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By H F. Yu, S Ueda, J Diao, W F. Gu, Y Sasaki, H Qin

The main reason for the underutilisation of steelmaking slags is their instability, which is influenced by their complex phase structure. Recycling and sustainable steelmaking slag face challenges due to the presence of magnesium oxide (MgO), a crucial component in steelmaking slag. This study investigates the phase evolution of steelmaking slag during the cooling process with different MgO contents, using high-temperature experiments and FactSage™ software, ver 8.1, (by Thermfact/CRCT and GTT-Technologies) thermodynamic equilibrium calculations. X-ray diffraction (XRD) and scanning electron microscope - energy dispersive x-ray spectroscopy (SEM-EDS) analyses identified main phases in steelmaking slag, including Ca2SiO4 (C2S), RO (MgO-FeO solid solution), Ca2Fe2O5 (C2F), and Ca3SiO5 (C3S). The study reveals that RO phase increases as the MgO content increases. C3S precipitates from steelmaking slag with a 12 mass per cent MgO content at a reduced temperature of 1400°C. It is important to note that C3S remains in the steelmaking slag even after slow cooling. This may be due to the substitution of Ca2+ in C3S by Fe2+ and Mg2+ ions. Intriguingly, the precipitation of f-CaO in steelmaking slag with 10 mass per cent of MgO content occurred after one year, while there was no manifestation of f-CaO precipitation in the steelmaking slag with 12 mass per cent of MgO content even after one year. This discrepancy suggests a potential stabilising effect of an appropriate amount of MgO in steelmaking slag. However, it is crucial to emphasise that this inference lacks supporting data, necessitating further research to validate this hypothesis. The significance of this work is in uncovering the complexities of steelmaking slag behaviour, providing essential insights for optimising recycling processes. The research sheds light on the factors that influence the phase composition and stability of steelmaking slag, contributing to the utilisation of steelmaking slags, promoting environmental sustainability, and enhancing the overall efficiency of metallurgical processes in the current industrial landscape.

Jun 19, 2024

By Kramadibrata S

Over the years many attempts have been made to develop a means of assessing the excavatability of rock. Most of them are based on an empirical rating system while some authorities still propose the use of seismic velocity as a direct predictor of the rippability of a rock mass. On the other hand there are a number of classical models which have been developed to define the cutting force required at the pick or tooth of continuous miners.While these methods have been applied with various degrees of success to the design of excavation systems there is no generally acceptable method of defining the excavatability or cuttability of a rock mass in terms of the machine power required to generate a particular rate of production.The authors have attempted to overcome this deficiency by recording the intact and rock mass properties at a number of surface mines where continuous miners were in use and to use this data to examine the relationships between the relevant dimensionless groups developed from a dimensional analysis of the problem.The results obtained are encouraging and it appears that this new approach to the problem could lead to civil and mining engineers having a means of predicting the cutting power required to excavate rock at a given rate at any site.While this paper does not include a review of past work it refers to a number of key publications which will give the reader an excellent introduction to the subject.It is important to point out that the research work reported in this paper is part of a doctoral research project at Curtin University of Technology on the Influence of Intact Rock and Rock Mass Properties on the Design of Surface Mines. One of the major objectives of this project is to explore the possibility of developing a technique for predicting the excavatability of rock in surface mines at the design stage.This paper therefore commences with a brief reference to the continuous surface miners in use at the test sites followed by some original work by the authors in applying Dimensional Analysis to analyse the macro-performance of mechanical excavators. Following an analysis of the experimental data the authors validate the wider use of the model by referring to its successful application in predicting the power required to excavate rock by continuous surface miners at other sites.

Jan 1, 1995

By BLAKE DAWSON WALDRON

I must acknowledge my strategic adviser, Mr Lance Grimstone, who contributed to the development of this paper, and collated the text and figures.

