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By R. J. Quevedo, Y. A. Sari, S. D. McKinnon

The large scale of cave mines leads to many challenges, including operational logistics and geomechanics design. In current practice, pillar stability assessment relies almost exclusively on stress analysis. However, stability is also affected by other factors including those related to operational aspects of the mining method, the effects of which are difficult to account for during the design stages. In this paper we present a case study of the application of a machine learning approach to evaluate the influence of these operational factors on pillar stability at the Chuquicamata underground cave mine in northern Chile. Due to the likely multi-factorial damage process leading to collapses and considering the different pillar conditions, a tree-based machine learning method was used and analysed to improve the understanding of the relative importance of the various contributing factors. Unlike stress analysis methods, it does not require any a priori knowledge of failure mechanisms, nor the calibration of associated controlling parameters. The proposed random forest model predicted pillar collapses with 80% accuracy despite limited samples to model from. The main contributing factors to collapses were found to be related to available pillar volume, cave front geometry, and time under abutment stress conditions. The effects and interactions of such factors were also studied, showing that careful and improved control over operational conditions can significantly reduce the likelihood of pillar collapses. These conclusions could not have been obtained from stress analysis alone, illustrating the complementary nature of conventional stress analysis and machine learning approaches.

Mar 14, 2024

By J. D. T. Steyl, G. B. Grobbelaar

Two examples are presented to show how first-principles – physical-dynamic, here termed behavioural modelling, is used to build digital twins of unit processes or plant sections. A digital twin equips the metallurgist, engineer, or manager with a powerful tool to conduct model-based design, solve process problems and improve operations. It is particularly relevant in the metallurgical industry where plant behaviour is highly integrated, rates respond to particle dynamics in complicated ways and mineral flows across flowsheet boundaries are variable. The two examples used to illustrate the behavioural modelling methodology are 1) a flotation column unit block and 2) a multi-compartment pressure oxidation (POX) autoclave. It simultaneously accounts for the causality of material and heat flow, takes care of mineral particle dynamics, physical and chemical reaction rates, the plant control system behaviour and operating heuristics. The basic modelling building blocks are briefly reviewed to show how the behavioural modelling attributes constrain the mass and heat flow dynamics to physically realistic values. This is the key aspect that allows project value to be created with minimal input data. Behavioural modelling is especially valuable where the process dynamics are poorly understood or controlled, where data-driven models fail to cope with complex and highly integrated plant behaviour, where quality data is lacking or limited funds are available to generate the data required to fill all the knowledge gaps. The paper uses the flotation column example to show how behavioural modelling and simulation can be used to optimise plant performance, while the multi-compartment POX autoclave model shows how it can be used to diagnose process problems. It also provides a solid basis to add a data-driven component – in other words, it makes a strong case for a hybrid first-principle– data-driven approach to solve process problems, especially where microscopic-level understanding is poor. Keywords: Digital twin, behavioural modelling, simulation, flotation column, pressure oxidation

Jan 1, 2020

By M. Rudolph, A. van Hall, G. Öktem, S. G. Özkan, A. Hassanzadeh

Despite the four-decade study on ultrasound’s (US) impact on mineral’s floatabilities, there is still not clarity regarding its role in mineral surface wettability. For this purpose, the present investigation endeavours ……the hydrophobicity and floatability characteristics of a chalcopyrite-pyrite-quartz (Cp- Py-Qtz) system in the presence and absence of an ultrasonic pre-treatment. The ultrasonic process was carried out by a sonopuls at a constant frequency of 20 kHz with an adjustable power level from 30 to 200 W. Initially, the impact of sonication time (15, 30, 45, 60 and 90 s as well as 10, 20 and 30 min) and power magnitude (30, 60, 90, 120 and 180 W) were evaluated while the dissolved oxygen, temperature, conductivity and pH were also monitored. Collector-less micro-flotation tests were carried out on the non-pre-treated and the US pre-treated samples at 60 W and 15 s. The samples’ hydrophobicities were determined by the drop shape analysis approach. The dissolved-oxygen level was varied using a mini bench pressurised water reactor to study the effect of O2(aq) concentration on the chalcopyrite and pyrite wettability characteristics. The results showed that the minerals’ hydrophilicities were relatively more sensitive to the sonication’s time than its power that resulted in reducing all three minerals’ hydrophobicities. In addition, it was found that the dissolved-oxygen content and creation of sub-micron sized bubbles led to an improvement on chalcopyrite and pyrite hydrophobicities. Finally, we proved that the Cp’s floatability increased and Qtz’s recovery reduced while being subjected to the ultrasonic irradiation (15 s at 60 W); however, Py’s recovery remained constant. This conclusion confirmed the possibility of a selective separation in ultrasound-assisted copper ore flotation. We recommend that further advanced investigations be conducted, using an X-ray photoelectron spectroscopy (XPS) and atomic force microscopy (AFM) to thoroughly understand the surface modifications induced by the ultrasonication

