ii rudarsko geološki forum, prijedor 2016
TRANSCRIPT
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100 ""
Marcela Gotelip Barbosa, G. Siboni, F.Guimaraes Vasconcelos, Armando Correa de
Araujo,Tim Sylow, Marie-Jeanne Venturini
OVERVIEW OF ARCELORMITTAL MINING OPERATIONS AND RESEARCH &
DEVELOPMENT FUNCTION .......................................................................................................... 1
Marcela Gotelip Barbosa, G. Siboni, F.Guimaraes Vasconcelos, Armando Correa de
Araujo,Tim Sylow, Marie-Jeanne Venturini
ARCELORMITTAL-A
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OVERVIEW OF ARCELORMITTAL MINING OPERATIONS AND
RESEARCH & DEVELOPMENT FUNCTION
Marcela Gotelip Barbosa1, G. Siboni1, F. Guimares Vasconcelos1, Armando Corra de
Araujo1, Tim Sylow1, Marie-Jeanne Venturini2
1ArcelorMittal Maizires Mining and Mineral Processing Research Center2ArcelorMittal Maizires Process Research Center
1. INTRODUCTION
ArcelorMittal is among the largest worlds producers of iron ore. With a geographically-
diversified portfolio of iron ore and coal assets, it is strategically positioned to serve the
network of steel plants, as well as supply the external global market. While ArcelorMittal
steel operations are important customers, the supply to the external market is constantly
increasing. ArcelorMittal Mining has a global portfolio of 14 operating units with mines in
operation and development. In 2015, the mines and strategic contracts produced 73.7 million
tons of iron ore and met 62% of the companys iron ore requirements. The company also
produced 6.29 million tons of coking coal and PCI (Pulverized Coal injection), meeting 15%
of the companys PCI and coal requirements.
Innovative thinking is encouraged across ArcelorMittal thanks to the influence of
ArcelorMittals research and development team. Research and development (R&D) helps thecompany to realize its ambitions in technological innovation, to support its sustainability
goals as well as ensuring future growth. With 1,300 full-time researchers in 11 research
centers across the globe, ArcelorMittal R&D is highly business oriented, ensuring a shorter
time to market and improved competitiveness in a variety of sectors, including Mining.
1. IRON ORE MINES
ArcelorMittal Mining currently has iron ore mining activities in Brazil, Bosnia, Canada,
Kazakhstan, Liberia, Mexico, Ukraine and the United States. Iron ore products include
concentrate for sintering and pelletising, pellets, and direct shipped fines and lump ore.. As ofDecember 31st2015, ArcelorMittals iron ore reserves are estimated at 4.3 billion tons run of
mine. The figure 1 is presenting the worlds map including the location of each iron ore mine.
a. ArcelorMittal Mining Canada (AMMC)
Located in Mont Wright, Quebec, AMMC is the largest iron ore producer among
ArcelorMittals mines. In 2015, the company produced 25.8 million tons of sinter feed
concentrate and pellets. The deposit is a banded iron formation with hematite as the only iron-
bearing mineral. The mine is an open-pit operation producing Run of Mine ore at
approximately 30% Fe. Beneficiation of the ore is achieved through crushing, screening,
grinding, classification and gravity separation. The resultant product is a coarse concentrate
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sold as sinter feed to the market. Prior to pelletizing, the ore is further classified and
concentrated by spirals at Port-Cartier. Sinter feed concentrate accounts for approximately
60% of total sales from AMMC.
Figure 1: World map showing the location of ArcelorMittal iron ore mines
b. Baffinland Iron Mines (BIM)
BIM is a 50:50 joint venture between ArcelorMittal Mining and Nunavut Iron Ores Holdings
LP, with ArcelorMittal Mining as operator. The main asset is the Mary River mine, an opencut operation, which commenced mining in late 2014.The mine is located in the north central
plateau of Baffin Island, approximately 160 km directly south of the settlement of
Mittimatalik (Pond Inlet) in the north of Canada, in the province of Nunavut well inside the
Arctic Circle. It is a high-grade hematite body in majority, with portions of magnetite and few
goethite encompassing also iron silicates and quartz as the gangue material. The process was
design to maximize the production of Lump Ore, with a crushing system followed by
screening. Today the production is divided in 70% of lump production and 30% of fines, with
an estimated total production of 6.5 Mtpa in the final stage of the project. One particularity of
Baffinland is that due the hard weather conditions in the polar circle, shipping is restricted to
the summer season, when all the production has to be shipped inside a 70 to 80 day window.
c. Minorca and Hibbing Mines
Minorcamine produces about 2.7 million tons of fluxed iron-bearing pellets and Hibbing
Taconite Mines produces about 5.1 million tons of pellets (data from 2015). Taconite, banded
iron formation composed mainly by magnetite and quartz, is mined in both mines and
processed by crushing, grinding, classification and magnetic concentration. In Minorca,
reverse flotation of quartz is also applied to further concentrate the ore. The final concentrate
from both mines are agglomerated in the pellet plants and sold in the US market.
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d.
ArcelorMittal Mexican Mines
Pea Coloradamine is located in the north of the Sierra Madre del Sur, in the north-west part
of the state of Colima, Mexico. This operation is a 50/50 joint-venture between ArcelorMittal
and Ternium S.A. The ore is composed mainly by magnetite, approximately 89% of itscomposition, and of different phases of gangue particles, including silicates. The ore is
processed by crushing, grinding, classification and magnetic concentration to produce, in
2015, 1.7 million tons of pellet-feed as ArcelorMittal production share. All pellet feed is
transformed into pellets.