Jan 1, 1994

By J Channell, G Phillips

"The Chesney deposit, located 4 km south-east of Cobar consists of three copper-gold rich shoots that lie within a larger, copper dominant lode. A fourth gold rich shoot is located adjacent to the main deposit. The deposit exhibits a strong structural control on mineralisation, as brittle deformation during subvertical extension provided the requisite structural traps to precipitate sulfides from solution. It is postulated that the Great Chesney Fault acted as a fluid conduit, conveying fluids to the site of orebody formation. The continual supply of fluids resulted in cyclic brittle deformation when the fluid pressure exceeded the lithostatic pressure and the tensile strength of the Great Cobar Slate.Evidence supports the hypothesis that ore deposit formation was controlled by: a supply of mineralised fluids transported along the Great Chesney Faulta suitable location for the mineralised fluids to accumulate, accommodated by vertical extension and dilation in certain locationsthe creation of suitable structural traps to capture the sulfides. These traps formed as a result of fault-valve behaviour. Understanding the controls on mineralisation enables prediction of the location of other deposits with similar emplacement history, or extensions of current deposits.CITATION:Channell, J and Phillips, G, 2014. Understanding mineralisation controls – a case study of the Chesney deposit, in Proceedings Ninth International Mining Geology Conference 2014 , pp 301–312 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Aug 18, 2014

Production targets are the basis for almost every decision made by operations personnel in the daily operation of a concentrator. It is the production targets that determine how the plant is operated and therefore determine its efficiency. However, to achieve maximum plant efficiency production targets must be relevant to the process and achievable.In RoseberyÆs case, for many years, production targets were taken from budget data which, with the exception of mill feed tonnes and grades were carried forward from the previous budget. The zinc concentrate grade target was fixed at 57 per cent Zn, a long-term average; it was the accepted æstandardÆ for Rosebery. Historical data showed this target was never consistently achieved and plant efficiency was highly variable.Investigations into the possible causes for the variable performance ruled out plant and mechanical issues. Despite the Rosebery concentratorÆs age the plant and equipment are more than serviceable and reliable. The mineralogy of the ore and specifically the iron content of the sphalerite was found to be the most significant determinant of zinc concentrate grade. It was found with increasing frequency that the zinc grade of the sphalerite in the ore was below the zinc concentrate grade target. Consequently on these occasions attempts to meet the required concentrate target destabilised the process resulting in plant inefficiencies.Using the correlation between iron and zinc, data modelling was able to accurately predict the maximum zinc concentrate grade achievable for any ore type. By using on stream analysis instrumentation the operations personnel are now provided with constantly updated concentrate grade targets. By knowing the mineralogical limits of the ore at the time it is being treated process stability is maintained allowing plant efficiency to be optimised. This has resulted in a step change in plant performance and a significant increase in revenue.CITATION:Harris, G R, 2012. Developing and implementing ore-specific targets for the Rosebery Concentrator, in Proceedings 11th AusIMM Mill OperatorsÆ Conference, pp 321-324 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Oct 29, 2012

By A Salama, J Greberg, B Skawina

In today’s underground mass mining, the orepass is an important part of the infrastructure as it connects two or more mine levels and is used for conveying or storing large amounts of material. One of the main risks related to an orepass is when operational or structural failure occurs, leading to long-term loss of functionality. Developing or rehabilitating an orepass when major failure has occurred is a time-consuming and expensive operation, and the risk of a long-term orepass loss motivates the development of alternative transportation strategies to ensure the continuation of smooth operations in large underground systems. The study presented in this paper has been done at the Loussavaara Kiirunavaara AB Malmberget mine in Sweden, where iron ore deposits are being extracted using sublevel caving. The study analyses the transportation system in one of the mine’s orebodies using discrete event simulation, with a focus on maintaining ore transportation in the event of the loss of an orepass.CITATION:Skawina, B, Greberg, J, Jonsson, K and Salama, A, 2016. Orepass loss mitigation approach in Malmberget mine, in Proceedings Seventh International Conference and Exhibition on Mass Mining (MassMin 2016), pp 617–622 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2016

