoring Device for Improved Construction Electricity Safety in Pumped Storage Power Station Construction

Abstract During the construction period of pumped storage power stations, there are prominent safety hazards in the use of electricity. Most of the underground chambers are in rocky environments, and it is difficult to meet the standard requirements for the grounding resistance of nearby distribution boxes. In addition, due to the high humidity and large amount of dust in the chambers, the grounding terminals are prone to oxidation. Furthermore, frequent blasting operations can cause poor contact of the grounding terminals, resulting in the inability to effectively guide the leakage current into the earth and causing electric shock injury. By using the TN-S wiring method, the leakage current of the equipment and distribution box is guided into the ground grid through the PE line. At the same time, a real-time monitoring system is established for the PE line, which monitors the integrity of the PE line resistance between the third-level distribution box and the transformer ground grid. When an integrity failure occurs, an alarm is quickly issued, enabling maintenance personnel to promptly detect and repair the fault, ensuring that all leakage currents have a good export channel, and further guaranteeing the safety of electricity users.

Extension of reliability information of Z-numbers and fuzzy cognitive map: Development of causality-weighted rock engineering system to predict and risk assessment of blast-induced rock size distribution

Blasting operations in surface mines have the primary purpose of fragmenting the rock mass into a size that is optimal and economically viable for the mine. This ideal size has an impact on every step of the mining operation, from loading and hauling through crushing and grinding. To put it another way, the economics of the mine or facility as a whole are significantly impacted by having the ideal size distribution. This research offers a causality-based rock engineering system (RES) for risk assessment and prediction of rock fragmentation generated by blasting in surface mines. The fuzzy cognitive map was utilized in the proposed approach in order to weight the RES in accordance with the reliability information provided by the Z-number theory. This model of the RES that is weighted according to causality and based on reliability is abbreviated as causality-weighted rock engineering system based on reliability (CWRESR). The construction of the model makes use of a database that was obtained from the Sarcheshmeh copper mine. The findings indicated that the overall risk level of fragmentation was somewhere between high and very high, with a vulnerability index (VI) of 69.73 cm. As a result, controlled blasting operations are strongly advised as a means of lowering the amount of risk. In addition, the suggested model was able to predict rock fragmentation with a coefficient of determination (R2) of 0.9462 and 9646 for the training and testing phase, respectively, demonstrating that the CWRESR model is superior to other models in its ability to predict rock fragmentation.

A stacked deep multi-kernel learning framework for blast induced flyrock prediction

Blasting operations are widely and frequently used for rock excavation in Civil and Mining constructions. Flyrock is one of the most important issues induced by blasting operations in open pit mines, and therefore needs to be well predicted in order to identify the safety zone to prevent the potential injuries. For this purpose, 234 sets of blasting data were collected from Sungun Copper Mine site, and a stacked deep multi-kernel learning (SD-MKL) framework was proposed to estimate the blast induced flyrock with confidence accuracy. The proposed model uses the stacking-based representation learning framework (S-RL) to achieve deep learning on small-scale training sets. A multi-kernel learning model (MKL) is used as the base module of S-RL framework, which uses a multi-feature fusion strategy to generate multiple kernels with different kernel length in order to reduce the effort in tuning hyperparameters. In addition, this study further enhanced the predictive capability of SD-MKL by introducing the boosting method into the S-RL framework and hence proposed a boosted SD-MKL model. For comparison purpose, several existing machine learning models were implemented, i.e., kernel ridge regression (KRR), support vector machine (SVM), random forest (RF), gradient boosting decision tree (GBDT), ensemble deep random vector functional link (edRVFL), SD-KRR and SD-SVM. Our experimental results showed that the proposed boosted SD-MKL achieved the best overall performance, with the lowest RMSE of 0.21/1.73, MAE of 0.08/0.78, and the highest VAF of 99.98/99.24.

Fiber optic vibration sensor for applications in the field of ground vibrations

We have proposed a vibration sensor based on a Michelson interferometer. The sensor was developed in the form of a triaxial accelerometer, calibrated, and ultimately validated with reference calibrated devices during blast operations. The sensing function principle is based on the push–pull principle of the mass–spring system of a total of three interferometers. A housing was designed and fabricated using 3D printing to allow the sensor to be operated in the field. Sensor calibration was carried out in an accredited laboratory. The measured sensitivity values were approximately 60 dB re rad/g for the vertical axis and 48–51 dB re rad/g for the horizontal axes in the frequency range of 1 to 50 Hz. The sensitivity values of the presented sensor are comparable to or surpass those of the Michelson-based sensors described in the state-of-the-art. The resulting amplitude–frequency calibration models of the sensor were assembled for all three orthogonal measurement axes so that the particle velocity can be measured. Finally, comparative measurement to reference seismic stations was carried out during a multi-hole bench blast at the Kotouc Stramberk quarry to validate the calibration models.

