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By D Laskovski, T Nguyen

Flotation surveying can be an extremely powerful means of determining opportunities for circuit optimisation and benchmarking. The type of survey employed depends heavily on the nature of the information required from the survey. It follows then that the type of survey determines the type of samples required; however, regardless of the type, the success or failure of a survey is often dependent on the correct used of appropriate sampling techniques at each sampling point. For a successful flotation plant survey it is vital that the correct samples be collected at the correct time in respect to the feed and each other. This paper discusses the current best practice sampling methods used in flotation surveying and discusses in detail the ideal sample masses based on theoretical analysis. Comparisons with JKTech’s database of real plant operations will then be made.CITATION:Laskovski, D and Nguyen, T, 2014. An investigation of current flotation survey sampling, in Proceedings Sampling 2014 , pp 99–106 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 29, 2014

By D François-Bongarçon

The theory of sampling (TOS), and therefore representative sampling, is based on three fundamental principles: sample correctness, which handles the search for non-bias; segregation, the effects of which must be eliminated or minimised; and variance control to help achieve the desired reproducibility. After briefly reviewing these very few, albeit essential first principles of Gy’s TOS, the state-of-the-art in TOS is examined through the prism of these principles and the prospects for the future are explored, with suggestions for further research where relevant. Regarding segregation, which is currently the focus of much research, new orientations are proposed. For correctness, which takes on a major importance when it comes to sampling devices, some new, important principles that have been put forth by recent research, are described in details.CITATION:François-Bongarçon, D, 2014. State of affairs and current research trends in theory of sampling fundamentals, including segregation and correctness, in Proceedings Sampling 2014 , pp 11–16 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 29, 2014

By J Webb

"It is vitally important in projects where the cut-off grade is low that there is no contamination during the sampling and sample preparation process, both in the ore and the waste zones. Failure to identify contamination could have devastating financial consequences for a project. The use of a variety of blank types is essential for ensuring that contamination is identified and eliminated at every stage of the sampling and sample preparation process. This paper examines an instance where an effective assay quality assurance and quality control (QA/QC) program was able to identify and eliminate a source of significant multi-element contamination in the prefeasibility stages of an open pit project. This examination includes the: evidence for contamination findings of the investigation process of resolving the problem and correcting the data evidence for both addition of metal and dilution through contamination potential impact on the project had this issue not been dealt with. It was through the use of a combination of blank types that the QA/QC process at Mount Isa Mines (MIM) identified a contamination issue at the first crushing phase of sample preparation. Identification of the contamination allowed MIM to stop assaying activities and work with the laboratory to correct the problem. The sampling program could then be continued with no further doubts about the sample preparation process. Contaminated samples were resampled from retained core. With the continuous monitoring provided by the blanks program, it was possible to ensure that only clean results went into the resource modelling data set. CITATION:Webb, J, 2014. When nothing is what you need – the importance of using blanks at all stages of sample preparation, in Proceedings Sampling 2014 , pp 49–54 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Jul 29, 2014

By J E. Powalisz

"The application of automated sampling techniques can help to provide a representative sample of process fluids and slurries that provide data for controlling process, custody transfer and quality assurance and quality control purposes.The automation of sampling provides a more consistent and reliable sampling process and eliminates many of the human-induced errors. Automated sampling can also provide a higher degree of safety in the plant/processing environment as well as better bottom-line results.Automated, fixed volume samplers can provide a very representative sample by integrating many small ‘grabs’ of sample from a process. Automated samplers can be controlled in a variety of ways to optimise the sampling process, including:cycle ratedwell timetotal volume.Properly selected and applied, an automated sampler can effectively isolate the operator from the process fluid during sampling and, to a great degree, during sample container collection.A variety of materials of construction, including seals, are available to handle demanding mining applications. Alloy 625, alloy 2205, AL6XN® and even certain plastics are used successfully in mining slurry applications.This paper explores the application of automated, fixed volume, batch style samplers in copper/nickel and gold processing, specifically pregnant leach solution and extraction fluids.Several specific field applications are discussed, including process installation details, hardware and operation.CITATION:Powalisz, J E, 2014. The application of automated samplers in copper/nickel processing, in Proceedings Sampling 2014, pp 35–42 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Jul 29, 2014

