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By Konstantinos Komnitsas, Evangelos Petrakis, Emmanouil Varouchakis

During the modeling of grinding systems, population balance modeling (PBM) which considers a constant breakage rate has been widely used over the past years. However, in some cases, PBM exhibited some limitations, and time-dependent approaches have been developed. Recently, a non-linear framework which considers the traditional linear theory of the PBM as a partial case was introduced, thus allowing the estimation of product particle size distribution in relation to grinding time or the specific energy input to the mill. In the proposed model the simplified form of the fundamental batch grinding equation was transformed into the well-known Rosin–Rammler (RR) distribution. Besides, the adaptability and reliability of the prediction model are among others dependent upon the operating conditions of the mill and the adjustment of the RR distribution to the experimental data. In this study, a series of grinding tests were performed using marble as test material, and the adaptability of the non-linear model was investigated using three loads of single size media, i.e., 40, 25.4, and 12.7 mm. The results indicate that the proposed model enables a more accurate analysis of grinding, compared to PBM, for different operating conditions.

Jun 24, 2023

By Mustafa Kumral, Yuksel Asli Sari

Economic evaluations of underground mining operations are made based on the estimated or simulated grades of the block model as the actual values are not known. Depending on how the planning decisions are made, the risk of the operation can be managed and decreased. This research introduces a mixed-integer linear programming (MILP) model for stope sequencing optimization that considers the uncertainty associated with net present value (NPV) using chance-constrained programming (CCP). The introduced method permits achieving a balance between maximizing NPV and reducing the associated operational risk through transforming the problem into a multiobjective problem, where the objectives are maximizing the NPV and minimizing the risk. This risk is expressed in terms of the standard deviation of project outcomes, taking into account multiple possible scenarios. In a case study, the efficiency of the method was demonstrated as a decision-making tool capable of translating a specified risk or reliability level into a stope sequencing plan.

Jan 1, 2024

By Robert Stefanko

The widespread and increasing use of electrical power for mechanized power has made a tremendous impact on the coal mining industry. It is probably the single most important factor influencing the productivity of a coal mine. In fact, as a part of a study of 62 coal mines in Pennsylvania and northern West Virginia, plus information that was obtained from other sources, it was possible to show a direct correlation between the rise in the use of electrical power and mine productivity, Figure 1. This increased power demand has not always been achieved through proper planning and a careful evaluation of all design parameters, many of which are not even recognized. The result has been increasing problems in power distribution and utilization establishing the need for scientific studies including considerable underground measurements. Because the cost of power for ton of coal produced is generally a relatively low value, inadequate attention is paid its distribution and utilization. Furthermore, a poor system of records and a lack of adequate measurements add further to a paucity of statistical evidence that would permit a rational analysis of overall power costs. However, the direct cost figure alone is highly deceptive; of much greater value is the indirect costs associated with the use (or misuse) of electrical power underground. Success in highly mechanized mining correlates directly with equipment utilization. Today's machines have tremendous potential capacities but profits are measured in terms of performance and not potential. Thus the reliability of electric motors is an extremely important factor for consideration and the effects of all parameters should be carefully evaluated. Thus while studies are continuing on the evaluation of primary and secondary mine electrical systems with interesting results, this paper will be limited to reporting on the results of a study of a specific motor application.

Jan 1, 1972

By J. Paraszczak

Underground metal mining involves many challenges. Economic slowdown, tough international competition and sometimes volatile metal prices put constant pressure on mine operators to increase productivity and to reduce production cost. At the same time, countries such as Canada, the United States and Australia are experiencing skilled labor shortages that might even stall development of certain mine projects. Furthermore, some stages of underground production take place in particularly hazardous environments where the safety and health of miners is at risk. Automation of some processes in underground mining is one of the promising avenues to alleviate some of these problems. Loading and haulage are among the most critical operations and areas where implementation of autonomous vehicles has been targeted. However, although health and safety issues are a primary driver to opt for automation, the latter should ensure an acceptable level of productivity and pro?tability. With this in mind, this article focuses on the factors affecting operational effectiveness of autonomous underground loading and haulage equipment and the means to maximize it.

