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By C. Gatschlhofer, A. Krammer, H. Raupenstrauch, T. Hochsteiner, A. Holzer, L. Wiszniewski

The rising demand for lithium-ion batteries, coupled with international climate targets, emphasises the need for circular economy and zero-waste recycling approaches. However, so far, no industrial-scaled solutions are available that fulfil global recovery targets. Therefore, a novel pyrometallurgical reactor was developed at the Chair of Thermal Processing Technology at Montanuniversitaet Leoben to avoid lithium slagging and recover volatile elements via the gas phase. Nevertheless, previous results show that several impact factors, including interfering elements or pre-treatment steps, strongly limit the recycling efficiency of black mass. To simulate the black mass and provide feedback on possible influences of pre-treatment steps, cathode material from production with additives of interfering elements was analysed regarding melting behaviour. The results show that formation of a further processable alloy was increased by almost 50% when pretreatment steps were excluded, indicating pre-treatment steps as a major influence to be considered within this specific recycling route.

Jan 1, 2023

By JESSICA W. A. AZURE, Kansake A. Bruno, Samuel Frimpong, AZUPURI G. A. KABA, FORSYTH A. KADINGDI, PROSPER E. A. AYAWAH

Blasting generates undesirable ground vibration with adverse effects on humans, the environment, and structures. Numerous studies have used various techniques including machine learning (ML) to predict the peak particle velocity (PPV) caused by blasting in surface mines. These studies used the validation-set approach to evaluate the performance of their predictive models. In this study, Random Forest (RF), Gaussian Process (GP), and Gradient Boosting Machine (GBM) were applied for the prediction of PPV using data from a large open-pit gold mine in Ghana. An ensemble model based on a stacked generalization approach was then built on these three base models to significantly improve the PPV prediction performance. The model evaluation was done using repeated tenfold cross-validation which resolves the challenges with the validationset approach for small datasets. The dataset used in this study comprised a total of 196 measurements taken from different blasting operations. R-squared ( R2 ), mean absolute error (MAE), and root mean square error (RMSE) were used to evaluate the model performance. The results obtained from these models revealed that all the base models did not significantly outperform each other. However, the stacked generalization approach had a better performance, with RMSE = 0.12, R2 = 0.96, and MAE = 0.07. This approach presented an improvement over the existing methods and will be useful for highly accurate predictions of ground vibration during blast design. The application of this model will be useful for efficient and effective blast design.

Nov 8, 2022

By Alok Bhowmick

The most uncertain and challenging part of a bridge design and construction is the ‘Choice of foundation’. During the planning and conceptual design stage, it is extremely important to choose the right type of foundation. Wrong choice can lead to disaster for the project. This paper will present three interesting case studies of past bridge projects, to demonstrate how correct choice of foundation can help to meet the project commitments while the wrong choice of foundations can led to huge delay in project completion, leading to time and cost overrun.

Nov 1, 2022

By Justin Srednicki, Chenming Zhou

Ambient electromagnetic (EM) noise (natural or manmade) is a major limiting factor for the design and operation of many electronic devices, including through-the-earth (TTE) communications and tracking systems. Extensive studies on EM noise were conducted by the US Bureau of Mines in the 1970s and 1980s. Changes in the design and operation of electrical equipment since then, however, have resulted in changes in the EM noise environment. This paper reviews past research on surveying EM noise in the extremely low-frequency (ELF) and very-low-frequency (VLF) bands, which are the operating bands for TTE systems, and introduces a new battery-powered, rugged, portable measurement system for surveying EM noise in mining environments. Examples of surveyed EM noise measurement results in and above an active underground coal mine, using the new system, are presented in terms of absolute magnetic field strength levels. A new metric, average noise level (ANL), is introduced to characterize the noise level relative to a particular frequency band for a specific location. It is found that EM noise in mining environments varies significantly with time and location. EM noise levels at frequencies of 30 Hz that shifted from power harmonics (e.g., 90, 150, 210, 270 Hz) are relatively low and their variation with time can be statistically modeled by a Gaussian distribution. The conclusions and findings presented in this paper can help better design and operate TTE communications and tracking systems in underground mines.

