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By Tom C. Palangio

The mining industry has come to rely on imaging devices to monitor and record all kinds of activities. We use them to measure, improve the process, ensure security, increase production and automate machinery. Cameras can be installed in locations most people wouldn?t want to live and be our ?eyes? to record the events at that location. This paper discusses digital imaging, the equipment, methods, techniques and information that can be learned from digital devices used in mining operations. The guy who said ?a picture is worth a thousand words? probably understated it. Images are the perfect way to document conditions before, after and even during mining activities. Today?s wide choice of cameras, camcorders and other digital devices allow us to record most activities and events. The technology is advancing at a high rate. Digital cameras and camcorders are marvels of technology with high resolution optics, compact size, automatic time and date stamp, great battery life, shutters to freeze motion and computers to auto focus and eliminate jitter (image stabilization). Add to this the economical price and ease of use and everyone in this industry should be aware of the possibilities.

Oct 1, 2010

By M. Olper

The zinc recycling is assuming more and more importance. A lot of zinc secondaries and wastes are ready to be collected and converted to zinc metal. The impurities present in these materials and the technical and economical problems that some traditional ways of treating these materials are facing, call for new technologies. The EZINEX Process, a fully hydrometallurgical process industrially proven to convert oxidised zinc bearing materials directly to zinc cathodes, is an innovative process that can deal with any kind of zinc waste/secondaries in a very powerful way. In this paper the highlights of this process will be shown and discussed to demonstrate the robustness of the process and its economic viability.

Jan 1, 2008

By E. J. Y., G. J. McLachlan, E. Amini, Koha

Inferring individual component flotation rates from recovery-time data of ore complexes is important in optimising and designing minerals processing plants. However, existing two-component first-order flotation models are computationally intensive to solve using conventional non-linear least squares regression. In this paper, a Convolutional Neural Network (CNN) is used to approximate the inverse function which predicts the two-component first-order model parameters using recovery-time data. This paper compares the accuracy and computation speed of the proposed CNN against traditional non-linear least squares regression on 1 million generated data based on a known two-component firstorder kinetic model. It is shown that using a CNN is possible to approximate the inverse function for the non-invertible two-component model with recovery-time as an input matrix for convolution operations. The CNN method is 450 times faster to compute than the Trust-Region (TR) non-linear least squares regression algorithm. However, the CNN model is less accurate compared to the TR algorithm overall. Keywords: Flotation kinetics, convolutional neural network, inverse problem, modelling and simulations

Jan 1, 2020

By Choulaj D

Mining and petroleum continue to be the two most vita components of our nation's development, says Sir Julius Char in the Australian Journal of Mining (Nov-Dec 1996). In fact the} account for 71.6% of export earnings, and 21.2% of Gros; Domestic Product in Papua New Guinea. The Misima and Porger, Mines, along with the Kutubu oil project, have essentially rescues PNG's economic viability since the loss of income from Panguna Though the industry cannot create employment in the manner o. intensive industries such as agriculture, it does provid( opportunities to local communities in terms of jobs, financia compensation, and infrastructure. Many ventures take many years and enormous capital investment to set up. There are three phases: exploration, feasibility, an( operations. The operations phase sees a massive development o infrastructure, or the actual `building' of the mine. For mining companies to commit to such lengthy time frames and high level: of investment, without returns, requires confidence and a degree of commitment from government on a number of levels - legislative stability, monitoring social development, and the provision o social infrastructure.

Jan 1, 1997

By Horowitz F. G, Lamb P

As mining operations become larger and more complex, larger volumes of geological, geophysical, and production data must be manipulated and interpreted on a day-to-day basis. Because of the large volume of data, more sophisticated data storage strategies, interpretation tools, and visualisation tools must be used by exploration and mining operations in order to remain competitive. Critical to better practice is an understanding of the basic syntax and character of geological and mining data, expressed in the form of a data model. In the short-term, a data model can be used to implement or evaluate schemes for data storage or exchange. It can be used to evaluate software. In the longer term, a data model is essential to the design of the next generation of software for 3D geological modelling. In this and the related paper (Lin et al, 1995) the data model in use by CSIRO is presented, together with: 1. examples of the use of data transfer in the linkage of existing mining software; and 2. examples of the use of the data model in software design.

