A Simple Technique for Selecting Coals for Non-Recovery Coke Making Process

New coke making technologies Session 22 1

Düsseldorf, 27 June – 1 July 2011

Acknowledgements

The authors acknowledge the partial financial support

of the Australian Coal Association Research Program

for this work. We gratefully acknowledge the

agreement of Illawarra Coke Company and BHP

Billiton to present the thermocouple measurements

from the Corrimal non-recovery coke ovens.

References

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[4] Arendt, P.; Lungen, H. B.; Reinke, M. (2006)

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[5] Kim, R.; Reinke, M.; Worberg, W. (2009)

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porosity and contraction behaviour. Fuel 62,

547-552.


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Swann, A.; Penny, G. (2003) Factors affecting

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(Associacao Brasileira de Metalurgia e

Materiais), 871-880.

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Lingard, G. (2006) Mechanism of fissuring

during coking and its impact on coke size

distribution. Proc. 4th Int’l Congress on the

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(2010) Fissure formation in coke. 1. The

mechanism of fissuring. Fuel 89 (7), 1654–

1662.

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formation in coke. 2. Effect of heating rate,

shrinkage and coke strength. Fuel 89 (7),

1663–1674.

[15] Jenkins, D.R.; Mahoney, M.R.; Shaw, D.E.

(2010) Fissure formation in coke. 3. Coke size

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1675–1689.

[16] Peyret, R; Thomas, D. (1983) Computational

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A Simple Technique for Selecting Coals for Non-Recovery Coke Making Process

H P Tiwari1, S Suri

2, P K Banerjee

1, S K Haldar

1, P Sarkar

1, R Agarwal

1

1 Tata Steel Ltd., Jamshedpur, India

2 Bhushan Power & Steel Ltd., Sambalpur, Orissa, India

Abstract

In general, Steel industries use many kinds of coking

coals from various sources for producing coke for blast

furnace usage. The selection process further varies

with the coke making technology adopted. The ratio of

inferior grade coking coal, which is relatively low in

price, has been increased in recent years in order to

reduce the coke cost to produce coke of desired

quality.

Possibility of predicting coke quality from the properties

of the coals in the blend has been attempted by many

authors. In this study a new method based on a

coefficient, named as Composite Coking Index (CCI),

has been proposed for assessing the suitability of a

coal as well as a blend for making coke of acceptable

properties. The index takes into account the various

coking properties of the coals of a given blend and

converts them into a single value. This value is

particularly important since each of these parameters

represents different aspects of the coking phenomena

with varying importance. Inter dependence of some of

these parameters also exists. This makes the

prediction process extremely difficult and majority of

cases, decision is taken based on experience. The

current method proposes a simple method for

prediction of coke properties from the properties of the

coals used in non-recovery coke making processes.

The Composite Coking Index (CCI) model predicts the

least expensive coal blend that would still comply with

the minimum coke quality requirements of a blast

furnace. The study confirms the existence of a

relationship between the CCI and the hot & cold

strength of coke. Actual plant data of a non-recovery

coke oven have been used for developing the model.

This model was further validated with some

commercial coke oven data for different type of blends

and operating parameters. The technique was

successfully used in selecting cheaper coals for

producing coke with CSR >65%, CRI <25% and M10

<6%. Details of the technique with some of the

predictions have been discussed in the paper.

Key Words

Composite coking index (CCI), CSR & non-recovery

coke making

Introduction

In Integrated steel plants, the cost of coke represents

the cost of producing hot metal. Therefore, in an effort

to sustain its cost the designing of low cost coal blends

for producing desired quality of coke. The optimization

of coal blend is ongoing process for any integrated

steel plant not a new process. In this regards the

selection and evaluation of coking potential of coal is

very crucial before using in coal blend for desired coke

quality.