Jan 1, 2020

By M. Z. Emad, A. Hassan, M. A. Raza, M. U. Khan, S. A. Saki

Digital modelling of a surface is crucial for Earth science and mining applications for many reasons. These days, high-tech digital representations are used to produce a high-fidelity topographic surface in the form of a digital terrain model (DTM). DTMs are created from 2D data-points collected by a variety of techniques such as traditional ground surveying, image processing, LiDAR, radar, and global positioning systems. At the points for which data is not available, the heights need to be interpolated or extrapolated from the points with measured elevations. There are several interpolation/extrapolation techniques available, which may be categorized based on criteria such as area size, accuracy or exactness of the surface, smoothness, continuity, and preciseness. In this paper we examine these DTM production methods and highlight their distinctive characteristics. Real data from a mine site is used, as a case study, to create DTMs using various interpolation techniques in Surfer® software. The significant variation in the resulting DTMs demonstrates that developing a DTM is not straightforward and it is important to choose the method carefully because the outcomes depend on the interpolation techniques used. In mining instances, where volume estimations are based on the produced DTM, this can have a significant impact. For our data-set, the natural neighbour interpolation method made the best predictions (R2 = 0.969, β = 0.98, P < 0.0001).

Feb 6, 2023

By Y. Qin, X. Fu, T. Qi, H. Tang

"The FASTMELT ® process was developed as an alternative to blast furnace ironmaking; in this process, high-quality molten iron is produced using direct reduction followed by high-temperature slag/metal separation. In this study, iron recovery and phosphorus removal from oolitic highphosphorus haematite using the FASTMELT® process was investigated. The performance of two reducing agents, coal and wood char, was compared. Direct reduction experiments indicated that with optimized reductant addition, the ore-char and ore-coal briquettes attained metallization values of 82% and 78%, respectively, and residual carbon contents of 0.24 and 2.35%, respectively. The slag/metal separation experiments revealed that molten iron containing 0.41% phosphorus and 0.021% silicon (by mass) was produced from optimally reduced ore-char briquettes, and molten iron containing 0.78% phosphorus and 0.91% silicon from optimally reduced ore–coal briquettes. The study indicates that carbonaceous materials with high CO2 reactivity are suitable for use in the FASTMELT ® process for phosphorus removal and iron recovery from oolitic high-phosphorus haematite. IntroductionIn China, the general depletion of iron-rich mineral reserves has led to many efforts to exploit low-grade and refractory ironcontaining resources for ironmaking (Liu et al., 2014). The efficient utilization of the highphosphorus oolitic haematite resources in central China has received considerable attention. The available resources of this material total approximately 3.0 billion tons. The oolitic units in the ore exhibit an onionlike structure with phosphorus-containing gangue layers of 2–10 µm thickness. The best concentrate produced from these resources contains approximately 50% iron (Fe) and 0.8–1.2% phosphorus (P) (by mass) (Ai, Yu, and Wei, 2009; Song et al., 2013; Wu, Wen, and Cen, 2011). Further removal of phosphorus from these ores using conventional beneficiation methods is not possible."