By Darold Woodward, Stefan Haydn, Petr Pospisil

Most fatalities in tunnel fires are caused by the spread of toxic smoke. Controlling the spread of smoke is crucial to provide a survivable environment for emergency egress. A properly designed and tuned feedback control loop is required to achieve a target flow that is slow enough to allow pedestrians to escape under different boundary conditions. Feedback control loops are also helpful in optimizing energy consumption under moving traffic conditions. A reliable feedback flow control system requires sophisticated engineering knowhow and experience, careful selection of instruments and controlled variables, and thoughtful implementation from design through field testing and commissioning.

Dec 1, 2022

By M. Ge, C. Mborah

"The acoustic emission/microseismic monitoring (AE/MS) technique has widely been used in the mining environment for the monitoring of rock mass stability. A successful implementation of the method is hinged on being able to accurately determine the P- or S-wave arrival which is then used in the determination of the source of an event. Determination of the arrival phase is usually performed visually by a human expert. However, the mining environment is characterized by high noise levels and different noise types of varying intensities. Therefore, performing manual phase picking on time series data acquired from the AE/MS monitoring system can sometimes be tedious and time consuming if not impossible. In this paper, a proposal for improving manual P-wave phase arrival picks is discussed. The method involves the filtering of the AE/MS data using a stationary discrete wavelet transform (SDWT) method. After filtering, appropriate scales/levels of the multi-level decomposition process are then selected for further processing. The first step in the picking process after filtering, requires the computation of the signal power and the root mean square (RMS) using the signal amplitudes at each scale. The preliminary P-phase arrival time is picked using the results of the calculated power and RMS values respectively. The calculated RMS values are then superimposed on the calculated power values to determine the final arrival picks. If the arrival time picks obtained by both methods coincide or are within 5 to 10 sample points, the pick is considered reliable. The accuracy of the method was verified using AE/MS data from two datasets obtained from two separate underground mines. The results of the study showed that the picking method is reliable and robust. Based on the data studied, it can be concluded that the method will help reduce the time usually spent in processing AE/MS data from mines. INTRODUCTION A successful identification and location of the source of acoustic emission/microseismic (AE/MS) events is mainly dependent on the onset time of the P-wave phase. In many seismic networks, initial picks are performed by picking algorithms followed by manual picks by human experts. If the waveform signal-to-noise ratio (SNR) is low, the P-wave onset time picks by the human expert is generally reliable and accurate [1]. However, for waveforms with high SNR, picking of the P-wave onset could be a major challenge. Also, due to the volume of data acquired daily, manual processing could be stressful and time consuming [2]. These challenges continue to provide motivation for alternative means of providing reliable and efficient ways of improving manual phase picking. The AE/MS technique has been an intergral part of safety monitoring in mines [3] due to its capacity to identify zones of instability in a monitoring area. To obtain an accurate source of an AE/MS event however depends on the quality of the data used for P-wave phase picking [4, 5]. AE/MS data from mines are generally contaminated with excessive background noise generated by the different mining operations. The AE/MS signals of interest are mostly buried in background noise which makes it difficult to pick the P-wave phase arrivals [4]. Processing AE/MS data acquired in noisy environment remains a major challenge due to the weak nature of AE/MS signals [6]. As noted by [4] most of the data recorded in the mine environment are mainly due to events not related to AE/MS events of interest. The complexity of data in this type of environment makes the detection and picking of P-wave arrival by human experts a very difficult task."