Designing of low cost coal blends for meeting the

requirements of blast furnace coke is very important in

coke making. The important coke properties that affect

the blast furnace performance are chemical

composition, size, strength, and reactivity. Keeping in

view reduces the % of HCC and maximum usages of

inferior grade coals in coke making. In order to reduce

the cost of production of coke further, the use of semi-

soft coals in the stamp charging blend and use of

optimum proportion of hard coal component from the

oven charge without impairing the strength

characteristics of resultant coke. Incorporation of such

blended semi-soft coals which are comparatively less

expensive than that of single source of semi-

soft/inferior coal can also reduce the overall cost of the

oven charge. With the above in view, it was felt

necessary to select requisite new source of coal for

decreasing cost of coke without affecting the coke

quality.

Blending is the process of mixing together coals of

different properties in the desired proportion. Since

most of the caking/coking properties are considered to

be additive in nature, a coal-blending model has been

developed and used for blend optimization. However,

the compatibility of individual coals in the blend plays

an important role in the quality of the final coke

product. The high-volatile coals normally have a lower

softening temperature when compared to the medium

and low volatile coals. The resolidification temperature

is also different for the different coals. However, quality

of coke depend upon the coal blend properties, thermal

cycle developed to maintain the optimize oven

temperature over the coking period by controlling the

primary air inlet, secondary air regulator and individual

oven draft.



The basic difference between heat recovery coke

making and by-product coke making is that in by-

product ovens the heat input into the coal charge is

provided by indirect heat transfer through the oven

walls from an independent heating system. As such,

coal is carbonized in the absence of air and a positive

internal pressure environment is created. In the heat

recovery process, heat input results from the complete

combustion of the volatile components of the coal

within and at the top of the oven chamber generated

during carbonization and a negative pressure is

created. The negative pressure results in significantly

reduced emission levels compared with by-product

ovens and the design requirements for the ovens are

simpler, cheaper and easier to maintain. The Non-

recovery coke making technology may accommodate a

wide variety of coals to produce superior quality coke

which should give the advantage of better coal

selection as compared to the by-products coke making

process.

The non-recovery stamp charging coke making

technology is eccentric compare with the recovery

stamp charging coke making technology. It contrasts

hereto the big advantages of the method could not be

denied: (a) extension of the range of charge coals; (b)

utilization of cheaper charge coal & (c) saving the

prime coking coal and, (d) the major advantage of this

technology is eco-friendly behavior for operation. The

above all assumption is applicable if the coal blend is

properly chosen.

The properties of coal like volatile matter (VM) are the

most important parameter for deciding the proportion of

different coal in a coal blend for non-recovery coke

making. Low VM coals generate less gas and hence,

the total thermal energy on its combustion may be

insufficient for attaining the coking temperatures in the

oven. On the other hand, a high VM coal results in

coke with high porosity and hence, poor CRI and CSR

characteristics. This was confirmed in the case of coal

blends with high VM resulting in coke with lower CSR

and lower yield. Also, the crucible swelling number

(CSN) is the coking property of coal and is the most

crucial parameter in deciding the right blend. The

higher CSN of coal blend indicate higher wall pressure.

Also, the maximum fluidity of coals and reflectance of

coal (Ro avg.) are equally important for selection of

coal for carbonization. Normally the target specification

for the blend is to maintain a maximum fluidity

(Gieseler’s fluidity) in the ranges of 200–1000 ddpm

and average reflectance of 1.2 to 1.3 %. The maximum

fluidity is used to predict the behavior of the plastic

phase during coking. The fluidity measurement is an

attempt to provide a practical test for comparing the

rheology of coals. The fluidity of coal blend determines

the bonding process during coke making. It has an

effect on the coke strength after reaction (1-5).

Possibility of predicting coke quality and optimization of

coal blend from the properties of the coals in the blend

has been attempted by many authors (6- 14). In this

study a new method based on a coefficient, named as

Composite Coking Index (CCI), has been proposed for

assessing the suitability of a coal as well as a blend for

making coke of acceptable properties. Thirty Five (35)

numbers of coking coals of wide varieties (prime hard

coking coals to weak coking coals) were chosen for

developing the CCI. The index takes into account the

various coking properties of the coals of a given blend

and converts them into a single number. This number

is particularly important since each of these parameters

represents different aspects of the coking phenomena

with varying importance. Inter dependence of some of

these parameters also exists. This makes the

prediction process extremely difficult and in majority of

cases, decision is taken based on experience. The

current method proposes a simple method for

prediction of coke properties from the properties of the

coals used in non-recovery coke making processes.