By S A. Campbell

There has been a reduction of coal production in the United States due to environmental pressure and the increased use of natural gas. This reduction will require coal producers and consumers to make decisions on whether to sample more to verify contract quality or sample less in an effort to cut costs.CITATION:Campbell, S A, 2014. The shrinking coal market in the US – sample more or sample less?, in Proceedings Sampling 2014 , pp 3–6 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 29, 2014

By P Minkkinen, J Lohilahti, T Korpela, B McPherson

Concentrator plants intake the ore from the mine and produce concentrates by extracting valuable minerals in a succession of process steps. Controlling the extraction process and detecting changes in the process requires updated information on a minute basis. Moreover, due to economical reasons, one must also collect metallurgical samples on a daily basis for monthly reporting.In this paper, we present how slurry samplers were installed in a Cu-Ni flotation plant to accomplish the aforementioned tasks. An on-stream elemental analyser at the plant is used for flotation process control by taking samples from selected process streams. Two process streams, the flotation feed and tail, are also equipped with metallurgical samplers. The metallurgical samplers were designed to have low head loss, be robust and easy to maintain and to collect slurry samples in a reliable way for the metallurgical accounting.Samples were collected by different sampler types and we discuss the uncertainty arising from sampling in the assays. Variographic analysis was carried out on the process data in order to find periodic components in the process, and estimate the correct number of shift sample increments and measurement error components. The metallurgical samplers were proven to be reliable devices with zero downtime and no sanding issues in the main process flows. In addition, we show that the tail measurement is the most important one for the plant recovery calculation, which emphasises the importance of the sampling accuracy in the tail stream.CITATION:Lohilahti, J, Korpela, T, Minkkinen, P and McPherson, B, 2014. Analysis of slurry sampling arrangements at a froth flotation plant, in Proceedings Sampling 2014 , pp 91–98 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 29, 2014

By L Lachance, F Flament, D Leroux, S Gariépy

The pervasiveness of poor sampling practices in and around mineral processing plants could seem intriguing in view of the various documented evidences both for causes and consequences. To this extent, this paper revisits the well-documented but lesser-known fact that failure to care sufficiently about how samples are selected and managed is the mother source of almost all sampling biases. The authors believe that the potentially huge leverage between causes and consequences as well as the diversity of people involved in sampling call for a globally integrated solution rather than for a point solution. Such a globally integrated solution has already been put forward by the AMIRA P754 Metal accounting – code of practice and guidelines (AMIRA, 2007) that inherently requires accuracy to be targeted, measured, reported and managed. Furthermore, always in order to avoid a point solution, several layers of protection against biases must be put in place with each one involving a different ownership whenever possible. This is exactly the role intended by the supervisory bias detection method described in this publication. It is based on the continuous monitoring of the expected value of reduced residuals obtained from statistical data reconciliation conducted in a metal accounting context. This supervisory bias detection method is developed in full details after which Monte Carlo simulations are done on a mineral processing plant example for showing how it can be successfully applied for detecting hidden biases.CITATION:Lachance, L, Leroux, D, Gariépy, S and Flament, F, 2014. Detecting sampling biases in metal accounting, in Proceedings Sampling 2014 , pp 109–120 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 29, 2014

By F F. Pitard

The optimisation of a metallurgical plant requires two kinds of sampling systems. The first provides samples to follow process changes, so in due time actions to maintain key parameters within pre-established specifications can be applied. The second provides composite samples at the end of a working shift to demonstrate that valuable constituents sent to the plant went to either a final concentrate or to the tailings in a logical, expected proportion. Contrary to what many experts may claim, these two kinds of sampling requirements are incompatible and cannot be fulfilled using the same sampling systems; this is because the required samples have completely different objectives. A process control sample is an instantaneous sample with increments taken at short intervals, and the objective is to know where the process is at any given time. Excellent accuracy is not a must, however good precision is recommended since a possible process correction may need to be made. Online analysers fed with in-stream stationary cutters or other kinds of in-stream or by-stream probes can do well for such a mission. A composite sample for metallurgical accounting with increments collected over a long period of time, for which excellent accuracy and good precision are both required, must be generated by sophisticated and extremely well-maintained cross-stream sampling stations. The use of process control sampling systems to achieve good metallurgical accounting has been promoted by several manufacturers of sampling equipment around the world; it is a grave mistake that is misleading top management and a practice that should be scrutinised and preferably eliminated. Furthermore, bias tests used to prove otherwise are often based on insufficient data to draw valid conclusions; a guideline is provided.CITATION:Pitard, F F, 2014. Sampling systems for process control and metallurgical accounting, in Proceedings Sampling 2014 , pp 25–32 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 29, 2014

