STUDY OF THE IMPACT OF OPERATIONAL PARAMETERS ON ......Blast Furnace as factors and PCA score as response to analyze the impact of input parameters on the Blast Furnace performance

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International Journal of Mechanical Engineering and Technology (IJMET)

Volume 10, Issue 08, August 2019, pp. 215-231, Article ID: IJMET_10_08_019

Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=8

ISSN Print: 0976-6340 and ISSN Online: 0976-6359

© IAEME Publication

STUDY OF THE IMPACT OF OPERATIONAL

PARAMETERS ON PRODUCTION OF HOT

METAL IN A BLAST FURNACE

KVLN Murthy and Vaddi Venkata Sundara Kesava Rao

Andhra Universty, India

ABSTRACT

The study made in this paper, is to analyze the Blast Furnace parameters based on

Hybrid multi-criteria decision making approach. The analysis is important as the

parameters cause the influence on the production process. The productivity as well as

quality can be improved by knowing these parameters in advance. The present work

examined is identification of various critical parameters of blast furnace in an

integrated Steel Plant by utilizing Response Surface Method based on GRA integrated

with PCA approach. GRA works like a discovery idea where known and obscure

components are aggregated to get optimum level of the multiple responses. Breeze

coke consumption, nut coke consumption, pulverized coal consumption, consumption

of sinter, composite quality index of sinter plant, sized iron ore consumption, pellets

consumption, Lime stone consumption, LD slag consumption, blast temperature, blast

pressure, blast volume and oxygen enrichment are considered as Blast furnace

operating parameters. Hot metal yield, % Si, %S, %P, %Mn, %CO2, %CO, %SOx,

%NOx and PM are considered as the output variables. . The grey relation coefficients

are subjected to principal component analysis to derive the principle component

scores which represent the aggregated response of multiple output variables. Finally,

response surface methodology is implemented by considering the input parameters of

Blast Furnace as factors and PCA score as response to analyze the impact of input

parameters on the Blast Furnace performance.

Key words: Grey relation analysis, Principal component analysis, Desirability

analysis

Cite this Article: KVLN Murthy and Vaddi Venkata Sundara Kesava Rao, Study of

the Impact of Operational Parameters on Production of Hot Metal in a Blast Furnace.

International Journal of Mechanical Engineering and Technology 10(8), 2019, pp.

215-231.

http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=8

1. INTRODUCTION

Multivariate models can be a good alternative to monitor complex processes like Blast

Furnace process. The models do not require complete theoretical knowledge about what is

going on inside the blast furnace at any given time, but they require good process knowledge.

With access to empirical data in the form of periods of good and stable operation in the blast



furnace, models can be set up to identify deviating operation and present which of the many

original variables that carry important information. Choosing of relevant variables requires

good process knowledge and the choice of reference data set is important for good model

performance. A reference data set that underestimates the variation in the process compared

with „normal‟ operation will produce a too sensitive model while overestimation of the

normal variability gives you a model that reacts slowly to significant changes of the process

state.

2. LITERATURE SURVEY

Erik Vanhatalo (2009), discussed the monitoring and control of a continuously operating

experimental blast furnace (EBF). A case study outlines the need for monitoring and control

of the EBF and the use of principal components (PCs) to monitor the thermal state of the

process. The case study addresses design, testing and online application of PC models for

process monitoring.

Vitor Maggioni Gasparini et.al. (2017) developed thermo chemical model in order to

monitor the performance of coke-based blast furnaces, focusing on tools for calculating and

graphically displaying parameters that facilitate interpretation of the internal phenomena. In

the study, the input parameters for the model consisted of the properties and consumption of

raw materials and the mass and thermal balances of the process. The thermo chemical model

is based on the calculation of the degree of reduction of the metallic burden in the preparation

zone, defined as the omega factor.

Angelika Klinger et al. (2009) presented the VAiron expert system which is fully

integrated into the online process optimization package.

Sujit Kumar Bag (2007) presented a method to predict blast furnace parameters based on

artificial neural network (ANN). The parameters like hot metal temperature (HMT) and

percentage of impurity of silicon content in molten iron are predicted in the study. The

simulation and plant trial results are compared to show the effectiveness of the approach.