The process starts with prediction of unique value of

coal blend named Composite Coking Index (CCI)

based on properties of coals. Based on the unique CCI

of coal, a simple model has developed to find out the

composite coking index of the blend and thereby, to

predict the coke quality. The unique futures of this

approach are that the model captures the past

experience of the coke making and converts then into a

single number for categorization of various coking

coals and optimization of coal blend cost.

The composite coking Index (CCI) model predicts the

least expensive coal blend that would still comply with

the minimum coke quality requirements of a blast

furnace. The study confirms the existence of a

relationship between the CCI and the hot strength of

coke. Actual plant data of a non-recovery coke oven

have been used for developing the model. This model

was further validated with some commercial coke oven

data for different type of blends and operating

parameters. The technique was successfully used in

selecting cheaper coals for producing coke with CSR

>65% and optimum CRI.



Experimental

A total of 35 numbers of coking coals were used for

evaluating the CCI of coal. All coals are characterized

through its proximate analysis, chemical analysis,

rheological and petrographic analysis (Tables 1 - 4).

The CCI of individual coking coals are shown in Figure

3. Total 104 number of coal blends using above 35

coking coals and also some non-coking coal for

developing and validation of CCI value were used for

present investigation.

All coal blend trials were conducted in an Industrial

Coke Oven Battery (selected ovens). The wide

varieties of coking coals were chosen for this purpose

and the blends were then subjected to high

temperature carbonization tests under a given set of

conditions.

In this study different coals collected from yard were

fed with the help of conveyor belt in to the identified

blending bunkers. After collection of the coal, the

blended coal was passed through the coal crusher and

crushed to the extent that > 90 % coal is below 3.0

mm. The moisture content was fixed at 11% (±1 %).

After crushing the coal was charged in stamping press

mold for formation of coal cake. Normally, the coal

cake is stamped with three layers at hydraulic stamping

station. The stamped coal cake density was around

1.06 – 1.08 t/m3 (on wet basis) and the weight of one

coal cake [13000 mm long X 3400 mm wide X 1000 –

1025 mm high] was approximately 46.50 - 48.50 MT.

After making coal cake, the charging plate is retracted

back along with the coal cake on the charging car. The

pusher cum charging car along with the coal cake

travels to the specified oven as per the oven schedule.

At the same time, quenching car also moves to the

specified oven for receiving ready hot coke. Both side

(charging and pushing) doors are opened after both the

cars are aligned. The ready hot coke is pushed to the

quenching tray by using pusher ram and the fresh cake

is charged in the empty oven. During trial the following

precautions were taken to maintain standard test

conditions:

The blending ratio was continuously monitored

through PLC to ensure the accurate percentage of

each coal.

The coal tower (which receives blended coal) was

emptied and cleaned before each experiment.

The ovens, in which the experiments were done,

were carefully selected to ensure set test

conditions.

Results & Discussion

The present investigation used a wide range of coking

coals (prime hard coking coal to inferior grade coking

coal) for the development of an index named CCI. The

selection criteria of individual coal was based on the

unique number of individual coal and predicting the

coal blend CCI for achieving targeted CSR through

maximum usages of semi-soft/inferior coking coal in

coal blend. The characteristic of all the coals is

presented in Tables 1 - 4.

The CCI model has been developed for individual coal

used specifically by coking properties of coals.

Because of differences in coal selection (type,

proportion), coal preparation, coking conditions and the

required CSR based on the blast furnace and

availability of coal for a particular plant. This CCI

concept is especially validated and successful only for

non-recovery coke making using a wide variety of

coking coal blends from different origins. The

coefficient of coal named CCI may be increased or

decreased based on the coking potential of individual

coal. The resultant Coke qualities (mainly CSR) may

vary with coke making technologies like top charged,

recovery and non-recovery stamp charging etc. The

model is based on the following coal properties: coal

ash, volatile matter (VM), crucible swelling number

(CSN), low temperature gray king test (LTGK),

maximum fluidity of coal (ddpm), alkalinity index, silica :

alumina ratio, reactive : inert ratio, reflectance (Ro

avg.) and V type distribution of coal, etc.

