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By D. Komljenovic, V. Kecojevic

Selection of the best possible truck type (model) is of crucial importance for the mine decision-makers. In this paper, authors contribute to the body of knowledge by developing a novel methodology for the selection of mining truck. Both the Coefficient of Technical Level (CTL) and Analytic Hierarchy Process (AHP) methods were used in this study. The CTL represents a mathematical function which takes into consideration the interrelations among the main technical parameters of mining trucks. The authors developed a hierarchical structure where both attribute categories and their importance, i.e. priority or weight in the selection process were determined. For each category, a number of sub-attributes and their priorities were assigned, and a pairwise comparison among them was performed using the AHP method. In the final part of the study, the CTL was integrated into the AHP and represents a holistic approach in the selection of mining trucks. A case study on 180 tonnes truck category offered by five manufacturers was carried out. A sensitivity study of obtained results was also performed indicating a very stable solution. The methodology presented in this paper may be used by mining operators to help in the selection of a specific type when acquiring a new truck. It may also be used by equipment manufacturers to help in analysis of potential benefits of envisaged modifications concerning an existing truck type and/or development of a new type.

Jan 1, 2007

By Tony Keller, Setsuo Maeda

Hand-Arm Vibration Syndrome (HAVS) was identified as early as 1918 in Bedford, Indiana in the U.S. Since then much research work has been done around the world in the areas of medical, epidemiological, engineering and legal aspects of HAVS. In Japan, much of the pioneering work in this field has been performed by Dr. Setsuo Maeda and his staff at the National Institute of Industrial Health (NIIH) in Kawasaki. Most recently, reports of work done by this group and by Dr. Ren Dong1 of NIOSH in the U.S., as well as many other suppliers and Japanese practitioners were presented at the 13th Japan Group Meeting on Human Response to Vibration held in Osaka2 during August 3-5, 2005. [ ]

Jan 6, 2006

By Dale Preece, Stephen Chung

Development of DMCBLAST-3D is continuing and now includes the capability to simulate the detonation of multiple blastholes in multiple rows that make up a conventional bench blast, either in a rock quarry or a surface coal mine. DMCBLAST-3D is a three-dimensional extension of the proven twodimensional rock blast modeling code, DMCBLAST. DMCBLAST has been under development since 1987 and has successfully predicted the physics, mechanisms and results of a number of different types of rock blasting including, coal mine bench blasting, mineral mine choke blasting and underground stope blasting. Substantial effort has gone into the qualification and verification of DMCBLAST by comparing computer code predictions with real blasts in the field. Comparison with field data has also provided significant guidance to the development of the code.

Jan 1, 2002

By P. Rambalee

A multiple coal fired boiler house provides steam for utility usage in various production plants. Maintaining stable header pressure and boiler availability is of critical importance for downstream consumers. Advanced Process Control comprising of a G2 based expert system and Model Predictive Control was implemented to improve the boiler house performance. The results yielded improvements in the main header pressure stability, reduction in boiler saturation and significant saving in coal consumption. All information used for this document is from a published whitepaper written and led by Prevlen Rambalee (Anglo Platinum) and Gustaf Gous (BluESP) titled, ?Control and Stabilization of a Multiple Boiler Plant: An APC Approach?.

Jan 1, 2012

A pilot scale AG milling campaign was conducted at the AngloAmerican Platinum pilot plant in South Africa. Different blends of hard:soft (silicate:chromite) and coarse:fine particles in the AG mill fresh feed were tested. The effects of multi-component feed on the pilot scale autogenous grinding are presented in this paper, and can be used as a guideline for multi-component feed blends for AG milling. Preliminary modelling and simulation for the multi-component AG mill model are also included.