Volcan/Sonora mine is located in the north of Mexico, near the cities of Obregon and
Guaymas in the state of Sonora. The ore is composed by iron oxides, as magnetite, iron
sulfides, as pyrite and the presence of apatites, as fluorapatite and chlorapatite. Other gangue
minerals are also encountered as quartz, calcite and grunerite. The crushing facilities at the
mine include crushing, a dry cobbing magnetic separator and four tertiary crushers. The ore isthen transported by truck to the concentration plant,
which is located 120 kilometers from the
El Volcan mine facility, and it includes two ball mills on line, a magnetic separation circuit,
flotation systems (sulfide and phosphate removal), a belt conveyor filter and a disposal area
for tails.The annual production in 2015 was 1.7 million tons of concentrate and pellets.
Las Truchasmine is located near the steel plant at the port of Lzaro Crdenas, south-east of
Mexico. Las Truchas mine is an integrated iron ore operation that includes mine exploitation,
crushing, dry cobbing pre-concentrate and final concentration performed by magnetic
separation. Mineralogical analysis of the ore show a presence of some sulfides
(pyrite/pyrrhotite) and the main iron-bearing phases are magnetite and hematite with someparticipation of goethite.The annual production of Las Truchas mine in 2015 was 1.8 million
tons of iron ore concentrate, lump and fines. All the three mines in Mexico supply iron ore to
the steelmaking facility located at Lzaro Crdenas, also in Mexico.
e. ArcelorMittal Brazilian Mines
Serra Azulmine lies within the iron quadrangle, in Itatiaiauu, which is located 70 km south-
west ward from Belo Horizonte, Minas Gerais State. The current operation consists of mining
and processing the friable itabirite (Banded Iron Formation), which produce lump and sinter
feed being sold on both domestic and international markets. Serra Azul ore is mainly
hematitic and main gangue phase is quartz. The beneficiation plant consists of crushing,classification, gravity (jigging and spiral concentration) and magnetic separation (wet rare
earth drums and WHIMS). The annual production of Serra Azul mine in 2015 was 1.5 million
tons of lumps and fines.
Andradeis an open pit mine situated in the south-east of Brazil, in the state of Minas Gerais.
The deposit is located in the Iron Ore Quadrangle and it is classified by Banded Iron
Formation. Principal minerals are hematite and quartz. The processing plant is composed only
by crushing and screening, no concentration is applied since only hematite body is exploited.
The sinter-feed produced is transported by railway to Joo Monlevade steelmaking plant,
which is around 8 km away. The annual production in 2015 was 1.5 million tons of fines.
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f.
ArcelorMittal Prijedor
Buvaopen-pit mine is situated near the town of Prijedor in the north-west of Bosnia and
Herzegovina. Its a Bilbao type deposit in Upper-Carboniferous to Lower Triassic limestone.
Principal minerals are goethite and siderite. The ore contains around 43% Fe and reserves areestimated at 180 Mt of ore. The goethite ore is processed in the GMS beneficiation plant to
remove mainly silica where crushing, scrubbing, classification and magnetic separation by
WHIMS and SLon are applied. The product is filtered by vacuum and press filtration before
being transported by railway to Zenica steelmaking plant. The annual production in 2015 was
2.1 million tons of lumps and fines.
g. ArcelorMittal Ukrainian Mines
ArcelorMittal Kryvyi Rih is the largest full-cycle metallurgical enterprise of the Mining and
Metallurgical Complex of Ukraine.
The open-pit mine in Kryvyi Rih is producing magnetite iron ore concentrate with Fe ~65%
from the mined crude ore at Fe ~34%. The processing plant has a capacity of approximate 25
million tons of crude ore per year and 10.1 million tons of concentrate per year (data from
2015). The beneficiation process includes crushing, grinding, classification and a series of
magnetic separator units to upgrade the ore and ultimately achieve a high iron grade (>65%)
magnetic concentrate.
The Ukrainian Underground mine is called Artyomand it is basically a hematitic ore, with
approximately 48-52% Fe content and 14-18% SiO2 content. The process flowsheet ofArtyom mine comprises only crushing and screening facilities to produce 0.9 million tons of
lumps and sinter feed (data from 2015).
h.
ArcelorMittal Kazakhstan Mines
ORKEN is the name of ArcelorMittal Iron ore department in Kazakhstan which manages the
four local ArcelorMittal mines. The total production of the mines is transported to
ArcelorMittal Temirtau steelmaking facility, also in Kazakhstan.
The Lisakovskymine is a sediment marine iron ore with goethite free or almost free oolithes.
The annual production is around 0.9 million tons of concentrate (data from 2015). The ore is
beneficiated by jigs, classifiers, and wet high intensity magnetic separators to yield a gravity-
magnetic concentrate, upgrading the ore from 39.8%Fe to 49%Fe. The phosphorus content is
quite high, varying from 0.6% to 0.8%P.
The Atasumine is located near the town of Karajan in the Atasu district. The mine deposit is
a skarn. The ore is composed by 25% Hematite and 75% Magnetite. At the beginning of its
operation in 1954, the mine was an open pit but until the 70s it was changed to underground
mine with a production of 0.9 million tons of lumps and fines per year in 2015. The
beneficiation of the ore is done by crushing, screening and gravity separation by jigging.
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The Kentobemine is an open pit mine with a capacity of 1.5 to 2Mt/y but the production was
only 0.9 million tons in 2015. The mine deposit is a skarn with massive magnetite ore. This
iron ore contains an important amount of sulfides. The process flowsheet comprises three
stages of crushing, classification and magnetic separators is applied to remove gangue
minerals but the concentrate remains with high level of sulfur content (>2-3 %).