By M J. Nimmo

extremely powerful tools for building predictive and generative models. ML can be used for building highly accurate regression and classification models. But without careful data science and statistical analysis, a regression or classification model could be highly biased and completely wrong – and we may not even realise. An example rock density estimation task will be used to illustrate the potential gains and possible pitfalls in using ML and AI in Mineral Resource estimation. What could possibly go wrong? Nothing! For most simple regression or classification problems, very little. For density estimation, using the global mean may be all that is possible, but the resource classification would need to reflect the uncertainty in global estimation of tonnage. Everything! From data collection and measurement, from selecting ML and AI techniques and designing the analysis workflow to our own cognitive bias. The things that could go wrong are vast and may include but not limited to asking the wrong question, not asking questions, data bias, measurement errors, insufficient data coverage, not collecting the right variables, not collecting enough observations, collecting clustered samples, unbalanced data, filtering out data without reason (outliers), clipping data (outliers), relying on automatic feature selection, including irrelevant variables, excluding relevant variables, geological interpretation, changing context, blindly following best practice, splitting small data sets, subsetting large data sets, using the wrong tool for the task, insufficient budget to complete the analysis, not allowing enough time for testing and experimentation (the science), focusing too much on building the model (the engineering), not learning from the data, our assumptions, our preconceptions, and our skill. This paper will explore the question of how training regression models to predict rock density are affected by gaps in the data (missing observations and missing variables). What could possibly go wrong?

May 24, 2023

By M Zoorabadi, B Hebblewhite, S Saydam, W Timms

Based on the cubic law, the hydraulic aperture of rock discontinuities has the most significant effect on the hydraulic conductivity of jointed rocks. The hydraulic aperture of rock discontinuities is a stress-dependant parameter and reduces with increasing applied stress. Furthermore, the rock stress increases with increasing depth. Therefore, hydraulic aperture is a depth-dependant parameter. In this paper, new empirical equations are introduced to calculate the hydraulic aperture of rock discontinuities at different depths. These equations were developed on the basis of back analysis of compiled packer tests from different actual cases. They provide the capability to calculate range of variation and average hydraulic aperture when the orientation of discontinuity and field stress tensor are given. Furthermore, it is possible to calculate the hydraulic aperture of rock discontinuities when its value is given for another depth. A hydraulic aperture of 7.7?µm was calculated by this method, comparing well with a reported site value of 8.3?µm for rock discontinuities at the Stripa mine project at a depth of 385?m. These new equations can be used by analytical or numerical methods to estimate the hydraulic conductivity of jointed rocks.CITATION:Zoorabadi, M, Saydam, S, Timms, W and Hebblewhite, B, 2014. New empirical equations to estimate the hydraulic aperture of rock discontinuities at different depths, in Proceedings AusRock 2014: Third Australasian Ground Control in Mining Conference , pp 375–380 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 5, 2014

By M Norbert, D O’Connor

The mainline running tunnels for Legacy Way comprise two tunnel boring machine (TBM) driven tunnels. Both TBM drives commenced at the Launch Portal adjacent the Mt Coot-tha Road. Excessive surface settlement arising from tunnelling would have had a significant impact to the road infrastructure. The geological structure above the tunnel springline is a mixture of fill, residual soil and weak rock. Given the combination of low ground cover and weak ground materials, along with the use of an open face TBM, an oversize pipe umbrella system has been installed above the tunnels. This pipe umbrella system is similar to that commonly adopted in conventional mined excavation support in soft ground except that the steel pipes are substantially larger in geometry and filled with concrete. It is acknowledged that similar systems with oversize pipe umbrella have been adopted elsewhere in the world in other projects. These systems often comprise of a full arch formed by conjoining oversize steel pipes (in contact only). By aligning the pipes in contact, the arching behaviour of the steel pipe would have been enhanced. However, this poses a degree of construction difficulty due to excavation and installation method of the pipes. The Legacy Way pipe umbrella system only consists of a partial arch at the tunnel crown and each pipe is spaced approximately 1200 mm apart with 900 mm diameter pipe, leaving a gap of approximately 300 mm between pipes. A reinforced concrete portal block was used to support the exposed ends of the pipes.This paper reviews the design analysis, the recorded deflections from the pipe umbrellas and surface monitoring during TBM launch and revisits the analysis using the newly available RS3 program, from Rocscience for 3D analysis of geotechnical structures.CITATION:Norbert, M and O’Connor, D, 2014. Starting the journey – launching of the Legacy Way open face tunnel boring machines in mixed face ground conditions, in Proceedings 15th Australasian Tunnelling Conference 2014 , pp 307–314 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 17, 2014