Jan 1, 2017

By k. Lebecki, S. Trzcionka

The prototype and test results of the instrument for quick determination of incombustible content in deposited dust as a protection against coal dust explosion propagation are presented. Presently existing methods do not permit to obtain quick information about incombustible content, needed for the proper protection of workings. As the operating principle for the e instrument the dependence of reflection of infrared radiation intensity on incombustible content in the sample is chosen. The construction is based on the modern electronics. The measurement will be done at the sampling site. The range of the instrument is 0-100% of incombustible, direct reading, autonomous supply, weight no more than 0.5 kg, and is intrinsically safe according to EN Standards. During the Conference the instrument will be commercialized.

Jan 1, 2003

[The following notations will be used: O an ore body. z a point in O. z&apos; another point in O. w0 (z) a point support of infinitely small dimensions w0 centred in z. w (z) a sample (or block) of finite dimensions kv centred in z. W (z) a block of size W greater than or equal to w. W&apos;(z) a block of size W&apos; greater than or equal to W. W" (z) a block of size W" greater than or equal to W&apos;. x (z) value of the regionalized variable x with support wO at point z. p (z) average value of x in w (z). pw WZ) = E[x(z&apos;)l zt in w(z) pw (z) average value of x in W (z). pwl (z) average value of x in W&apos; (z). pwU (z) average value of x in W" (z). Note that pW (z), pw (z), pwf (z) and pw,, (z) are (regular- ized) regionalized variables with support w, W, W&apos; and W" respectively. The relative sizes of wO, w, W&apos;, W" and O are represented schematically in Fig. 7.1. The variance of pw (z) when w (z) takes all possible positions in O is known as the dispersion variance of w in O and is denoted by a? (w in O) or a\: 0; = a2(w in a = Ew,z, inn {[p~ (z) - p121. (7.1) For convenience, the following simpler notation will be used: a> Ew in n [(p w - pl21. Consider now a large block W (z) in O with average value p~(z), and all the possible smaller blocks (or samples) w (z&apos;) of size w in W (z). We can calculate the variance of the blocks w (z&apos;) in the block W(z), which is the dispersion vaciance of w in W (z), from the relationship a2 [w in W(z)l = Ewcz,, in W(Z) {[PW (z&apos;) -pw (z)l21. (7.2) Provided the intrinsic hypothesis is satisfied (§3.4.4), this variance is a function only of the dimensions of the supports w and W, and is independent of the position z of the block W (z). We can therefore define the dispersion variance of w in W, as follows: u2 (win W) = Ew in w [(ptti - PW)~]. (7.3) In cases where the intrinsic hypothesis is not satisfied, the variance of samples w in blocks W may vary only slightly from block to block, so that the average dispersion variance of w in W (z) for all possible W (z) in O can be used: ayw in W) = Ew(,, ~n n {a2 [W in W (z)]}. (7.4) The following example will help to illustrate the importance of the variance of dispersion. Assume that we plan to develop a bedded coal deposit Q. We consider mining the ore body with only one giant dragline, which will extract each day a block of coal of size W&apos;. The total area mined in a year will have a size W". We want to know whether this scheme is acceptable, given that the daily variation in ash content must not exceed a given percentage specified in the sales contract, which is renewable on a yearly basis. On a given day, if we mine the block W&apos; (z), the ash content will average pwT (z). In a period of one year, the expected daily variation in ash content is measured by the expected variance of pwf (z) in W", i.e. by a2 (W&apos;in W"). An alternative mining method might be to use two smaller draglines, each one mining W&apos;/2 of ore per day, and Wn/2 per year. If the draglines are used to mine two distinct sections of the deposit, the expected daily variation in ash content will be u2 (W&apos;/2 in Wj1/2)/2 Other situations, where economic decisions cannot be made without knowledge of the dispersion variance, are illustrated in § 7.4 and 7.5 below. Remark: The notation a2 (w in W) is used in this publication because of its clarity. An alternative notation, which is increasingly accepted in the literature, is s2w/W. ]

Jan 1, 1978

By H. Mulder, M. C. Bekker

The principal objective of this investigation was to conduct a thorough literature review on the status of project readiness mechanisms, tools, techniques, and frameworks for mining projects. The review aimed at identifying common readiness evaluation criteria as well as potential shortcomings that prevent the establishment of a generalizable project readiness index. The literature review included a general overview of capital project performances and the importance of project readiness assessments to improve project delivery success. The study then progressed towards literature involving mining projects and how this differs from infrastructure and industrial project assessments. The paper concludes by summarizing the current state of mining project readiness assessments, the unique and differentiating factors to be considered, and suggests recommendations towards the development of generalizable readiness assessment criteria for mining projects.