Atansor open-pit mine is located in north-central Kazakhstan, about 45 km north-west of
Stepnogorsk. The deposit is a moderately dipping skarn magnetite of magmatic origin, but the
ore has been oxidized and enriched due to weathering in some zones. The main result of
weathering is martite, with hematite and goethite also present. The mine produces hematitic
(martite) lump and fines and the beneficiation plant comprises crushing, screening and
magnetic separation. The annual production in 2015 was 0.4 million tons.
i. ArcelorMittal Liberia
ArcelorMittal Liberian deposits all belong to the Nimba mountain range, in the north-east of
Liberia. All ArcelorMittal Liberian deposits have approximately the same geological
configuration: first itabirites, a metamorphosed Banded Iron Formation (BIF) then a
weathering profile composed of a transitional zone and finally a laterite horizon, or canga.
Deposits are composed of three iron oxides: magnetite, hematite and goethite and quartz as
the main (and sometimes the only one) gangue mineral. ArcelorMittal actually possesses 3
concessions in the Nimba region: the Tokadeh, Gangra and Yuelliton deposits. The Tokadeh
is nowadays producing a Direct Shipping Ore (DSO) product and exploitation of the
transition zone should begin in a near future. The DSO is processed only by dry crushing andscreening to produce 4.3 million tons of sinter-feed (data from 2015).
2. Research and Development activities
a. Mining and Mineral Processing
The ArcelorMittal group is actively developing its raw material base to raise self-sufficiency
levels, and for this reason, the research Centre of Mining and Mineral Processing (MMP) was
created in 2008 within ArcelorMittals Global Research & Development in Maizires-ls-
Metz, France. Since its establishment, MMP has been developing processing solutions forboth existing operations and new projects, with iron ores from Europe, Asia, America and
Africa. The main missions of MMP are to contribute to ArcelorMittals overall strategy to
increase iron ore self-sufficiency, to provide technical assistance to existing mineral
processing plants improving efficiency, increasing safety, decreasing environmental impacts
and operation costs, to develop conceptual level studies for the beneficiation of new potential
sources of iron ores, to characterize raw material sources for iron making and to identify and
assess emerging trends in the field of iron ore characterization and processing. In addition,
MMP supports sales and raw material purchasing activities. In terms of expertise, MMP
performs:
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Mineralogical characterization and interpretation
Design, supervision and conduction of mineral processing test-work programs
Process flowsheet design and simulation
Equipment sizing and selection (pre-scoping/scoping level) Plant audits and identification of potentials for optimization
Benchmarking of mineral processing unit operations.
As example of the latest achievements of MMP team, it is possible to mention the
development of new sinter-feed fines products from Liberia and the development and
subsequent marketing and sale of Baffinland lump at a premium price in the pellet market. In
addition to that, the design for reconfiguration of the crushing circuits at Baffinland enabling a
50% improvement in throughput. Other examples are the improvements achieved on the low
grade ore concentration flowsheets for Bosnia and Pea Colorada (Mexico), these
achievements being very important to supporting sustainable ongoing operations at thesemines. At last, but not least, the work performed for tailings thickening in the Ukrainian
operation which has the potential to reduce energy costs in the Ukraine by several million
dollars per year.
b. Research activities for AM Prijedor and AM Zenica
Since 2009, MMP has been contributing to technological improvements in the GMS
beneficiation plant of ArcelorMittal Prijedor.
Since the exploitation in Jezero Pit in Omarska
Mine was over some years ago, ArcelorMittal Prijedor started to exploit the Buva Mine. The
two deposits are near to each other but they have different mineralogical characteristics and itaffects a lot the mineral processing of the ore. Fe content in Buvac Pit is lower by
approximately 4% against Jezero Pit. Therefore, the studies were focusing on plant
optimization in order to improve the beneficiation of this new ore trying to keep as much as
possible the design of the plant. Laboratory and pilot scale tests were performed throughout
the years, the feasibility of the project was evaluated and finally in 2014, two magnetic
separators (SLon) to treat the -1+0.5mm ore fraction and one filter-press, to recover the -
0.025mm fraction, were installed at GMS plant adding value to the process. Research and
development activities are still going on especially for the quality improvement of the -
0.025mm fraction and possible recovery of Fe rich tailings pond material.c. Sinter pot tests with Prijedor ores for Zenica sinter plant
ArcelorMittal Maizires Process Research Center, located in the same site as MMP, is
equipped with a sinter pot pilot to simulate the sintering process and access results as
productivity, sinter quality and energy consumption. This tool allows to characterize various
ores mixes and fluxed and to define optimal sintering operational conditions.
In 2009, first study is done in a context where the sinter strand at Zenica was bottleneck in
terms of hot metal production so the increase of sinter productivity was considered as first
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priority. Different possibilities were studied to attain this objective and sinter pot test was
performed to optimize the sinter mix with a maximum of local iron ore. Significant increase
of productivity was obtained replacing the initial ore mix with 80% of Bosnian sinter-feed ore
and 20% of Kryvyi Rih concentrate, with very fine size distribution, by 100% of Bosnian
sinter-feed ore with addition of 1% burnt lime in mix and optimization of the moisture leading
to improvement of granulation process. However, the solid fuel consumptions were increased
with the increase of Bosnian BPR goethite ore.
Recently, another series of trials were performed in the sinter-pot aiming to characterize the
sintering performances of Buva (BPR and BPR+) and carbonate ores. The objective of the
trials is to assess the possibility of using binary mixes: BPR or BPR+ with carbonate ore
without negative impact on sintering conditions and sinter quality. The usual Buva ore used
in Zenica sintering plant is called BPR and it contains around 10% of -0.025mm. With the
new configuration of the GMS plant, filtration residue of very fine ore in form of dense cake,from press filter, is available and can be used in the sinter plant. 7 to 10% of this very fine
compound is added to BPR to constitute BPR+. The sinter pot tests show a light but
acceptable decrease of productivity (-1t/m2/d) with BPR+ compared to BPR.