Jan 1, 1995

By Côme Carbonneau

The ten -year case history of La Société Québécoise d'Exploration Minière (SOQUEM) is reviewed under the headings of the company's raison d'être, objectives, policies, structure and final goal. A brief analysis of its past operations is presented: aborted efforts, findings and developments. A projection is made of the future earnings, in terms of estimated present value, of its three main discoveries - Louvem , Niobec and Selim.

Jan 1, 1978

By Norman O. Lotter, Virginia Lawson, Mika Muinonen, Dominic Fragomeni, Jorge F. Oliveira

Development of metallurgical flowsheets from drill core to production of finished metal involves two main domains: process mineralogy to sample the ore and develop a successful flowsheet producing saleable concentrate; and extractive metallurgy to convert that saleable concentrate to finished metal. Effective flowsheet development for a new or brown-field concentrator and processing facility is essential to project schedule and return on investment. Over the last decade in particular, the integration of geology, mineralogy, sampling, and mineral processing have empowered the development of an improved practice in flowsheet development. The use of geometallurgical units as a basis for best-practice sampling delivers representative study material to the laboratory. Modern automated mineralogy characterises the whole rock assembly and paymetal mineral hosting, to address downstream process implications. Batch scale flotation testing then uses this information to design and perform well-focused strategies. At early stages of the project, the Extractive Metallurgy team is engaged to determine processing constraints or other economic drivers in conversion of the saleable concentrate into finished metal. The team evaluates available technologies, assesses the impurities and key properties of the given concentrate. In many cases, changes to mineral processing flowsheet are required to maximize value per tonne of concentrate. Site conditions, availability of infrastructure, energy and power all play an important role. Modern metallurgical simulation tools such as FactSage™ and METSIM™ can quickly define the appropriate test program for each process. Bench scale testing and appropriate scale of piloting can confirm simulations and provide design criteria. A case study demonstrates how effective use of these tools and a multidisciplinary team can accelerate flowsheet development, while reducing costs and ensuring that the designed processing facilities maximize project value with minimum risk.

Jan 1, 2014

By F F. Pitard

Observation from attending many conferences around the world reveals that many people are constantly shifting from the word ‘error’ to the word ‘uncertainty’ without a clear vision of what the subtle difference is. The same applies to a large swath of the scientific literature, in which these two concepts are unfortunately often used synonymously, which is a scientific flaw of the first order. In practice, as demonstrated in the Theory of Sampling (TOS), there are both sampling errors and sampling uncertainties. Sampling errors can easily be preventively minimised, while sampling uncertainty for a preselected sampling protocol is inevitable. P M Gy’s work, supported by G Matheron’s critical reviews, demonstrates beyond any possible doubt that sampling uncertainty cannot be achieved if sampling errors are not preventively minimised by applying very stringent rules that are too often ignored by sampling practitioners. A clarification, in line with the TOS, is presented to mitigate the confusion created by circles favouring the use of uncertainty versus the use of the word error. Both words are very different in nature and scope and they are both necessary in their due place and time.CITATION:Pitard, F F, 2017. From errors to uncertainty – a clarification for proper use by the Theory of Sampling, in Proceedings Eighth World Conference on Sampling and Blending , pp 355–358 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

One Data Set for the Lifecycle Complete data captured at the logging stage benefits the entire life cycle Exploration Technical Studies Production and Milling Visible Light Spectrum Hyperspectral, put simply, is the interaction of light with matter based on the nature and chemical structure of the specimen. SpectrumWe develop the library to match the liberation size fraction Analyses every 1mm2 1 meter of NQ core = 35 000 analysis data Crystal size detection : 0.1 micron

