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By Catharine L. VanOmmeren

Emulsion quality characteristics such as shelf life, handling, and sensitivity are critical to the end-user. It is therefore important that the consumer be prepared to ask his supplier specific questions. To do so, he needs to have a basic understanding of what an emulsion is and how emulsion properties can affect safety, performance, and environmental impact. The criteria to be covered include: energy, velocity, sensitivity, minimum priming, shelf life, pumpability, effect of AN quality, over-the-road handling, and formulation The consumer will learn how each of these properties can directly affect him. The information will enable him to set minimum standards in product selection, blending, and application.

Jan 1, 1989

By Uli Leiste

Two papers are presented which examine Air Deck Blasting, one from a computational aspect and the other from a shock aspect.

Jan 1, 2016

By Carlos Campos Junior, Leonardo Soares Chaves, Raul Carretero Hoz, Renato Jacome Costa, Davi Bastos Martins de Oliveira

The Vale’s Serra Sul mine, also known as S11D project, is one of the world’s largest open pit iron ore mines, located in Brazil’s Amazon region. The mine produces around 90 Mt of high-quality iron ore annually. Its unique orebody enabled the S11D project to adopt a truckless mining system with In-Pit Crushing and Conveying (IPCCs) for ore exploitation and waste stripping. The IPCCs system requires crushing the waste, mainly composed by the jaspilite lithology, and is also conveyed to waste deposits several kilometers away from the mine. Due to Amazon’s environmental restrictions, the project was designed with preliminary geological knowledge, leading to the installation of primary crushers not designed for the hardest jaspilite, with uniaxial compressive strength (UCS) up to 500 MPa. This resulted in low waste crushing performance, affecting stripping ratio and threatening future production. A project was developed to improve the fragmentation of the jaspilite, allowing higher waste IPCC production, and reducing the risk to the production due to low performance of waste crushing and conveying. The first challenge was the independence of the geological and the geomechanical models, while only the geological model was considered in blast designs, the geomechanical model describes six waste groups of jaspilite classified according to their strength. A new geological model was then created to incorporate the geomechanical classification, allowing different blasting design according to the rock strength of the jaspilite. The blasting pattern on most compact jaspilite was reduced 27%, from 22m² to 16m² in 9 7/8” diameter holes, with a proposed theoretical reduction of 23% of the P80, from 326mm to 250mm. The second challenge was the implementation of the new design, as this pattern size and the high powder factor (2.9 kg/m3) were paradigms for such a large iron ore mine. The measured results have shown 25% reduction of the P80, the crushing rate was increased by 17% (230t/h), saving around $2.4 million annually in crushing and conveying system maintenance.

Jan 21, 2025

By Stephen Mansfield

Analysing how accurately a blast design is implemented is one of the cornerstones of a successful operation, and critical for mine optimisation. This paper presents a concept that involves the collection and analysis of data for the five stages through which a blast transitions, from budget through to implementation.

Feb 1, 2020

By M. Szumny, P. Mertuszka, K. Fuławka

Mining-induced seismicity is one of the adverse side effects of mining with the use of explosives at great depths. Disturbance of the primary stress state by the development of the network of excavations combined with the continuous generation of dynamic loads by the detonation of explosives in mining faces has a negative impact on the level of safety of crew and machines. To ensure the stability of excavations it is necessary to identify in detail the nature of the seismic hazard by means of continuous seismic measurements. Unfortunately, the technologies used so far are characterized by high costs of installation and further maintenance, which clearly limits the possibility of using a large number of stations in a small area. A breakthrough in this matter may be the technology of microelectromechanical systems, which is several times cheaper than standard solutions used in mining seismology. This technology, with the appropriate configuration of devices, can be successfully used to monitor dynamic phenomena generated by mining activities. As part of this study, the assumptions and configurations of a prototype 42-channel accelerometer network using MEMS technology have been presented. The network was installed in the Lubin underground copper mine in Poland in a seismically active area, where multi-face destress blasting are conducted regularly. As a result, 14 tri-axial accelerometers were installed in the immediate vicinity of the active mining front at a distance of about 1,000–4,000 ft (300–1,200 meters) from the closest faces. Highfrequency recordings are collected based on algorithms that trigger the system when the Peak Particle Acceleration set value is exceeded or on the basis of a manual external triggering device connected to the blasting machine. During the measurement period, several high-energy seismic tremors and several dozen of waveforms generated by blasting were recorded. The obtained results are the basis for assessing the effectiveness of the destressing measures and for the analysis of geomechanical risk based on periodic analysis of the velocity of seismic wave propagation

Jan 1, 2024

By Ricardo González Dumuihual, Carlos Scherpenisse

A monitoring campaign to record dynamic pressures generated by detonation of a single blasthole was implemented at Barrick Gold´s Pueblo Viejo Open Pit mine (PVDC), located in the Dominican Republic . Pressure amplitudes beyond the dynamic compressive strength of the electronic detonator´s capsules have the potential to damage these electronic devices, generating misfires along with all the associated risks and entailed safety implications. In most instances, misfires generated from high dynamic pressures are a direct consequence of drilling deviations resulting in blasthole distances smaller than originally designed.

Feb 1, 2020

By Emmett J. Ientilucci, Martin Held, Xuesong Liu

Mining is crucial for advancing technology and science by providing essential resources and materials. Quarry blasting, a necessary process in mining, releases these resources and chemicals, driving research and engineering improvements. However, NOx (nitrogen oxides) is an undesired contaminant produced during quarry blasts, posing a significant negative impact on mining activities. Therefore, detecting and quantifying NOx emissions is essential for designing effective blasts, analyzing their dynamics, and reducing contaminant production. Image processing and machine learning, including deep learning methods, have been applied to solve smoke plume detection and segmentation tasks. Most research has focused on smoke from campfires or forest fires, which are important for early fire and smoke detection. However, there is limited research on quarry blast smoke using machine learning methods, which presents challenges for recognizing the smoke plume from complex background scenarios and objects, including collapsed rocks, scattered soil dust and dirt, and the dynamic movement of the scene due to blast vibrations. Detecting NOx within the smoke plumes is further challenging because NOx must be distinguished from the overall smoke plume, as they share many common spatial features. To resolve this issue, we propose an ensemble method for NOx detection that combines smoke plume segmentation with NOx fume detection. First, we apply a smoke plume segmentation model to identify the smoke plume regions. Then, we use these masked-out smoke plume regions to detect NOx based on its unique color features. The first stage of the ensemble method helps block out complicated confusers in the background, such as reddish and brownish dirt. Next, we specifically design a color classifier for NOx to further improve its sensitivity. We evaluate the overall video-wise performance using accuracy, false alarm, and miss rates based on sampled image frames from videos. Additionally, we assess the mean Intersection over Union (mIoU) for smoke plume segmentation and the mean Average Precision (mAP) for image-wise performance from an algorithmic perspective. In summary, this paper represents the first attempt to apply machine learning approaches to NOx detection in a memory-efficient and time-efficient manner.

Jan 21, 2025