By R Minnitt

Two methods of calibrating the parameters K and Alpha for use in Gy’s formula for the fundamental sampling error were investigated. Approximately 480 kg of broken ore from Harmony’s Target gold mine in South Africa was used to compile the replicate samples for two experiments. The duplicate series analysis (DSA) and segregation free analysis (SFA) methods differ in terms of their approach to recovering the sized materials required for the calibration procedure. In this experiment the DSA method split off six more-or-less equal masses of material from a 75 kg lot that was successively crushed to finer and finer fragment sizes. The sublots so formed were split into 32 samples and fire assayed for gold. The SFA method used approximately 250 kg of broken ore crushed to 95 per cent passing 2.50 cm, which was then passed through successively finer screens until a sublot of less than 0.0075 cm was obtained. Each of the 15 sublots obtained in this way were split using a riffle splitter into 32 samples that were fire assayed. A total of 672 fire assays were used in these experiments. The sampling parameters K and Alpha were used for the compilation of a nomogram and to calculate the gold liberation size of the ores. While there are no definitive results in terms of the actual gold grain sizes against which the calculated sizes can be compared, it appears that the DSA method produces more acceptable results.CITATION:Minnitt, R, 2014. A comparison of two methods for calculating the constants K and alpha in a broken ore, in Proceedings Sampling 2014 , pp 165–178 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 29, 2014

By D W. Stevens

Mechanical sampling provides a definitive value for the product sampled but the results are not instantaneous, meaning that decisions on process control can only be made hours after the sample is taken. Chemicals are added, tailings are sent to waste, stockyards are laid and the data for the decision-making is historical. Online analysers provide instantaneous information, allowing operators to make immediate and cost-saving decisions on the process. However, pending the specific online analysis application, there is a need for reference sampling for dynamic calibration/bias updating of the analysers so that they can deliver the desired precision and accuracy. What would be the economic justification for combining mechanical sampling with online analysis to reduce laboratory costs and provide immediate process control with certainty of accuracy? Approved sampling according to the Theory of Sampling, with immediate process control information.CITATION:Stevens, D W, 2014. Justification for integrating mechanical sampling with online analysis, in Proceedings Sampling 2014 , pp 135–138 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 29, 2014

By M Graham, R Brunning, C Andringa-Bate, S Westergren

In 2010, Rio Tinto Iron Ore’s operations commenced the planning and design of a sampling and analysis system for the new 50 Mt/a Cape Lambert Port B (CLB) facility. Current sampling systems at the ports consist of automated robotic, mass-based sampling protocols employed at Dampier, and semi-automated, time-based sampling systems operating at Cape Lambert Port A.The new sampling system at CLB has brought together a fully automated sampling and analysis system utilising time-based sampling, with the capacity to sample ore products of medium quality variation as defined in ISO 3082 (ISO, 2009). The CLB system is required to sample at shiploading rates from 6000 t/h to 13 440 t/h and provide full load-port analysis. The sampling approach is to proportionally extend primary increments (of varying mass relative to shiploading rate) to a standard length using variable speed aero-belt conveyor systems. This enables a fixed number of secondary cuts to be achieved for all primary increment masses and to be delivered to the automated cell. The cell is required to be capable of concurrently processing four individual shipments, each containing multiple cargoes.Achieving the target sampling precision for all export cargo sizes has culminated in the planning, design and construction of the largest automated iron ore port laboratory in the world. The CLB sampling and analysis cell was commissioned in Q3 and Q4 2013. This paper discusses the sampling philosophy and commissioning outcomes.CITATION:Brunning, R, Andringa-Bate, C, Graham, M and Westergren, S, 2014. Cape Lambert Port B shiploading, sampling and analysis, in Proceedings Sampling 2014 , pp 79–90 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 29, 2014