May 1, 2013

By A Gustafson, D Johansson, K Jonsson

The underground mining process, from in situ characteristics of the unmined rock mass to the final mill product with fully liberated minerals, consists of a chain of unit operations. Some of them influence fragmentation while fragmentation impacts others. From a production point of view, fragmentation is a key parameter for the proper functioning of many unit operations and affects total production; it influences the ability to load, haul and crush the rock later in the process. Fragmentation varies because of rock mass strength, the presence of joints, the chosen explosive, specific charge (kg/m3) and quality of drill holes. The efficiency and result of unit operations such as drilling, blasting, loading and crushing depend on the rock properties, which vary throughout a mine. Generally speaking, operations are not well adapted to the actual rock properties, leading to a non-optimised flow in production. This paper presents the initial part of a project that will build knowledge on the impact of fragmentation on each step of the production chain in an underground mine. It identifies the key parameters of fragmentation, which influence the overall energy consumption and productivity in a mining operation through interviews, mine visits and a literature review. In the subsequent stages of the project, a number of field tests in the case study mine will address important segments of the production process where fragmentation is a major obstacle to improvements. Optimising the entire process, rather than isolated unit operations, will lead to increased productivity, decreased amount of interruptions and lower energy consumption.CITATION:Gustafson, A, Jonsson, K, Johansson, D and Schunnesson, H, 2016. From face to surface – a fragmentation study, in Proceedings Seventh International Conference and Exhibition on Mass Mining (MassMin 2016), pp 555–562 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2016

By M. O. Holowaty, C. M. Squarcy

Bituminous coal furnaces give way to coke, and by 1880, the American iron and steel industry was growing at a tremendous rate. In the twentieth century, the number of operating blast furnaces was cut in half while pig iron capacity more than doubled. Bituminous coal furnaces Attempts to relieve the shortage of charcoal by the use of bituminous coal in American furnaces began quite early. Use of bituminous coal in the blast furnace was restricted to areas lacking anthracite deposits but rich in deposits of bituminous coaL The largest concentration of bituminous coal furnaces was in Western Pennsylvania, West Virginia and Western New York state. Other areas included Kentucky and southern portions of the states of Indiana and Illinois. The first trials were made in Pennsylvania around 1780. Gradually several more furnaces began charging coal with their burdens as a partial substitution for charcoal. These so called cheaters were numerous, especially in Western Pennsylvania, which around 1810 began to expand its ironmaking capacity to catch up with the Lehigh Valley and the Schuylkill According to the existing references, complete bituminous coal burdens began to be used in this area only around 1840 when the ironmakers hastily adopted the hot blast apparatus introduced from England. The majority of these furnaces were built, however, after 1844 when an extensive experimental program, carried out at the Shenango Steam and Water Hot Blast Charcoal, Coke and Raw-Coal Furnace produced favorable results, with coal containing up to 35 pct volatile matter. The furnace was located near the Shenango River on the Lackawannok Run, five miles northwest of Mercer, Pennsylvania. It was built, owned, and operated by David Hogeland of Mercer. The first furnace was built in 1836 or 1837 as a charcoal furnace. Soon, however,

Jan 1, 1961

By J. Cilliers, R. Seitz, D. Drinkwater

Mineral processing produces a saleable product from high-cost ore. The Mineral Processing Engineer must constantly improve the separation to create the most value from the ore and reduce the associated costs. Graduate Metallurgists (or Chemical Engineers) have had the university theoretical training required, but often are illprepared to perform their plant roles. The in-work Metcelerate programme was designed specifically to develop the practical skills that Mineral Process Engineers require, by combining three key elements: 1. Knowledge transfer through self-paced, on-line training; 2. Practical skills acquisition through assessed, practical plant exercises; 3. Skills development through online tutorials. A final process improvement project adds significant value to the operation that far exceeds the training cost. Experience from Metcelerate clients has indicated improved staff retention, greater confidence, and pro-active process intervention.