By J Sargeant, T Levi, J Theiler, B Beamish

Two coal samples (810 Panel and Area 900) collected from Clarence Colliery have been tested under a controlled heating procedure to evaluate the gas evolution trends corresponding to increasing temperature. Gas evolution data are normally used to identify key gas indicators and validate the trigger action response plan settings for the mine as part of the Principal Hazard Management Plan for Spontaneous Combustion. The samples were located some 6 km apart and there is a significant coal-type difference between them. At low temperatures (<100°C), the Graham’s ratio (GR) values for the two samples are almost identical. However, at elevated temperatures, there is a significant difference, with the 810 Panel coal having higher GR values than the Area 900 coal. This is most likely due to a difference in the coal reactivity. The 810 Panel coal does not evolve ethylene until temperatures in excess of 140°C are reached, whereas Area 900 coal evolves ethylene once temperatures in excess of 120°C are reached. The 810 Panel coal also produces significant ethane evolution (200 ppm) at low temperatures (50°C) due to the presence of retained ethane seam gas, which decreases as the temperature increases due to desorption from the coal. Once the coal temperature exceeds 130°C, the ethane evolution increases again due to coal oxidation reactions, reaching levels of 50 ppm. In contrast, the Area 900 coal has no significant low-temperature ethane evolution, and ethane evolution only increases once the coal temperature exceeds 120°C. There are also noticeable differences in the trends of other gases evolved from the coal. These findings have implications for the interpretation of gas monitoring results from the two different areas of the mine. They also illustrate the need for obtaining results from each new area of a mine as it develops so that changing gas evolution trends can be identified in advance of mining.CITATION:Beamish, B, Sargeant, J, Levi, T and Theiler, J, 2015. Gas evolution trends for coal from two different areas at Clarence Colliery, in Proceedings The Australian Mine Ventilation Conference, pp 273–278 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Aug 31, 2015

By M Iglesias-Martínez

The potential for iron ore in the Central African Archean belts has received much less attention by comparison to their equivalents in other cratonic regions of West Africa, South Africa, Australia and Brazil. Historically, iron ore is known to occur in South Cameroon and North Gabon with significant iron ore deposits (eg Mbalam in Cameroon, Avima in the Republic of Congo, Minkébé, Bélinga and Boka-boka in Gabon), having been the subject of geological exploration programs carried out by government institutions and exploration companies. Although the presence of strong aeromagnetic anomalies, undeniably related to the presence of greenstone belt formations, has been recognised, this study is the first attempt to assess the potential of banded iron formations (BIF) in the Northern part of the Gabonese Republic in the region between Minvoul and Minkébé (>10.000 km2). Deep lateritic weathering, inaccessible equatorial rain forest and high operational costs are some of the reasons why only limited exploration has been undertaken in the area. This work aims to improve the geological knowledge and to highlight the potential for iron ore of the eastern margin of the Ntem Complex units dated between 3.3–2.7 Ga. Field work showed a series of mafic rocks (amphibolite, anorthosite and gabbro), schist and BIF (described as itabirites in neighbouring areas). Samples from Minvoul BIF showed an extensive and deep weathering to powdery hematite revealing the presence, in thin sections, of iron oxides including magnetite, hematite and martite, primary and secondary quartz, garnet, hornblende, anthophyllite, cummingtonite and epidote. Mineral assemblages and coarse grain size suggest a metamorphic grade ranging from medium to upper amphibolite facies. The existence of known economic iron ore deposits in neighbouring regions combined with an extensive weathering enrichment process are promising factors for potential iron ore discovery in this remote area.

Sep 18, 2023

By Barnes E. L S

With the introduction of diesel powered loading and hauling equipment, a number of Australian mines producing less than one million tonnes per year from shallow orebodies have been dev- eloped by decline. This method of entry has the advantage of providing both flexible access and early prod uction. However, as the depth of mining increases so does the operating cost of haulage, due to the need for more trucks. This cost, together with increased capital requirements for both equipment and infrastructure, leads to the point where the alternat- ive of shaft hoisting must be con- sidered. With an established decline it is possible to provide an ore hoisting shaft using a drilled raise with subsequent stripping to a suitable size. Using DCF techniques and known equipment operating and capital costs, an economic comparison of the two systems is made to determine the limit- ing depth to which truck haulage can be used.