Parag Sen (2015), presented a case study of an Indian pig iron manufacturing organisation

to model the CO emission from the blast furnace by applying Six Sigma. In the study, it was

suggested that coke consumption is the most important parameter to influence CO emission

from the perspective of cost. Frequent high concentration of CO implies that heat is leaving

the furnace in the form of coke consumption, which needs to be improved using best available

technologies.

Ural Juan JIMÉNEZ et al. 2004 developed neural network based models to predict Blast

furnace hot metal temperature using set of variables such as blast parameters along with the

ore to coke rate. The model has been developed departing from actual plant data supplied by

Aceralia from its steel works located in Gijón.

V.R. Radhakrishnan et al. (2000) developed a neural network and trained with output

variables: quantity of hot metal and slag as well as their composition with a set of thirty three

process variables.

Angela X. Ge (1999) modelled the blast furnace using a neural network approach using

eleven imput variables. The author predicted the hot metal temperature which is the most

important parameters of the blast furnace as output.

Mohanty, I et al. (2011) studied feed-forward neural networks for predicting hot metal

temperature. For the first set they used twenty four inputs variables which reduced to fifteen

input variables based on the method that measures the entropy of different input variables

while categorizing the output.

Study of the Impact of Operational Parameters on Production of Hot Metal in a Blast Furnace


Erik Vanhatalo et al. (2007) discussed the design and analysis of an experiment performed

in a continuous process (CP). A full factorial design with replicates is used to test three types

of pellets on two levels of a process variable in an experimental blast furnace process. The

authors propose a multivariate approach to the analysis of the experiment, in form of principal

component analysis combined with analysis of variance

AL. Kundu et al. (2004), dealt with the various factors which contribute towards low

silicon and low sulphur hot metal production. The authors concluded that actions namely:

consistency in input quality of BF burden; optimal control of heat input to blast furnace;

stable hearth condition; optimisation of slag chemistry, blowing practice; stock level, control

of hanging & scaffolding are needed.

Ram Pravesh Bhagat (2011), analyzed the explanatory variables affecting the coke rate

statistically. The study showed that the variable, burden rate was the most significant one

followed by temperature of hot blast. A change in burden rate has been mainly reflected by a

change in weight of raw limestone in the burden.

J. Gavel (2017) made a review on nut coke utilisation in the iron making blast furnaces.

The nut coke utilisation in a mixture with the ferrous burden is proved beneficial in the iron

making and its usage varies from few kilograms to as high as 140 kg/thm. The shaft

permeability increases with nut coke size and concentration.

Shun Yao et al. (2018) proposed optimization model and applied to analyze the effects of

coke ratio, coal rate, blast temperature and other factors on the cost, CO2 emission and

solution loss, and some measures to save cost, reduce emissions and reduce solution loss.

Yoshiyuki (2005) overviewed the effect of centralized gas flow principle on the

enhancement of blast furnace functions, summarizes our blast furnace operations historically

and technologically, and provides a view towards future blast furnace operation.

Masaru HOSOKAWA et al. (2014) studied the mechanism of the hydrothermal reaction

of BF slag was investigated by focusing on the reaction at the slag surface. The surface

reaction behaviour was reproduced using slag plate samples, which adjusted the effective

amount of hot water participating in the reaction.

Zheng gen Liu et al. (2016) studied the effect of three major influence process parameters,

carbon addition ratio, ore particle size, and coal particle size on the compressive strength of

high alumina iron ore–coal composite hot briquette (AlCCB) with the application of response

surface methodology.

Shujun Chen et al. (2019) investigated the effects of the simultaneous injection of MgO

and magnesite powder on the combustion of coals, properties of the primary slag, and

softening-melting properties of the burden. The authors concluded the technology of MgO

injection into tuyeres with pulverized coal was beneficial for blast furnace operation.