Sep 1, 2022

By P. N. Harris

The Colloquium on &apos;Recent Mining and Metallurgical Developments in the Orange Free State&apos; was held at the Civic Centre in Welkom on the 4th and 5th May, 1983. The Chairman of the Orange Free StatejKlerksdorp Branch of the Institute and Acting General Manager of Unisel Gold Mining Company, Mr D. A. Parfitt, opened the proceedings, and Professor A. N. Brown gave the welcoming address. Mechanization of mining The South African Institute of Mining and Metallurgy is to hold a one-day colloquium on &apos;Developments in the Mechanization of Mining Operations&apos; during September 1984. Surfac,e mining and quarrying The Second International Surface Mining and Quarrying Symposium is being organized by The Institution of Mining and Metallurgy in association with the Institute of Quarrying.

Jan 1, 1983

By Donald J. Murdock

Column flotation has been developed over the past ten years as an alternative to conventional flotation. It has been used mainly in the cleaning applications of sulphide flotation, where its ability to wash the froth of entrained gangue and produce a higher-grade product has given it a distinct advantage over other technology. The benefits of column flotation can be summarized as follows: Improved concentrate gradeby 2 to 5 per cent . Significantly lower operating costs . Savings in capital investment . Superior control. Since the initial development of the equipment, a number of design improvements have been developed that now form the basis of the Cominco column-cell design. The purpose of this article is to discuss some of those design innovations, and present some typical industrial results.

Jan 1, 1991

The 91st Annual General Meeting of The South African Institute of Mining and Metallurgy was held at Kelvin House, Johannesburg, on 17th August, 1988. Mr B.c. Alberts (President) was in the Chair, and declared the meeting open at 16hOO. Obituaries The President announced the deaths of the following members of the Institute:

Jan 1, 1988

By A. V. Z. Brink, D. M. O’Connor

Recent research into the prediction of mining-induced seismic events by the monitoring of small microseismic events at Western Deep levels Gold Mine is described. Measurement and interpretation techniques and results are presented. It was found that the occurrence of the larger events near longwall faces on the Carbon leader reef is preceded for some hours by an increased rate of microseismic emission and spatial concentration of the microseismic events in the zone of eventual failure. The research is currently being expanded with the aim of producing a practical rockburst early-warning system.

Jan 1, 1983

By T. Szendrei and S. Tose

Historical approaches to the problem of flyrock based on correlation studies and regression analysis, including artificial neural networks and similar techniques, are inherently incapable of addressing two core issues – root causes of flyrock and projection velocity. A further shortcoming of correlation techniques is that they give no information on the influence of rock size and shape on the flight distance. The scaled depth of burial model for crater blasting in the collar zone and bench face does not specifically address the question of flyrock velocity. A third approach, based on flight trajectory calculations, often neglects the very significant effects of air resistance on the trajectory. Some trajectory models incorporate air resistance but use an implausible fragment velocity model that cannot propel sizeable rocks to distances much beyond 150 m. Nonetheless, trajectory calculation incorporating the effects of air drag affords the most promising approach to the prediction of flyrock range. A unique and insightful feature of the proposed realistic flight modelling is that it collapses all suspected causes of flyrock, many of which are not well understood, to just a single parameter – the launch velocity. This indicates that the root causes of flyrock lie in the mechanisms of momentum transfer to broken rock and suggests new avenues of study.