Jan 1, 2023

The green field site is one that has no history of mining and as such it presents the designer with a blank canvas, on which any number of drill and blasting options can be drafted. No matter which mining method is selected all technology and experience will need to be acquired from outside sources, applied quickly and produce the right results. North Limited, in joint venture with the Sumitomo Group (20 per cent), have developed AustraliaÆs first block cave mine at Northparkes in New South Wales, Australia. This innovative and large scale mining project was taken from a green field site in November 1993 to a fully commissioned block cave mine in December 1997. NorthparkesÆ Endeavour 26 mine is now producing at a rate of 3.9Mt per annum from a 27Mt ore block. Prior to 1993 no history of mining activity existed at Northparkes. The rock had never been blasted and many questions were raised about how it would behave. While it was clear that the Endeavour 26 (E26) orebody contained geotechnical properties appropriate to block caving, the blasting methods required to establish a block cave mine had never been used in Australia and no designs were available. The joint venture partners had no prior experience with block caving and it was immediately apparent that this blasting technology would have to be acquired from foreign sources and applied to the unique local conditions. Conceptual designs for the block cave drill and blast were first drafted in 1994, first issued for drilling in December 1995 and first blasted in January 1996. 470 km of drill metres and four million tonnes of rock were blasted to complete the undercutting project by September 1997. This paper will provide those developing new drill and blast projects with an inside view of how one small technical team solved many design challenges to successfully construct AustraliaÆs first block cave mine under strict quality controls and ambitious time constraints.

Jan 1, 1998

By J Zeni

In 2001, the Springvale Coal Mine determined a requirement for a 360 m deep vertical airway to ventilate ongoing underground longwall mining operations. The project, referred to as the Springvale No 3 Ventilation Shaft, was completed in 2003 - 2004 by blind boring a 4.25 m diameter hole from the surface to the coal seam at 360 m depth. The shaft was completed with a 3.5 m inside diameter lining made from a continuous watertight steel and concrete composite pipe. Presented from both the clientÆs and the contractorÆs perspective, the following paper describes the process of constructing a blind bored ventilation shaft for this application. Beginning with the clientÆs definition of the requirements and specifications for the shaft, the paper describes the steps followed by Springvale Coal prior to awarding the contract. Springv aleÆs process involved a thorough site investigation including procurement of geotechnical data for prospective contractors and the management of issues relating to safety, quality and the environment. The clientÆs view is followed by a description of the planning and works performed by their nominated shaft drilling contractor, Ardent Underground Pty Ltd.The contractorÆs description of the works includes the details of the blind shaft drilling and lining construction process used for this application. The design of the shaft lining installed at Springvale Coal was the first of its type to be used in Australia. The successful installation of a watertight composite shaft lining and the safety of the method, which did not require any personnel to enter the shaft or the mine, were among the positive outcomes from the project.

Jan 1, 2005

By W. A. Gibson, A. D. Trujillo

The Santa Rita copper deposit first served as a source of native copper for Indian implements and weapons. In 1801 Santa Rita copper, trans- ported by mule train to Chihuahua, began to be used commercially for making coins. It was a small- scale operation made difficult by Indian raids, transportation costs and depletion of the higher grade ores. There was, moreover, no known method for economically extracting copper from the under- lying low grade sulfide ores. Originally, copper and copper oxide ores were mined by underground methods, but there was virtually no development of the deposit-which became United States territory after the Treaty of Guadalupe Hidalgo in 1848-for almost a hundred years. A number of small underground mines had been started during that time, but it was not until 1873 that all the principal claims were consolidated under one control.

Jan 1, 1966

By Uwe König

Traditionally quality control of iron ore sinter, its raw materials and raw mixtures has relied on time-consuming wet chemistry. The mineralogical composition that defines the properties is often not monitored. XRD analysis in combination with Rietveld quantification and statistical data evaluation using Partial Least-Square Regression (PLSR) has been successfully established to determine the mineralogical composition and process parameters such as the FeO (Fe2+) content and basicity of iron sinter within an analysis time of less than 8 minutes per sample. Both methods take the full XRD pattern into account and can be simultaneously applied on the same measurement. In addition the Rietveld method was used to quantify the phase content of raw mixtures to monitor the composition of blending beds.

