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By C. Perez

In mineral processing plants it is important to estimate rock composition, size and grindability to improve control of the grinding process. It is well known that variation in ore grindability and size distribution directly affects mills power consumption and throughput. This paper extends a general machine vision approach for on-line estimation of rock mixture composition using color information. Our proposed rock lithological recognition method requires the following steps: division of each image into sub-images, color feature extraction using the Binary Quaternion-Moment-Preserving thresholding technique (BQMP) and support vector machines (SVM) for classification. The BQMP thresholding technique splits the image in two and chooses representatives of each half using the histogram as features. The statistical parameters of the color data can be expressed using the quaternion moments into the representatives. This method can be recursively applied to the sub-images, obtaining 5x2n features in n iterations. Once the feature vector has been computed, each vector is assigned to one of three classes using a classifier. The method was tested on a database containing 20480 sub-images (64x43 pixels) of five ore types as follows: massive sulphide (MS), disseminated sulphide (DS), net textured (NT), gabbro (G), and peridotite (P). These ore types were assigned to three grindability classes: soft (MS), medium (DS and NT) and hard (G and P). The database was divided in 2 subsets: 15360 sub-images used for training and cross validation and 5120 sub-images used for test. The classification accuracy was compared with a method previously published based on a mixture of principal components analysis (PCA) for color, and wavelet texture analysis (WTA) for texture feature extraction. WTA-PCA approach reached a 76% accuracy in test while our BQMP method reached 90% of classification accuracy using only color features. Experimental results show that our proposed method yields excellent results compared to previously published results. Keywords: rock classification, lithological classification, grindability estimation

Sep 1, 2012

By Sami Makni, Ahmed Bouajila

"To remain competitive in an open market, mining companies are more and more requested to optimise their complex and highly multivariate processes. The increasing amount of process data, brought about by intensive use of instrumentation and the progress in computer data acquisition, enhances the need for more and more powerful and robust tools for the analysis and modelling of the process data. Now, with the emergence of multivariate data analysis (MDA) techniques, on-line clustering of mineral process behaviour is possible and could help in the extraction of valuable process operation indicators. MDA can be used in different mineral processes such as flotation, grinding, smelting etc. In this paper, the focus will be on grinding process as it is the one of the most costly node in the mineral processing scheme (i.e. power consuming) and it is a highly multivariable process. Particularly for primary grinding mills (SAG/AG), more instrumentation outputs and more powerful and significant process indicators are needed for efficient control and optimisation.MDA is a novel approach suitable for understanding mineral process behaviour compared to the traditionally approaches based on empirical and phenomenological modelling. Multivariate techniques can be used for several purposes. In this paper, the following objectives will be addressed:operation diagnosis: Shift differentiation and operation test differentiation can be showed by using MDA tools such as principle component analysis and factorial discriminant analysis.operation performance monitoring: operation regimes can be evaluated by clustering based on an economic criterion performance such as costs, tonnage, product quality, profit, etcIdentification of operation behaviour (monitoring) and selection of drift correction trajectories (control):MDA can be used to identify periods of unusual or abnormal process behaviour and to diagnose, if it is possible, causes for such behavior."

Jan 1, 2003

By E. Scheepers

Within a phosphorus producing submerged arc furnace it was found that the continuous fluctuation of the furnace between a flowrate-driven state (high throughput) and a thermodynamic-driven state (low throughput) caused both techniques to have similar overall, predictive abilities and resulted in a linear, ARX-type, adaptive prediction model as the model of choice. Secondly, this pre-diction model was developed, tested and then shown to have a reasonable, 8-hour-ahead predictive accuracy (R2, coefficient of determination) of 30% (±6%) on future Pslag values. This inherent relationship exists because, at the precise moment of a Pslag prediction, the furnace already contains the metallurgical memory (input variables to the linear model) needed to ensure that some predictive possibilities will always exists - all as a result of the long residence times in the furnace. Residence time, however, is not a directly adjustable variable but rather a fully dependent variable and a function of an array of interconnected and interactive variables. In fact, this applies to virtually all input variables, reemphasising the importance of innate metallurgical memory. Thirdly, predictive control possibilities were explored by simulating the set-points of two fully independent variables used by operators to control the process: the ratio of fixed carbon-to-P2O5 and the ratio of silica gravel-to-pellets. This linear, predictive control model showed only a slight improvement with an 8-hour-ahead predictive accuracy of 35% (±7%). This highlights how ineffective current adjustments are in optimally steering the process and how difficult even incremental improvements in feed-forward and predictive control can be. Finally, it is shown that fundamental design-, sampling- and process restrictions currently associated with the process will always limit the predictive and especially control accuracy or meaningfulness of any dynamic model. These restrictions include the size of the furnace resulting in long residence times, 8-hours sampling intervals, an extremely complex and interactive process and a 16% spatial analyses variation on the Pslag values ? the very value that the model is to predict. The point is made that, given the current status quo, even the perfect dynamic prediction model can not improve on an 8-hour-ahead prediction of 30% (±6%). This barrier can only be pierced with e.g. tidier and more frequent sampling regimes and other upfront capital investments, a decision that becomes a cost accounting exercise and that can only be taken by the management structure. An investment demanding ever-increasing attention is CFD software and it?s potential to shed more light on the complex interactions within the furnace.

