Outlining of high quality coking coal by concentration–volume fractal model and turning bands simulation in East-Parvadeh coal deposit, Central Iran

Abstract Identification of various mineralized zones in an ore deposit is essential for mine planning and design. This study aims to distinguish the different mineralized zones and the wall rock in the Central block of North Anomaly iron ore deposit situated in Bafq (Central Iran) utilizing the concentration-number (C-N) and concentration-volume (C-V) fractal models. The C-N model indicates four mineralized zones described by Fe thresholds of 8%, 21%, and 50%, with zones <8% and >50% Fe representing wall rocks and highly mineralized zone, respectively. The C-V model reveals geochemical zones defined by Fe thresholds of 12%, 21%, 43% and 57%, with zones <12% Fe demonstrating wall rocks. Both the C-N and C-V models show that highly mineralized zones are situated in the central and western parts of the ore deposit. The results of validation of the fractal models with the geological model show that the C-N fractal model of highly mineralized zones is better than the C-V fractal model of highly mineralized zones based on logratio matrix.

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Justification of technological and spatial planning solutions of the combined mining system for mining the reserves of the Elegest coking coal deposit

MINING INFORMATIONAL AND ANALYTICAL BULLETIN ◽

10.25018/0236-1493-2020-12-48-10-16 ◽

2020 ◽

pp. 10-16

Author(s):

A.S. Hovhannisyan ◽

V.V. Agafonov ◽

Sh.G. Shoyduk

Keyword(s):

Spatial Planning ◽

Coal Deposit ◽

Mining System ◽

Coking Coal

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The impact of the compositional nature of data on coal reserve evaluation, a case study in Parvadeh IV coal deposit, Central Iran

International Journal of Coal Geology ◽

10.1016/j.coal.2018.02.003 ◽

2018 ◽

Vol 188 ◽

pp. 94-111 ◽

Cited By ~ 3

Author(s):

Hossein Molayemat ◽

Farhad Mohammad Torab ◽

Vera Pawlowsky-Glahn ◽

Amin Hossein Morshedy ◽

Juan José Egozcue

Keyword(s):

Central Iran ◽

Coal Deposit ◽

Coal Reserve ◽

The Impact

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New Coal Basin of the U.S.S.R.

Geological Magazine ◽

10.1017/s0016756800074963 ◽

1944 ◽

Vol 81 (3) ◽

pp. 132-132

Author(s):

I. Andronov

Keyword(s):

Soviet Union ◽

Southern Urals ◽

Economic Life ◽

Coal Basin ◽

The Southern Urals ◽

High Quality ◽

Coking Coal ◽

The Soviet Union ◽

Power Base ◽

Set Up

Until quite recently only a few specialists, even in the Soviet Union, knew about the Poltava and Bredin anthracite deposits in the Southern Urals. Under the stern conditions of war, however, this coal basin in the east of the Soviet Union has sprung to economic life. A new power base has been set up, new shafts sunk, all of them working and with reserves of coal before them sufficient for many years. Many of the Bredin pits contain coking coal, and the Poltava-Bredin coal district will soon be able to supply many of the large Urals works with high-quality fuel.

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APPLICATION OF RQD-NUMBER AND RQD-VOLUME MULTIFRACTAL MODELLING TO DELINEATE ROCK MASS CHARACTERISATION IN KAHANG Cu-Mo PORPHYRY DEPOSIT, CENTRAL IRAN / ZASTOSOWANIE METOD MODELOWANIA NUMERYCZNEGO ORAZ MODELOWANIA FRAKTALNEGO DO ANALIZY JAKOŚCI SKAŁ W CELU OKREŚLENIA CHARAKTERYSTYKI GÓROTWORU W OBSZARZE ZŁOŻA Cu-Mo W KAHANG, ŚRODKOWY IRAN

Archives of Mining Sciences ◽

10.2478/amsc-2013-0071 ◽

2013 ◽

Vol 58 (4) ◽

pp. 1023-1035 ◽

Cited By ~ 1

Author(s):

Amir Bijan Yasrebi ◽

Andrew Wetherelt ◽

Patrick J. Foster ◽

Peyman Afzal ◽

John Coggan ◽

...