The model was used to calculated CCI number of

individual coals. The relationship between CCI and the

properties of the coals was also investigated, and a

blending design system using CCI, which was different

from the conventional methods, was developed. The

model also predicts the least expensive coal blend that

would still comply with the minimum coke-quality

requirements of the blast furnace. The study also

confirms the existence of a relationship between the

coking properties of coal and the hot strength of coke if

the operating parameters are constant. This CCI

prediction methodology is successfully used for

selection of coal blend only for non-recovery coke

making process.

Figure 1 and 2 shows that the model used in this

investigation of find out the coking potential of coal as

well as coal blend in terms of CCI. The proportions of

the individual coals are the inputs for calculating the

CCI of individual coal as well as coal blends.



Results showed that the decreasing CCI value of

individual coking coal will decrease the coking potential

of individual coal. Figure 3 clearly showed that the

composite coking index (CCI) of individual coking coal

varies from 2.12-7.89 which represents the wide range

of coking coals (prime hard coking coal to inferior

grade coking coal) used. From this investigation, it was

found that Coal AK is having minimum CCI i.e. 2.12,

while, Coal A has the maximum CCI i.e. 7.89.

Figure 4 and 5 showed that the effect of CCI of coal

blend on coke CSR. It may be seen from Figure 4 that

CSR increases with increasing CCI value of coal blend

(correlation coefficient is 0.7552). The coal blend

having CCI 4.08 is producing the minimum CSR i.e.

63.09, whereas, the CCI of 6.32 is producing higher

CSR i.e. 69.51 (based on the plant trials of 18 numbers

of coal blends). Also, Figure 5 showed that a similar

increasing trend of CSR value with CCI during the

validation trials conducted (correlation coefficient is

0.6692). The coal blend having CCI 3.77 is producing

the minimum CSR i.e. 61.82 and the CCI of 6.67

producing the higher CSR i.e. 71.50. This CCI module

is first developed based on 18 numbers of trial coal

blends at selected ovens of a commercial coke oven

battery and after that it has been validated with 84

numbers of commercial plant data (Non-Recovery

Coke Making Process). The CCI model is now

successfully used in the non-recovery coke making

technologies.