N. Spirin, V. Shvidkiy, Y. Yaroshenko and Y. Gordon (2014) discussed the fundamentals

of the blast furnace process to achieve a highly efficient operation of the blast furnace with

combined blast. The major types of combined blast and supplemental fuels are as follows:

oxygen enrichment, natural gas, oil and pulverized coal injection. The authors concluded that

the energy efficiency of blast furnace operation depends on the compliance of operating

parameters to the developed principles of combined wind.

3. PROPOSED METHODOLOGY

The performance of a blast furnace is determined by many parameters such as:

Composite quality index of sinter plant

Coke consumption

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Nut coke consumption

Pulverized coal consumption

Consumption of sinter

Sized iron ore consumption

Pellets consumption

Lime stone consumption

LD slag consumption

Blast temperature

Blast pressure

Blast volume

Oxygen enrichment

The application of proposed methodology is useful to continuous monitoring and

diagnostics of blast furnace process faults or improving hot metal quality. Sinter quality

control and productivity are important because allow blast furnace operate at low fuel rate,

stable and efficient operation, and economically profitable. It is possible to see the quality

requirements for sinter to be used as burden materials in the blast furnace (Mochón et al.

2014; Cores et al. 2010a).

The frame work for the proposed integrated methodology is presented below.

Figure 1 Frame work for the proposed integrated methodology

Process Parameters of smelting Process in blast furnace: Smelting is the process of

producing hot metal by the physical and chemical reactions in the blast furnace. The reactions

can affect the quality of the hot metal. Composite quality index of sinter plant, blast furnace

operating parameters like: coke consumption, nut coke consumption, pulverized coal

Composite quality index of sinter plant

Selection of smelting process input and output parameters

Literature review on smelting process parameters in the blast furnace

Data collection on input and corresponding output parameters

Data on output parameters GREY relation analysis

Grey relation coefficients Principal component analysis

Principal component scores Response surface method

Obtain critical parameters



consumption, consumption of sinter, composite quality index of sinter plant, sized iron ore

consumption, pellets consumption, Lime stone consumption, LD slag consumption, blast

temperature, blast pressure, blast volume and oxygen enrichment are considered as input

variables.

Blast furnace Process Quality Indices: In this thesis, Hot metal yield, %Si, %S, %P,

%Mn, %CO2, %CO, %SOx, %NOx and PM are considered as quality indices or output

parameters of smelting process in blast furnace.

3.1. Case Study

A case of an integrated Steel Plant in the area of production of hot metal in a Blast Furnace is

presented to study the impact of operational parameters on production of hot metal in the

Blast Furnace with the application of the proposed hybrid methodology.

3.1.1. Input parameters

Blast furnace process consists of a multivariate system which is subjected to a large number

of inter-influencing variables affecting the performance of the blast furnace. It is necessary to

isolate the inter-influence of the variables to understand the role played by each variable on

the performance of the blast furnace.

For the production of quality hot metal, it is essential to identify and optimise the various

key parameters e.g. raw materials quality, burden distribution, blowing conditions, slag

characteristics and cohesive zone behavior.

Besides acting as fuel/reducing agent, raw materials such as varieties of coke, pulverized

coal and iron ore with higher Fe content in helps in the reduction of slag volume. Nut coke in

the wall area helps to reduce reduction gas and heat requirements in the wall area. Since

coking coal / coke is scarce and costly, PCI is considered very relevant to minimize total

coking coal consumption as well as cost of production Flux material such as Lime stone when

charged in the blast furnace gets calcined inside the blast furnace. This calcination reaction

needs heats which result into increase in the specific fuel consumption. If these fluxes are

charged through sinter or pellets then the calcination reaction takes place outside the blast

furnace and the blast furnace working volume is more effectively used by the iron bearing

materials. This in turn improves the blast furnace productivity. Blast furnace productivity

greatly depends on the quality of sinter. It improves BF operation and productivity and

reduces coke consumption in blast furnace Sinter should have optimum grain distribution,

high strength, high reducibility, high porosity, softening temperatures greater than 1250 deg

C, constant FeO content in the range of 7-8 % and constant basicity. Fuel (pulverized coal/

natural gas/ coke oven gas/oil/coal tar) injected at the tuyere level is normally accompanied

by oxygen enrichment of the hot air blast. The injection of oxygen to the air blast reduces the

specific flow of the gas causing a reduction in the top temperature and an increase in the

adiabatic temperature (RAFT) in the tuyeres.