Oct 14, 2022

By Carlos Herrera, Paulo Alcazar, Alex Conco, Fremy Flores

Las redes neuronales artificiales son un subconjunto del machine learning y están en el corazón de los algoritmos de Deep learning reflejando el comportamiento del cerebro humano, permitiendo así que las soluciones tecnológicas reconozcan patrones y resuelvan problemas comunes en los campos de la IA. Este modelo matemático es capaz de aprender a través de un aprendizaje supervisado de patrones de conocimiento. Se investigó una solución que involucra cuatro tipos de sistemas.

Sep 30, 2022

By Z. Botha

Sheryl Sandberg, the COO of Facebook, published a book titled 'Lean In', where she invites women to sit at the table. In summary, to 'sit at the table' entails not only embracing opportunity, but to actively seek out opportunities in leadership positions. Sandberg points out that, unfortunately, apart from institutional obstacles, a woman's own perceived limitations have a negative impact on her leadership ambitions. To create an environment where diversity will flourish, where everyone has a seat at the table and everyone has a voice, safety must be established first. To establish safety within a diverse team, there is power in relationships and connections. This study will first and foremost review theories in literature as to why diversity and inclusion are so important and the value that diversity brings to our industry. The study also recommends a few focus areas for improving diversity and inclusion in the industry.

Sep 1, 2022

By Bülent Erdem, Zekeriya Duran, Tuğba Doğan

The objective of this study was to evaluate the potential health risk associated with exposure of dozer operators to whole-body vibration. Measurements were made on 31 dozers used in different surface mines in Turkey. Predictions of adverse health effects of vibration were based on the standards BS 6841:1987, ISO 2631–1:1997, ISO 2631–5:2004, and the EU Vibration Directive:2002. Operators experienced high and comparable levels of vibration acceleration and dose during all dozer tasks: pushing the material (DOZE), moving forward (FORWARD), moving backward (REVERSE), and ripping (RIP). Specifically, DOZE, FORWARD, REVERSE, and RIP showed high potential of adverse health effects in terms of vibration dose according to the worst-case axis and vector sum criteria and a moderate adverse health effect for acceleration according to the worst-case axis criterion. Operators of large dozers were exposed to lower levels of vibration. However, no statistically significant relationship between vibration exposure levels and the propelling mechanism and the service time of dozers, operator experience, operator age, and ground type was found.

Jun 16, 2022

By C Sylvestre

Leaders have invested heavily in traditional safety management to improve safety performance over the years. This primarily involved: • fixing the environment (eliminating the hazard) • improving the system (more/less rules, procedures etc) • making safety more conscious (more/better interactions about the importance of safety). These approaches have helped improve safety performance significantly during the last 50 years and will continue to play a role in the future. Although these approaches may be part of an overall solution, they are not enough to prevent many incidents. Today, these three approaches are used more than before, and are used better than ever before. Yet, workplace fatalities in Australia have stagnated from 2013 to 2019, as have the number of incidents, even serious ones. In the USA, workplace fatalities have increased by 15 per cent in the same period.

May 5, 2022

By C Sylvestre

For the last 50 years, traditional safety management has improved safety performance by: • Fixing the environment (eliminating the hazard). • Improving the system (more/less rules, procedures etc). • Making safety more conscious (more/better interactions about the importance of safety). Although these approaches certainly have merit, and may be part of an overall solution, they are not enough to prevent many modern-day incidents. Today, these three approaches are used more than before, and are used better than ever before. Yet, workplace fatalities in Australia have stagnated from 2013 to 2019, as have the number of incidents, even serious ones. In the USA, workplace fatalities have increased by 15 per cent in the same period.