Jan 1, 2014

By G. Zaitsev

In mining and mineral processing, ammonium and nitrate from residues of explosives and other sources are discharged to the aquatic environment. Nitrogenous compounds in mine water can adversely affect the aquatic environment. The aim of this work was to study the biological removal of nitrate from cold inorganic mine water at the pilot-scale in an operating underground mine. Seven-months pilot testing on biological removal of nitrate from inorganic mine water was successfully performed in denitrifying fixed-bed biofilm reactors. The feed to denitrification bioreactors contained 11 mg NO3--N/L. Methanol was added as an external carbon source to the denitrifying bio-reactor since the inorganic mine water contained only traces of organic compounds (<CODCr (50 mgO2/L). Nitrate removal reached 75 g NO3--N/m3/d with removal efficiencies above 95% at temperature below 4°C. Thus, removal of explosives-residues from cold inorganic mine water was shown in pilot-scale biofilm reactors.

Jan 1, 2014

By C. Kleine

Coal sorting with an x-ray-transmission (XRT) sensor has experienced a lot of development since first described by Jekinson et.al. in 1974. Powerful computers and increasingly sensitive X-ray scintillation counters enabled the development of high-performance sensor-based sorting machines. The first industrial installation of a belt-type XRT-sorting for coal is in operation since 2004. It is upgrading high quality coal from 1% ash to 0.7% ash for cathode production. In comparison to the high quality coal which is treated with a belt-type sorter, the chute type sorter has been successfully optimized for high volume coal treatment of lower quality coals. This project was funded by the German Environmental Fund (Project Nr.244444). In cooperation with the RWTH Aachen University and Isambane Processing, CommodasUltrasort(now Tomra Sorting Solutions) conducted extensive pilot test work under production conditions in 2010 for the destoning of power station coal at Arnot Power Station in South Africa. The experience gained during this pilot test phase led to an optimized, redesigned PRO Secondary XRT Chute sorter which is now in operation at a South African coal mine. This flexible semi-mobile modular destoning plant is treating a size range of -120 mm +30 mm at a throughput of 150 tph. The implementation of this plant not only reduces the ash content, abrasiveness index and sulfur content significantly, but also reduces royalties, water treatment and transport costs.

Sep 1, 2012

It is well known that the crushing and grinding steps in the mineral processing of ores represent a substantial proportion of total energy, cost and carbon emission in mining. For instance a recent study calculated that 36% of the energy consumed by gold and copper producing mines in Australia was attributable to comminution (Ballantyne et al, 2012). Understanding this component of a business is essential in a world seeking to lower production costs, improve earnings and reduce its carbon footprint. The issue commands the attention and support of industry leaders in the mining sector, government, equipment manufacture, engineering design and research organizations. Furthermore, several researchers have often stated that globally, comminution processes consume between 3-4% of the world?s electrical energy production (Daniel and Lewis-Gray, 2011). In 2009-10, Australia?s Federal Government Energy Efficiency Opportunity (EEO) participants in the mining sector used 336.5 PJ, representing approximately 6 percent of Australia&apos;s total energy use. It is evident that the current practices of breaking rocks in tumbling mills are relatively inefficient (Fuerstenau and Abouzeid, 2002). Research and improved engineering design has established that a range of improved blasting, crushing and grinding techniques and flow sheets may lower project costs and carbon footprint. These include relatively simple process flow sheet strategies such as gangue rejection or pre-concentration prior to size reduction, a better combination of grinding technologies (both current and new systems), and selection of coarser grind sizes where ore and waste mineralogy allow. Yet, despite this systematic and detailed research, the industry remains somewhat reluctant to implement these strategies. CEEC (Coalition for Eco-Efficient Comminution) was established in 2011 to support knowledge sharing and change in an area of high-energy consumption for the global mineral industry. CEEC is a not-for-profit industry company, funded by grants from world wide industry companies, whose mission is to accelerate knowledge and technology transfer in the field of energy-efficient comminution. The key benefit to the mining sector arising from CEEC?s activities will be an increase in the rate at which more energy-efficient comminution data sets, processes, and technologies are developed, demonstrated, shared and applied. This will contribute directly to the industry?s target of reducing operating costs and carbon emissions. CEEC International Ltd and JKTech Pty Ltd teamed together to facilitate the inaugural CEEC Workshop, in June 2012. The purpose of the workshop was to produce a roadmap to provide guidelines for how (1) operations can achieve best EEC practice in the short to medium term, in collaboration with vendors, engineering companies and other service providers, and (2) what R&D is needed to achieve paradigm change in EEC in the medium to long term, e.g. 75% reduction in comminution energy in 10 years.