Aug 16, 2017

Trans. No. Place Date Vol. Page 1. Wilkes-Barre Pa......May.71 1 3 2. Bethlehem, Pa.........Aug., '71 I 10 3. Troy, N.Y............Nov..: 71 1 13 4. Philadelphia, Pa........Feb., 72.. 1 17 5. New York. N. Y.*......May, '72.. 1 20 6. Pittsburgh. Pa.........Oct..' 72.. 1 25 7. Boston, Mesa..........Feb., '73.. 1 28 8. Philadelhia, Pa.*......May, '73.. 2 3 9. Easton, is............Oct..' 73.. 2 7 10. New York, N. Y.......Feb..' 74 . . 2 11 11. St. Louie. Mo.*........May,' 74 . . 3 3 12. Hazleton, Pa...........Oct., '74.. 3 8 13. New Haven Conn......Feb., '75.. 3 15 14. Dover,N.J.*..........May, 75.. 4 3 15. Cleveland,O...........Oct. 75. 4 0 16. Washington D. C......Feb.'' 76.: 4 18 17. philadelphia, Pa.......June,: 76.. 5 3 18. Philadelphia Pa........Oct 76.. 5 19 19. New York, N. Y.......Feb: '77.. 5 27 20. Wilkes-Barre. Pa.*.....Mag :77.. 0 3 21. Amenia N. Y..........Oct. 77.. 6 10 22. Philadeiphia Pa........Feb.;' 78. . 6 18 23. Chattanooga' Term.*. . . May' 78. . 7 3 24. Lake George: N. Y....: Oct. 78. . 7 103 25. Baltimore, Md.*.......Feb:' 79.. 7 217 26. ~ittsburgi, Pa.........~a;' 79.. 6 3 27. Montreal, Canada......~ept.;' 79.. 8 121 28. New York, N. Y.*......Feb., '80.. 8 275 29. Lake Superior, Mich....Aug., '80. . 9 1 30. Philadelphia, Pa.*......Feb., '81. 9 275 31. Staunton, Va..........May, '81. .10 1 32. Harrisburg, Pa.........Oct.. '81..10 119 33. Washington, I). C.*....Feb., '82. . 10 226 34. Denver. Colo..........Aug.. '82. . ll 1 35. Boston. Mass.*.........Feb.. '83..11 217 38. Roanoke, Va...........June, '83.. 12 3 37. Troy, N. Y...........Oct.. '83. .12 175 38. Cincinnati. O.*.... Feb, '84..12 4 39. Chicago Ill............May, '84. . 13 1 40. philadelphia, Pa........Sept., '84. . 13 285 41. New York, N. Y.*......Feb., '85. . 13 586 42. Chattanooga, Tenn.....May, '85..14 1 43. Halifax, N. S..........Sept., '85.. 14 307 44. Pittsburgh, Pa.*........Feb.. '86 . . 14 587 45. Bethlehem. Pa.........May, '86 .. 15 Ixiii. 46. St. Louis, Mo..........Oct, 86.. 15 Ixx. 47. Scranton, Pa.*.........Feb.', '87. . 15 lxxvi 48. Utah and Montana.....July '87.. 16 xvii. 49. Duluth, Minn...........July' '87. -16 xxiv. 50. Boston. Mass.*.........Feb.: '88. . 16 xxviii. 51. Birmingham, Ala.......May, '88. .17 xix. 52. Buffalo N. Y..........Oct. '88.. 17 xxiv. 53. New york, N.Y........feb.' ;89 . . 17 xxxi. 54. Colorado..............June' 89 . . 18 xvii. 55. Ottawa, Canada........Oct., 89. . 18 xxiv. 56. Washington, I). C.*. . . Feb. 90. .18 xxx. 57. New York, N. Y........sept:, '93. .19 vii. 58. New York, N. Y.*......Feb. '91. . 19 xxv. 59. Cleveland, O.......... June, :91 . .a xvi. 60. Glen Summit, Pa.......Oct, 111. . 20 lxi. 61. Baltimore, Md.*.......Feb.: '92. .21 xix. 62. Plattsburg, N. Y.......June, '02. . 21 xxxiii. 03. Reading. Pa...........Oct., :92. .21 xliv. 64. Montreal. Canada*.....Feb. 93..21 65. Chicago, Ill............aug.: '93..22 XI. 66. Virginia Reach. Va.*. ...Feb., '04. .24 xwl. 67. Bridgeport Coon.......Oct., '94..24 xxx?. 08. Florida ...'............Mar., '95. .25 xix 69 Atlanta. Ga......... Oct 95. 25 xi 70. Pittsburgh. Pa.*........Feb., '96.. 26 xvii. Trans. No. Place Date Vol. Page 71. Colorado..............Sept. '96. .26 xxix. 72. Chicago, Ill............Feb.. "97.. 