Jan 1, 2012

By N. Plint, D. Philips, J. Sweet, B. Durant

A pilot scale AG milling campaign was conducted at the AngloAmerican Platinum pilot plant in South Africa. Different blends of hard: soft (silicate: chromite) and coarse: fine particles in the AG mill fresh feed were tested. The effects of multi-component feed on the pilot scale autogenous grinding are presented in this paper, and can be used as a guideline for multi-component feed blends for AG mil

Jan 1, 2012

By M. D. Dolan

In this study, the development of a new method for investigating the diffusion of metal cations in multi-component slag systems, using microprobe analysis is discussed. This method allows the analysis of the diffusion of multiple species within a single system. This project has focused on the diffusion characteristics of manganese, iron, calcium and silicon in silicate slags, in order to simulate industrial steel and ferromanganese production. The diffusion behaviour of transition metals is of particular interest because of the effects of their variable valence. This study identified the variation in diffusivity of slag components with changes in composition and temperature of multi-component slag systems. The molecular structure of the experimental calcium-aluminosilicate slags was investigated to determine the chemical and physical properties that influence diffusion behaviour. This experimental data was used to develop predictive models for diffusivity. These models enable the estimation of the diffusivity of many slag components in complex slags, over a range of compositions.

Jan 1, 2004

By Gordon A. Irons

"Powder injection for melt refining is widely practiced in the steel industry, and is gaining acceptance in the non-ferrous industries. In these commercially-important systems, the thermodynamics and kinetics may be very complicated. More than one element may be removed by refining, and often the refining agents are not just one compound. In this paper, the thermodynamic and kinetic aspects of these processes are reviewed. A number of examples are given, such as the desulphurization of iron with a variety of reagent mixtures, and selenium removal from copper with several different reagent combinations.IntroductionI am very pleased and honored to present a paper at this Symposium recognizing the many contributions of Professor Howard Womer. Even at the time I was an undergraduate student, the WORCRA process was part of the curriculum. I remember at the time that I was fascinated by the concept, and due to it and the many other rapid developments in Process Metallurgy, I chose it as my field of study. I think the Symposium is testimony that Professor Womer has touched the lives of many others as well.My original assignment was an overview of the basic aspects of gas and solids injection. However, I reviewed most of this material at the Savard/Lee Symposium [1]. In the present paper, I will present some of the basic issues related to refining with powder injection, using work that we have recently completed in both ferrous and non-ferrous systems."

Jan 1, 1996

By J. Ga?parík

The optimal selection of machine or machine group for building processes is very important role of building planner during the process of building planning. During this process building planner have to analyze several factors influencing the final effective decision concerning this problem. There are several factors and criteria for effective selection of building machines. In our contribution there are analyzed: ability of machines to realize designed building process (quality aspect), duration of mechanized process (time aspect) and minimizing of energy consumption (cost and environmental aspect). Selected scientific methods and theories of problem solution can be divided into these groups: theory of system (creation of building machine selection method), multi-criteria optimizing method (analysis of mechanized building process from the point of more optimal criteria), queuing theory (application during the mathematical modeling of mechanized soil processes), method of scientific analysis (analysis of factors influencing final decisions), method of scientific synthesis (creation of optimizing method implemented into building processes and possible application in construction sector). Multi-criteria optimizing method were during our research work implemented into soil processes and selected building group machines (excavators and trucks), which are very often used in construction and mining processes. This method can be considered as a multi-level decision making process based on multiple parameters. In our contribution is presented key mathematical models for model example solution and software built in Java, which has been created as a support for method described in this contribution. Application of this method and software will increase the effectiveness of building machine selection from the point of key criteria of optimizing: quality, time and energy consumption.

Aug 1, 2013

By S Van Duin, I Porter

This paper discusses how advanced manufacturing technology (AMT), which is fundamentally reliant on and developed via multi-disciplinary skills, has benefitted non-mining industries, and what lessons can be derived from this experience to benefit the mining industry, particularly underground coal mining. Examples of AMT use in æconventionalÆ industry, some drawn from the more than 25 years experience in many different industries of the Automation and Robotics team at the University of Wollongong, are described to show typical benefits, risks and opportunities expected in using AMT. Applying AMT to mining brings many unique challenges and this paper expands on these and then provides practical examples of how a multi-disciplinary team can develop new processes and systems leading to productivity, safety and other benefits for underground coal mining.