3.1.2. Output parameters

The performance of a blast furnace is generally evaluated by the level of its productivity, fuel

rate and the quality of the hot metal. Superior quality hot metal with lower and lower silicon

and sulphur contents is required for the production of quality steel through LD-CC route. Low

silicon and sulphur operation contributes not only to reduction of the heat required to reduce

silica in the blast furnace but also to the cost reduction in the steelmaking process.

Hot metal contains carbon (C), silicon (Si), manganese (Mn), phosphorus (P), sulphur (S),

trace metals and some gases besides iron (Fe) which is the main constituent of HM. P and S

are considered as impurities in the Hot metal. Hot metal contains around 3.5 % to 4.5 % of C

with S content less than 0.05 % and P content can be up to 0.12 %. Basic grade of Hot metal



has less than 1.0 % of Si, and lower than 1 % of Mn. This type of Hot metal is mainly used

for steel making. The composition of Hot metal especially the impurities such as S, P, and

trace elements depends on the quality of the burden materials consisting of ore, coke and

fluxes as well as quality of coal used for the injection. Si is the main element which decides

whether the Hot metal is of basic grade or foundry grade. A low Si content in Hot metal while

lowering of the refining costs during steel making also reduces BF energy consumption since

the Si transfer reactions are endothermic. Si in the Hot metal originates from silica (SiO2) in

coal, coke and the ore burden. A low S content of Hot metal is desired to avoid expensive

desulphurization before steelmaking. Additionally, S in the Hot metal retards C dissolution

from coke and coal char and hence the consumption of char. Most of the S in the Hot metal

originates in the coke and coal.

The quality of Hot metal of a blast furnace is determined by many parameters such as:

Hot metal yield, % Si, %S, %P, %Mn, %CO2, %CO, %SOx ,%NOx,, PM

3.1.3. Process control systems

Process control refers to the methods that are used to control process variables when

manufacturing a product. Two systems have been envisaged in the Blast Furnace Automation

Control system.

Burden handling system:

To control the proportioning of burden materials with due consideration for coke moisture

content & batching accuracy, this system also gives relevant data connected with the

operation of the Burden Handling Complex to the process personnel. This consists of the

following major local systems for monitoring and control.

i) Material levels in the bins

ii) Weighing material batch wise

iii) Moisture content of Coke

iv) Availability and transfer of the material batches to the Blast furnace top.

v) Over filling of chutes.

The centralized monitoring and control system

i) Automatic data acquisition on the process run, state of equipment.

ii) Automatic processing of incoming data and recording.

iii) Calculation of process variables quantities per cast per batch.

iv) Delivery of corrective responses to local control circuits.

v) Optical and acoustic signaling for variations of basic process parameters.

3.1.4. Data Collection

The data on process variables are collected for 30 days in three shifts and their statistical

measures are shown in the Table-1.