Dec 13, 2022

By A. V. L. Narasimham

The practice of refractory lining in Fe Cr furnaces is purely based on the behavior of various refractories used in different parts of the Furnace at elevated temperatures. The choice of the pattern of lining is mostly done on traditionally based concepts. Refractory Lining of a furnace is the most crucial enabler for operational excellence for Ferro Alloys production. During the last few years, no major technological change has taken place in ?Refractory Lining Management?, only freeze lining concept is becoming popular. Many ferro chrome furnaces today, have the refractory lining problems especially at tap holes, resulting in shutdowns and production losses. The author with his experiences for the last 37 years in furnaces of all sizes tried to attempt to investigate into the reasons for premature failure of lining.

Jan 1, 2007

By F. Nakhaei, A. Noury, S. Ghodrati

To achieve maximum efficiency in any purification stage, the flotation should be continued until the concentrate grade reaches the feed grade. During sampling from rougher cells of the Iranian Sarcheshmeh copper concentrator plant 1, it was revealed that the concentrate grade of the last five cells of rougher circuit was close to feed grade. Therefore, it was suggested that the concentrate of the aforementioned cells be mixed with rougher feed. In this study, one of eight rougher circuit rows was selected and evaluated. The floatability tests were performed to compare the flotation behaviour of rougher feed and mixture of concentrate of the last five cells with rougher feed in the laboratory scale by a tree analysis process. The results showed that mixing of rougher last five cell concentrate with feed has a detrimental effect on flotation efficiency that leads to a 3% decrease in recovery. It was also observed that with grinding of this concentrate, recovery can be improved. Keywords: Efficiency, flotation, rougher, tree analysis, floatability, concentrate, mixing

Jan 1, 2020

By R. Hoare

TSG Consulting works with many of the major global mining companies using simulation modelling as an integral component in their development team&apos;s processes to determine which combination of infrastructure options, operating performance and operating rules best achieve project goals. All systems can be divided into a series of subprocesses. Conventional management and design practices focus on the optimisation of these system components. However, the interaction between subprocesses means that improvements made to the individual components do not necessarily translate into total system performance improvement. TSG Consulting advocates a `total integrated system approach&apos; to understanding the behaviour and performance of complex interacting systems. In a constraint-based integrated set of processes, the instantaneous system capacity is limited by the subsystem component capacity that is the bottleneck at that point in time. Additionally, real world stochastic and dynamic influences tend to have knock-on effects across subsystem boundaries. This necessitates the requirement for simulation modelling to develop and validate solutions that are sufficiently robust to deliver sustainable outcomes. The key to maximising production is to understand the causes and effects the inherent variability has on component performance, subprocess performance and most importantly, the total integrated system performance. The correct use of simulation modelling techniques allows study teams to evaluate the relative merits of different capacity enhancement proposals. Put simply, when done well, simulation modelling provides a quantitative and rational process to enhance the knowledge and insight needed to make informed decisions.

Jan 1, 2007

By S. Kauvosaari, M. Olofsson, O. Haavisto, M. Kosonen, J. Martikainen

The target in grinding circuit operation is usually to maximise the throughput and energy efficiency while keeping the product particle size on an optimal level for downstream operations. Furthermore, feed ore variations in size and hardness need to be compensated in order to stabilise the process. This is a challenging task for any circuit and requires good instrumentation, combined with careful control design. This study investigates the control of a two-stage iron ore grinding circuit with an AG and pebble mill at LKAB Kiruna KA3 concentrator in northern Sweden. A new control approach based on multivariable model predictive control (MPC) is designed, implemented and evaluated for the circuit. Additionally, advanced instrumentation including a volumetric charge measurement for grinding mills is utilised. The MPC control approach starts with step tests that are used to model the effect of manipulated variables (MVs) like feed rate, water addition and primary mill speed on controlled variables (CVs) such as final product particle size, power consumption and volumetric charge of the mills. In the control implementation phase, the models are then utilissed together with current process measurements and known operating limits to predict optimal future changes for the manipulated variables in order to reach the desired process output values. It is shown that the new control approach improves the stability of the circuit and reduces energy consumption. Keywords: Model predictive control, grinding, optimisation

