model matematic de optimizare a proprietatilor sinterului

8/11/2019 Model Matematic de Optimizare a Proprietatilor Sinterului

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MATHEMATICAL MODELLING AND OPTIMISATION OF

IRON ORE SINTER PROPERTIES

E. Donskoi1, J. R. Manuel1, J. M. F. Clout2, Y. Zhang3

1CSIRO Minerals, Pullenvale, Kenmore QLD, Australia2Fortescue Metals Group Limited, West Perth, Australia

3Hamersley Iron Rio Tinto Ltd., Shanghai, CHINA

ABSTRACT. The quality of iron ore sinter is a critical factor determining the

productivity of blast furnaces for ironmaking. Csiro has therefore been developing

capabilities for predicting sinter characteristics, which enables sinter quality to be

improved/optimized and preliminary assessments to be made of the suitability of

specific ores or ore blends for sinter production.

An extensive database of pilot-scale sintering experimental results has been

used to create mathematical models for predicting different sinter properties. Inaddition to size distribution and other physical and chemical characteristics which are

usually used for sinter quality prediction, the mineralogical and textural

characteristics of iron ores intended for sintering have also been taken into account.

This approach has been quite successful, the variation of sinter reduction degradation

index (RDI) that is accounted for by explanatory variables (R-sq) being 87%, for

example.

Optimisation criteria have been developed that take into account several sinter

characteristics simultaneously and optimisation of different iron ore blends to produce

target sinter characteristics has been carried out. Modelling results and their

preliminary validation are discussed.

Introduction

In spite of the development and introduction to industrial production of a large

number of new iron making processes (1), the major source of iron production (more

than 90%) is still the blast furnace. The purpose of a blast furnace is to remove

oxygen from iron oxides and to convert iron oxides into liquid iron which can be

cooled down to produce pig iron for further processing into steel. Iron oxide can be

fed to the blast furnace in the form of raw ore (lump), pellets or sinter. In current

ironmaking practice, sinter comprises up to 70-85% of the total ferrous burden.

Sinter is produced by agglomerating fine ore, utilising coke or another

carbonaceous material as fuel. Specific amounts of different additives, such as fine-

sized limestone, magnesite, dolomite, quartzite, serpentine, hydrated lime, etc, arealso added to balance sinter chemistry for the blast furnace and flux matrix melt-

forming reactions.

The main aim of iron ore sintering is to produce a strong and reducible

agglomerate (2). To produce sinter at a plant, the granulated sinter mixture is fed onto

the sinter strand and ignited from the top. Air is drawn down through the bed with

suction fans and a flame front propagates through the bed. During high temperature

treatment, the sinter mix is partially melted, and when the sinter is cooled the melt

solidifies into a bonding phase for the unreacted materials (see Fig 1).


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Figure 1. Reflected light optical photomicrograph of hematite sinter structure,

showing remnant hematite nuclei, the melt-derived matrix and porosity.

Several different approaches have been used to develop mathematical models

simulating the iron ore sintering process (3-5). Usually such models describe the heat

and mass transfer, drying and condensation of water, gas flow, coke combustion, and

charge melting and solidification phenomena that occur during the process. They can

also include modelling of phenomena like shrinkage of the bed, variation of granule

diameter, channelling factors, void fraction (5), etc. Such modelling enables

calculation of different parameters inside the bed like composition, solid and gas

temperature, and porosity. These models are very good for understanding theunderlying mechanisms of the process and in prediction of the bed behaviour.

Unfortunately, no previous mathematical phenomenological models have been

developed that connect the parameters described above and some of the parameters of

the sinter which are extremely important for the industry, including Tumble Index

(TI), Reduction Degradation Index (RDI) and Reducibility Index (RI) (for definition

of these parameters see the Appendix). Parameters such as Productivity and Fuel

Rate can be obtained from such modelling. However, due to the extreme complexity

of the process, results obtained from the resulting models can be significantly

different from experimental results.