Figure 1: Model for calculating the CCI of coals

Figure 2: Model for calculating the CCI of coal blends

Figure 3: CCI value of individual coking coal

Figure 4: Effect of CCI of coal blend on coke CSR

Figure 5: Effect of CCI of coal blend on coke CSR

S. No 1 2 3 4 5 6 7 8 9

Properties Coal

A Coal

B Coal

C Coal

D Coal

E Coal

F Coal

G Coal

H Coal

I

Proximate Analysis, (adb) %

Ash 8.70 8.45 8.10 8.00 9.50 9.76 9.50 10.20 8.6

VM 23.90 25.98 22.10 21.68 22.00 21.90 25.00 23.50 25.12

S 0.53 0.58 0.47 0.52 0.54 0.54 0.63 1.10 0.58

P 0.020 0.028 0.023 0.002 0.018 0.018 0.038 0.095 0.060

Na2O 0.50 0.04 0.40 0.60 0.57 0.57 0.39 0.10 0.46

K2O 0.90 0.02 0.80 3.01 0.08 0.08 0.15 1.57 1.00

Rheological Properties

CSN 8.5 7.5 8.0 8.5 7.5 7.5 9.0 9.0 8.0

GK G5 G6 G4 G6 G5 G4 G10 G7 G4

Max. Fluidity, ddpm

999 1100 1100 5272 500 500 4500 4915 886

Ash Constituents, %

SiO2 61.50 66.50 58.00 57.29 62.62 62.62 55.89 61.38 52.46

Al2O3 29.70 26.50 32.50 27.86 29.56 29.56 32.88 26.56 35.94

Fe2O3 2.70 2.40 3.10 7.82 4.16 4.16 4.70 3.43 9.17

CaO 0.80 0.20 0.90 3.41 2.00 2.00 3.17 2.42 0.96

MgO 0.50 0.40 0.50 0.00 1.00 1.00 1.17 0.30 0.01

Reflectance of Coal , %

Ro, % 1.24 1.19 1.17 1.18 1.16 1.16 1.18 1.11 1.15

Maceral Analysis, %

Vitrinite 64.70 75.30 58.00 70.80 68.40 68.40 64.60 69.10 56.40

Exinite 1.00 1.30 1.00 0.40 5.40 5.40 0.80 0.00 2.90

Inertinite 30.30 19.30 37.00 24.10 21.40 21.40 29.10 25.10 35.70

MM 4.00 4.10 4.00 4.70 4.80 4.80 5.50 5.80 5.00

Vitrinite distribution, %

V6

V7 14

V8 23 1 3

V9 9 2 3 1 1 10 3 3

V10 5 1 7 10 8 8 15 23 18

V11 47 1 66 41 54 54 21 52 35

V12 18 0 25 39 35 35 31 21 37

V13 14 6 0 5 2 2 16 4

V14 12 21 2

V15 4 24

V16

Table 1: Details Properties of Characterized Coal

S. No 10 11 12 13 14 15 16 17 18

Properties Coal

J Coal

K Coal

L Coal

M Coal

N Coal

O Coal

P Coal

Q Coal

R


Ash 6.62 9.34 9.00 8.31 13.10 8.95 7.52 17.23 9.20

VM 24.90 22.51 20.10 25.85 26.10 20.95 20.52 20.30 25.00

S 0.73 0.64 0.50 0.60 0.57 0.44 0.60 0.60 0.60

P 0.010 0.049 0.006 0.102 0.010 0.060 0.053 0.080 0.030

Na2O 0.76 0.31 1.04 0.32 0.35 0.22 0.78 0.12 0.51

K2O 3.15 1.35 0.40 0.51 2.46 0.66 1.66 1.17 0.69


CSN 8.0 9.0 7.5 7.5 9.0 7.5 7.5 5.5 8.0

GK G4 G7 G5 G5 G6 G2 G4 G G4

Max. Fluidity, ddpm

2248 2679 446 3794 518 262 65 1480 250

Ash Constituents, %

SiO2 57.97 66.88 62.58 28.40 64.69 51.42 52.26 63.34 50.14

Al2O3 24.26 26.40 28.06 19.65 27.46 34.46 20.59 30.07 32.78

Fe2O3 10.40 3.41 4.17 2.16 2.36 5.69 15.68 2.91 9.35

CaO 2.05 1.32 1.04 0.81 1.37 2.08 6.81 1.81 2.30

MgO 1.42 0.33 1.04 0.53 1.30 5.47 2.22 0.58 1.12


Ro, % 1.17 1.09 1.11 1.19 1.04 1.15 1.17 1.24 1.17

Maceral Analysis, %

Vitrinite 72.30 49.20 61.10 76.25 87.50 66.00 58.70 51.20 68.60