Jan 1, 1973

By P Minkkinen

"Pierre Gy showed in his early works that the results in estimating the variance of the mean value of continuous lots depends on sampling mode (random, stratified or systematic), except in the theoretical case that the measurand is randomly distributed in the lot. The experimental variance estimate calculated from samples taken from the lot is independent of sampling mode but depends on sample size and the type of the lot heterogeneity. Variography can be used to estimate the variance of the lot mean for different sampling modes.Gy also defined weighting error, which is made if a simple average is calculated from the assays of samples taken from a continuous object (eg from a process stream) where the flow rate varies with time and is correlated with the concentration of the analyte in the stream.The effect on sampling uncertainty of the three sampling modes and their interaction with the following factors in estimating the uncertainty of the mean concentration is studied using simulated lots, and lots having heterogeneity patterns similar to those of real data sets, including the effect of: the type of heterogeneity (random, grading and clustered heterogeneity patterns) increment size and number employing composite sampling weighting error. CITATION:Minkkinen, P, 2017. Dependence of the variance of lot average on the sampling mode and heterogeneity type of the lot, in Proceedings Eighth World Conference on Sampling and Blending, pp 319–330 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

May 9, 2017

By H Benzer, N A. Aydogan

It is well known that the high pressure grinding rolls (HPGR) can only be operated efficiently under choke feeding conditions in which a material bed is built up between the rollers to ensure interparticle breakage. By increasing the fines amount in the material bed up to a specific level with the recycling load, a uniform material bed is created and higher pressure gradient along the roller width is obtained which results in compact flakes including more fines. Extensive test work has been carried out with an industrial HPGR unit grinding cement clinker in a cement plant in Turkey. During the test work the recycling load of the circuit was changed to achieve different ratios to the fresh feed rate in order to identify the effect of the recycling load on the circuit performance. Six different tests were performed with the circuit at steady state. For each test, samples were collected from HPGR fresh feed and discharge, and total feed, then the particle size distributions of the samples were determined and mass balances around the circuit were calculated. The conclusion was that the reduction ratio of the HPGR increased with increase in recycling load. The flake densities showed the same trend with more compacted flakes being produced for higher recycling loads.

Jan 1, 2009

By C V. Deutsch

"Geometallurgical variables often cause problems to conventional geostatistical workflows. There are many variables; some are compositional and some are non-additive. They often show: complex multivariate relationships undersampling relative to conventional grade variables unequal sampling; that is, all variables are not available at all data locations.A variety of approaches have been developed over the years, but geostatisticians remain mostly focused on modelling rock types and a few grade variables with an abundance of data. The problems are reviewed and a chained workflow is presented where appropriate techniques are employed in sequence to permit reliable spatial prediction of geometallurgical variables. The spatial models may be used to assess the value of additional data, for prediction of process performance, for improved mine planning and other optimisation tasks. The chained workflow will include data transformations such as ratios and normal scores that make the variables amenable to geostatistical modelling. Data imputation techniques to account for missing values are considered with special consideration to spatial correlation with the available data. Decorrelation techniques such as the principal components, min/max autocorrelation factors and projection pursuit multivariate transformation are employed to manage massively multivariate modelling. Full distribution modelling must be considered to avoid biases in the prediction of non-additive variables. The post-processing of geometallurgical models must be streamlined for robustness and efficiency. The precise workflow varies according to the specific purpose of the study. Guidelines and thoughts on best practice are presented.CITATION:Deutsch, C V, 2013. Geostatistical modelling of geometallurgical variables - problems and solutions, in Proceedings The Second AusIMM International Geometallurgy Conference (GeoMet) 2013, pp 7-16 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Sep 29, 2013