In the work described here, a different approach based on empirical modelling

was employed. CSIRO (Commonwealth Scientific and Industrial ResearchOrganisation) has performed a large number of sintering experiments over recent

years. These experiments were performed with many different iron ores and blends,

covering a wide range of sintering behaviour. From this work, a large database has

been built up, which contains data on the resulting sinter, including physical, chemical

and mineralogical parameters of the blends and specific conditions under which the

experiments were performed. The most significant variables for modelling sintering

outputs were identified and regression models developed. Further optimisation

criteria have been developed and then modelling was performed to find optimal iron

ore blends within realistic constraints.

The effect of different variables on sinter properties has been studied by

different authors. Loo et al (6) and Hsieh (7) showed that the properties of the rawmaterials used for sintering significantly affect the melt formation and assimilation,

nucleus

matrix

pore

pore

pore

matrix

1mm

hematitenucleus


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sintering reactions and production of bonding phases. Hosotani et al (8) showed the

influence of alumina on melt formation in the sintering process and its further effect

on sinter properties. Higuchi and Heerema (9) showed the influence of sintering

conditions on the reduction behaviour of pure hematite compacts.

No articles have been found in the open literature where all the dependencies

of raw material properties and sintering conditions were combined in separate modelsfor predicting sinter parameters, although a number of attempts are known to have

been made within industry using an empirical approach to modelling sinter properties.

It is believed that none of these considered the textural characteristics of iron ore used

for sintering, while CSIRO research has shown that textural characteristics

significantly affect the quality of the sinter and should be taken into account during

modelling of sinter properties.

Initial task

The initial task was to find the best sinter mix composition for nine different

ores (further referred to as ores A, B, C, G, H, I). The ore blends were selected onthe basis of different criteria, such as lowest cost, highest TI, highest productivity and

lowest fuel rate. Simultaneously, the blends had to contain minimum percentages of

particular ores and have chemical compositions acceptable for specific blast furnace

requirements. Two sets of iron ores were developed, ie, high quality and

correspondingly higher cost blends, and lower cost blends with acceptable quality.

The constraints for the high quality blends were:

(1) Final sinter Total Iron 57-58%, SiO2< 5%, basicity = 1.8-2.0, MgO = 1.6-2.5%.

(2) Initial blend not less than 30%, but not more than 60%, in total of ores A, B, Cand D.

The constraints for the low cost blends were:

(1) Final sinter Total Iron < 57%, 4.3% < SiO2< 5.5%, basicity = 1.8-2.0, MgO =1.6-2.5%

(2) Initial blend with 60% or more in total of ores A, B, Cand D.(3) Acceptable sinter TI >65% (pot-grate equivalent).

Approach

CSIRO has been conducting pot-grate sintering (pilot scale) on different ores

and blends for a number of years. The technique used is consistent for all sintering

runs. Information about the initial sinter mixture and sintering results were put into a

sintering database. The modelling was based on 95 carefully selected optimum runs

from CSIRO pot-grate testing in which the return fines were in balance and there were

no abnormalities in physical or process variables.

Variables which had the greatest influence on sintering performance and

which were mathematically most significant were used. `Major variables which were

used for modelling were total iron content, the amount of alumina in the -1mm size

fraction, blend moisture content, coke level, bulk density of the iron ore mixture

without coke or fluxes, basicity (ratio of CaO to SiO2 by mass), loss of mass duringignition (LOI) up to 371 C (LOI 371), LOI 900, ore mass percentages in the -0.63


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mm, -1 mm and +4 mm size fractions, and the proportion of dense hematite ore

texture. Details on ore textural classifications have been provided by Box et al (10).

All variables characterising sintering are interrelated, so different sets of variables can

be chosen for modelling. However, the set of variables mentioned above was found to

be the most efficient.

Table 1. List of objective functions used in the modelling (Prod=Productivity).