Jan 1, 2020

By B. Oladipo, E. Govender-Opitz, T. V. Ojumu

Bioleaching, one of the several important processes of biohydrometallurgy is an accepted technique for the extraction of desired metals from sulphide ores. Although the mechanism is well understood, the slow metal extraction rate due to passivating layer and ferric ion precipitation, especially in a typical chalcopyrite leaching, impact negatively on the overall efficiency of the process. Iron precipitates are common in many biohydrometallurgical operations and these precipitates represent the exit route for undesired iron, alkali ions or sulphate ion from the processing circuit. While there have been reports on approaches to slow the formation of these precipitates by manipulation of various operating conditions such as pH, redox potential and temperature, the precipitates become highly significant over the long term in the continuous operation of bioleaching and may render heap impermeable. Although there is evidence supporting the nature of the passivation to be precipitates of iron, their scavenging mode of reducing extraction efficiency is still not well understood. This paper thus discusses iron precipitation in biohydrometallurgical processes, including its formation and significance on bioleach processes. The paper also details the associated passivating species identified with heap bioleaching of sulphide minerals and provides some insight into sorption mechanism that limits metal recovery in a typical bioleach operation. Keywords: Iron precipitation, bioleaching, chalcopyrite, passivation, jarosite, metal sorption

Jan 1, 2020

By A. Pareek, V. S. Masampally, V. Runkana

Mineral processing plants, in general, consist of comminution and separation circuits. Rougher flotation is considered as the heart of the separation circuit with concentrate grade and mineral recovery being the key productivity and quality parameters that affect the downstream operations and profitability. Online prediction of these parameters can help improve process performance in real-time. Past efforts to predict flotation cell performance have focused on building physics-based or machinelearning (ML) based models. Although physics-based models are popular in process design applications, ML models are more appropriate for online monitoring, control and optimisation due to their lower computational requirements. Deep learning (DL) is gaining prominence nowadays for application in industrial operations where a large amount of data is available. We have developed a DL model based on long short-term memory (LSTM) neural networks to forecast the two key performance indicators (KPIs), namely, mineral recovery and concentrate grade in a mineral processing plant. Rougher feed characteristics such as mineral grade and particle size distribution, upstream measurable disturbances such as throughput of the comminution circuit and pulp density, and control variables such as aeration rate, pH, and collector addition rate are considered as explanatory variables to train the model. The model was tested with data from an industrial mineral processing plant. For real-time dynamic predictions, the trained model appropriately accounts for mismatch in measurement frequency of real-time process data and off-line laboratory analysis data. Such a time series model can be used as a state observer in model predictive control techniques for optimal control of unit operations in a mineral processing plant. Keywords: Mineral processing, flotation, time-series, dynamic modelling, deep learning

Jan 1, 2020

By F. Roelofse, D. Grobler, and M. Purchase, C. Gauert, E. Kotze

Scandium is an important industrial metal for which demand is projected to increase in the future. Although many Sc deposits are secondary, Sc is scavenged by clinopyroxene during fractional crystallization of primary, mafic-ultramafic magmas. Sc may thus occur in sub-economic concentrations in mafic-ultramafic intrusions. In this work, we present new data on the concentration of Sc in the Bushveld Complex (BC) of South Africa. The eastern and western limbs of the BC are considered to be largely pristine, primary magmatic deposits, whereas the northern limb shows evidence of largescale, localized crustal contamination. Samples from the primary magmatic cumulates of the eastern limb, from the mineralized Flatreef of the northern limb, and from the crustal-contaminated Footwall Assimilation Zone (FAZ) of the Flatreef were analysed for Sc. Despite the FAZ containing abundant clinopyroxene, interpreted to have recrystallized from the original cumulates in the presence of melted sedimentary rocks, no significant differences are seen in the concentration of Sc compared to other cumulate rocks of the BC containing less-abundant clinopyroxene. The concentration of Sc in the analysed samples is mainly controlled by mineralogy, with anorthosites, chromitites, and harzburgites containing under 20 ppm, and norites and pyroxenites containing 20–40 ppm. The parapyroxenites of the FAZ are less enriched in Sc than expected, suggesting that Sc may have been lost during alteration and recrystallization.

Sep 1, 2022