Jan 1, 2017

By R. L. Miller

"Miners worldwide use a tremendous amount of energy in producing finished minerals for the end user market. In accomplishing this goal, they incur energy costs that can range from 20% to 30% of the total cost of production, so there is fertile ground for cost savings. To unlock this huge value in cost reduction, energy analysis is the key and is broken into several segments: 1. Fuel analysis for power generation 2. Energy supply & demand 3. Opportunity cost for lack of power 4. Backup power 5. Power contracts 6. Energy efficiency 7. Renewable energy 8. Continuous energy management FUEL ANALYSIS FOR POWER GENERATION What is the first thing we evaluate when looking at power supply alternatives for the mining industry’s large power requirements? Is it 1) site, 2) engineering, procurement, and construction companies, 3) independent power producers, 4) turbine vendors, 5) grid connections, or 6) fuel supply, logistics, and infrastructure? In evaluations of power supply alternatives for mining companies with special emphasis in developing countries, there is one over-riding conclusion: reliable fuel supply drives the energy generation economic engine. Evaluating several different fuels including liquefied natural gas (LNG), natural gas, diesel, heavy fuel oil, and liquefied petroleum gases (LPG) among others, the best project outcomes are those where fuel supply, pricing, logistics, and infrastructure are given an extensive amount of time during the analysis stage. In an assessment of the twenty-year costs of generating power from multiple fuels, each fuel’s costs were placed into three separate net present value (NPV) buckets of: 1) capital, 2) operating and maintenance, and 3) fuel. Within a range of 65% to 85% for all fuels, fuel cost was the largest component of the NPV of the life cycle power generation costs. So if 65% to 85% of your life cycle power generation cost is one item, shouldn’t miners spend a corresponding amount of time in the analysis stage fully understanding this key determinant for a successful power project? In developing countries where power generation infrastructure may not exist or does not exist at the same level as is found in North America and Europe, fuel analysis becomes even more important. One of the key questions for fuel importation in developing countries is the draft at existing ports to allow larger vessels to dock at the jetty. Can larger ocean-going tankers dock at the port for off-loading, or is the port’s draft too shallow, thus forcing a Single Point Mooring (SPM) system or Conventional Buoy Mooring (CBM) system to be constructed at substantial capital expense? Once fuel can be imported, sufficient onshore storage capacity should exist, or must be built to off-load the entire fuel cargo from the larger ships. Additionally, the throughput capacity of the pipeline from SPM/CBM must allow a rapid unloading of the fuel from ship to shore to eliminate steep demurrage costs for the project."

Jan 1, 2017

By R. Adsero

The volume of data produced by operating mines has increased substantially over the last 10 years. However, the volume of data and analytical capability has been negatively correlated to equipment productivity for at least the last seven years. In simple terms, as the volume of data has increased equipment performance has declined. Table (2010) tells us that ?The calamity of the information age is that the toxicity of data increases much faster than its benefits?. More data does not guarantee improving productivity; but more data is also not the cause of declining productivity. Similarly, when equipment performance does start to improve, (it must ? there really is no option if mines want to remain competitive), it will not be the data nor analytics which causes it; it will be the people using the resources at their disposal; including the data. Data and analytics can help if the toxicity of the data can be managed. The simplest form of business improvement using data is benchmarking. The objective of benchmarking is to understand and evaluate the current position of a process in relation to ?best practice? and to identify areas and means of performance improvement. The application of benchmarking involves four key steps: (1) Analyze and understand in detail existing processes; (2) Analyze and understand the processes of others; (3) Compare your own performance with that of the others analyzed; and (4) Design and implement the changes necessary to close the performance gap(s). There are however, many in the mining industries who have shunned equipment benchmarking because they believe that, ?every mine is a pilot?. In other words, the differences between mine operations make comparisons between them of little value. This paper will present a valid approach to benchmarking equipment performance and reliability, accounting for the differences and similarities between operations, producing a result which can be used to add value to mines around the world.