By J E. Powalisz

Obtaining a representative sample of bulk materials in process industries is critical to providing valid data for process control and custody transfer (valuation). The accuracy of a sample, that is to say how well the sample represents the material in the process, is directly related to how well the process can be controlled and to how much a product may be worth. Sampling accuracy then is in fact related to bottom line profitability.Sampling theory offers information and equations on how to take a representative sample from a process, however, the actual practice of extracting a sample from the process can be more of an art.Key sampling variables include product specifications such as bulk density, particle distribution, material conveyance, practical sampler location, sampler type and sample frequency and volume/mass.This paper discusses basic sampling theory, sampling methods, types of samplers, how to develop a ‘real world’ sampling system and current manual and automated samplers.Key points include ‘practical precision and variance’, material properties, flow properties, direct sampling versus indirect sampling, sampler types and applications.CITATION:Powalisz, J E, 2014. Bulk solids sampling, in Proceedings Sampling 2014 , pp 179–188 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 29, 2014

By W P. Breytenbach

"The phased application of statistical process control (SPC) infers that processes are classifiable as pre-SPC (or phase zero), phase I and phase II processes, with different objectives being pursued in the different phases. Phase zero processes, defined as processes affected by assignable causes of variation, are characterised as follows: out-of-control with unknown autocorrelation structure having an unknown statistical distribution not performing consistently on target with minimum variability potentially incapable of conforming to the product specifications. The objectives of phase I SPC is to obtain estimates of the mean (µ), variance (s2), probability density function (f(X)) and the autocorrelation structure, as well as transforming the process into a phase II process (defined as a process conforming to specifications and performing on target with minimum variability). As in any other type of research or pragmatic improvement intervention, sampling is required is to achieve these objectives. The challenge of deciding a phase I sampling plan is multifarious since it has to cope with the aforementioned effects of assignable causes and provide for rigour estimates of the unknown parameters. Furthermore, deciding sampling plans in phase I invariably presents a classical catch-22 situation: appropriate sampling is required to determine the autocorrelation structure of a variable, but knowledge of the autocorrelation structure is required to formulate an appropriate sampling plan. This paper suggests, for random variables, guidelines to formulate sampling plans, and presents a methodology to estimate the inherent process parameters of process variables and an approach to identify assignable causes in phase I. CITATION:Breytenbach, W P, 2014. Rational sampling and standard deviations for random variables in phase i application of statistical process control, in Proceedings Sampling 2014, pp 141–154 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Jul 29, 2014

By G M. Jeffress, D C. Arne

There are two discrete aspects to collecting portable X-ray fluorescence (pXRF) data to obtain reliable results and to comply with reporting requirements under the 2012 edition of the JORC Code (JORC, 2012) – sampling method and quality assurance. A robust sampling program is required to obtain representative material for analysis. The appropriate method should be established for a particular project using a small orientation study. A variety of approaches to sampling soils, drill cuttings, rock faces and diamond drill core are discussed. These include using multiple spot analyses, scanning using a single operating beam and the collection of cutting saw pulps. The operator must also be aware of the attenuating effects of analysing through sample bags of different material types for various elements as well as the limitations of current pXRF instruments in reporting reliable data for certain elements. The analysis of the samples must also be accompanied by a quality assurance program in order to generate data that are ‘fit for purpose’. This includes recording X-ray tube resolution on a routine basis, using matrix-appropriate standards for which total elemental concentrations are available, using silica blanks, capturing sufficient duplicate analyses to constrain data precision and obtaining check assays to calibrate pXRF data against laboratory data. Examples of quality control approaches and data are presented. Table 1 of the 2012 JORC Code also recommends that information on instrument make and model, reading times and calibration be provided in any reporting of pXRF data. To this should be added information on operating mode and temperature, software version and moisture content of the samples. A robust sampling methodology with a suitable quality assurance/quality control program should produce pXRF data of sufficient quality for public reporting purposes, provided that the data are presented using appropriate cautionary language and adequate supporting information.CITATION:Arne, D C and Jeffress, G M, 2014. Sampling and analysis for public reporting of portable x-ray fluorescence data under the 2012 edition of the JORC Code, in Proceedings Sampling 2014 , pp 57–64 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 29, 2014