Other ore named Carbonate ore (~26%Fe, ~14% CaO, ~2.4% SiO2, ~4.8% MgO ) is also
available in Ljubija deposit close to the current mine. This ore very poor in Fe but with high
amount of CaO and MgO could replace a part of fluxes such dolomite and a part of limestone
in the sinter blend while allowing increasing the level of iron in sinter. Results were
encouraging and showed that carbonate ore can be used in the sinter plant replacing fluxes in
the order of maximum 30% in the sinter mix without decrease of productivity, with increaseof Fe content in sinter (+1.8%) but also increase of MgO content in sinter to 2.2%. This level
is high but not detrimental in Zenica conditions. Sinter quality is slightly worse compared to
when only BPR and fluxes are used.
These encouraging results have to be confirmed during industrial trials in Zenica sinter plant
at end of 2016.
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ARCELORMITTAL-A
Marcela Gotelip Barbosa1, G. Siboni1, F. G. Vasconcelos1, A. Corra de Araujo1,
T. Sylow1, M.J.Venturini2
1ArcelorMittal Maizires Mining and Mineral Processing Research Center2ArcelorMittal Maizires Process Research Center
1.
ArcelorMittal . , , . ArcelorMittal-a , . ArcelorMittal Mining 14 . 2015. , 73.7 , 62% . , , 6.29 PCI (eng Pulverized Coal injection), 15% PCI.
ArcelorMittal-u, ArcelorMittal-a . (R&D) ,, . 1300 11 , ArcelorMittal R&D , , Mining.
2.
ArcelorMittal Mining , , ,, , -. , ,
. 31. 2015. , ArcelorMittal-a 4.3 . 1. .
2.1. ArcelorMittal Mining Canada (AMMC)
Mont Wright-u, Quebec, AMMC ArcelorMittal-u. 2015., 25.8 . . , 30% Fe. , , , . , .
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, Port-Cartier-u. 60% AMMC-a.
1: ArcelorMittal-a
2.2.Baffinland (BIM)
BIM 50%:50% ArcelorMittalMining-a Nunavut Iron Ores Holdings LP. Mary River ,2014. . Baffin, 160 km Mittimatalik (PondInlet), , Nunavut - ., , . , , . 70% 30% , 6.5 . Baffinland-a ,
, , 70 80 .
2.3. Minorca Hibbing
Minorca 2.7 , Hibbing 5.1 ( 2015). , , , , . Minorca, .
-.
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2.4. ArcelorMittal
Pea Colorada Sierra Madre del Sur, - Colima, . 50%:50%
ArcelorMittal-a Ternium S.A. , 89%, , . , , , 2015. , 1.7 ArcelorMittal-ov . .
Volcan/Sonora , Obregon y Sonora. , ,, , . , . , cobbing
. , 120 El Volcan, , , ( ), . 2015. 1.7 .
Las Truchas Lzaro Crdenas, - . Las Truchas , , cobbing - .
(/) , . LasTruchas 2015. 1.8 . LzaroCrdenas, .
2.5. ArcelorMittal
Serra Azul (iron ore quadrangle), Itatiaiauu, 70 km - Belo Horizonte, MinasGerais.
(), . Serra Azul . , , ( ) (WHIMS). Serra Azul 1.5 .
Andrade -, Minas Gerais. . .
,
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. Joo Monlevade, 8 km.2015. 1.5 .
2.6. ArcelorMittal
-. bilbao - -. . 43% Fe 180 mil.t . - ,, WHIMS i SLon . ,, , 2015. 2.1 .
2.7. ArcelorMittal
ArcelorMittal Kryvyi Rih .
Kryvyi Rih Fe~65% Fe ~34%. 25 10.1 ( 2015. ). , ,, (>65%) .
Artyom , 48-52% Fe 14-18% SiO2 . Artyom 0.9(2015. ).
2.8. ArcelorMittal
ORKEN ArcelorMittal-ovog ArcelorMittal-ova . ArcelorMittal Temirtau, .
Lisakovsky . 0.9 ( 2015.). , , -, 39.8% Fe 49% Fe. , 0.6% 0.8% P.
Atasu Karajan, Atasu. . 25% 75% . 1954. , 70- 0.9 2015. .
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, .
Kentobe 1.5 2 mil. t/god
0.9 2015. . . . , , , , (>2-3 %).
Atanasor , 45 km -Stepnogorsk-a. , . , . ()
, . 2015. 0.4 .
2.9. ArcelorMittal
ArcelorMittal Nimba , - . ArcelorMittal-ova : , (BIF), , , , canga . :, , ()
. ArcelorMittal, , 3 Nimba : Tokadeh,Gangra i Yuelliton . Tokadeh (DSO) .DSO 4.3.(2015. ).
3.
3.1.
ArcelorMittal
, 2008. (MMP), ArcelorMittal-ov Maizieres-les Metz, . MMP -, , , , . MMP-a ArcelorMittala , , , , , , . MMP :
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. . , .
, (, +) . : +, , . 10% -0.025 mm. (-) 7 10%-, +. , ,, (-1 t/m2/d).
, (~26% Fe, ~14% CaO, ~2.4% SiO2, ~ 4.8% MgO). , CaO i MgO. ( ), .30%, , Fe (+1.8%), MgO 2.2%. , . , .
, 2016.
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1, . 1
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. Fe, () 550 km2. , (, ). , , 60-, , . . () -,. Fe-. - -
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. 3 , . 452,3% 1500km2. 400 , , 120 km2 (8% ) - . . , , . 60.
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ArcelorMittala - , , .
2014. 2015. , (2016). , 1:50.000, , , . c1.
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METALOGENETSKAKARTA
LJUBIJSKEOBLASTI
RUDARSKIINSTITUTD.O.O.PRIJEDOR
,2015.god.