Exinite 3.80 0.80 1.40 1.80 1.80 0.80 1.10 1.20 2.00

Inertinite 20.10 44.50 31.90 17.60 2.60 27.00 35.60 37.40 16.00

MM 3.80 5.50 5.60 4.35 7.80 6.00 4.60 10.20 2.70


V6

V7 2 13

V8 7 5 18 4 2

V9 27 8 6 20 5 8

V10 18 24 3 36 4 12 12 25

V11 3 39 30 11 38 15 15 14 48

V12 4 22 50 31 2 35 30 29 15

V13 5 2 20 14 40 18 33 2

V14 21 0 4 8 10 11

V15 11 1 7 1

V16 2 3




S. No 1 2 3 4 5 6 7 8 9

Properties Coal

A Coal

B Coal

C Coal

D Coal

E Coal

F Coal

G Coal

H Coal

I


Ash 8.70 8.45 8.10 8.00 9.50 9.76 9.50 10.20 8.6

VM 23.90 25.98 22.10 21.68 22.00 21.90 25.00 23.50 25.12

S 0.53 0.58 0.47 0.52 0.54 0.54 0.63 1.10 0.58

P 0.020 0.028 0.023 0.002 0.018 0.018 0.038 0.095 0.060

Na2O 0.50 0.04 0.40 0.60 0.57 0.57 0.39 0.10 0.46

K2O 0.90 0.02 0.80 3.01 0.08 0.08 0.15 1.57 1.00


CSN 8.5 7.5 8.0 8.5 7.5 7.5 9.0 9.0 8.0

GK G5 G6 G4 G6 G5 G4 G10 G7 G4

Max. Fluidity, ddpm

999 1100 1100 5272 500 500 4500 4915 886

Ash Constituents, %

SiO2 61.50 66.50 58.00 57.29 62.62 62.62 55.89 61.38 52.46

Al2O3 29.70 26.50 32.50 27.86 29.56 29.56 32.88 26.56 35.94

Fe2O3 2.70 2.40 3.10 7.82 4.16 4.16 4.70 3.43 9.17

CaO 0.80 0.20 0.90 3.41 2.00 2.00 3.17 2.42 0.96

MgO 0.50 0.40 0.50 0.00 1.00 1.00 1.17 0.30 0.01


Ro, % 1.24 1.19 1.17 1.18 1.16 1.16 1.18 1.11 1.15

Maceral Analysis, %

Vitrinite 64.70 75.30 58.00 70.80 68.40 68.40 64.60 69.10 56.40

Exinite 1.00 1.30 1.00 0.40 5.40 5.40 0.80 0.00 2.90

Inertinite 30.30 19.30 37.00 24.10 21.40 21.40 29.10 25.10 35.70

MM 4.00 4.10 4.00 4.70 4.80 4.80 5.50 5.80 5.00


V6

V7 14

V8 23 1 3

V9 9 2 3 1 1 10 3 3

V10 5 1 7 10 8 8 15 23 18

V11 47 1 66 41 54 54 21 52 35

V12 18 0 25 39 35 35 31 21 37

V13 14 6 0 5 2 2 16 4

V14 12 21 2

V15 4 24

V16


S. No 10 11 12 13 14 15 16 17 18

Properties Coal

J Coal

K Coal

L Coal

M Coal

N Coal

O Coal

P Coal

Q Coal

R


Ash 6.62 9.34 9.00 8.31 13.10 8.95 7.52 17.23 9.20

VM 24.90 22.51 20.10 25.85 26.10 20.95 20.52 20.30 25.00

S 0.73 0.64 0.50 0.60 0.57 0.44 0.60 0.60 0.60

P 0.010 0.049 0.006 0.102 0.010 0.060 0.053 0.080 0.030

Na2O 0.76 0.31 1.04 0.32 0.35 0.22 0.78 0.12 0.51

K2O 3.15 1.35 0.40 0.51 2.46 0.66 1.66 1.17 0.69


CSN 8.0 9.0 7.5 7.5 9.0 7.5 7.5 5.5 8.0

GK G4 G7 G5 G5 G6 G2 G4 G G4

Max. Fluidity, ddpm

2248 2679 446 3794 518 262 65 1480 250

Ash Constituents, %

SiO2 57.97 66.88 62.58 28.40 64.69 51.42 52.26 63.34 50.14

Al2O3 24.26 26.40 28.06 19.65 27.46 34.46 20.59 30.07 32.78

Fe2O3 10.40 3.41 4.17 2.16 2.36 5.69 15.68 2.91 9.35

CaO 2.05 1.32 1.04 0.81 1.37 2.08 6.81 1.81 2.30

MgO 1.42 0.33 1.04 0.53 1.30 5.47 2.22 0.58 1.12


Ro, % 1.17 1.09 1.11 1.19 1.04 1.15 1.17 1.24 1.17

Maceral Analysis, %

Vitrinite 72.30 49.20 61.10 76.25 87.50 66.00 58.70 51.20 68.60

Exinite 3.80 0.80 1.40 1.80 1.80 0.80 1.10 1.20 2.00