Sep 26, 2011

By Brian E. Davies, Bobby G. Wixson

INTRODUCTION The impact of modern mining and ore processing on the environment is readily minimised. Our improving knowledge of how pollutants are released to and move through the environment has been accompanied by better expertise in controlling these emissions. For example, when the new mines and smelters were developed in the Viburnum Trend of Missouri a sulfur dioxide pollution problem was quickly recognized and soon controlled. (Wixson 1974) But older operations were not efficiently organized and, despite their smaller scale, they often caused major pollution problems. The British mining fields are among the world's oldest and during the heyday of their operation (approximately 1750-1900) they were worked with- out regard to their environmental impact. The Rivers Pollution Commissioners in the mid-nineteenth century were led to remark that ore dressing was carried on in a slovenly and wasteful manner and the complaints by farmers, that river borne mine wastes were poisoning both their crops and stock, were justified. (Frankland and Mortan, 1984). The Comissioners report led to the 1876 River Pollution Act of Parliament, the first modern environment protection legislation. The development of mining since the last century is characterized by improved regulation of the pollution potential of ore dressing, smelting and refining. The collective memory is unfortunately long and pessimistic. A serious problem arises when old mines are reopened especially if the mine has not been worked for several years since local action groups may soon blame the new owners for causing problems which, in reality, are longstanding and were caused by the use of earlier state of the art technology. It is therefore essential that during the early development stages prior to reopening that comprehensive studies be carried out to identify and quantify local contamination problems. These baseline studies should include surveys on the nature and composition of old tailings disposal areas and other wastes on the mine properties. The composition of air and water should be monitored. Soils are long term sinks for all heavy metals and particular attention should be paid to soils within any city areas near the mine since there are sources of heavy metals in urban areas which are not connected with mining and ore processing. This paper describes some of the results of such a study around a prospective cobalt mine in Southeastern Missouri. It is restricted to the soil survey of forest and agricultural land which took place in and around Fredericktown, Missouri. The city is located at approximately 37° 30' North and 90° 20' West and lies 143 kilometers south of St. Louis. There are several metal mines near Fredericktown, including Mine La Motte, which is the oldest and was first opened in 1720. [Figure 1] is a location map of the area. The survey described here was in connection to the reopening of the Madison Cobalt Mine which was first worked in 1947.

Jan 1, 1985

By Pamela Castillo, Marcia Muñoz, Rodrigo Luca, Brian Townley, Fernando López

In the present study we discuss the value of multi-element geochemistry as a tool for semi-quantitative to quantitative bulk mineral characterization in geological and geo-metallurgical modeling of porphyry copper type deposits, with an emphasis on hydrothermal alteration associations, types and intensities. In mine production of porphyry copper deposits much effort is placed on ore grade and geological modeling, applied for predictive mineral processing behavior, from blasting, grinding, leaching or flotation, down to waste disposal and environmental management. High confidence geological models are commonly based on geological mapping of drill core and surface/ underground exposures, combined with discrete mineral characterization (e.g., petrography, QEMSCAN®, spectroscopy, etc.). Unfortunately these are expensive and applied to limited numbers of samples, thus lacking sufficient representation and not suitable for geo-statistical modeling. In addition, geological mapping always presents uncertainty, based mostly on qualitative estimates. The combination of information usually provides geological models that are hard to combine with geo-metallurgical data, much less with mineral processing predictive behavior models. The advent of multi-element geochemistry, at low cost, has allowed employment of such tools massively, yet little use is given to the data, usually focused on elements of economic interest (main ore and credits) and elements of negative impact to concentrates and/or process. Geochemistry reflects the mineral composition of rocks, thus allowing characterization of lithology, alteration types and intensities, and mineralization. As examples of such applications we present generic classification techniques which were developed on various porphyry copper deposits of northern Chile, these based on multi-element geochemistry, allowing for semi-quantitative to quantitative high resolution modeling of key aspects of lithology, hydrothermal alteration and mineralization, and which may be cross-referenced to metallurgical test samples and by that, to geo-metallurgical properties of rocks and modeling. In addition, as data is quantitative and continuous, modelling of gangue can benefit from geo-statistical modeling.