Autori:
Prof.drAleksandarGrubi,akadem
ik
Prof.drRankoCviji,dipl.in.geologije
DrAleksejMiloevi,dipl.in.geologije
Miodragelebi,dipl.in.rudarstva
0
2km
4km
6km
Neogen-kvartarne
uglavnom
kontinentalnetvorevine
Marinsketerigeno
-karbonatne
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22
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Tmtc
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x
LEGENDA: G
ornjifli:pjeariialevrolitisa
rijetkimpojavama
mikrokonglomera
ta
Olistostromskila
n:
pjearsko-alevro
litskiflini
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Rasjed:utvren,pretpostavljen
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[1] , . , . 1991. . -, . ArcelorMittala, .
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[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
Garai, V. i Jurkovi, I. 2012. Geochemical characteristic of different iron ore types from theSouthern Tomaica deposit, Ljubija, NW Bosnia. Geologia Croatica, vol. 65, No. 2, str. 255-270. Zagreb., . , . 2003.
. : , . 63- 137. . ., ., , . , . 2006. . II , .32-34. . .., ., ., ., 2016. . ., . 1988. . - .., . 1971. . , . XI. . 1-146. ., . 1973. - .
. ,.Strmi-Palinka, S., Spangenberg, J. E. i Palinka, A. L. 2009. Organic and inorganicgeochemistry of Ljubija siderite deposits, NW Bosnia and Herzegovina. Min. Deposita, vol. 44,
No. 8, str. 893-913. Springer Verlag., . 1981. . -, . 1-135. . ., ., ., 1980. 1981-1985. , . 8, "" , . 7-14.
-
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OVERVIEW OF MATHEMATICAL MODELS FOR PIT
OPTIMIZATION AND PLANNING
Abstract: Contemporary mining has constant declining trend in business conditions. Achievement
possibilities of economic and other goals of mining projects are being compromised by constant
deposit deterioration where exploitation is being held, as well as a significant dose of uncertainty
associated with geological, economic and technical parameters of production. In such complexbusiness conditions, mathematical models, able to respond on significant challenges, i.e. to offer
optimal solutions which shall guarantee maximal fulfillment of projected goals, are considered to be
necessity.
Current approach to optimization and planning of mines, can be divided into two categories conventional
(deterministic) and stochastic. Their main characteristics of conventional methods are based on
deterministic approach during adoption of relevant input parameters. In last two decades, adoption of
stochastic models is considered to be serious alternative. Numerous results of scientific work strongly
suggest that stochastic mathematical models significantly better describe the nature of contemporary
mining, and represent alternative capable of offering better solutions.
-
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This paper gives an overview of the basic conventional and stochastic algorithms for optimization and
planning of open pit, with a critical focus on the advantages and disadvantages of both approaches.
Keywords:optimization, mine planning, mathematical models, deterministic and stochastic approach.
1.
, , [1]. . , , . , , . , 1.
1.
., , , , , . ,
:
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1.
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)
[11]Lerch Grossmann 2D
[13]
Lerch Grossmann 3D
[14, 15]
[12] 3[14] , [16]
-
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. , . , . , () . . , () .
, Lerch Grossmann 2D . .. , .
Lerch Grossmann 3 . . . : (. arcs) . Lerch Grossmann 3D () , 3D .
, . , 2.
2.
Johson
[17]
Gershon
[18][20]
/,
[19]
Dowd Anur
[21]
() (1969) . , . .
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.
() .
[20] . .
Dowd Anur [21] . .
. .
2.1.
, . , , .
. [22,23].
.
.
3.
, , . .
, . .
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.
, . . Goovaerts (1997) , , (. smoothingeffect) . Ravenscroft [24] . . , , , .
, Godoy Dimitrakopoulos [25] (.Simulated annealing) .
Ramazan Dimitrakopulos [26] . . , .
-, . , , . , . , , .
( ) . , , ()
[27].
-
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3.1.
,
, .
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:
[1] Erdem, ., Gyagler T., and Demirel, N., Uncertainty assessment for the evaluation of netpresent value: a mining industry perspective, The Journal of The Southern African Institute ofMining and Metallurgy, 2012, Volume 112, str. 405 - 412
[2] Ramazan S, Open pit mine scheduling based on fundamental tree algorithm, doktorska disertacija,2001, Colorado School of Mines.
[3] Darwen, J, P, Genetic Algorithms and Risk Assessment to Maximise NPV With Robust Open-PitScheduling, Strategic Mine Planning Conference, 2001, Perth
[4] Dowd, P, A, Pardo-Igzquiza, E, The Incorporation of Model Uncertainty in GeostatisticalSimulation, Geographical and Environmental Modelling, 2002, Vol.6, str. 147-169.
[5] Benndorf, J. Dimitrakopoulos, R., New efficient methods for conditional simulation of large
orebodies. Orebody Modelling and Strategic Mine Planning, The Australasian Institute of Mining,2007.
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[6] Dimitrakopoulos, R, Conditional simulation algorithms for modeling orebody uncertainty inopen-pit optimization. International Journal of Surface Mining Reclamation and Environment,1998, Vol. 2, No. 4, str. 173-179.