Feb 23, 2014

By Y. Zhang, C. Zhou, M. Girman

Wireless technologies are increasingly being implemented in underground mines for various purposes such as communications, tracking, remote control, remote sensing, and environmental monitoring. However, the adoption comes with risks as common wireless systems, such as WiFi, Bluetooth, IWT (Innovative Wireless Technologies) wireless system, often operate in the unlicensed ISM (industrial, scientific, and medical) band. The shared nature of the ISM band can lead to interference among devices and consequently cause performance degradation and reliability issues. In this study, we developed two wireless communication evaluation boards as a tool to evaluate the performance and reliability of a wireless system with the presence of electromagnetic interferences (EMI) from other wireless systems in underground environments. We adjusted various parameters including signal power from adjacent frequency channels, noise power within the same channel, the movement of personnel, and communication packet size. Our findings indicate that, while the power of the EMI source plays a key role, the motion of personnel in the tunnel also has a strong impact on performance, and this impact increases as the power of EMI sources increases. In noisy environments, smaller packet sizes are preferable as they enhance the likelihood of successful transmission. High levels of interference or noise can lead to increased packet loss or error rates, potentially causing communication links to fail completely or become ineffective. The evaluation boards utilized in this study are based on a practical wireless transmission system deployed in a remote controller for a heavy mining machinery used in underground coal mines. The results and findings presented in this paper can aid in the design of wireless systems that are resilient to EMI in underground mining environments.

Feb 1, 2025

By J. L. Bellanca, L. R. Swanson

DISCLAIMER The findings and conclusions in this paper are those of the authors and do not necessarily represent the official position of the National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention. Mention of any company or product does not constitute endorsement by NIOSH. ABSTRACT Proximity detection systems (PDSs) for mobile machines have the potential to decrease injuries and fatalities. Early adopters of the technology have identified some challenges, which presents an opportunity to explore and improve the integration of mobile PDSs in underground coal mines. The current study applied the task-technology fit framework to investigate the fit between mobile PDS technology and mining relative to health and safety, from the perspective of leaders at two coal mines. Quantitative results from the study show that mine leaders evaluated mobile PDS favorably for training and ease of use, system feedback, user authorization and experience, and less favorably for safety, compatibility, task completion, and reliability. Qualitative results reveal specific task, mine, and system characteristics that may have influenced leaders' evaluations. The study includes considerations for safe technology integration. INTRODUCTION Striking, pinning, and crushing accidents related to mobile machines continue to be a major concern in underground coal mining. The National Institute for Occupational Safety and Health (NIOSH) linked 22 of the 75 underground mine fatalities reported between 2011 and 2015 to power haulage [1]. The Mine Safety and Health Administration (MSHA) asserts that proximity detection systems for mobile machines (mobile PDSs) could help to prevent these types of fatal injuries [2]. Mobile PDSs use a collision avoidance technology that allows the mine to establish hazard zones and employ electromagnetic sensors to detect workers equipped with a miner-wearable component (MWC) working near these mining machines. The authors characterize a mobile PDS as an automated technology, because—in line with Lee & See’s [3] definition of automation—a mobile PDS actively “selects data, transforms information, makes decisions, or controls processes” (p. 50). When the system detects workers in the established hazard zones, it issues alerts and slows or stops the mobile machine. Even though a mobile PDS may be a viable and promising safety solution, there are still a number of questions regarding how well this automated technology will fit with mining tasks and conditions. Using a mixed-methods approach, the current study explored factors that influence the fit of mobile PDSs in the underground mining environment.

Jan 1, 2019

By A. Bhattacherjee

Industries in general and the mining industry in particular have become more safe than ever before. However, there is a need to increase the search for the causes of mining work-related injuries and to contribute to their reduction through job design, work procedures, and worker training. Mine accident and hazard control has to be practiced in a manner which is consistent with the overall objectives of the mine management, among which are the optimum allocation and effective utilization of the resources. The purposes of this paper are to (i) review the role risk analysis in safety management with references from mining and other industries; (ii) discuss the risk measures appropriate for injury analysis; (iii) present a set of analytical tools to injury experience data in under-ground coal mines; (i v) apply these tools on case study example; (v) discuss the results of the case study; and (vi) amplify the application limitations and the future research needs.