27 xvii. 73. Lake Superior..........July '97. .27 xxx. 74. Atlantic City, N. J.*. ..Feb.: :98. .28 xvii. 75. Buffalo N. Y..........Oct. 08. .28 XXXVI. 76. New York,N. Y.*......Feb.: '99. .29 xvii. 77. California.............Sept., ;99. . 29 xlix. 78. Washington, D. C.*.....Feb., 00. .30 xix. 79. Canada...............Aug.. '00. .30 xlv. 80. Richmond. Va.*........Feb.. '01. . 31 xix. 81. Mexico................Nov..'01.. 32 cxviii. 82. Philadelphia Pa. $......May, :02. . 33 xxxv. 83. New Haven, Conn......Oct.. 02.. 33 xlvii. 84. Albany N. Y.*........Feb '03. .34 xxiii. 85. New York, N. Y........Oct.': '03. .34 xi. 86. Atlantic City. N. J.*....Feb., '04. .35 XXIII. 87. Lake Superior..........Sept.'04. .35 dii. 88. Washington. D. C......May' '05. .36 xlii. 89. British Columbia.......July, '05. . 36 90. Bethlehem. Pa.........Feb. '06. .37 xli. 91. London, England.......July: '06. .37 xlviii. 92. New York, N. Y........April, '07. . 38 lii. 93. Toronto, Canada.......July. '07. .38 lix. 94. New York, N. Y........Feb., :08. .39 xli. 95. Chattanooga, Tenn.....Oct.. 08..39 xlviii. 06. New Haven Conn......Feb. '09. .40 xli. 97. Spokane,Wash.........Sept:.'09. .40 xlviii. 98. Pittsburgh, Pa.........Mar.,'10. .41 xxxviii. 99. Canal Zone............Nov.,'10. .41 dv. 100. Wilkes-Barre, Pa.......June. ' 11. . 42 xxxiv. 101. San Francisco, Cal......Oct.. '11..42 xliv. 102. New York. N. Y.*......Feb 12. .43 Ixxvii. 103. Cleveland, Ohio........0ct;' '12.. 44 wii. 104. New York, N. Y.*......Feb.. '13. .45 xv. 105. Butte, Mont...........Aug., '13. .46 ?' 108. New York, N. Y........Oct., 13..47 vii 107. New York. N. Y.*......Feb.. '14..48 xv. 108 Salt Lake City. Otah Aug. :l. 49 5: 109. Pittsburgh, Pa.........Oct., 14. .50 vii. 110. New York, N. Y.*......Feb., '15. .51 xvi. 111. San Francisco, Cal......Sept., '15. .52 xii. 112. New York,N. Y.*......Feb., '16..54 xvi. 1 Arizona...............Sept.,'l6. .55 VII 114. New York, N. Y.*......Feb., '17: .56 vii. 115. St. Louis, Mo..........Oct.. '17..57 vii. 116. NewYork,N. Y.*......Feb.. '18..59 xvii 117. Colorado.....:........Sept.,'18. .60 vii. 118. Milwaukee, Wis........Oct.. '18. .60 x:!: 119. NewYork,N. Y.*......Feb., '19..61 xi. 120. Chicago...............Sept.,' l9.. 62 VII. 121. New York, N. T.*......Feb., '20..63 ix. 122. Lake Superior District. .Aug., '20.. 66 ix. 123. New York, N. Y.*......Feb., '21. .66 xiii. 124. Wilkes-Barre, Pa...... Sept.. : 21.. 66 xix. 125. New York, N. Y.*......Feb., 22. .67 ix. 126. San Francisco. Cal......Sept.,:22. .68 xxix. 127. New York, N.Y.*.....Feb., 23. .69 xxix 128. Canada...............Aug., '23. .69 xxxv! 128.129. New York, N. Y.*......Feb., ;24. .70 xxxi. 130. Birmingham, Ala.......Oct. 24. .71 xxxiii 131. NewYork.N Y.*......Feb.: '25.. 71 xxxviii. 132. 131.Salt Lake City, Utah .. .Sept., '25. .73 xvi 133. New York, N. Y.*......Feb., '26. .73 xxii. 134. Pittsburgh Pa ....... Oct.. '26. .74 xv. 135. New York,' N. Y.*............Feb., '27. .75 x::: 135.136. NewYork,N. Y.*......Feb., '28..76 104 137 NewYork.N. Y.*......Feb., '29. .291 116 138.San FranciscoYork,N.Y. Cal..........Oct. 29..308 406 139. New York, N. Y.*...Feb., '30..30H 1321