[7] Dimitrakopoulos R, Farrelly C, Godoy M, Moving forward from traditional optimization: Grade
uncertainty and risk effects in open pit design, Mining Technology, 2002, Vol. 111, str. 82-87.[8] Dimitrakopoulos R., Ramazan S, Uncertainty based production scheduling in open pit mining,
SME Transactions, 2004, vol. 316.[9] Pitkanen, P, Open Pit Optimization, Calculating the Optimum Pit Limits, Helsinki University of
Technology, Laboratory of Rock Engineering, 1997, ISBN 951-22-3877-2.[10]Osanloo, M, Gholamnejad J, i Karimi, B, Long-term open pit mine production planning: a review
of models and algorithms, International Journal of Mining, Reclamation and Environment, 2008,Vol. 22, No. 1, str. 3-35
[11]Pana, M. T, Carlson, T.R, A Description of Computer Technique Used in Mine Planning of theUtah Mine of Kennecott Copper Corp, VI APCOM, 1966, State College, Pennsylvania
[12]Philips, P, A, Optimum Design of Open Pit, X-ti APCOM, Johanesburg, South Africa, 1973.[13]Lerchs, H and Grossmann, Optimum design of open pit mines, Transactions CIM Bullitin, 1965,
vol. 58, str. 17-24.[14]Johnson, T, B, i Sharp, W, R, A ThreeDimensional Dynamic Programming, Method for Optimal
Open Pit Design, Bureau of Mines, 1971, U.S. Dep. of the Interior[15]Zhao, Y, Kim, Y, C, A new Optimum Pit Design Algorithm, XXIII-ti APCOM, 1992, str. 423-
434[16]Davis, R, E, Williams, C, E, Optimisation Procedures for Open Pit Mine Scheduling, XI-ti
APCOM, University of Arizona, Tucson, Arizona, 1973, Vol. 1C, str. 1-18[17]Johnson, T.B., Optimum production scheduling, Proceedings of the 8th International Symposium
on Computers and Operations Research, 1969, str. 539 562.[18]Gershon, M.E., Optimal mine production scheduling: evaluation of large scale mathematical
programming approaches. International Journal of Mining Engenering, , 1983, vol. 1, str. 315-329[19]Dowd P.A., & Onur A.H., Optimization of Open-pit Mine Design-Part 1: Optimal Open-pit
Design, Transactions of the Institute of Mining and Metallurgy, (Section A, Mining Industry), vol.102, 1993, str. A95-A104
[20]Dimitrakopoulos R., Strategic mine planning under uncertainty, stochastic optimization forstrategic mine planning: a decade of developments, Journal of Mining Science, 2011, Vol. 47, str.138-150
[21]Dimitrakopoulos R., Griffin W.H, Geology-based Conditional Simulation in the Athabasca OilSands Deposit, Alberta, Canada, Natural Resources Research, 1993, Volume 2, str. 49-61
[22]Dimitrakopoulos R, Ramazan S, Stochastic integer programming for optimizing long termproduction schedules of open pit mines: methods, application and value of stochastic solutions,Mining Technology, Transactions of the Institute of Mining and Metallurgy, Section A, 2008,Vol. 117, No. 4, str. 155-167.
[23]Erdem, ., Gyagler T., and Demirel, N., Uncertainty assessment for the evaluation of netpresent value: a mining industry perspective, The Journal of The Southern African Institute ofMining and Metallurgy, 2012, Volume 112, str. 405 - 412
[24]Ravenscroft, P, J, Risk analysis for mine scheduling by conditional simulation, Transactions ofthe Institute of Mining and Metallurgy, (Section A, Mining Industry), 1992, Vol. 101, str. 104-108.
[25]Godoy M. C., Dimitrakopoulos R., Managing risk and waste mining in long-term productionscheduling, SME Transactions, 2004.
[26]Ramazan, S, Dimitrakopoulos, R, Production scheduling with uncertain supply: A new solutionto the open pit mining problem, COSMO Stochastic Mine Planning Laboratory, Technical Report,2007, str. 257-294.
[27]StevanoviD, Kolonja, B, Stankovi, R, Kneevi, D, Bankovi, M, Application of stochastic
models for mine planning and coal quality control, Thermal Science, 2014, vol. 18, str. 1361-1372
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, , ArcelorMittal Maizieres ArcelorMittal .
, , :
.
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2.
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: , .
3.
, cutoff 35% Fe. Na ovaj nain je prosjeni sadraj u ulaznoj rudi bio 48%Fe.
cutoff 32% Fe, . 46.5% Fe. 10%.
3.1. -
:
(-8+0 mm) (-40+8 mm), i () .
70% Fe 80%.
20% 80%.
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, , 1.
1. -
, (+0-150 mm) , . :
(1 1.), GISL-72
30 20 mm (2), -150+30 mm 40 mm (3), GISL-62 1.6 mm (4), GISL-62 20 1.6 mm (5), (Barmac ) -20 +1.6 mm (19)
- (I),
-5 +0 mm (6), ( -3 +0 mm)
250 mm (7) (10b),
I
III
II
IV
-
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Jones DP-317 (9), (8) a (11) (12), 30m (IV),
Slon 2500(15, 18) (16) (14, 17), - (II),
(10a) Bilfinger GHT - 2000 (20) (III).
, , -.
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3.2.1.
-2009. - , .
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VSI, 2 x 150 t/h, RF SH 3000 x 7300 DD-2 , UR 915/1500-VA-Sond, VT 40 05 HC, 3KW. 2. .
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2.
(1 2) , , .
1.
AsortimanMasa namokro,
mil t
Masa nasuvo,
mil t
Fe% Mn% SiO2% H2O%Iskoritenje
mase na
mokro,%
Iskoritenjemase na
suvo,%
Iskoritenje
Fe,%
Procentualniudio
asortimana %
Obogaenje,
%
Ulazna ruda 69844 58125 46.99 1.84 14.61 16.73 100 100 98.49 100%
Krupni
koncentrat,
APR
7707 6804 53.53 1.56 7.80 11.60 11.03 11.71 13.33 16% 6.54
Sitni
koncentrat,
BPR41608 34544 50.27 2.02 10.76 16.94 59.57 59.43 63.57 84% 3.27
Ukupni
koncentrat49315 41348 50.81 1.94 10.27 16.11 70.61 71.14 76.91 3.81
Mulj 20529 16776 35.14 1.85 29.54 29.39 28.86 21.58 -11.85
ODNOS APR:BPR 16:84 (SA TERCIJARNIM POSTROJENJEM)
2.