May 5, 2022

By Ehsan Moosavi, Kamyar Tolouei, Mehran Gholinejad

The literature defines rockbursts in slightly different ways based on their generation mechanisms, field phenomena, and degree of damage, but in a way that is almost consistent. Generally, a rockburst refers to seismic events that mainly happen in underground excavations, for purposes such as underground mining, road and railway tunnels, nuclear power, etc., and that are followed by the collapse and outburst of rocks. Long-term rockburst prediction can be abstracted as a classification problem mathematically, in which we can introduce deep learning to solve it. Further, using deep learning in rockburst prediction can overcome drawbacks such as subjectivity and inconsistency brought from previous traditional approaches. A rockburst data-set, was employed to evaluate the current method for predicting rockburst grade, and the good results of overall success rate were obtained. The results indicated that the proposed algorithms can speed up parameter optimization search, the proposed method is robust model and might hold a high potential to become a useful tool in rockburst prediction research.

Apr 26, 2022

By J. A. Batista-Rodríguez, F. J. López-Saucedo

A new methodology is proposed for the preliminary assessment of coal blend quality, which combines gamma-ray spectrometry and magnetic susceptibility measurements. This methodology does not replacethe conventional methods of assessing coal quality, but can help to control the quality of the coal blending process, as well as reduce the amount of proximate analysis required and the determinations of calorific value and total sulphur. The methodology includes five steps. First, both geophysical measurements aremade with handheld equipment. The samples with magnetic susceptibility ≤ 1 × 10-3 SI are then separated. From these samples, those with total gamma intensity ≤ 20 nGy/h are separated. These samples have a higher calorific value and lower ash and moisture content. To separate the samples of better quality and with lower total sulphur content, in step four the samples with total gamma intensity > 20 nGy/h are separated from those with magnetic susceptibility ≤ 1 × 10-3 SI. This last group includes samplesthat will cause less total sulphur-related environmental pollution. In the final step, the quality of both radioactive sample groups is corroborated from the calorific value measurement and ash, moisture, andtotal sulphur contents in at least 10% of all coal blend samples from both groups. In this study, more than1000 samples were analysed with this new methodology, and the results are remarkably similar to the ASTM classification of the Sabinas Basin coals.

Apr 2, 2022

By Pratima Meshram Abhilash

In this review, availability of germanium in primary and secondary resources and its recovery from these resources are presented. With nearly 40% germanium consumed in fiber optics and scarcity of resources, the global consumption outplays the primary production. Germanium exists as a substituted element in zinc sulfide matrix of lead–zinc sulfides and lignites. Nearly 60% of the germanium supply is met by zinc refining residues, and about 20–30% of germanium is produced from coal and its by-products. Germanium recovery from various resources such as zinc plant residues, coal and coal plant waste, spent optical fibers, and copper flue dust has been examined. Bioleaching was exploited by few researchers to extract germanium from zinc sulfides, sulfide tailings, jarosite, coal fly ash, and flue dust. This review covers the various work carried out by different chemical separation processes, viz., chemical precipitation, solvent extraction (SX), ion exchange (IX), and membrane processes, as well as biological approaches.

Jan 16, 2022

By Siddhartha Roy, Hemant Agrawal, Ram Madhab Bhattacharjee, Devi Prasad Mishra

Heat stress is a major concern for the health, safety and productivity of miners working in hot and humid underground mines. At greater depth, heat inside underground mines increases due to geothermal gradient, heat released from high-capacity machinery, exposed rock surface, auto-compression of air, human metabolism, etc. Prolonged physical work in thermal environments severely affects the health, safety and efficiency of miners. In order to address this important issue, a systematic literature review (SLR) has been conducted. This SLR aimed to identify the research gap in the area of heat stress effect on underground miners and its control. An extensive literature search was carried out on numerous digital databases, grey literature and conference proceedings to identify relevant research work for this review. Moreover, the impactful studies, methodologies and preventive measures undertaken during the past five decades on the effect of heat stress on underground miners were retrospectively analysed, with a broader aim to explore the need for further research on this pertinent topic. Overall, 571 references were identified from the literature review, out of which 51 research articles addressing the sources of heat, heat stress effects on miners, heat stress indices and control measures for heat stress in underground mines were selected as primary papers for detailed review. Pre-developed fatigue is one of the prime causes of health hazard, safety and productivity loss of miners working in underground high heat stress zones. Hence, development of a better heat stress index integrating personal, environmental parameters and pre-developed fatigue (PDF) of miners is essential.

