A review of the influence of blast fragmentation on downstream processing of metal ores

Kinyua, Eric Munene; Zhang, Jianhua; Kasomo, Richard M.; Mauti, Dalmus; Mwangangi, Jackson

doi:10.1016/j.mineng.2022.107743

Cited by 14 publications

(5 citation statements)

References 85 publications

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“…One critical geochemical property is the mineral composition of the rock. Different minerals have varying degrees of hardness and brittleness, which can lead to variations in how the rock fractures and fragments during blasting [24]. The presence of certain chemical elements or compounds within the rock can also influence fragmentation behavior.…”

Section: Geomechanical Properties Influencing Fragmentationmentioning

confidence: 99%

“…Several authors' work has mention that good blast fragmentation in mining operations provides several advantages. [7] explained that efficient blasting enhances ore recovery, reduces energy consumption during crushing and grinding, improves overall processing efficiency, and minimizes downstream processing costs. [8] and [9] also explained that optimal fragmentation also facilitates better ore handling, transportation, and ultimately results in increased productivity and profitability for mining operations.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Rock and Explosive Properties for Fragmentation Characterization: An Application of WipFrag

Otokiti,

Adebayo

2024

IJEATS

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This study investigates the rock explosive properties in selected Lokoja quarries, Nigeria, with the goal of characterizing fragmentation for optimized downstream operations. The analysis includes porosity, UCS values, and permeability assessment in Gitto quarry, highlighting advantages in material application. Comparisons between rock formations (Q1 and Q2) reveal varying compressive strengths, crucial for determining appropriate explosive energy for efficient fragmentation. Blast design parameters from Q1 and Q2 indicate consistent values, aiding in operational planning. Fragmentation analysis, conducted using WipFrag software, delineates size ranges and classifies the blast as having a moderate distribution. Correlations between blast fragmentation size and powder factor underscore the impact on efficiency. A classification chart and table are presented for convenient interpretation of results, providing valuable insights for enhancing blasting practices in Lokoja quarries and ultimately improving productivity. The fragmentation analysis result carried out in this study using WipFrag software shows that the 50%, 80% and Maximum block size passing size ranges from 539.94 – 1349.53 mm, 690.07 – 1907.81 mm, 808-2280 mm respectively. The blast fragmentation sizes are classified as moderate distribution blast based on the uniformity index value ranging from 1.95 to 2.4. The relationship between blast fragmentation size and powder factor was evaluated using linear correlation coefficient. It was noted that, X20, X50, X80 has high R2 values greater than 60% respectively

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Section: Geomechanical Properties Influencing Fragmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Rock and Explosive Properties for Fragmentation Characterization: An Application of WipFrag

Otokiti,

Adebayo

2024

IJEATS

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“…Blasting technology is widely used in mining engineering for rock breaking. The size of rock fragments after blasting directly affects subsequent operations, such as shoveling, transportation, and crushing [1][2][3][4]. Maintaining appropriate rock fragment sizes can reduce blasting costs and enhance mining productivity.…”

Section: Introductionmentioning

confidence: 99%

Identification of Rock Fragments after Blasting by Using Deep Learning-Based Segment Anything Model

Zhao,

Li,

2024

Minerals

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Rock fragmentation is an important evaluation indicator for field blasting operations. This paper applies a deep learning-based method, the Segment Anything Model (SAM), to automatically segment rock fragments. To review the SAM’s segmentation performance, 83 images of rock fragment collected from the mine site were used as the test dataset. Pixel-level accuracy (PA), intersection over union (IOU), and dice coefficient (Dice) were employed to evaluate the model pixel-level segmentation performance. The results showed that the SAM exhibited excellent segmentation performance on the test data (PA = 94.5%, IOU = 94.4%, Dice = 95.4%). The coefficient of determination (R2) values for the 50% and 80% passing sizes (X50 and X80) were 0.970 and 0.991, respectively, which demonstrated that the SAM could achieve high precision measurement of rock fragmentation. Additionally, the effectiveness of the SAM was further evaluated by comparing it to commercial software, and the generalizability of the SAM was verified on two other datasets. The findings revealed that the SAM not only outperformed the Split-Desktop V 4.0 on the test dataset but also achieved comparable accuracy to previous studies on the two other datasets. The SAM could be regarded as a useful tool to provide fast and accurate feedback for field blasting.

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“…Recently, intelligent algorithms have gained widespread use in predicting blast-induced outcomes, including the predicting of adverse effects, e.g., backbreak [4], dust emissions [5], and vibration [6], the predicting of direct blasting results, e.g., block degree [7] and throwing distance [8]. Moreover, blasting impacts the operations of various production segments in open-pit mines [9]. For example, bulk ore produced by blasting implies additional loading, transportation, and crushing costs [10].…”

Section: Introductionmentioning

confidence: 99%

Prediction and Optimization of Blasting-Induced Ground Vibration in Open-Pit Mines Using Intelligent Algorithms

Guo

Zhao

Ping-feng³

2023

Applied Sciences

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Prediction and parameter optimization are effective methods for mine personnel to control blast-induced ground vibration. However, the challenge of effective prediction and optimization lies in the multi-factor and multi-effect nature of open-pit blasting. This study proposes a hybrid intelligent model to predict ground vibrations using a least-squares support vector machine (LSSVM) optimized by a particle swarm algorithm (PSO). Meanwhile, multi-objective particle swarm optimization (MOPSO) was used to optimize the blast design parameters by considering the vibration of particular areas and the bulk rate of blast fragmentation. To compare the prediction performance of PSO-LSSVM, a genetic-algorithm-optimized BP neural network (GA-BP), unoptimized LSSVM, and BP were used, by applying the same database. In addition, the root-mean-squared error (RMSE), the mean absolute error (MAE), and the correlation coefficient (r) were regarded as the evaluation indicators. Furthermore, the optimization results of the blasting parameters were obtained by quoting the established vibration prediction model and bulk rate proxy model in MOPSO and verified by field tests. The results indicated that the PSO-LSSVM model provided the highest efficiency in predicting vibrations with an RMSE of 1.954, MAE of 1.717, and r of 0.965. Furthermore, the blasting vibration can be controlled by using the two-objective optimization model to obtain the best blasting parameters. Consequently, this study can provide more specific recommendations for vibration hazard control.

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A review of the influence of blast fragmentation on downstream processing of metal ores

Cited by 14 publications

References 85 publications

Evaluation of Rock and Explosive Properties for Fragmentation Characterization: An Application of WipFrag

Evaluation of Rock and Explosive Properties for Fragmentation Characterization: An Application of WipFrag

Identification of Rock Fragments after Blasting by Using Deep Learning-Based Segment Anything Model

Prediction and Optimization of Blasting-Induced Ground Vibration in Open-Pit Mines Using Intelligent Algorithms

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