Jan 1, 1991

By Yi Yang, Xiuzhu Su, Wei Sung Ng, Shuiping Zhong, Miao Chen

With over 260 tons of gold reserves, the Shuiyindong gold mine is one of the largest Carlin-type gold deposits in China. A particular challenge in the processing of the ore is the presence of carbonaceous compounds, which can cause substantial losses in recoveries via the preg-robbing and adsorption of gold. To investigate the structural properties of the native carbon and to compare between different characterization techniques for such compounds, pre- and post-flotation mineral samples containing 0.9–4.8% non-carbonate carbon from the Shuiyindong mine have been examined via mineralogical and thermogravimetric approaches, as well as spectroscopic techniques, including X-ray photoelectron spectroscopy (XPS), near-edge X-ray absorption fine edge structure spectroscopy (NEXAFS), solid-state 13C nuclear magnetic resonance (NMR) spectroscopy, diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy, Raman spectroscopy and preg-robbing measurements. NEXAFS and DRIFT yielded the most reliable results, indicating that 60–75% of the organic carbon in all samples consist of aliphatic alkenes with substantial unsaturated content, highly similar to activated carbon, with the remainder being graphitic and humic. Speciation by XPS was sub-optimal due to spectral overlaps between graphitic and aliphatic carbon, and between carboxylic and carbonate functionalities, while NMR spectroscopy could not provide reliable speciation of the carbon groups. The preg-robbing strength was strongly correlated to the aliphatic content and structural disorder in the carbon, while no such relationship was found with the graphitic and humic content.

Nov 29, 2021

By J. Paraszczak

Application of teleoperated and automated (or ?autonomous?) mobile equipment in underground metal mines is a promising avenue for overcoming some of the challenges facing mine operators. These include, but are not limited to, the issues related to safety and health of underground workers, better use of the shift time leading to increased productivity, skilled manpower shortages, and remoteness of some mine sites. Autonomous load-haul-dump machines and trucks offer several benefits over man-operated ones, but their use at their full capacity is a challenging and complex task. This paper makes an overview of commercially available equipment and systems and their applications. It examines factors affecting adversely availability, utilization, and production efficiency of autonomous vehicles and compares them to conventional man-operated machines. Technical and operational aspects are discussed and their criticality is analyzed. Based on that, recommendations concerning means and measures that may help raise operational effectiveness are given. It is stressed that due to a scarcity of publicly available information on the performance and cost of autonomous loading and haulage operations, more field studies and in-depth analysis are needed.

Feb 23, 2014

By Y. Xi

In this paper we present an availability approach for determining the capacity of a major bucket wheel excavator (BWE) system to be used in Yuabaoshan surface coal mine in China. Based on previous time studies on other BWE systems for delay times due to internal and external causes, and a four-level maintenance and repair schedule, we develop a model taking into account all random failures and present a fast and simple solution to this highly complex problem. A procedure employing the Markov renewal theory for determination of the distribution of random failure times and failure rate of the whole system, as well as some other factors useful for calculation of the overall system capacity are discussed in the paper.

Jan 1, 1993

By Harry Asche

Tunnels are frequently specified by owners to be designed to a structural code, often a bridge code. In the USA, AASHTO have created a tunnel code, based on structural principles, the AASHTO LRFD Tunnel Design Specification. The Load Resistance Factor Design method (LRFD) described in the AASHTO title is an approximation to the probabilistic design of structures, based on the independence of load and resistance. This methodology is well known to be problematic in geotechnical design, but it is widely used. The paper considers the difficulties of this approach and describes alternative approaches that would result in more appropriate designs for tunnels.