Jan 14, 2022

By Dejan Petrović, Pavle Stojković, Jelena Ivaz, Saša Stojadinović

In spite of the fact that flyrock phenomenon represents the real threat for personnel and machinery, it still remains insufficiently investigated. There are efforts, in the mining community, to explain the causes of flyrock, but the models capable to accurately predict flyrock occurrence and define the flyrock distance still do not exist. This article is an attempt to establish such a predictive model. The model utilized the adaptive nature of artificial neural networks and combined it with the accuracy of numerical modeling of physical phenomena. Necessary data were collected by high-speed camera recordings of actual blasts at three different surface mines and processed for further use for artificial network training. The result was a neuro-numerical couple which was capable to predict flyrock occurrence, estimate the launch velocity of flyrock fragments, and calculate the maximum distance of flyrock fragments. The safe distance was then calculated by multiplying the flyrock distance with a factor of safety. The article explains the procedure for data acquisition and processing, artificial network construction, training, and validation. It also explains the principles of flyrock distance calculation and, finally, provides a definition of factor of safety and safe distance. The results showed that the prediction was satisfactorily accurate and reliable.

Oct 22, 2021

By JESSICA W. A. AZURE, Samuel Frimpong, AZUPURI G. A. KABA, FORSYTH A. KADINGDI, PROSPER E. A. AYAWAH

This study developed machine learning (ML) models for predicting rock formation reactive forces experienced by a hydraulic shovel bucket during excavation. To do this, rock formation in the form of muckpile was modeled as granular ball elements in three-dimensional particle flow code (PFC) using linear bonding logic. A typical field-size hydraulic shovel bucket modelled in AutoCAD was used to excavate the rock formation and the three-dimensional shovel bucket resistive forces required to cut through the rock formation were measured and recorded. The experiments involved different muckpile height, bulk densities, repose angles, and average fragment size. Six ML algorithms, artificial neural network (ANN), K-nearest neighbor (KNN), linear, random forest (RF), regression tree (RT), and support vector machine (SVM), were evaluated on their ability to predict the shovel bucket resistive forces. The input variables used for the prediction of the resistive forces were the muckpile bulk density, angle of repose, height, and fragment size. The results showed that the ML techniques are useful and reliable methods of predicting rock formation resistive forces based on the rock formation properties. This research is a preliminary step towards developing reliable models for accurate prediction of excavation forces/torques which could potentially enhance hydraulic shovel process automation.

Sep 27, 2021

By BHAMIDIPATI S. SASTRY, ANMAYA NAIK, ASIF AHMAD, Srivatsan Jayaraman Sridharan, ADITYA PANDEY

Wet kata cooling power (WKCP) as measured by a kata thermometer is a well-known heat stress index used in the mining industry. Although, over the years, several researchers proposed different heat indices and indicated limitations of the kata thermometer, WKCP is used in the South African gold mining industry due to its simplicity and rapidity of assessment. This paper presents a comparative analysis of the newly developed empirical expression for the WKCP and some of the more commonly used empirical models. For this purpose, WKCP measurements are obtained based on experiments conducted in a low-speed wind tunnel. Based on a regression technique, the analysis presented aims to assess the disagreement between the empirical model predictions and actual measurements. The correlation is also presented between WKCP and the well-established thermoregulation models using parameters from the low-speed wind tunnel experiments, including dry-bulb temperature (Td), wet-bulb temperature (Tw), barometric pressure (P), and wind speed (v). It is found that the proposed empirical model for the WKCP represents the bioclimatic interaction quite well under the specific parameters that defines the harsh thermal environment and air condition, such as metabolic activity, clothing of the person, and the velocity of the ventilating air in which the person is working. The statistical analyses performed show that the empirical model proposed for the WKCP produces satisfactory estimates of the thermal stress experienced under lower air velocities, light clothing, and higher metabolic activity rates.

