A review on the prediction and assessment of powder factor in blast fragmentation

Powder factor can be defined as the quantity of explosives (kg) required to break a unit volume or tonne (t) of rock. The prospect of excavating rocks by blasting is characterized by a specific consumption of explosives. In the past decades, researchers have come up with several precise approaches to predict powder factor or specific charge in blast operations other than through trial blast. Research in this area has focused on the relationship between rock mass properties, blasting material and blasting geometry to establish the powder factor. Also, the interaction between specific energy and particle size embodied in the theory of comminution that is less dependent on local conditions has been studied. In this paper, the various methods for powder factor estimation based on empirical and comminution theory modelling as well as machine learning approaches in both surface bench blasting and underground tunnel operations have been reviewed. The influence of intact rock properties on powder factor selection and the influence of powder factor selection on post-blast conditions have also been discussed. Finally, the common challenges that have been encountered in powder factor estimations have been pointed out in this regard.

Epipolar Multitarget Velocity Probability Data Association Algorithm Based on the Movement Characteristics of Blasting Fragments

Fragments generated from the blast-fragmentation warhead after blasting are typically multiple, fast, small, and dense. In light of the epipolar multitarget feature of blasting fragments, this paper utilizes the movement characteristics of blasting fragments for modeling. Then, the modeling results are adopted in probabilistic data association (PDA) algorithm of multitarget tracking. A novel epipolar multitarget velocity PDA (VPDA) algorithm is proposed based on the movement characteristics of blasting fragments. This algorithm forms the movement characteristics with the finite element simulation results of warhead blasting fragments, utilizes the Doppler velocity probability to reassign the association probability, and updates the state and covariance of each target through the probability weighted fusion. Simulation results demonstrate that, the computational complexity of the proposed algorithm is close to that of PDA algorithm, and the association success rate and the state value update error approximates to the association effects of joint probabilistic data association (JPDA) algorithm, which can effectively track the fragments with identical velocity while reducing the complexity of the epipolar multitarget tracking algorithm, and can respond to the group target tracking scenario.

Experimental investigations of the response of a portable container to blast, fragmentation, and thermal effects of energetic materials detonation

The detonation of an energetic material (EM) is manifested in the form of blast wave, fragmentation of casing material, and thermal effects. These effects are very destructive and cause injuries-being fatal-and structural damage as well. The attenuation of these effects is a prime focus. C4 explosive weighing 104 g was tested as surface burst. Peak overpressures of 1362 kPa and fireball radius of 0.65 m were measured. A multi-layer container comprised steel liner, Kevlar woven fabric, and laminated glass fiber reinforced polymer (GFRP) was developed and investigated to counter the combined blast, fragmentation, and thermal effects of EM detonation. Commercially available shaving foam was characterized and used as filling material inside the container. The foam bubbles have shown a good stability with time. The shaving foam quenched the fireball and afterburning reactions owing to rapid heat and momentum transfer mechanism. The containment system provided more than 80% overpressure reduction with respect to an equivalent open-air detonation and also restricted any escape to lateral directions. Coupled Euler-ALE (Arbitrary Lagrangian-Eulerian) approach was employed to numerically simulate the blast wave parameters. A good agreement is obtained between the simulation and experimental results.

Development of a predictive model of rock fragmentation for Nui Phao open-pit mine in Vietnam using multiple-output Neural Networks and Monte Carlo Dropout technique

Precise and reliable prediction of blast fragmentation is essential for the success of mining operations. Over the years, various machine learning models using artificial neural network have developed and proven to be efficient in predicting the blast fragmentation. In this research, we design multiple-output neural networks to forecast the cumulative distribution function (CDF) of blast fragmentation to improve this prediction. The model architecture contains multiple response variables in the output layer that correspond to the CDF curve’s percentiles. We apply Monte Carlo dropout procedure to estimate the uncertainty of the predictions. Data collected from a Nui Phao open-pit mine in Vietnam are used to train and validate the performance of the model. Results suggest that multiple-output neural network models provide better accuracy than single-output neural network models that predict each percentile on a CDF independently. Whereas, Monte Carlo dropout technique can give valuable and relative reliable information during decision making. Article highlights: • Precise and reliable prediction of blast fragmentation is essential for the success of mining operations. • A predictive model based on the multi-output neural network and Monte Carlo dropout technique was designed to predict the fragmentation CDF curve in the blasting operation of an open-pit mine. • The predictive model was proven reliable and provided better accuracy than models based on a single-output neural network.

Prediction of rock fragmentation using the Kuznetsov-Cunningham-Ouchterlony model

SYNOPSIS Assessment of blast fragment size distribution is critical in mining operations because it is the initial step towards mineral extraction. Different empirical models and techniques are available for predicting and investigating the consequences of blasting, one of which is the Kuznetsov-Cunningham-Ouchterlony (KCO) model. In this paper we summarize the advances in the empirical models from inception until now, and explore the improvements that have been made so far with particular emphasis is on the most recent KCO model. Utilization of the model and the errors that arise between expected and the actual outcomes are analysed. The results indicate that the KCO model remains useful for predicting the blast fragmentation at limestone mine sites, despite the availability of other advanced prediction models. It is also a valuable instrument for pre-surveying the impact of varying certain parameters of a blast plan. Keywords: blasting, rock fragmentation, modelling, prediction.