Sep 1, 2012

By Osvaldo A. Bascur, J. Pat Kennedy

"The profitability of mining and metallurgical operations continues to lag behind that of other industries despite the technical advances that have occurred in recent years. A primary reason for poor performing in this economic sector is the lack of integration of mining and metallurgical information. The mine cut-off grade can be optimized if mining and metallurgical engineers have the data to optimize milling for the type of ore in hand.Integration of mine transactions, stockpile inventories, process flows and assays is the key to determining the economics for each blast strategy in a mining site. Real-time information from manufacturing assets greatly affects the profitability and flexibility of a company, but gross errors from faulty sensors, inaccurate instrument calibration, or incorrect process models may corrupt actionable information. New software tools can model the process, sift through available data to identify and eliminate gross errors, and use statistical data reconciliation to create materially balanced and consistent readings. The reconciled data is unified for use by both monitoring and decision making processes.Creating a real-time information environment where upgrade and change are allowed is becoming an accepted methodology to accelerate and simplify the integration of applications such as production monitoring, equipment performance management and yield and inventory management.This paper describes the data integration requirements in a mine, concentrator and port for unification of the redundant data. The capture of the mining events, stock pile management and the metallurgical information is the base requirements for a dynamic environment to reduce the overwhelming amount of information to unified set of information. Part of any decision support program should entail these results to continuously improve the measurement system to address the costs in mine and milling the ore."

Jan 1, 2003

By Sepideh Javanshir

The process of liquid?liquid extraction of gold from chloride solution is complicated by the presence of many variables acting simultaneously in an unknown non-linear fashion for the determination of the final extraction efficiency. In practice, it is quite difficult to develop a model for predicting various properties of such process. Therefore, an alternative approach based on Artificial Neural Network (ANN) is considered to assist in the simulation process. Artificial neural networks (ANN) are powerful tools that can be used to model and investigate various highly complex and non-linear phenomena. This paper presents application of Multi-Layer Perceptions (MLP) to construct relationship that will be employed to develop a quantitative method for the estimation of the extraction efficiency as a function of hydrodynamical parameters such as stirrer speed, [AuCl4]- and DBC concentration, organic and aqueous flow rate. The model is shown to be in excellent agreement with the experimental data (R2 value for training and testing data was 0.9992 and 0.9915 respectively). Therefore, it can be used to optimize the parameters of liquid?liquid gold extraction with dibutyl carbitol (DBC).

Sep 1, 2012

The effect of the acid anion on chalcopyrite (CuFeS2) leaching has been examined, under care-fully controlled conditions, in a range of acids in stirred reactors and columns at pH 1. Solution speciation calculations were performed to determine possible rate controlling species. The relative leach rates of Cu from chalcopyrite can be explained quantitatively entirely on the basis of solution speciation without the need to invoke any rational relating to surface structure/speciation. The rate of Cu release is first order with respect to Fe3+ activity. The stirred batch reactor experiments at fixed pH 1, Eh of 750 mV (SHE) and 75? showed that chalcopyrite leach rates were rapid in both HClO4 and H2SO4 with 100 % Cu recovery after 73h and 97 h respectively. However, the leach rate in H2SO4 was slower as compared to the HClO4 leach due to a slower increase in the activity of Fe3+ due to the formation of FeSO4-. The presence of Cl- in the leaches (HCl and H2SO4 with NaCl added) led to a low activity of Fe3+, due to the high activity of FeCl2+, resulting in very low leach rates. For the column leach experiments dissolution of Fe and Cu from the pH 1 HCl eluted chalcopy-rite column with quartz proceeded at several times the rate of any other column (chalcopyrite with and without quartz and/or pyrite leached with H2SO4, HClO4, HCl). Three observations indicate that galvanic interactions are not leach rate controlling role: a) The rate of leaching was not uniformly increased for the non-quartz containing columns where the two sulfide minerals, chalcopyrite and pyrite, are in physical contact as compared to the analogous quartz containing columns where they are not; b) there exists a reasonable correlation between the rate of Cu release and the effluent Fe3+ activity for all columns thus indicating that the leach rate is determined by solution speciation; and c) in all cases the proportion of Fe to Cu leached indicates preferential dissolution of Cu over Fe. However, the proportion of Fe to Cu is either the same or greater than expected on the basis of the single mineral columns. This indicates that chalcopyrite is not dissolving preferentially to pyrite.