AsortimanMasa namokro,
mil t
Masa nasuvo,
mil t
Fe% Mn% SiO2 % H2O%Iskoritenje
mase na
mokro,%
Iskoritenjemase na
suvo,%
Iskoritenje
Fe,%
Procentualniudio
asortimana %
Obogaenje,
%
Ulazna ruda 69844 58125 46.99 1.84 14.61 16.73 100 100 98.49 100%
Krupni
koncentrat,
APR
20775 18606 53.53 1.56 7.80 11.60 29.74 32.01 36.46 45% 6.54
Sitni
koncentrat,
BPR28540 22742 48.58 2.27 12.29 20.01 40.86 39.13 40.45 55% 1.59
Ukupni
koncentrat49315 41348 50.81 1.94 10.27 16.11 70.61 71.14 76.91 3.81
Mulj 20529 16776 35.14 1.85 29.54 29.39 28.86 21.58 -11.85
ODNOS APR:BPR 45:55 (BEZ TERCIJARNOG POSTROJENJA)
Fe.
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II - , 2016
3.2.2.
- 30 7t/h .
. , , , .
. 30 .
. , . () .
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Ecochem A-1 30 , 30 . 3.
3.
-
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II - , 2016
3.2.3.
, .(-1mm).
. ,:
SLon 2500, Outotec, // 75 -125 t/h, 200-400m3/h. 15m ,1mm .
4. .
4.
3.2.4.
30% -25m. -25m (-) -, , , ., -.
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II - , 2016
:
Fe, % 45-55 -0.025+0 mm 90%SiO2, % 6-16 -0.5+0.025 mm 10%Mn, %. 2Al2O3, % 1-4
() , .
, 2012. , 1.7 m2, 3.
3.
Pumpanje
mulja
(min)
Presovanje
(min)
Produvavanje
(min)
Ostalo
vrijeme
(min)
Trajanje
ciklusa
(min)
Tokom
pumpanja
(bar)
Tokom
presovanja
(bar)
I 3.0 3.5 1.0 4.0 11.5 3.0 16.0 100 31.8 20 175
II 3.0 3.5 0.0 4.0 10.5 6.0 15.0 70 30.33 21 218
III 3.0 3.5 3.0 4.0 13.5 6.0 16.0 70 30.39 21 158
IV 2.0 3.5 1.0 4.0 10.5 3.0 16.0 90 19 215
I 3.0 3.5 0.0 4.0 10.5 5.7 16.0 100 30.09 20 192
II 3.0 3.0 0.0 4.0 10.0 5.6 15.0 120 27.69 21 229
III 3.0 3.5 0.0 4.0 10.5 5.6 15.0 120 29.8 21 203
IV 3.0 2.5 0.0 4.0 9.5 5.2 15.0 130 30.42 19 238
I 3.0 2.7 0.0 4.0 9.7 5.8 15.0 120 31.86 20 207
II 3.0 3.0 0.0 4.0 10.0 5.5 16.0 120 30.99 21 229
III 2.0 3.2 0.0 4.0 9.2 5.9 15.0 110 31.45 21 232
I 3.0 3.5 0.0 4.0 10.5 5.6 16.0 100 28.75 19 211
II 3.0 3.5 0.0 4.0 10.5 5.7 16.0 100 30.35 17 213III 4.0 3.5 0.0 4.0 11.5 5.6 15.0 100 30.32 23 198
IV 2.0 3.0 0.0 4.0 9.0 5.8 15.0 70 28.38 14 147
I 3.0 3.0 0.0 4.0 10.0 5.1 15.0 100 30.98 16 164
II 3.5 3.1 0.0 4.0 10.6 5.0 15.0 120 31.2 15 159
III 3.5 3.0 0.0 4.0 10.5 3.9 15.0 100 31.29 13 134
6/9/2012 1.4 42
6/16/2012 1.3 25
6/8/2012
1.4 38
1.4 42
Prosjena
debljina
keka
(mm)
Stepen
filtracije
kg/mh
6/7/2012 1.3 34
Udio
vrstog
u
mulju
%
Parametri filtracije (trajanje) PritisakKoliina
ulaznog
mulja
(l)
Vlaga u
keku
(%)
DatumPranjenje
keka
Gustina
ulaznog
mulja
kg/dm
, Bilfinger GHT 2000,11917 l = 11.917 m3, 710.7 m2 103 . , , , 50 , : , , , , , ,
. 5. , 4. .
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II - , 2016
5.
4.
Asortiman
Masa na
mokro,
mil t
Masa na
suvo,
mil t
Fe% Mn% SiO2% H2O%
Iskoritenje
mase na
mokro,%
Iskoritenje
mase na
suvo,%
Iskoritenje
Fe,%
Procentualni
udio
asortimana %
Ulazna ruda 30 24 46,50 1,97 15,55 19,60 100,00 100,00 100,00
Krupni
koncentrat, APR4 3 52,30 1,51 9,09 13,50 13,32 14,33 16,12 19,89
Sitni koncentrat,
BPR16 13 50,78 2,02 10,51 17,00 53,64 55,38 60,47 80,11
Ukupni
koncentrat20 17 51,09 1,92 10,22 16,30 66,96 69,71 76,59
Mulj 10 7 35,94 2,09 27,83 33,04 30,29 23,41
4.
. , . , (10%:90% 45%:55%). , ,
.
, 7t/h, 30.000 t . , ,, 48%, 46.5% Fe.10%.
, . , 1.3 mil t ,
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-0.025mm 48% Fe. , . , , -.
Acknowledgements
Significant contribution and support to ArceloMittal Prijedor, in the research related to theproject of GMS Processing Plant optimization, has been provided by ArcelorMittal Miningand Mineral Processing Research Centre, Maizieres-les-Metz/France.