Prediction of Rock Fragmentation in Bench Blasting Operations Based on Multi Parameters—A Case Study

Prediction of Rock Fragmentation in Bench Blasting Operations Based on Multi Parameters—A Case Study

A newly developed blasting cut in tunnels; application of “combined method” in small to medium-sized tunnels

Drilling and blasting method have been widely used for driving tunnels in hard rock. When designing a blast operation especially in case of long tunnels, the primary objective of contractors is to ensure optimized blast parameters for achieving the most economical and time-saving technique of excavation. Choosing a proper blasting cut has a crucial role in the successfulness of the whole blast round in terms of quality, time and cost. The first aim of cut is to break and throw rocks and create a cavity for the upcoming production detonators. An attempt has been made here to test and develop an innovative cutting system for small to medium-scale tunnels where classical wedge cut is physically difficult to execute; what is called in this paper “combined or quasi-wedge method”. Proposed drilling and blasting patterns have been first put in computerized drilling machines and then tested in the tunnels located in Rishikesh-Karnaprayag Railway Project, Package 2, driven in sedimentary rock mass. After thorough investigation of trial-and-error blasts economically and technically, necessary changes have been made to guarantee optimization and find proper design configurations. In comparison to conventional parallel cut, the newly developed cut leads not only to the betterment of cycle time for excavation but also improvement of specific drilling and specific charge, the two main performance indicators of blast operations. Vibration, fragmentation, pull length, fly rock and profile quality, as pro-blast parameters, were also found highly satisfactory.

Prediction of rock blasting induced air overpressure using a self-adaptive weighted kernel ridge regression

Blasting operations are widely recognized as the most frequently used rock breakage approach in the field of Civil and Mining Engineering. However, the induced air-overpressure (AOp) can result in structural damages to surrounding buildings and therefore needs to be well predicted and subsequently minimized. In this study, a novel self-adaptive weighted kernel ridge regression (Sa-WKRR) was proposed to predict blast induced AOp, which used a self-adaptive weighting strategy to improve the performance of traditional kernel ridge regression (KRR). The model was developed and validated on two blasting datasets. Subsequently, the performance of the proposed Sa-WKRR was compared with 9 other machine learning models, i.e., KRR, kernel random vector functional link (KRVFL), Sa-WKRVFL, transductive KRR (TKRR), ensemble deep RVFL (edRVFL), artificial neural network (ANN), random forest (RF), support vector machine (SVM) and multivariate adaptive regression splines (MARS). The optimal performance of these models were obtained using grid search method and compared by three evaluation indices, root mean squared error (RMSE), mean absolute percentage error (MAPE) and correlation coefficient (R). The results demonstrated that the proposed Sa-WKRR has the best performance in two datasets, with RMSE of 0.46/1.98, MAPE of 0.30% and 1.20%, and R of 0.9991/0.9235 in Case study 1 and RMSE of 1.03/3.22, MAPE of 0.76%/2.74%, and R of 0.9965/0.9373 in Case study 2. Findings revealed that the proposed Sa-WKRR emerged as the most powerful and stable technique in predicting blast induced AOp compared with other machine learning models.

Determining the Pocket Charge Design for Effective Fragmentation of Caprock in Surface Mine Blast Operations

Determining the Pocket Charge Design for Effective Fragmentation of Caprock in Surface Mine Blast Operations

Application of Soft Computing, Statistical and Multi-Criteria Decision-Making Methods to Develop a Predictive Equation for Prediction of Flyrock Distance in Open-Pit Mining

Blasting operations in open-pit mines generally have various management strategies relating to flyrock. There are empirical models for calculating the flyrock distance, but due to the complexity and uncertainty of rock properties and their interactions with blasting properties, there are still no models that can predict the flyrock distance that may be applicable across mining operations in general. In this regard, the Jajarm bauxite mine complex was used as a case study. The purpose of this study was to develop and evaluate different methods that can predict flyrock distance. For this purpose, soft computing models were developed using generalized regression neural network (GRNN), gene expression programming (GEP) and genetic-algorithm-based GRNN (GA-GRNN) methods. To obtain statistical models, multivariable regression was applied in the form of linear and nonlinear equations. A flyrock index was introduced using a classification system developed by incorporating fuzzy decision-making trial and evaluation methods (fuzzy DEMATEL). In order to achieve this goal, the data of 118 blasts in eight mines of the Jajarm bauxite complex were collected and used. Following this, four performance benchmarks were applied: the coefficient of determination (R2), variance accounted for (VAF), root-mean-square error (RMSE) and mean absolute percentage error (MAPE). The performance of the models was evaluated, and they were compared with each other as well as with the most common previous empirical models. The obtained results indicate that the GA-GRNN model has a higher performance in predicting the flyrock distance in actual cases compared to the other models. At first, data on factors that were the main cause of flyrock (and had a direct impact on it) were collected and classified from different blasts. Then, using the collected data, 19 different combinations were established, which can be used to provide the appropriate predictive equation. The purpose of this work is to more accurately predict flyrock and prevent heavy damage to buildings and mining machines across the mining complex.