Jan 1, 2014

By Andrew Logan

Newcrest has a strong history of growth over the past 20 years, yielding value from previously marginal ore bodies. We have grown regionally in Asia Pacific through our operations in Australia, Indonesia and Papua New Guinea, and also established a beachhead in West Africa. The company has a reputation of realizing value from large, low-grade deposits adopting step change approaches. Newcrest is focused on an overall strategy of gold, low cost, long life and moderate growth. Capability step changes play a large delivery role. They transition from a business offline-to-online approach as opportunities mature. Newcrest believes in a) Knowing what ?good? looks like, b) An open innovation approach to actively working with others, c) The leap frogging leverage of making the ?old new again?, d)An adopt-adapt-develop methodology enabled by rapid prototyping/piloting, e) Getting our handsdirty with test work and plant analysis, and f) Staying focused on a few high business leverage areas. This paper describes Newcrest?s approach and our experiences in establishing renewed metallurgical processes, specifically aimed at increasing innovation adoption. Some of our step changes discussed include: 1) Early waste rejection to avoid high energy processing downstream, 2) Broader gold product channels to reduce site based processing needs, 3) Lower energy processing, and 4) Geo-Metallurgy approaches. Keywords: Newcrest, gold, step change, open innovation, adopt, adapt, develop, offline, online, prototyping, old new again, waste rejection, low energy, geo-metallurgy

Sep 1, 2012

By P. Ying

An auxiliary system connected with SEM-EDX for rapid identification of mineral and rock sample has been developed. With the aid of a synthetic spectrum library created, using the compare and match techniques provided by INCA software in EDX the mineral or rock sample names for refer-ence can be given at the same time analyzed.

Jan 1, 2014

By Minna Päällysaho

For the first time, the idea of sustainable development was recognised at a high political level at the UN Conference on Environment and Development held in Rio de Janeiro in 1992 (UNEP, 1992). Then the governments of the world committed themselves to promote sustainable development in their national activities and international co-operation. In 2001, the European Council approved the EU Strategy for Sustainable Development (European commission, 2011). This complements and supports the EU?s so-called Lisbon Strategy (European council, 2000) on competitiveness. The revised Strategy for Sustainable Development was approved in June 2006. Recently sustainability has had an important role in policy and decision-making while the public awareness towards limits of the environment has been rising. However, the indicators of industrial sustainability satisfy primarily the needs of corporate management and global investors. The information is often broad and non-specific and might occasionally even give a false impression. Moreover, contradictions between environmental, economical and social aspects also point out the unreliability of existing sustainability indexes. Therefore, the current state of affairs serves the interests of neither the industry nor the policy makers. Current corporate sustainability indexes and indicator systems need to be assessed with scientific methods encompassing the identification of their strengths and weaknesses. This requires quantitative and qualitative assessment and synthesis of existing indexes and indicator systems, providing new data on the functionality of existing corporate sustainability indicators. Therefore, the assessment lays the foundation for the development of new, more balanced sustainability index. Our first aim in this multidisciplinary article is to analyse existing global sustainability indexes for process industry. Second, we will introduce a first version of a new sustainability index for process industry, which includes the environmental, social and economic impacts of the plant, as well as legislation aspects.