Jan 1, 2018

By R Carstens, L Munsamy, L Prinsloo, C Huang, C Zhang, R Liang, I Canbulat

With recent advances in machine learning, data-based automated decision-making has been proven successful with promising outcomes in many industries. Its application in the mining sector can be ground-breaking; particularly the data-driven analytics that can support decision-making in the mine and consequently enhance mine safety, efficiency and sustainability. However, the current data science applications in the mining industry are mostly noncomprehensive, especially inadequate in evaluations, thus often requiring further study to be potentially beneficial to operations. Furthermore, it was found that existing literature paid limited attention to understanding the role of such techniques. Hence, in this paper we aim to build an end-to-end machine learning powered data-driven framework for intelligent geo-hazard analytics to support decision-making for underground mining data management, where multiple types of data are managed in an integrated database to support the unified machine-learning model. Here we present the framework as data-driven workflow processes, including data gathering, data preparation, data processing and data presentation. In addition, we present an application scenario of the proposed framework, where falls of ground (FoG) is managed with the historical FoG investigation reports, related panel hazard ratings, compliance data and rock mass characteristics. A demonstration can be dynamic forecasting of high-risk areas for FoG based on both spatial and temporal factors, including a 3D data visualisation. Such a scenario shows the potential of the proposed framework to establish connections among various data fields across a diverse category of data. Furthermore, the results offer important insights for ground instability management and can potentially optimise the underground operation flow.

Nov 29, 2022

By A. K. Majumder

The multi-gravity separator (MGS) is a novel piece of equipment for the separation of fine and ultra-fine minerals. However, the published literature does not demonstrate its use in the separation of coal fines. Therefore, an attempt was made to study the effects of different process variables on the performance of an MGS for the beneficiation of coal fines. The results obtained from this study revealed that among the parameters studied, drum rotation and feed solids concentration play dominating roles in controlling the yield and ash content of the clean coal. Mathematical modeling equations that correlate the variables studied and the yield and ash contents of the clean coal were developed to predict the performance of an MGS under different operating and design conditions. The entire exercise revealed that the MGS could produce a clean coal with an ash content of 14.67% and a yield of 71.23% from a feed coal having an ash content of 24.61%.

Jan 1, 2007

By Patricio Vergara Lara, Jorman Barrera

The selection of the production level elevation is one of the most critical strategic decisions faced by large-scale block or panel caving projects. This elevation largely affects project timelines, capital expenditures, resource recovery, and may even constrain the mine’s maximum production capacity. Over the years, many computational tools have been developed to assist in this decision, most of them based on the same principle of maximizing the project value given its production level elevation. However, for large and inclined deposits, or those extending significantly in the vertical dimension and where extraction cannot be achieved from a single level, traditional selection techniques involving multiple production horizons often lead to suboptimal results. This paper proposes a methodology ensuring that the selection of production level elevations in a multiple horizons (“lifts”) case is globally optimized. Input parameters include economic and cost variables, maximum extractable column height, and deepening costs. The methodology is demonstrated through a real case study to illustrate its potential impact on project value.

Feb 22, 2026

Mine design and selection of ore processing capacity depend upon a correct estimate of the grade-tonnage distribution. The estimate must reflect what can be recovered and processed. The McCoy/Cove operation in central Nevada, USA, is a gold and silver mine. Case studies of recoverable reserve estimation for gold and silver were made using multiple indicator kriging and the log-affine variance correction. From this model, the grade-tonnage distribution was constructed. Optimal pits were generated by utilizing the distribution, and the results were compared with the pit generated by ordinary kriging. Studies indicate that the proposed method for multi-metal recoverable reserve estimation is more accurate than ordinary kriging in terms of both tons and the grade. Using recoverable reserves for mine design will maximize global recovery of mineral resources.

Jan 1, 1997

By E. Cetin

Orebodies that contain more than one economically important mineral are generally evaluated by parametric cut-off grades. This approach often leads to mis-valuation of mineral deposits because cut-off grades are not based on the grades of each individual mineral and, because of the parametric formulation, are only indirectly related to the individual grade distributions. The only realistic approach is a formulation that accounts separately for each component mineral. The grid search method can be used as a means of multi-mineral cut-off grade optimization in this context. This paper describes the use of the grid search method in cut-off grade optimization for multi-mineral deposits. The authors introduce the general concepts of the method and formulate its application to cut-off grade optimization; they describe a software implementation of the method that can accommodate cut-off grade optimization for mineral deposits that contain up to three economic minerals. Keywords cut-off grade, optimization, grid search, multi-mineral deposits.