Pressure Drop and Gas Flow in an Oxygen Blast Furnace Analyzed by a Combination of Experimentation and a Porous Model

As a modification of the conventional blast furnace (BF), the top gas recycling-oxygen blast furnace (TGR-OBF) has been continuously studied in the context of the technological transformation of low-carbon metallurgy. As it has a set of new gas inlets in the stack and changes the blast operation system in the hearth, the pressure drop and the in-furnace gas flow are the primary problems to be solved in the TGR-OBF’s industrialization. In this paper, a two-dimensional model of a whole blast furnace, based on a softening-and-melting experiment and porous-medium theory, is established. The in-furnace pressure drop and the gas velocity with different oxygen concentrations and tuyere heights are studied. The results show that the suitable height of the stack tuyere is the same as that of the elevation as the cohesive zone inside the furnace. With the gradual increase in oxygen enrichment, the permeability of the cohesive ore layer (COL) increases, while the gas flow through the coke layer (CL) decreases gradually up to 10%. The simulation results provide a theoretical basis for the TGR-OBF to reduce the coke rate and keep the pressure drop from increasing, or even to enable it to decrease.

Several Tree-Based Solutions for Predicting Flyrock Distance Due to Mine Blasting

Blasting operations involve some undesirable environmental issues that may cause damage to equipment and surrounding areas. One of them, and probably the most important one, is flyrock induced by blasting, where its accurate estimation before the operation is essential to identify the blasting zone’s safety zone. This study introduces several tree-based solutions for an accurate prediction of flyrock. This has been done using four techniques, i.e., decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost). The modelling of tree-based techniques was conducted with in-depth knowledge and understanding of their most influential factors. The mentioned factors were designed through the use of several parametric investigations, which can also be utilized in other engineering fields. As a result, all four tree-based models are capable enough for blasting-induced flyrock prediction. However, the most accurate predicted flyrock values were obtained using the AdaBoost technique. Observed and forecasted flyrock by AdaBoost for the training and testing phases received coefficients of determination (R2) of 0.99 and 0.99, respectively, which confirm the power of this technique in estimating flyrock. Additionally, according to the results of the input parameters, the powder factor had the highest influence on flyrock, whereas burden and spacing had the lowest impact on flyrock.

Transient 3D CFD study of pulverised coal combustion and coke combustion in a blast furnace: Effect of blast conditions

The pulverised coal combustion and coke combustion in the raceway of an ironmaking blast furnace (BF) is the key to stable and efficient BF operation, especially under various blast conditions, yet their dynamic behaviour is still unknown. In this study, a three-dimensional transient CFD model is developed to investigate the impacts of the variation of key blast conditions on the in-furnace phenomena in an industrial scale BF. This model integrates the two-fluid model and discrete particle model to simulate the dynamic raceway and co-combustion of coke and pulverised coal in the lower part of the BF. A set of blast operating schemes, i.e., blast rate and oxygen enrichment, are numerically examined to illustrate and quantify their influence on the raceway evolution and combustion of coal and coke, respectively. The simulation results show that a higher blast rate benefits the formation of a larger raceway, i.e., with the rising blast velocity, from 90 m/s to 180 m/s, the raceway is expanded in terms of depth (0.4 m to 1.1 m), width (0.13 m to 0.44 m), and height (0.23 m to 0.76 m), which indirectly contributes to enhancing the combustion of coal and coke. The oxygen enrichment operation is also helpful to the co-combustion of coke and coal and increases the internal and surrounding temperature of the raceway, which produces more reducing gas for the BF. The average coal burnout rate is enhanced by 15 % after the blast rate is raised from 90 m/s to 180 m/s and the oxygen mass fraction is raised from 25 % to 35 %. This study offers a cost-effective tool to understand and optimise the blast operations toward stable and efficient BF operation.