Table 1 Input variables

S.No. Variable Mean Standard

Deviation Min Max

1 BF Coke (kg/thm) 443.6 3.5 436.9 452.6

2 Nut Coke (kg/thm) 26.2 3.2 16.3 33.7

3 Pulverised Coal (kg/thm) 63.3 5.4 50.9 74.1

4 Sinter (kg/thm) 1169.2 72.4 1017.0 1328.1

5 Sized Iron Ore (kg/thm) 427.4 11.2 399.9 451.1

6 Pellets (kg/thm) 64.9 5.1 50.6 74.3

7 Limestone (kg/thm) 2.2 0.1 2.0 2.5

8 LD Slag (kg/thm) 2.9 0.0 2.8 3.0

9 Mn Ore (kg/thm) 0.6 0.0 0.5 0.7

10 Quartzite (kg/thm) 0.9 0.0 0.8 1.0

11 Sinter Quality Composite Index 1.6 0.3 1.0 2.9

12 Blast Temp 1017.1 17.4 984.7 1058.1

13 Blast Pressure (kg/cm2) 3.1 0.1 2.8 3.3

14 Blast Volume (Ncum/min) 5035.6 228.8 4582.8 5561.9

15 O2 Enrichment (%) 2.9 0.6 1.9 4.3

Table 2 Output variables


Deviation Min Max

1 HOT Metal Yield (t/d/cum) 1.8 0.2 1.3 2.2

2 %Si 0.8 0.0 0.8 0.8

3 %S 0.0 0.0 0.0 0.1

4 %P 0.1 0.0 0.1 0.1

5 %Mn 0.1 0.0 0.1 0.1

6 %CO2 25.3 1.1 23.1 27.8

7 %CO 24.0 0.8 22.1 25.8

8 Sox (mg/mm3) 152.1 12.5 125.5 179.0

9 Nox (mg/mm3) 127.1 18.9 100.1 175.5

10 PM (mg/mm3) 21.4 4.0 10.9 27.7

3.1.5 Data application in the proposed methodology

Study of impact of operational parameters on production of hot metal in Blast Furnace is

carried out by adopting the proposed hybrid method. Initially, grey relation analysis is

conducted by considering the data on ten output variables of the blast furnace.

3.1.5.1. Normalized matrix

During normalization yield of hot metal is considered as benefit type and the other variables

are considered as cost type for normalization.

3.1.5.2. Absolute differences

Absolute differences matrix outputs of blast furnace is calculated following the standard

formula.

3.1.5.3. Grey relation coefficients

Grey relation coefficient matrix outputs of blast furnace is calculated as per the procedure.

3.1.6. Principle component analysis

Principal Component Analysis (PCA) methodology is employed using SPSS 14 software to

determine the principal component scores from grey relation coefficients of Blast Furnace

output process parameters. Results of the principal component analysis are presented and

discussed below.



Input data for the principle component analysis: Grey relation coefficients are considered as

input data to the principle component analysis.

Eigen value and eigen vector: Eigen values and eigen vectors are determined for the

matrix using SPSS-statistical software and are presented in the Table-3.

Table 3 Total Variance Explained

Component

Initial Eigenvalues Extraction Sums of

Squared Loadings

Rotation Sums of Squared

Loadings

Total % of

Variance

Cumulative

% Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 7.071 70.715 70.715 7.071 70.715 70.715 6.899 68.992 68.992

2 1.800 18.003 88.718 1.800 18.003 88.718 1.973 19.725 88.718

3 0.983 9.832 98.549

4 0.082 0.815 99.365

5 0.051 0.515 99.879

6 0.005 0.051 99.931

7 0.003 0.030 99.961

8 0.002 0.018 99.979

9 0.001 0.011 99.990

10 0.001 0.010 100.000

Principal components based on eigen values: Only factors with an eigenvalue of more than 1

will be considered as significant and will be extracted. The value of 1 is the SPSS default

setting Kaiser stopping criterion for deciding how many factors to extract. The principal

components are shown in the Table-4.

Table 4 Component matrix

Output Variable Component

1 2

HMY 0.993 0.088

Si 0.995 0.083

S 0.992 0.085

P 0.991 0.099

Mn 0.981 0.032

CO2 0.996 0.063

CO 0.996 0.078

SOx 0.125 0.110

NOx –0.278 0.939

PM –0.299 0.929

Weighted principal component values (t-values): Weigthed PCA values are determined as

per the procedure. The weights of the two principal components are 0.797 and 0.203. Then t-

values are determined and are presented below Table-5.



Table 5 Indicating the t-values

Output Variable t-Values

HMY 0.810

Si 0.810

S 0.808

P 0.810

Mn 0.788

CO2 0.807

CO 0.809

SOx 0.122

NOx –0.031

PM –0.050

Principal component scores: The PCA scores are determined as per the procedure. The PCA

scores of 200 samples are considered as single response which aggregated from the multiple

responses (six output variables). In this paper, PCA is adopted to obtain a single variable by

aggregating the multiple variables.