Jan 1, 1930

By Mark Van Kleek

CONTENTS ?The Falcon C ?Data from a Production Trial ?Methodology ?Scale-up ?Test Results ?Circuit Configurations ?Conclusion

Jan 1, 2006

By Cesar H. Oboni

The development of specific risk-based methodologies to guide and prioritize clearing decisions in countries contaminated by Unexploded Ordnance and Land Mines (generically defined as Explosive Remnants of War: ERW) either at country-wide scale or road-network-scale was used in an unexpected way when a large mining company asked Riskope (www.riskope.com) to develop a multi-modal transportation network risk assessment plan, which included ocean going vessels, ports, wharves, railroad, trucking and other modes of transportation. What had been a first-in-the-world attempt in the humanitarian landmine clearing industry turned out to be extremely useful and equally innovative in the extractive corporate world. Instead of a country contaminated by man-made hazards such as Explosive Remnants of War (ERW) we were now dealing with a very complex multi-modal transportation system afflicted by all sorts of potential hazards from quakes to climate change, sabotage to labour strikes, and vulnerable infrastructures to potential energy crises. Interestingly enough the method developed to deal with ERW risk based clearing was also capable of solving the transportation risk evaluation problem, after necessary customization. This paper shows how ?out of the box? thinking and industry cross dissemination can bring significant evolution and a competitive edge even to unexpected areas of the world-wide mining industry. The results of the logistic system risk assessment shed new light on the management of the system, changed the way insurance coverage was purchased, and led to significant potential cost reductions in a period of global recession.

May 1, 2010

By A. Makinen

There is an increasing trend between increasing consumption of water and the production of metal concentrate. Along with this trend we see that ore grades are declining and getting more complex to concentrate around the world. This will not only complicate processing but also generate new requirements for the management of water and waste streams on mining sites. The mining industry is a large user of water and its water consumption is growing rapidly. The processing of lower quality ores requires greater volumes of water. For these reasons, the need for efficient use of water and water management at mine sites is becoming more urgent. The perception of water in the mining industry has changed from thinking about it only in terms of "water treatment" to actually seeing it as a valuable resource, a resource that needs proper management. This resource management needs to involve various aspects, such as optimizing water usage of the entire mine or concentrator site, minimizing fresh water usage, minimizing or eliminating waste generated in water treatment and implementing practices for water reuse and water recycling. This paper presents trends and new developments relating to waste and water management in the mining industry.

Jan 1, 2015