By G J. Lyman

The theory of sampling has progressed slowly over the last century. The concepts of correct sampling were known to mining engineers such as D W Brunton in the 19th century. It was recognised early in the 20th century that the Poisson distribution of particles carrying the target analyte was the key to the uncertainties between samples of a given mass. However, no efforts were made until the 1980s to determine the entire sampling distribution as a function of sample mass. The variance of sampling was made clear by Gy and others, but none attempted to determine the entire sample assay density function until J H Venter in South Africa applied the method of characteristic functions to the problem.Venter applied the method to a specific case of very low analyte concentrations in samples of very fine particle size where his assumption of constant sample mass was justified. When his general methodology is applied to the more general problem, it can be shown that the assumption of constant sample mass is no longer justified and leads to some anomalous results for sampling variance. Venter’s method can be expanded to allow for a statistically variable sample mass and to take into account the size distribution of particles that carry no analyte.This paper presents the basis of Venter’s work and shows how the approach can be easily modified to yield results for the variance that are consistent, for example, with the work of Gy. It is also demonstrated that the entire sampling distribution can be determined without undue computational burden when sufficient information on the material heterogeneity has been collected.CITATION:Lyman, G J, 2014. Determination of the complete sampling distribution for a particulate material, in Proceedings Sampling 2014 , pp 17–24 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 29, 2014

By J T. Carswell, K Sutton

Quality control (QC) of reverse circulation drill sampling commonly involves regular duplicate subsampling to monitor the extent of imprecision or random error in the subsampling system. In gold mining in Australia, the subsampling tool is usually a cone splitter.Other sources of error, including delimitation error and bias (systematic error) in the subsampler are often assumed to be negligible or ignored for being too difficult to QC.This paper describes actual sampling errors and error mitigation based on quality assurance and quality control (QA/QC) of surface and underground reverse circulation drill sampling at a high-nugget gold mine at Sunrise Dam, Western Australia.CITATION:Carswell, J T and Sutton, K, 2014. Sources of sampling error and implications for quality assurance and quality control in surface and underground reverse circulation drilling, in Proceedings Sampling 2014 , pp 65–70 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 29, 2014

By R Shaw, T T. Neidel, C Adams

Sampling equipment manufacturers endeavour to ensure that the mechanical equipment installed in sampling systems are designed and manufactured to adhere to the requirements of the International Organization for Standardization (ISO) or other standards relevant to the application. The mechanical parameters necessary to achieve these standards can be well controlled and requirements such as constant speed can be met through good mechanical design and engineering. However, when the equipment is installed and put into operation, the ISO compliance of the system is out of the hands of the equipment manufacturer and handed to the operator, who may be a generalist control system integrator.Making a sampling system perform according to standards, and being able to monitor and report on compliance require that the sampling system knowledge and the control integration is combined and applied in an integrated approach. Using a dedicated library of control routines developed for sampling can change a system from having compliant equipment to being a compliant system. The library has to include monitoring of relevant parameters and control and reporting on sampling parameters.CITATION:Neidel, T T, Adams, C and Shaw, R, 2014. Control and monitoring of International Organization for Standardization compliance for industrial sampling systems, in Proceedings Sampling 2014 , pp 121–126 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 29, 2014