Jun 13, 2021

By T. Panhorst

Evaluation of through-deck blasthole sample reliability at the Lone Tree gold mine revealed a non-systematic bias in the sampling system. Ore is primarily hosted within the Powerline fault zone. Clay gouge, present in the fault zone and typically enriched in gold content, forms agglomerates during blasthole drilling leading to a segregation error during sampling. Replicated samples split from blasthole cuttings piles show through-deck samples to be either significantly enriched or depleted by 1.5 standard deviations or more from the mean gold content of their respective cuttings pile quality of through-deck samples correlates strongly with quantity of clay present.

Jan 1, 1999

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 Lihong Zhou, DAVOOD BAHRAMI

A reliable and stable ventilation system is essential to the safe operation of underground mines. The stability of a mine ventilation system becomes extremely critical when responding to a fire incident, as an unstable ventilation system will pose a risk of airflow reversal. The reversed airflow could bring fire contaminants, such as toxic gases and smoke, unexpectedly to working areas. In the past few years, there has been a growing interest in the study of ventilation network stability using the concept of resistance sensitivity, which is described as an indicator of how the airflow in an airway is reacting to a resistance change of other airways. Several methods of calculating the resistance sensitivity in a mine ventilation network have been developed by researchers and scholars around the world. However, those proposed methods rely heavily on a vast number of mine ventilation simulations, which is time-consuming and computer-resource intensive, especially for a large-scale mine ventilation network. In this study, a derivative method calculating the resistance sensitivities with a single mine ventilation simulation is developed and implemented into the MFIRE mine fire simulation software. The results from the derivative method were verified against the results from a traditional method. The derivative method is proved to be reliable and accurate.

Dec 1, 2022

By Hakan Uyguçgil, Beril Bayraktar, Adnan Konuk

In mining, which is one of the industries with the highest risk of occupational accidents, a primary goal is to enhance safety through the thorough investigation of accidents using reliable methodologies. In this research, the data from 13,653 accidents reported in the Karadon Hard Coal Enterprise between 2000 and 2011 were analyzed and compared using the life-table and Kaplan-Meier methods. Accident records were classified and analyzed into four categories: environment-related, equipment-related, person-related and other accidents. Findings in terms of survival time indicate that environmentrelated and person-related accidents had the highest risk in Karadon, with equipment-related accidents having the lowest. This study established the reliability of using survival analysis methods to investigate occupational accidents in the mining sector. Given that environment-related and person-related accidents occur in a relatively short period, such as one year after starting work, it is recommended that research be done to improve workplace and occupational safety measures and broaden the training given to miners. Additionally, it is determined that increasing the use of automation and mechanization in underground coal mines, combined with developing a thorough equipment safety training program to raise awareness of the danger, can help prevent person-related occupational accidents caused by the interaction between equipment.

Feb 1, 2024

By Lihong Zhou, DAVOOD BAHRAMI

A reliable and stable ventilation system is essential to the safe operation of underground mines. The stability of a mine ventilation system becomes extremely critical while responding to a fire incident since an unstable ventilation system will pose a risk of airflow reversal. The reversed airflow could bring the fire contaminants such as toxic gases and smoke unexpectedly to working areas. In the past few years, there has been a growing interest in the study of ventilation network stability using the concept of resistance sensitivity, which is described as an indicator of how the airflow in an airway is reacting to a resistance change of other airways. Several methods of calculating the resistance sensitivity in a mine ventilation network have been carried out by researchers and scholars around the world. However, the proposed methods heavily rely on a vast amount of mine ventilation simulations, which are very time consuming and computer-power intensive, especially for a large-scale mine ventilation network. In this paper, a derivative method calculating the resistance sensitivities with a single mine ventilation simulation has been developed and implemented into a mine fire simulation software, MFIRE. The results from the derivative method were verified against the results from a traditional method. The derivative method has been proven to be reliable and accurate.

Jun 7, 2022