Sep 1, 2012

By C. Igathinathane

Coal is the most widely used fossil fuel in the world, providing more than 25% of world?s electrical power. Since, the behavior of coal particles in crushing and grinding circuits, concentration operations, and solid-liquid separations is strongly dependent on size, a reliable and accurate measurement of particle size and particle size distribution (PSD) is a vital aspect in coal cleaning. This paper presents a method for the characterization of ground coal particles using image analysis. We utilized a machine vision (MV) approach that used a document scanner as the imaging device and a user-coded ImageJ plug-in that processed the image and automated the PSD analysis and output results in textual and graphical forms. This approach used a sum of volumes (SVolume) as weighting factor for particle length as the primary dimension of analysis and was successfully applied to ground coal. The plug-in was further improved for computational performance in this study. Mechanical sieving (MS) was also used to compare the MV results. MV results showed that the PSD of ground coal followed a uni-modal normal distribution, and the log-normal plot against particle size exhibited a linear trend for most of the range. MS results, however, had three linear segments, when the width-based sieving results were transformed to lengths by applying the shape factor (width/length), but a definite deviation between the PSD plots was observed. This study demonstrates successful application of the MV SVolume approach for PSD analysis of ground coal, which can be extended to similar particulate minerals and products. Keywords: dimension, fiji, image j, image analysis, coal, particle size, plug-in

Sep 1, 2012

By R. C. Miranda

Flotation is an operation widely used in mineral processing because of its aptitude to recover minerals of interest very efficiently. Flotation is often responsible for making possible the exploitation of mineral deposits of low concentration. In flotation various processes phenomena occur, some of which have still not been explained scientifically. Due to this fact, there are no models that can be widely applied in flotation control. Thus, in most cases, the control is done by experienced operators who are able to visually classify the types of foam into different categories and each one of them is associated with an operating actuation. In this situation, image analysis is configured as a solution that has already been employed in several ways. In this paper, a control strategy based on the colour of froth on a direct flotation of a nonferrous metal is presented. Moreover, the paper reveals the theoretical basis of the method, the critical points, difficulties and improvements that can be implemented. Data of grade were collected from the mineral of interest and RGB (red, green and blue) data of final concentrate of the froth. The mineral grade was collected and analysed during one month of operation and RGB was obtained using a camera. It was verified that the red colour showed the same trend of the grade curve in the concentrate. Thus, the use of this online data in optimising control was introduced. The laboratory grade is used to calibrate the colour set point. Therefore, it was possible to monitor in real time the trend of enrichment and depletion of a non-ferrous mineral in a flotation. It was possible to establish an online control strategy based on simple data acquisition. The result was an increase of 10% on production.

Sep 1, 2012

By M. Karamoozian

Modelling and optimisation of the power draw of large SAG mills are important due to the large power draw which modern mills require (5~10 MW). The cost of grinding is the single biggest cost within the entire process of mineral processing. Traditionally, modelling of the mill power draw has been done using empirical models. Although these models are reliable, they cannot model mills and operating conditions, which are not within the model database boundaries. Also, due to their static nature, the impact of the changing conditions on the power draw, within the mill, cannot be deter-mined using such models. Despite advances in computing power, on-line modelling of large plant scale mills could be a time consuming task. On the other hand, using on-line parameters for model-ling, result in more reliable control of SAG mill without performing any more costly tests. In this paper, SAG mill power draw is modelled using neural network approach. Six inputs, namely: solid percent, two bearing pressures, noise, recycle and feed rate were used to model the power draw. The produced model shows an acceptable correlation coefficient, R2 =0.9623.

Jan 1, 2014

By O. A. Bascur

"In recent years, metal producing companies have increased their investment in automation and technological innovation, embracing new opportunities to enable transformational change. Internet of Things (IoT) is one of many transformational initiatives, which can fundamentally revolutionize how industrial complexes operate. Whatever the issue, the answer is in the abundant and growing quantity of real time data and events collected in the grinding and flotation circuits in a mineral processing plant. Based on earlier work, today a grade recovery model permits identifying the best operating conditions in real time. We will review the strategy for increasing the value of the abundance instrumentation in current plants. An optimal Gaudin size distribution model provides augmented information from traditional sensors to find the optimal grind cut to reduce metal losses in the flotation circuits. The large amount of sensors in a flotation circuits permits to estimate the recovery and to find the optimal froth depth and aeration using an air holdup flotation model. The paper will describe the strategy of classifying data for on line generation of insights to using operational intelligence tools. We will discuss the implementation Recovery/Grind strategy with industrial examples in non-ferrous mineral processing and iron beneficiation.INTRODUCTIONThe declining supplies of high grade ores and increasing consumption of mineral and metal products have demanded higher operational efficiencies with minimum capital investments. The objective of copper flotation objectives are based on maximizing the dollars return on the concentrate produced. The economic efficiency is maximized by manipulating the circuit along the grade/recovery curve relationship. To achieve such objective improved stabilization of the grinding and flotation circuits are required (Oosthuizen, Craig, and Jamsa-Jounela, 2017)."