Table 6

S.No. PCA Scores S.No. PCA Scores S.No. PCA Scores S.No. PCA Scores

1 4.626 51 3.541 101 3.235 151 4.099

2 3.957 52 2.549 102 4.304 152 3.378

3 2.487 53 3.102 103 3.854 153 2.953

4 2.771 54 3.617 104 2.634 154 2.881

5 3.642 55 3.167 105 5.363 155 2.520

6 5.169 56 2.125 106 3.246 156 2.245

7 4.354 57 2.339 107 3.156 157 3.260

8 3.842 58 3.027 108 2.788 158 1.919

9 2.678 59 3.417 109 4.084 159 2.077

10 4.207 60 4.049 110 2.649 160 3.585

11 1.997 61 2.919 111 4.793 161 4.519

12 2.452 62 4.700 112 2.748 162 2.195

13 3.980 63 2.730 113 2.683 163 4.566

14 2.361 64 2.685 114 2.238 164 2.892

15 3.879 65 2.972 115 2.096 165 3.237

16 2.234 66 2.478 116 2.822 166 2.993

17 2.409 67 2.814 117 2.855 167 3.322

18 4.663 68 3.135 118 2.913 168 2.093

19 3.356 69 3.506 119 2.730 169 3.326

20 4.035 70 2.400 120 2.344 170 2.878

21 3.120 71 3.167 121 3.372 171 2.442

22 3.257 72 2.898 122 2.410 172 2.178

23 2.921 73 2.272 123 3.245 173 2.519

24 4.027 74 4.285 124 2.323 174 2.267

25 2.414 75 3.826 125 3.477 175 2.489

26 2.724 76 4.709 126 3.136 176 2.896

27 3.470 77 4.358 127 2.322 177 2.138

28 2.670 78 1.900 128 3.098 178 3.434

29 2.233 79 2.333 129 4.126 179 2.801

30 2.341 80 3.393 130 2.600 180 2.302

31 2.205 81 2.854 131 3.654 181 3.827

32 2.521 82 4.258 132 2.949 182 3.568



S.No. PCA Scores S.No. PCA Scores S.No. PCA Scores S.No. PCA Scores

33 2.483 83 3.348 133 3.217 183 2.687

34 4.279 84 2.922 134 2.969 184 2.536

35 3.887 85 3.636 135 2.907 185 2.330

36 3.027 86 2.729 136 2.652 186 2.184

37 2.822 87 2.902 137 2.542 187 2.809

38 3.545 88 2.791 138 2.071 188 2.452

39 2.355 89 3.196 139 2.161 189 3.906

40 2.337 90 4.310 140 3.862 190 2.238

41 2.313 91 2.571 141 3.372 191 3.795

42 3.626 92 4.427 142 2.396 192 3.634

43 3.114 93 3.008 143 2.089 193 4.808

44 3.628 94 5.694 144 2.234 194 3.099

45 2.193 95 3.068 145 3.652 195 3.250

46 2.519 96 2.396 146 3.933 196 2.535

47 2.537 97 2.562 147 2.447 197 2.086

48 2.453 98 2.434 148 3.381 198 2.720

49 3.636 99 4.039 149 3.539 199 3.187

50 2.656 100 2.635 150 1.971 200 4.227

3.1.7. Response surface method

In this paper, Response surface Methodology is adopted to know the critical input factors of

sintering process that effect the Overall quality of the process aggregated from the six output

factors. Hence input parameters of sintering process are considered as factors and PCA score

that represent the overall quality is considered as response and Response Surface

Methodology using the Design Expert Software (Version10) is implemented.

Data on the input factors and response of the 200 samples are fed to the Response Surface

Model to the DOE module of Design expert 10.0. The results are presented in the following

and are discussed.

Analysis of Variance (ANOVA): The significance of model terms is evaluated by the F–

test for analysis of variance (ANOVA). The ANOVA analysis for significant factors is only

shown in Table-7.