Optimisation criteria

1 Cost 6 TI/Cost

2 Prod/cost 6a TI-Cost*20.43

2a 100+prod-cost*52.1 7 TI

3 Productivity 8 Prod*TI

4 Fuel Rate 8a Prod+2.55*TI

5 Prod/Cost/Fuel Rate 9 Prod+2.55*TI-cost*52.1

5a 100+prod-cost*52.1-FuelRate*0.91 9a Prod*TI/cost

10 Prod*TI/Fuel Rate/Cost 14 Fe total/cost

10a Prod+2.55*TI-Fuel Rate*0.91-

cost*52.114a Fe total-19.73cost

11 Fe total*Prod/cost 15 TI/Fuel Rate

11a Prod+2.64Fe total-52.1cost 15a 100+TI-2.8FuelRate

12 Prod*TI*Fe total/Cost/1000 16 Prod*TI*Fe total/1000

12a Prod+2.55TI+2.64Fe total-52.1Cost 16a Prod+2.55TI+2.64Fe total

13 Prod*TI*Fe total/Cost/Fuel Rate 17 TI*Fe total/Cost

13a Prod+2.55TI+2.64Fe total-

52.1Cost-0.91FuelRate

17a2.55TI+2.64Fe total-

52.1Cost

After modelling the sintering process parameters, ie, productivity, RDI, TI and

fuel rate, optimisation objective functions (optimisation criteria) were developed.

These objective functions included separate sintering process parameters and their

combinations (see Table 1). For the same combination of sintering parameters, two

different objective functions were developed. One objective function was just a

simple combination of products and ratios of sintering parameters, eg,

(Prod*TI/FuelRate/Cost) which aims (if maximised) to maximise productivity and TI

while minimising cost and fuel rate. Another objective function with the same goal as

the previous example is the sum of sintering parameters with weighting coefficients

(eg, Prod+2.55*TI-Fuel Rate*0.91-cost*52.1). These coefficients correspond to therespective variability of the parameters involved (standard deviations) obtained from

the database of experimental results. If special preference needs to be given to

maximisation or minimisation of certain parameters, the weighting coefficients can be

changed correspondingly.

The optimisation spreadsheet was created in Microsoft Excel and contained

information about the nine individual ores from which blends were created. This

information includes chemical and textural composition, cost, bulk density and size

distribution information. The spreadsheet also contains information about coke and

fluxes that have to be added to meet sinter composition requirements. Among these

fluxes were limestone, magnesite, dolomite, quartzite, serpentine and hydrated lime.

To find optimal blends, the objective functions described above and the ExcelSolver function were used for iterative optimisation. Concurrently with optimisation


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the model and/or is a result of random measurement errors. For other modelling

parameters, the proportions of the variation of the response variable (modelling

parameters) that were explained by explanatory variables (initial characteristics of the

mixture) (R-Sq) were 82% for productivity, 88% for TI and 90% for fuel rate.

More than 100 theoretical blends corresponding to different optimisation

criteria and constraints were investigated. From this initial list, results for one of theexercises are shown in Figure 2, the aim being to find the blend with the best

productivity corresponding to a given modelled Tumble Index. The trade-off between

productivity and tumble index can be clearly seen.

Tumble Inde x

Productivit

y

7271706968

50

49

48

47

46

45

44

43

1

8

76

5

4

32

Sca tterplot of Productivity vs Tumble Index

Figure 2. Relationship between maximum sinter productivity and Tumble Indexproduced by the optimisation process.

Some other results for the blend optimisation procedure are presented in

Table 2 (these blends are different from blends in Figure 2). The optimisation criteria

were:

1 Minimum cost

2 Maximum Productivity

3 Minimum Fuel Rate

4 Maximum TI

5 Maximum (Productivity+2.55*TI-cost*52.1)

6 Maximum (Productivity+2.55TI+2.64Fe total)

Table 2. Examples of blend calculations for various optimisation criteria.Sinter parameters % of iron ore in the blend

FeTotalsinter

TIPro-duc-tivity

FuelRate

CostBlend

RDIBasi-city

B C D E F G H I

1 57.00 67.72 41.88 51.43 0.745 32.0 2.00 48.87 0.00 28.50 0.00 0.00 0.00 0.00 0.00