Keyword(s):

Rock Mass ◽

Fractal Model ◽

Mine Planning ◽

Central Iran ◽

Rock Classification ◽

Porphyry Deposit ◽

Planning And Design ◽

Fractal Models ◽

Rock Quality ◽

Rock Mass Characterisation

Abstract Identification of rock mass properties in terms of Rock Quality Designation (RQD) plays a significant role in mine planning and design. This study aims to separate the rock mass characterisation based on RQD data analysed from 48 boreholes in Kahang Cu-Mo porphyry deposit situated in the central Iran utilising RQD-Volume (RQD-V) and RQD-Number (RQD-N) fractal models. The log-log plots for RQD-V and RQD-N models show four rock mass populations defined by RQD thresholds of 3.55, 25.12 and 89.12% and 10.47, 41.68 and 83.17% respectively which represent very poor, poor, good and excellent rocks based on Deere and Miller rock classification. The RQD-V and RQD-N models indicate that the excellent rocks are situated in the NW and central parts of this deposit however, the good rocks are located in the most parts of the deposit. The results of validation of the fractal models with the RQD block model show that the RQD-N fractal model of excellent rock quality is better than the RQD-V fractal model of the same rock quality. Correlation between results of the fractal and the geological models illustrates that the excellent rocks are associated with porphyric quartz diorite (PQD) units. The results reveal that there is a multifractal nature in rock characterisation with respect to RQD for the Kahang deposit. The proposed fractal model can be intended for the better understanding of the rock quality for purpose of determination of the final pit slope.

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Influence of the prime hard coking coal in stamp charge cokemaking: true or false

Metallurgical Research & Technology ◽

10.1051/metal/2020046 ◽

2020 ◽

Vol 117 (4) ◽

pp. 412

Author(s):

Hari Prakash Tiwari ◽

Vishwesh M. Shisani ◽

Bhavendra Kumar Sahu ◽

Rabindra Kumar Sabat ◽

Damodar Mittal

Keyword(s):

Blast Furnace ◽

Raw Material ◽

Furnace Productivity ◽

Specific Productivity ◽

Hot Metal ◽

High Quality ◽

Coking Coal ◽

Coal Blend ◽

Working Volume

The production of hot metal through the blast furnace route is stilled the most cost-effective and highly productive process and probably remains the coming decades besides developed many alternative ironmaking technologies. In the recent past, the working volume of the blast furnace has been increased drastically to increase the blast furnace productivity. This means the blast furnace performance is more correlated to specific productivity which measures the efficiency in terms of ton hot metal. These modern blast furnaces favour high quality of coke, i.e. high coke CSR and M40 value, high iron content sinter and pellets. These high quality of input raw materials increased blast furnace efficiency and productivity. Generally, cokemakers increases the ratio of prime hard coking coal in the coal blend to achieve the high quality of coke. This increase in prime hard coking coal is not desirable for coke oven batteries because it creates high oven wall pressure and high coke cost and also not suitable for raw material security. The present investigation highlights few cases which clearly show that the high quality of coke (coke CSR: 69–71) may be easily produced with the optimal proportion of prime hard coking coal in the blend if the selection of coals is proper. Results confirmed that upto 30% primary hard coking coal with 15% non-coking coal in the coal blend produce an excellent quality of coke which naturally requires a careful selection on the blend component. The optimum composite coking potential (CCP) value of 4.6–4.9 is the ideal value for producing coke CSR in the range of 69–71 in recovery stamp charge cokemaking process in the real-time plant operation. Therefore, it is necessary to select the right coals for the coal blend based on the adopted cokemaking technologies to conserve the reserve of prime hard coking coal, oven health and cost-effectiveness.

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