Sep 2, 2021

By M. Salimov, J. Sattarvand

The conventional visualisation method of remote equipment is through the installation of one or more cameras on the machine, and sending a live feed to the operator’s flat display. However, the performance of the teleoperation can be enhanced using better visualisation through virtual reality (VR). This paper presents the process of the design and manufacturing of a robotic platform that will be used in the next phase of the project for a VR-assisted teleoperated excavator. The robot consists of a loading boom and arm arrangement installed on the undercarriage of a CAT300-9D. All hydraulic cylinders are replaced with electric actuators, and crawling motors are substituted by electric motors. The pieces of the boom and the arm, as well as the bucket were designed in Autodesk Inventor, and manufactured from aluminium sheets using a desktop and CNC machine. Parts are joined by pins and welding as needed. An Arduino-based control system is developed for the robot, and motor controllers are used to convert the power commands from the microcontroller to the high-amperage actuators. The control system is capable of communicating with a wireless Play Station controller to operate remotely. The manufactured robotic excavator is planned to be utilised in multiple autonomy research projects.

Sep 1, 2021

By M. Z. Naghadehi, F. F. Nasategay, J. Sattarvand, R. Battulwar, E. E. Gohari

After the recent increase in catastrophic failures of tailings dams such as the Burmadinho, Brazil failure in 2019, the mining industry has actively engaged in the stabilisation of tailings dams. Most of the tailings storage facility (TSF) guidelines suggest a laborious action that can be facilitated through imaging by unmanned aerial vehicles (UAVs). Although UAVs are not unknown to most mining operations, automated rill detection through aerial image processing is a new concept. This study utilises images from sites in the U.S. and employs semantic segmentation with a UNet architecture to detect location and size of these rills. The developed model can be used as a tool for monitoring surface erosion of TSFs.

Sep 1, 2021

By F. C. Harris, S. J. Gaab, J. Sattarvand, S. Duzgun

Underground mines are inherently dangerous due to potential hazards such as fires, explosions, and caving of rock. Although these events are rare, once they occur a speedy and safe egress of mineworkers is critical to survival. Static evacuation systems use exit signs or directional lines but can get obstructed or be hardly visible in smoky or dusty conditions. This paper presents the investigation of the effectiveness of a smart evacuation technology in underground mining, where a real-time guidance system leads individuals on an optimised path to the egress while avoiding danger zones, compared to conventional practices. Participants simulated an evacuation from a virtual reality mine, using the conventional system and the smart evacuation system with real-time guidance. The highest reduction in evacuation time using the smart method amounted to nearly 40% on average. Moreover, 83% of participants preferred the smart guidance system, and agreed that it could enhance safety.

Sep 1, 2021

By Chiranjibi Santi, Amit Kumar Gorai, VIVEK KUMAR HIMANSHU

Air overpressure (AOp) and peak particle velocity (PPV) are the most undesirable effects of blasting in mines. It is an urgent need to design a predictor model for AOp and PPV for universal applications to minimize environmental effects and damages that occur due to blasting. The present study attempts to design an artificial neural network (ANN) model for the prediction of AOp and PPV in different conditions. In this study, the blast design parameters (number of holes, depth of the blast hole, stemming length, spacing, burden, distance of vibration monitoring location from the blast site, charge weight per delay) of two active mines (coal and iron ore) are considered in four different conditions for training and testing of the model for the prediction of AOp and PPV. The model was trained and tested in four different combinations of data (trained using data of one mine and tested using data of another mine, trained and tested using data of coal mine, trained and tested using data of an iron ore mine, trained and tested using combined data of both the mines) for examining the applicability in different conditions. The results indicate that R2 values are ranged from 0.886 to 0.908 and 0.8728 to 0.8959, respectively, in the prediction of PPV and AOp. A comparative study of the performances of the developed model with the other empirical model is also demonstrated. For this, the site constants of different empirical models were estimated individually for both the mines. The study results indicated that the ANN model performs much better than all the empirical models. It can be inferred from the results that blast vibration cannot be accurately predicted only from charge per delay and distance from the blast hole. The ANN model was considered many other factors and thus can predict the vibration level more accurately.

Jul 30, 2021