2 59.38 68.37 49.30 54.09 0.867 30.0 1.80 0.00 24.25 0.00 31.12 8.08 0.00 0.00 17.37

3 58.00 72.60 44.15 44.24 0.867 29.4 1.89 0.00 0.00 24.46 0.00 0.00 2.06 52.48 0.00

4 58.00 72.69 43.24 44.37 0.865 30.2 1.89 0.00 0.00 30.34 0.00 0.00 0.57 48.90 0.00

5 58.00 72.66 43.78 44.32 0.860 30.1 1.89 0.00 0.00 28.77 0.00 0.00 1.71 48.61 0.006 59.66 72.28 43.91 44.29 0.906 29.6 1.80 0.00 0.00 24.66 0.00 0.00 14.52 43.01 0.00


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Relationship Between Experimental TI and Theoretical TI

y = 0.7707x + 31.863

R2= 0.8689

y = 0.6719x + 37.791

R2= 0.6802

82.0

82.5

83.0

83.5

84.0

84.5

85.0

85.5

86.0

86.5

87.0

66.0 67.0 68.0 69.0 70.0 71.0 72.0

Model Predicted TI (%+6.3mm)

CompactTI(%+

2mm)

Low Cost Blends

High Quali ty Blends

Figure 3. Relationship between experimental (measured) TI and theoretical

(predicted) TI.

Figure 4 compares experimental and modelled results for Tumble Index and

RDI for several single (100%) ore blends. As can be seen, the experimental results are

quite close to the modelling results. These single ore blends were included in the

database on which modelling was performed. Strictly speaking, such a comparison

does not demonstrate the predictability of models for new blends that are not part ofthe modelling database. As already pointed out, pot-grate sintering experiments are

very expensive to perform and require large amounts of raw materials, which limits

the number of blends and conditions which can be feasibly tested. Unfortunately, at

this stage there is not enough data to demonstrate the predictability of the model for

new blends, but this will be the subject of further research and validation of the

approach outlined above.


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Tumble Index Modelling

60

62

64

66

68

70

72

74

D G H I A

Experiment

Prediction

RDI Modelling

0

10

20

30

40

50

60

D G H I A

Experiment

Prediction

Figure 4. Comparison of experimental results and modelling results for various single

ore blends for a) Tumble Index and b) RDI.

Conclusions and Future Development

A mathematical modelling method for predicting sinter properties has been

presented. The ability of the models to reflect the variation in sinter properties via

explanatory variables was quite high (R-Sq values were 82% for productivity, 88%

for TI, 86% for RDI and 90% for Fuel Rate), which demonstrates the potential for

reliable prediction of major sinter characteristics. Such modelling enablesdetermination of major parameters affecting various sinter properties and a better

understanding of the effect of different parameters such as chemical composition,

density and ore textural characteristics, on sinter quality. The modelling showed that

not only should physical, chemical and mineralogical characteristics of iron ores and

fluxes be taken into account, but the textural characteristics of the iron ores

comprising the blend should also be included.

Comparison of modelling results with results from compact scale sintering

demonstrates the capability of both methods for predicting a range of sinter

characteristics.

Optimisation criteria and the method described above open up the possibility

of developing blends with specified characteristics and/or testing the feasibility of


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creating sinter with the required properties from a specified set of iron ores. The

effect of substitution of one iron ore for another in a given blend can also be studied.

To further validate the method, the results of more recent pot-grate sintering

runs need to be analysed. As part of further work, the database will be significantly

extended not only by including more recent results, but also by including more

chemical and textural parameters characterising the initial sinter mix. To take intoaccount the effect of the larger number of parameters on sinter properties, the

principal component analysis method will be used.

Acknowledgments

The financial support of Hamersley China for this work and permission to

publish the results is gratefully acknowledged. The contributions from Mr Jasbir

Khosa and Dr Liming Lu of CSIRO Minerals, who provided pot-grate sintering data

and information for this paper, are greatly appreciated. Dr Ralph Holmes is thanked

for reviewing the final manuscript.