Inertinite 20.10 44.50 31.90 17.60 2.60 27.00 35.60 37.40 16.00

MM 3.80 5.50 5.60 4.35 7.80 6.00 4.60 10.20 2.70


V6

V7 2 13

V8 7 5 18 4 2

V9 27 8 6 20 5 8

V10 18 24 3 36 4 12 12 25

V11 3 39 30 11 38 15 15 14 48

V12 4 22 50 31 2 35 30 29 15

V13 5 2 20 14 40 18 33 2

V14 21 0 4 8 10 11

V15 11 1 7 1

V16 2 3




S. No 19 20 21 22 23 24 25 26 27

Properties Coal

S Coal

T Coal

U Coal

V Coal W

Coal X

Coal Y

Coal Z

Coal AA


Ash 7.30 8.98 10.49 9.22 9.22 10.50 8.00 9.50 14.95

VM 22.80 22.15 26.20 26.15 26.15 23.50 31.50 24.50 24.50

S 0.36 0.40 1.10 0.43 0.48 0.69 0.61 0.41 0.64

P 0.046 0.045 0.012 0.017 0.038 0.020 0.033 0.044 0.010

Na2O 0.30 0.11 0.37 0.48 0.48 0.20 0.60 0.30 0.23

K2O 0.80 0.78 1.98 0.65 0.65 0.98 0.90 0.80 0.75


CSN 6.0 7.0 6.0 7.0 7.0 5.0 9.0 5.5 5.0

GK G1 G4 G1 G2 G1 G G10 G G

Max. Fluidity, ddpm

400 25 588 506 600 548 7000 200 2700

Ash Constituents, %

SiO2 50.90 49.83 61.99 61.56 61.56 47.43 50.50 50.90 63.86

Al2O3 27.90 39.45 31.42 22.84 22.84 37.27 37.00 27.90 30.05

Fe2O3 10.90 5.40 3.21 7.27 7.27 11.29 4.10 10.90 2.93

CaO 3.60 3.05 0.75 1.25 1.25 0.68 1.50 3.60 1.65

MgO 1.20 1.38 0.28 1.45 1.45 0.23 0.40 1.20 0.53


Ro, % 1.12 1.12 1.00 0.95 0.95 1.10 0.92 1.04 1.03

Maceral Analysis, %

Vitrinite 54.00 48.50 53.80 72.20 72.20 55.00 76.00 61.20 49.00

Exinite 1.00 0.00 0.80 5.60 5.60 2.00 3.00 5.40 2.40

Inertinite 42.00 47.30 39.20 19.80 19.80 38.00 18.00 28.60 39.80

MM 3.00 4.20 6.20 2.40 2.40 5.00 3.00 4.80 8.80


V5

V6

V7 4 2 2 2 3

V8 13 13 13 4 35 4

V9 2 5 24 30 30 10 56 25

V10 30 20 36 44 44 27 6 36

V11 62 56 16 9 9 30 30

V12 6 17 5 1 1 8 4

V13 2 2 8 1

V14 1

V15

V16


S. No 28 29 30 31 32 33 34 35 36 37

Properties Coal AB

Coal AC

Coal AD

Coal AE

Coal AF

Coal AG

Coal AH

Coal AI

Coal AJ

Coal AK


Ash 8.50 10.50 9.50 13.00 7.66 7.39 9.55 9.50 8.39 10.30

VM 27.50 17.50 27.50 33.00 34.57 31.78 18.65 33.50 17.60 31.33

S 0.55 0.59 0.50 0.70 0.69 0.97 0.63 0.55 0.33

P 0.050 0.055 0.050 0.020 0.014 0.016 0.026 0.015 0.051

Na2O 0.26 0.50 0.59 0.55 2.69 0.56 0.17 0.57 0.20 0.27

K2O 1.53 0.90 1.17 0.90 4.68 1.82 0.94 1.04 1.46 1.09


CSN 5.0 7.5 6.0 5.5 7.5 8.0 5.0 5.5 3.0 0.0

GK G1 G6 G3 G G4 G3 G G F A

Max. Fluidity, ddpm

400 20 3500 1500 8174 2630

8 36 150 0 0

Ash Constituents, %

SiO2 60.02 54.50 47.86 52.40 54.75 71.54 63.59 62.92 53.50 62.63

Al2O3 31.44 28.50 28.56 35.80 33.15 17.98 28.62 20.30 35.94 29.16

Fe2O3 3.43 6.40 13.33 5.55 2.96 5.36 2.54 9.14 4.27 5.88

CaO 2.29 2.40 3.61 1.25 0.89 1.91 3.82 1.96 2.97 0.52

MgO 1.03 0.70 1.61 0.40 0.89 0.83 0.32 1.11 1.66 0.46


Ro, % 0.98 1.60 1.03 0.80 0.79 0.93 0.93 0.72 1.27 0.78

Maceral Analysis, %

Vitrinite 52.00 71.00 54.00 70.00 61.30 76.50 55.00 70.00 54.00 50.10

Exinite 1.20 0.00 2.90 2.00 1.20 5.50 1.20 2.00 0.60 1.20