By K M. P Singh, K M. K Sinha, G S. Jha, T Gouricharan, K K. Sharma

The majority of the powerplants in India are fed with non-coking coals, with the coals dispatched from their respective mines via wagons loaded by a rapid loading system or pay loader. The usual top size of coal is 200 mm and sampling is carried out manually from wagons. The manual method of wagon top sampling of large size raw coal is not only difficult, but also violates some of the fundamental principles of sampling. As per sampling requirements, samples are to be drawn from the full depth of the wagons, which is impossible to be done manually. Since the ash distribution in the different size fractions is not homogeneous, the results from the samples, which do not reflect the true size distribution of the lot, are likely to be biased. More importantly, sample collection by shovel from the top is a function of human discretion and not governed by the equiprobability rule. In another case, the sample has been collected by a cross belt-type auto mechanical sampler for 200 mm size coal at one of the super thermal power stations. Test analysis shows that coal collected using a cross belt-type mechanical sampler also has significant deviations from stopped belt coal samples of 200 mm size due to size segregation. Studies carried out on wagon top sampling through specially designed experiments reveal some interesting features. The presence of bias in wagon top sampling is apparent when compared with the ‘stopped belt’ sample. In this paper, a comparative study between manual wagon top sampling/auto mechanical sampling with respect to stopped belt sampling has been conducted for coal of size 200 mm. In addition, precision studies have been performed for wagon top sampling/mechanical sampling for Indian thermal powerplants.CITATION:Sinha, K M K, Sharma, K K, Jha, G S, Singh, K M P and Gouricharan, T, 2014. A comparative study of manual wagon top sampling and auto mechanical sampling of 200 mm size coal with respect to stopped belt sampling of thermal coal at Indian super thermal powerplants, in Proceedings Sampling 2014 , pp 43–48 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 29, 2014

By J Chan, T Baumgartl, A Plante, C Peltre, P D. Erskine

Mine soils are often organic matter and carbon poor due to disturbance, topsoil stockpiling and mixing with spoil. They present an opportunity to observe the accumulation of soil carbon arising from rehabilitation or ‘green carbon’ through organic matter inputs and may be considered for carbon credits. Soil carbon is known to improve multiple soil characteristics and is viewed as an indicator of soil health.When attempting to estimate soil carbon levels in the Australian coal mining regions, both coal and black carbon (BC) need to be quantified. Coalmine soils contain coal particles that are difficult to distinguish from organic matter using standard carbon analyses. BC, a material arising from the incomplete combustion of organic matter and fossil fuels, displays some similar characteristics to coal and is ubiquitous in Australian soils due to bushfires. The presence of coal and BC complicate the determination of green carbon levels in mine soils.A method to quantify coal and BC in mine soils from the Bowen Basin using thermal analysis and multivariate curve resolution is presented. By determining BC and coal contributions it is then possible to calculate green carbon levels in rehabilitated mine soils. Calculated green carbon levels are compared with bulk density as soil quality indicators.CITATION:Chan, J, Baumgartl, T, Erskine, P D, Peltre, C and Plante, A, 2014. Distinguishing carbon fractions in coalmine rehabilitated soils, in Proceedings Life-of-Mine 2014 , pp 491–498 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 16, 2014

By I H. Wilson

After decades of turmoil, Rwanda launched its Vision 2020 in 2000 aimed at reconstructing government and addressing poverty. Implementation focused on the agricultural sector, infrastructure and education. Later economic development and poverty reduction strategies recognised that expansion of tantalum, tin and tungsten mining could potentially diversify Rwanda’s economy and improve the balance of trade. In 2005, comprehensive environmental legislation requiring environmental impact assessment for all new mines was introduced. Since 2006, government-operated mines have been privatised and illegal artisanal miners encouraged to form cooperatives. The government passed a new Mining and Quarry Exploitation Law in 2008 and approved a new Mining Policy in 2010. This policy was influenced by the Africa Mining Vision (AMV), which the African Union approved in 2009 to ensure African countries obtain reasonable benefits from exploitation of their mineral wealth. The Rwandan mining industry has grown significantly over the last five years and the second economic development and poverty reduction strategy, released in September 2013 forecasts a further three-fold increase in production in the next five years. The strategy prioritises the mining industry and proposes new legislation, better information, greater transparency, encouragement to investment and use of best environmental and technical practices. It also proposes that Rwanda develop a ‘green’ mining sector featuring a ‘model’ mine that is fully funded through green investment and incorporates renewable energy systems, water treatment systems, forestry schemes and local training. Many international development partners have contributed to the current legislative framework and are monitoring environmental and social performance of mining in Rwanda. Local communities have generally benefited from mining and the new mines show improved environmental performance. The major challenges are dealing with the legacy of past haphazard mining practices and establishing linkages between the mines and local industries to create broader economic development.CITATION:Wilson, I H, 2014. Rwanda and the Africa mining vision, in Proceedings Life-of-Mine 2014 , pp 427–438 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 16, 2014