By R C. A Minnitt

The two methods for calibrating the parameters K and (alpha) for use in Gy’s equation for the fundamental sampling error (FSE), the duplicate sampling analysis (DSA) and the segregation fractionalised analysis (SFA) are described in detail. A case study using identical broken reef material from a Witwatersrand-type orebody is calibrated using the DSA and SFA methods and the results are compared. Classically, the form of Gy’s equation for the FSE raises the nominal size of fragments given by dN to a power of 3. Gy’s formula is a theoretical formula applicable in the case when fragments are randomly collected one by one, which is obviously never the case. This is because the fragment shape factor (f) and the granulometric factor (g) were introduced to take care of this anomaly. More recent formulae, such as those suggested by François-Bongarçon and Lyman, are empirically based formula. A later modification of Gy’s formula raises dN to the power ? (alpha), the latter term being calibrated with the coefficient K in the DSA and SFA methods. The preferred value of ? (alpha) for low-grade gold ores used by sampling practitioners in the mining industry is 1.5. A review of calibration experiments for low-grade gold ores using the DSA and SFA methods has produced values of K that vary between 70 and 170 and values of alpha in the range 0.97 to 1.30. The average value for alpha is found to be 1, rather than 3 as originally proposed in the classic form of Gy’s equation or the industry preference for 1.5. It is suggested that for low-grade gold bearing ores, the equation for the FSE should raise dN to a power of 1. Such an equation for the variance of the combined FSE plus the grouping and segregation error, now referred to as the short-term component of the quality fluctuation error (QFE1), greatly simplifies the characterisation of gold ores, which now only requires the calibration of K for a given mass and established fragment size. The implications for the heterogeneity test from the generalised equation are that provided the fragments have been screened to within a narrow size range, any particular size will return a value for K that is acceptable for use in the sampling nomogram.CITATION:Minnitt, R C A, 2017. A generalised form of Gy’s equation for gold ores – empirical evidence, in Proceedings Eighth World Conference on Sampling and Blending , pp 331–350 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By C Xuan, J H. Park, Q Wang, C Shen, D Kumar, W Mu

Dissolution of non-metallic inclusions in the metallurgical slag is of vital importance for cleanliness control of the steel manufacturing. With the development of high temperature confocal laser scanning microscope (HT-CLSM), new insights have been obtained due to its in situ observation characteristics, higher resolution and precise control. However, HT-CLSM measurement has the limitation, eg the slag composition cannot include high amount of transition metal oxides. In addition, it is time consuming for the experimental procedure and not so simple to succeed for every measurement. It is known that digitalisation has made a significant progress in recent years. Machine learning (ML), a sub-domain of artificial intelligence (AI), is the key enabling technology for the digitalisation of the material science and industry. The database for ML model is collected using almost all available HT-CLSM experimental data and subsequently the established database is trained by different ML methods. Unseen data is used as the benchmark of the ML model. Al2O3 dissolution is the main process to be predicted in the current study. A good agreement between the HT-CLSM data and the ML model prediction results show the possibility to apply ML in process metallurgy.

Jun 19, 2024

By S Hapugoda, J R. Manuel, M J. Peterson, A Poliakov, E Donskoi

The occurrence and deportment of manganese in a number of ferromanganese and Mn-bearing iron ore samples from supergene-upgraded deposits, as well as from magnetite banded iron formation (BIF) and magnetite skarn deposits, was characterised in order to provide a basis for improved understanding of the processing characteristics of different ore types and investigate the potential for development of a robust geometallurgical classification for Mn-oxide ores. An understanding of the Mn mineralogy in mixed Fe-Mn deposits is necessary to inform any attempt to separate the Fe and Mn during beneficiation and to understand and predict the behaviour of lump and fine ore products during processing.A suite of analytical techniques was utilised, including hand sorting of lump ore particles, optical microscopy, X-ray fluorescence (XRF)/inductively-coupled plasma mass spectrometry (ICP-MS) chemistry, X-ray diffraction (XRD), scanning electron microscopy (SEM)/electron probe microanalysis (EPMA) and gas and solid phase pycnometry, in order to elucidate manganese mineralogy, mineral chemistry and mineral textures. Manganomelane group minerals (eg cryptomelane, hollandite), pyrolusite and lithiophorite were the most common manganese oxides in the samples examined. From the microprobe work, there was evidence of probable substitution of Mn, as well as Al and Si, into the structures of goethite and hematite. Manganomelane minerals were often intimately associated with goethite, in particular, and a clarification of this association and the associated Si and Al impurities is fundamental for improved understanding of beneficiation and sintering behaviour of Mn-oxide ores. Due to the poor or very fine crystallinity of some supergene manganese deposits and of some Mn oxide minerals, in particular the extent of overlapping peaks and their variable mineral chemistry, XRD was a problematic technique to equivocally determine Mn mineralogy.For lower-grade, earthy, poorly microcrystalline ores the mineralogical and textural association between mixed Mn, Fe and Al (¦ Si) phases was also critical. The deportment and potential separation of these elements in these deposits, especially Al, was complex, as they were characterised by forms of æhydrousÆ cryptomelane with higher levels of Fe-Al-Si substitution, as well as intimate intergrowths of ochreous-earthy goethite (probably aluminous in part) with cryptomelane, lithiophorite and/or kaolinite.Trace amounts of alkali elements in ferromanganese deposits were considered likely to be preferentially structurally associated with Mn oxides, while lateritic deposits with significant amounts of lithiophorite could contain elevated levels of deleterious impurities; Co and Ni in particular.A combination of solid and gas phase pycnometry proved to be a useful technique for broadly discriminating hematitic, goethitic and mixed Mn oxide-goethite lump particles based upon differences in their envelope and apparent densities. Based upon these preliminary results and ongoing Raman spectroscopy work on manganese deposits sensu stricto, Raman spectroscopy is a technique considered to have promise during targeted research to better link the optical and compositional properties of ferromanganese ore particles. CT-scan (computed tomography scan) analysis could also be combined with Raman spectroscopy and pycnometry to link mineral composition, mineral texture, ore particle textures and particle porosity/permeability to the high-temperature processing behaviour of lump and fines products.CITATION:Peterson, M J, Hapugoda, S, Manuel, J R, Donskoi, E and Poliakov, A, 2013. Textural characterisation of manganese minerals to improve the geometallurgical characterisation of ferromanganese and iron ores, in Proceedings Iron Ore 2013 , pp 281-300 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Aug 12, 2013