Jan 1, 2018

By Richard A. Williams

The mineral processing community have been amongst the pioneer developers and early adopters of key platform technologies in analytical methods, chemistry, physical separation and advanced modelling. Fundamental research will continue to have a significant impact driven by the continued development of sustainable practices. Major drivers and challenges for change in our industry in the next two decades include: reduction in energy uses in processing; discovery of new energy materials; adoption of green chemistry routes; safeguarding of water resources; and demonstration of adherence to best practice at all times. With increasingly global mining enterprises, the provision of internationally accepted benchmarks for performance will continue to drive the creative process. This lecture highlights major opportunities around a new paradigm of Multi-scale Measurement coupled with Multi-scale Modelling, based on advanced sensors, communications and computing. Connecting real-time measurement sensors with dynamic simulation models opens up new horizons for visualising the process behaviour from an experimental and theoretical basis. Examples are given of the potential of this methodology and its implications for better design.

Jan 1, 2003

New technology for on-line particle size analysis of mineral slurries has opened up new applications for particle size measurement. In some cases the particle size distribution of the final concentrate is an important quality issue. This paper discusses established on-line particle size analysis technologies and presents experiences on the use of a new technique employing laser diffraction in several minerals processing operations. The main innovations in the new instrument are a simple dilution system and multiple scattering correction algorithm, which allows wide solids content variations of diluted sample in the measurement cell. Representative sample for laser measurement is produced by a simple and reliable dilution system. The applications discussed are; Blaine number measurement of Fe concentrate before pelletizing, grinding of variable batches of ore and paste thickener control.

Jan 1, 2014

By Girolamo Belardi, Silvia Serranti, Giuseppe Bonifazi, Roberta Palmieri

Ore textural and structural characterizations play a fundamental role in any preliminary study (i.e. comminution, classification, separation) addressed to the design and set up of beneficiation flow sheets. The possibility to utilise automatic tools able to perform a classification, and a quantitative description, of the mineralogical and petrographical characteristics of raw ore samples thus represents an important step to define, to control and to verify the entire set of parameters, ore-texture-related, that can affect the final beneficiation results. The procedure usually adopted is based on the analysis of polished sections and more in details the recognition of the different mineralogical species and associations and the measurements of some quantitative parameters estimating both the geometrical characteristics of each species and the possible spatial relationships between them. This approach, originally carried out by experts, utilising optical and/or electronic microscopy, was ‚replaced? during the years by more and more sophisticated devices integrating analytical facilities and acquired data processing. Such integrated analytical devices formed the breakthrough platforms into what is now known as modern Process Mineralogy. In this paper the possibilities offered by chemical imaging based devices and techniques, to perform ore minerals recognition/classification, and to perform their topological assessment, are presented. Tests have been carried out and are discussed in order to analyze the problems related to the development of suitable and low cost strategies addressed: i) to realize a full mapping of ore minerals/gangue inside selected samples, ii) to define the optimal data-flow handling to optimize the speed of processing versus the quality of the results and iii) to start to define logics and procedures finalized to implement innovative smart detection engines, chemical imaging based, for mineral processing addressed to perform both a preliminary characterization of the ore, at laboratory scale, and a further possible control of the different processed minerals flow streams.

Jan 1, 2014

By Olgac Kangal, Ali Guney, Gulay Bulut, Guven Onal, Ayhan A. Sirkeci

This paper presents the results of pilot scale column flotation applications in the two different copper concentrators in Turkey. The Kure Copper Plant is located in the Black Sea Region of Turkey. In Kure, a very fine grinding requirement for an adequate level of liberation makes a successful flotation difficult and consequently the final concentrate can hardly assay above 16% Cu Column flotation tests were conducted at the plant site using two different column types The experiments proved that the grade of the concentrate could be increased from about 14% Cu to 25-26% Cu. Murgul Cakmakkaya Copper Concentrator is the largest copper concentrator in Turkey. Although concentrate production capacity is around 170,000 tons annually with 22-23% Cu content, this figure tends to be much lower than that. In Murgul a Turbo Column of Multotec Company of South Africa was employed. As a result of systematic column flotation tests, the grade of the concentrate was increased from 20.3% Cu to 28.7% Cu.

Jan 1, 2003