Aug 1, 2013

By Hooman Askari-Nasab, AHLAM MAREMI, EUGENE BEN-AWUAH

In oil sands mining, tailings are voluminous unwanted byproducts generated from extracting the desired mineral from the mined ore. Dyke construction and backfilling activities require well-managed techniques to facilitate progressive reclamation at the earliest opportunity. This directly affects the sustainability and profitability of the mining operation. This research introduces a goal programming framework that simultaneously optimizes the (1) production schedule with limited duration stockpiling and directional mining, (2) dyke construction schedule, (3) size, shape and location of tailing cells and (4) mining and processing production targets. The results show that in creating in-pit tailing-cell designs, decreasing the mining-cell volume and increasing the number of mining cells improves the net present value (NPV) of the operation due to increased operational flexibility.

By Hooman Askari-Nasab, AHLAM MAREMI, EUGENE BEN-AWUAH

In oil sands mining, tailings are voluminous unwanted by-products generated from extracting the desired mineral from the mined ore. Dyke construction and backfilling activities require well-managed techniques to facilitate progressive reclamation at the earliest opportunity. This directly affects the sustainability and profitability of the mining operation. This research introduces a goal programming framework that simultaneously optimizes the (1) production schedule with limited duration stockpiling and directional mining; (2) dyke construction schedule; (3) size, shape, and location of tailings-cells; and (4) mining and processing production targets. The results show that in creating in-pit tailings-cells designs, decreasing the mining-cells volume and increasing the number of mining-cells improve the net present value of the operation due to increased operational flexibility. The model generated a uniform schedule for ore and practical tailings-cells designs for backfilling activities. Integrating tailingscells optimization into oil sands mine planning and waste management ensures the efficient use of in-pit mined-out areas required for sustainable operations.

Apr 14, 2021

By Venkataramana Runkana, Aditya Pareek, Vishnu Masampally, Nagaravi Kumar Varma Nadimpalli

"Recovery and grade of the ore produced are two important metrics, which define the profitability of a mineral processing plant. Achieving high grade and high recovery at the same time in a mineral processing plant is difficult due to conflicting nature of the two objectives. To explore the operability limits of the plant operations and find the set of operating conditions that can help achieve the best possible recovery and grade, there is a need to solve a multi-objective optimization (MOO) problem. Past research focused on building a plant-wide simulation model or on optimization of individual units or a part of a mineral processing plant like grinding circuit, crushing circuit, etc. However, it is necessary to consider optimization of the complete plant in order to optimize the plant performance. Here, we have formulated the mineral processing plant performance optimization as a MOO problem. We have applied machine-learning algorithms such as random forest to build models to predict the recovery and grade of the ore. A non-dominated sorting-based multi-objective evolutionary algorithm called NSGA-II (Non-dominated Sorting Genetic Algorithm II) was employed to solve the MOO problem. Since the number of parameters that influence recovery and grade are quite large, we have applied machine-learning algorithms for feature selection or identification of key process variables. Pareto optimal conditions were determined for manipulated variables like grinding mill throughput, speed ratio at SAG mill, speed of centrifugal pump, water addition at different locations etc. The results obtained are useful in identifying the operability of a mineral processing plant to achieve the optimum grade and recovery for a given feed grade and the processing circuit. INTRODUCTION Copper is majorly mined or extracted as copper sulfides from large open pit mines in porphyry copper deposits. Among all the available ores, sulfides are the most commercial ores, especially chalcopyrite (Greenwood and Alan, 1997). Copper grade in these ores generally lies between 0.4 to 1.0%. In order to perform smelting operation, the grade of copper has to be improved to around 30% (Biswas et al. 2013). This targeted copper grade is achieved through two stages of mineral processing. Firstly, the copper in the ore has to be liberated by crushing the mineral rock. Secondly, the liberated copper in the crushed ore is then physically separated from other minerals like nickel by froth flotation techniques."

Jan 1, 2018