APPENDIX

Fuel Rate - weight (in kilograms) of dry coke required to produce one tonne of

product sinter.

Productivity - amount of sinter product expressed as tonnes per square metre

of grate area of sintering machine per day.

Reducibility Index (RI) - the reducibility test is carried out to evaluate the

reduction behaviour of various iron oxide burden materials in the middle zone of the

blast furnace shaft and is a measure of the amount of oxygen removed from the

sample under certain standard conditions.

Reduction Degradation Index (RDI) - is a measure of the breakdown of the

blast furnace burden materials under conditions chosen to resemble those in the upper

part (low temperature zone) of the blast furnace shaft. In the test, a 500 g sample of -

19 + 16 mm sinter is reduced isothermally at 550 C for 30 minutes in a fixed bed

with a 30% CO/70% N2gas mixture flowing at 15 L/min. After cooling under N2,the

sample is tumbled for 30 minutes at a speed of 30 rpm and then screened at 3.15 mm.

The RDI is the weight % of tumbled product passing 3.15 mm.

Tumble Index (TI- Sinter Strength) - weight % +6.3 mm material remaining

after the tumble treatment in which 15 Kg of -40+10 mm sinter is tumbled in a 1 m

diameter drum for 200 revolutions at a speed of 25 rpm.

REFERENCES

1. Girija, P. and Gejierstam, J.(Ed.). Tradition and Innovation in the History ofIron Making. Nainital, PAHAR, 2002.

2. German, R.M. Sintering theory and practice. John Wiley & Sons, 1996.3. Nath, N.K. and Mitra, K. Mathematical modeling and optimization of two-

layer sintering process for sinter quality and fuel efficiency using genetic

algorithm. MATERIALS AND MANUFACTURING PROCESSES 2005,

20(3), 335-349.

4. Patisson, F., Bellot, J.P., Ablitzer, D., Marliere, E., Dulcy, C. and Steiler, J.M.

Mathematical-Modelling of Iron-Ore Sintering Process. IRONMAKING &STEELMAKING 1991 18(2), 89-95.


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5. Cumming, M.J. and Thurlby, J.A. Developments in Modeling and Simulationof Iron-Ore Sintering. IRONMAKING & STEELMAKING 1990 17(4), 245-

254.

6. Loo, C.E., Williams, R.P., Matthews, L.T. Influence of Material Properties onHigh-Temperature Zone Reactions in Sintering of Iron Ore.

TRANSACTIONS OF THE INSTITUTION OF MINING ANDMETALLURGY SECTION C- MINERAL PROCESSING AND

EXTRACTIVE METALLURGY 1992, 101, C7-C16.

7. Hsieh, L.H.. Effect of Raw Material Composition on the Sintering Properties.ISIJ INTERNATIONAL 2005, 45(4), 551-559.

8. Hosotani, Y., Konno, N., Kabuto, S., Kitamura M. and Abe T. Influence ofAlumina Component on Melt Formation in Sintering Process and Sinter

Quality Improving Technology. TETSU TO HAGANE- JOURNAL OF THE

IRON AND STEEL INSTITUTE OF JAPAN, 83 (6), 347-352.

9. Higuchi, K. and Heerema, R.H. Influence of Sintering Conditions on theReduction Behaviour of Pure Hematite Compacts. MINERALS

ENGINEERING 2003, 16 (5), 463-477.10. Box, J., Phillips, J. and Clout J.M.F., 2002. Use of Geological Material Types

for Predicting Iron Ore Product Characteristics. In: Proceedings of the 1st

Japan-Australia Symposium on Iron and Steelmaking, Fuwa-Ward

Symposium, ISIJ, Kyoto University, April 4-5, 2002.

11. Clout, J.M.F. and Manuel, J.R., 2003. Fundamental investigations ofdifferences in bonding mechanisms in iron ore sinter formed from magnetite

concentrates and hematite ores. Powder Technology, 130, 393-399.

model matematic de optimizare a proprietatilor sinterului

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