[1]
[2]
[3]
[4]
[5]
[6]
Plant Optimization Report, ArcelorMittal Mining and Mineral Processing Research CentreMaizieres-les-Metz, ArcelorMittal Prijedor, Buvac Mine, October 2012Recapitulation of lab tests of hydrocyclone overflow filtration, ArcelorMittal Prijedor,ArcelorMittal Mining and Mineral Processing Research Centre Maizieres-les-Metz, April2012, , ArcelorMittal, 2012, ArcelorMittal , 2014,2015 - - ,ArcelorMittal , 2014
- - , ArcelorMittal, 2014
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II- , 2016
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II - , 2016
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2.
, , , , , . 1996, 2006, 2010-2015. , , 1 .
1
1996 1.093.325 390.930 127.041 11.920 - - 1.623.216
2006 1.571.082 1.979.575 *512.115 *6.301 - - 4.069.073
2010 1.547.098 2.278.258 794.363 - - -
2011 2.085.640 2.658.219 927.755 31.529 23.383 - 5.703.143
2012 2.037.646 1.935.516 1.087.927 87.204 89.677 -
2013 1.907.499 2.480.622 852.930 105.660 180.967 -
2014 1.750.304 2.272.748 1.211.401 101.612 148.952 -
2015 2.030.832 2.531.303 1.212.406 100.622 165.014 10.000 6.040.177
*
1
-
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2
()
1996 1.623.2162006 4.069.073
2011 5.703.143
2015 6.040.177
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1996 2015
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1.623.216 4.440.629 10.000.000
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.
1. , , , (, )(,).
2. 1996. 2015. 1.623.216 6.040.177 .
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[2]
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http://www.doiserbia.nb.rs/http://www.nspm.rs/ekonomska-politika/neoliberalni-koreni-svetske-ekonomske-krizehttp://www.efzg.unizg.hr/http://www.globalisationanddevelopment.com/http://www.naturalresources.gr/.../%20Globalisation%C2%A0and%20the%C2%A0Industrial%C2%A0Minerals%C2%A0Industry%C2%A0http://www.kfbih.com/http://www.kfbih.com/loc/default.wbsp?p=17&n=1234&naslov=Odr%9Eivo%20Upravljanje%20Mineralnim%20Sirovinama%20-%20projekat%20%20SARMahttp://www.google.ba/url?sa=t&rct=j&q=globalization%20and%20mining&source=web&cd=1&cad=rja&ved=0CC4QFjAA&url=http%3A%2F%2Fcatapa.be%2Fen%2Fmining&ei=UZjDUKb6Kqb14QT_84DYCg&usg=AFQjCNEEFmQ5KvRy2yUjK4Z70k2d7SdNlghttp://www.doiserbia.nb.rs/http://www.doiserbia.nb.rs/http://www.google.ba/url?sa=t&rct=j&q=globalization%20and%20mining&source=web&cd=1&cad=rja&ved=0CC4QFjAA&url=http%3A%2F%2Fcatapa.be%2Fen%2Fmining&ei=UZjDUKb6Kqb14QT_84DYCg&usg=AFQjCNEEFmQ5KvRy2yUjK4Z70k2d7SdNlghttp://www.kfbih.com/loc/default.wbsp?p=17&n=1234&naslov=Odr%9Eivo%20Upravljanje%20Mineralnim%20Sirovinama%20-%20projekat%20%20SARMahttp://www.kfbih.com/http://www.naturalresources.gr/.../%20Globalisation%C2%A0and%20the%C2%A0Industrial%C2%A0Minerals%C2%A0Industry%C2%A0http://www.globalisationanddevelopment.com/http://www.efzg.unizg.hr/http://www.nspm.rs/ekonomska-politika/neoliberalni-koreni-svetske-ekonomske-krizehttp://www.doiserbia.nb.rs/ -
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[2] , ., , . . VI Me ''2013''. , . 171180, 2013.
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[6] www. nezavisne.com/ekonomija/analiza[7] 20162021
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A SIGNIFICANCE OF THE APPLICATION OF NEW STRATEGIES
AND TECHNOLOGIES IN THE EXPLOITATION OF COAL LAYERS
IN THE REPUBLIC OF SRPSKA AND SERBIA
1, . 2, . 3, . 4
: . . 8 , .
. . .
: , , , .
bstract:Mineral and energetic resources in the Republic of Srpska are significant. This is a study on
the field of energetic mineral raw materials (coal) in the last decades. Production stagnated and there
was a fall in production in some mining fields and in the other there was a development. The field of
the underground exploitation had its deteriorating process in majority of the criteria. The examples of
it in the underground exploitation in Serbia in JP PEU Resavica (with 8 mines) in the mine copper
basin RTB Bor, which are now in a very difficult situation where the new solutions are being found for
their further survival. Open-pit mining especially of the energetic raw materials had a relatively better
development in the majority of the coal basins in the Republic of Srpska and Serbia because of the
better conditions for the exploitation and production of the energy in thermal power plants.
In this paper the basic guidelines are indicated how to overcome the difficult conditions in mining of
the Republic of Srpska through the application of the new technologies of the exploitation.
1 , , -mail:
[email protected] , , -mail:
[email protected] , , -, -mail:
, , -, -mail: [email protected]
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The examples are given as well as other mining countries richer than us which search for the best
solutions.
Key words:New strategy, new technology, mechanization, new technologies, mechanization andautomation in the mines, alternative technologies.
1.
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3.1.
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[4]:
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8.200 / ( 16.894 / ), Zafiowka 3.400 / ( 4.600) Jas -Mos 3.000 / ( 5.050 /).
,
.
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ITERATURA
[1] . , . , . , . :
, 2/2002, , , , 2002.