Inertinite 41.00 23.00 38.25 24.00 33.10 13.40 38.20 25.00 41.50 42.60

MM 5.80 6.00 4.85 4.00 4.50 4.60 5.60 3.00 3.90 6.10


V5 13

V6 8 28 17

V7 7 37 2 3 30 28

V8 23 50 42 22 33 16 19

V9 37 0 13 68 46 14 2 13

V10 22 40 8 15 9 20 8

V11 7 10 3 2 41 2

V12 2 22

V13 12

V14 66 3

V15 18

V16




Conclusion

Selection of coking coals for coke making is a very

complex process. This is due to the fact that large

numbers of coking properties are used for assessing

the coals for their commercial exploitation. In this

study, a simple index named “Composite Coking Index

(CCI)” has been proposed which incorporates the

effects of these properties. Accordingly, a simple model

has been developed for selecting individual coals and

designing coal blend for coke making process. The

model has been validated with actual plant data from a

non-recovery coke making technology. The key futures

of the model are:

Identifying the coking potential and categorization of

different coking coal grades (like prime hard, hard

semi-hard, soft, semi-soft and weak coking coal)

Selection of coal and blend design for achieving coke

of desired properties (this may not be meet desired

level, if coking is not completed in optimum

conditions).

Selection of cheaper coals for reducing coke cost

Prediction of coke properties from individual

properties of blend constituents (for non-recovery

coke making technology)

The above correlation to be developed for others

coke making technologies (like top & stamp charged

recovery coke making).

It is also concluded that this model is not applicable

for calculating the CCI of non-coking coal.

Acknowledgement

We are thankful to the Chairman, METEC

INSTEELCON®2011, for permitting to present this

paper in the conference. We are also thankful to Mr.

Swapnil Gupta (Sr. Software Engineer, Wipro

Technology, Hyderabad) for making this easier model

and also the CRDSS, Tata Steel Ltd., Jamshedpur for

their cooperation and support.

References

[1] Grigore M., Sakurovs R., French D., Sahajwalla V.:

Mineral matter in coals and their reactions during

coking, International Journal of Coal Geology, 2008,

76, P. 301–308

[2] Kumar P. P.; Barman S. C.; Singh S; Ranjan M,:

Influence of coal fluidity on coal blend and coke quality,

Ironmaking and Steelmaking, 2008, Vol 35, No 6, P.

416-420

[3] MacPhee J. A.; Giroux L.; Gransden J.F.; Price J.T.:

The synergistic effect between macerals of different

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[4] Banerjee P. K.; Tiwari H. P.: Impact of beneficiation

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A Simple Technique for Selecting Coals for Non-Recovery Coke Making Process

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