By M I. Pownceby, N Karimian, A Frierdich

These may be found with goethite through mechanisms including direct substitution or coprecipitation of nanocrystalline phases. Understanding P incorporation in goethite has significant implications for Australia’s iron ore industry as it lowers their commodity value. The current study tests a novel low-temperature technique to extract P impurities from synthetic goethite phases under O2-free ambient conditions by utilising Fe(II)-catalysed recrystallisation. The objective of this study was to examine the effect of the Fe(II)-catalysed recrystallisation of goethite on the geochemical behaviour (mobility and speciation) of co-associated P. To achieve this, we tracked changes in labile P (aqueous and 1M NaOH extractable) concentrations over time in goethite suspensions which were reacted with Fe(II)aq for 14 days under circumneutral pH conditions and room temperature conditions. Temporal changes in Fe mineralogy were investigated via synchrotron-based X-ray absorption spectroscopy. Iron K-edge extended X-ray absorption fine structure (EXAFS) spectroscopy confirmed goethite as the only mineral phase in P-free treatments while goethite (~86 per cent) and feroxyhyte (~14 per cent) were the main phases in 1.0 per cent P-containing treatments after a 14- day reaction with Fe(II)aq. According to our findings, the treatment of P-goethite with Fe(II) for a duration of 7–14 days, followed by a 24-hour extraction of the reacted solids using 1M NaOH, results in the removal of approximately 79.5 per cent of the initial P content. This removal encompasses both the aqueous fraction (~4.5 per cent) and the surface adsorbed fraction (~75 per cent) from the 1 per cent P-bearing mineral phase. Notably, this approach demonstrates an improvement in P extractability, with approximately a 10 per cent increase compared to the 1 per cent P-goethite sample that was not reacted with Fe(II).

Sep 18, 2023

By J-P Franzidis, E V. Manl, K Runge

Over recent years a number of techniques have been developed as part of the Australian Minerals Industries Research Association (AMIRA) P9 project to represent mathematically or measure directly subprocesses occurring in flotation cells. These include the determination of a:machine parameter (bubble surface area flux, Sb); froth parameter (froth recovery, Rf); entrainment and drainage parameters; and ore floatability parameter (Pi). A cost-effective methodology was developed for using these techniques to derive models of flotation circuits. This methodology was applied to derive a model of the flotation circuit at the Western Mining Corporation (WMC) Ltd Mt Keith Nickel Concentrator. The model was used to simulate a number of minor circuit flowsheet changes, the results of which are discussed in this paper. One year later, a second study of this plant was conducted. In this second study, data were collected while operating the plant in the flowsheet configurations that were simulated from the model developed from the first study. The results obtained from the second study showed reasonable agreement with the trends predicted from the first study model; discrepancies can be explained in terms of the model assumptions. This suggests that this simple circuit modelling methodology can be useful in assisting plant engineers to optimise flotation circuits.

Jan 1, 2000