Prediction of blasting induced air-overpressure using a radial basis function network with an additional hidden layer

Blasting operations are the most conventional and frequently used rock breakage approach in the field of Civil and Mining Engineering. However, the side effects induced by blasting may cause severe damages to surrounding areas. Air-overpressure (AOp) is one of the side effects induced by blasting operations, which is defined as the air pressure wave generated by blasting operation that exceeds normal atmospheric pressure. It can result in potential structural damage and glass breaking and therefore needs to be well predicted and subsequently minimized. In this study, 76 sets of blasting data were collected to develop a predictive model to estimate AOp value. However, due to the small size of dataset, it is hard to determine the complexity of the model. Therefore, for the purpose of developing a machine learning model with appropriate complexity, a radial basis function network with an additional second hidden layer (RBF-2) is proposed, which is trained by incremental design principle and modified Levenberg–Marquardt algorithm. The performance of the proposed RBF-2 is compared with those of five other machine learning techniques, i.e., multilayer perceptron (MLP), RBF, MLP optimized by genetic algorithm (GA-MLP), multi adaptive regression spline (MARS) and random forest (RF). The results demonstrate that the proposed RBF-2 network outperforms other models with RMSE of 2.02/1.98, MAPE of 1.32%/1.40%, and R of 0.9828/0.9735 in training/testing stage. Findings revealed that the proposed RBF-2 network emerged as the most efficient, powerful and robust technique in predicting blast induced AOp compared with other machine learning models.

Оценка качества капсюлей-детонаторов (с пиротехническим замедлением) неэлектрических систем инициирования по времени их срабатывания

Blasting operations in surface and underground mining are characterized by the need to protect the environment from harmful effects of the detonation products of explosives, the fly rock zone, the seismic manifestations of the blast, as well as airborne shock waves. Non-electric blasting systems by various manufacturers are used in Russia, but all of them have certain advantages and disadvantages. It can be stated that the blasting caps of the applied blasting system do not always correctly execute the firing times specified by their manufacturer. Mismatches of the actual firing times from the rated ones might be so great that the applied blasting systems sometimes produce disturbances in the order of charge firing, which, in turn, may cause detonation failure in the boreholes. Studies have shown that in order to reduce the risk of detonation failures, regardless of their causes, it is recommended to initiate the borehole at two points with placing the second primer in the upper part of the borehole, or using extended priming charges.

An Evaluation on the Impact of Ore Fragmented by Blasting on Mining Performance

In open-pit mines, the blast operation should be effectively optimized, leading to minimization of production costs through the application of specific technical specifications. However, there is inadequate information in the literature to link blasting to comminution stages. To this end, the effective parameters for the performance of mining unit operations were scrutinized in this work. In this regard, the rock fragmentation distribution (RFD) caused by blasting was considered the main determinative criterion for providing the optimum conditions for the blasting operation at Sarcheshmeh copper mine. By carrying out a statistical analysis of the experimental data, operational parameters affecting the blasting were optimized. The relationship between parameters was obtained using the technique of regression and in accordance with the evaluation criterion under which correlation coefficient (R2) was used to determine the best fitting model. A high correlation coefficient of the loading cycle of the machine’s bucket (Cl) with the independent variables showed that the C1 was more affected by the RFD, as well as the dimensions of the blast block. Because of the wide variations in the nature and structure of rock mass in different mines, in each case, sufficient data should be collected, and these relationships should be analyzed statistically for each individual mine showing wide ranges of fractures and cracks. Therefore, due to these wide variations of ore characteristics, with the current data it seems very difficult to quickly find a significant operational relationship between downstream processes such as crushing efficiency and blasting operations. Therefore, the focus of this research was limited to the effective parameters for blast efficiency. According to the analysis of the data obtained from 20 blasts under different operating conditions, the diameter of the hole was 241.3 mm (such as blast number 20), the ratio of length to width of the explosive block was about 6 (average blasts with high fragmentation efficiency), and the best index of mining operations was 0.22 (such as blast number 20).

DEVELOPMENT OF A 3D NUMERICAL MODEL FOR SIMULATING A BLAST WAVE PROPAGATION SYSTEM CONSIDERING THE POSITION OF THE BLASTING HOLE AND IN-SITU DISCONTINUITIES

Blasting operations are one of the most important parts of geotechnical and mining projects. Most rocks naturally have a series of discontinuities that significantly affect their responses to blast waves. In this paper, the propagation of a blast wave in one intact rock and four rocks with different joint conditions are simulated by a 3-dimensional distinct element code. The results showed that the joint in the model acted as a wave barrier and passed part of the waves, absorbed a portion, and reflected the remaining part into the model. In other words, a discontinuity reduces the energy of the wave and causes more wave attenuation. In addition, a shorter distance between the joint and the hole causes slower wave propagation and greater damping. Moreover, the results showed that the smaller the angle between the discontinuity and axis of the blast holes, the more stress occurs in the rock bench.