Table 7 ANOVA results

Source Sum of df Mean F-value p-value

Model 115.3682 21 5.493725 10452.65 <0.00001 Significant

A-BC 0.00466 1 0.00466 8.866243 0.00331

B-NC 0.001456 1 0.001456 2.770149 0.097797

C-PC 0.00722 1 0.00722 13.73635 0.000281

D-SR 0.000136 1 0.000136 0.259528 0.611076

E-SO 0.024198 1 0.024198 46.04123 <0.00001

F-PE 0.010811 1 0.010811 20.56898 <0.00001

G-LS 0.00235 1 0.00235 4.472185 0.035843

H-LD 0.007521 1 0.007521 14.30994 0.000212

J-Mn 0.007221 1 0.007221 13.73887 0.00028

K-QU 2.51E-05 1 2.51E-05 0.047823 0.827145

L-SQI 0.027151 1 0.027151 51.65869 <0.00001

M-BT 2.5E-06 1 2.5E-06 0.004756 0.945094

N-BP 0.005854 1 0.005854 11.13865 0.001029

O-BV 0.002173 1 0.002173 4.134558 0.043501

P-OE 4.13E-05 1 4.13E-05 0.078617 0.779506

AB 0.00326 1 0.00326 6.202925 0.01367



AC 0.023721 1 0.023721 45.1319 <0.00001

BP 0.005856 1 0.005856 11.14237 0.001027

DL 1.66E-05 1 1.66E-05 0.031569 0.859178

LP 0.002858 1 0.002858 5.437825 0.020824

MN 0.002875 1 0.002875 5.47091 0.020446

Residual 0.093554 178 0.000526

Cor Total 115.4618 199

The Model F-value of 9655.29 implies the model is significant. There is only a 0.01%

chance that an F-value this large could occur due to noise.

P-values less than 0.0500 indicate model terms are significant. In this case Blast furnace

Coke (BF), pulverized coal consumption (PC), sized iron ore (SO), Consumption of Pellets

(PE), Consumption of lime stone (LS), Consumption of LD slag(LD), manganese ore

consumption consumption (Mn), Sinter quality Index (SQI), Blast Pressure (BP) and Blast

volume (BV) are obtained as significant model terms.

Combined effect of BF coke (BC) & Nut Coke (NC), blast coke (BC) and pulverized coal

consumption (PC), Nut Coke (NC) & Oxygen enrichment (OE), Sinter Quality Index (SIQ) &

Oxygen enrichment and Blast Temperature and Blast pressure is arrived as significant model

terms.

Blast Furnace Coke (BF):

Coke consumption is the most important parameter to influence CO emission from the

perspective of cost. Frequent high concentration of CO implies that heat is leaving the furnace

in the form of coke consumption

Nut coke (NC):

Nut coke (size < 40 mm) is charged in mixture with the ferrous burden in the blast furnace to

take advantage of better permeability, enhanced reduction kinetics and to lower the expensive

regular coke requirement during smelting. Nut coke is charged in wide range of size 10 – 40

mm and concentration from 2-35 % (Dharm Jeet Gavel et al., 2016)

Pulverized coal (PC):

Replacement of metallurgical coke by pulverized coal (PC) injected in blast furnace (BF)

tuyers is a major economical challenge, due to the high price of coke and unfavourable effect

of its production for the environment. But the difficulty consists in necessity of complete

gasification of coal particles within raceway and compensating for the negative changes in

technology. Theoretical and experimental researches of PC burning process under conditions

of raceway have been carried out. Methods and designs for intensifying burning have been

developed. Among them there are enriching blast with oxygen and its rational use.

Sized iron ore (SO):

For proper blast to be maintained there should be sufficient space between pieces of iron ore.

Iron ore lumps received from the mines are crushed in lump ore crushing unit and after

screening, the sized iron ore of 10mm to 50mm size along with other raw materials through

belt conveyor is charged into blast furnace. Quality of sized Iron-ore (%) is in the range of Fe

66.90 + 0.5, SiO2 0.90 + 0.25 and loss on ignition 1.56. Iron ore fines screened down are sent

to sinter plant for sinter making.

Consumption of Pellets (PE):

As a promising method to strengthen the blast furnace smelting and to realize reduced fuel

operation, high-proportion pellet charging has become the practice of BF iron making. Use of

pellet gives rise to uniform bed permeability in comparison with iron ore or sinter. This leads

to better gas–solid contact resulting in higher productivity at reduced coke and fuel rate

(Ashis Agarwal, 2018).



Consumption of lime stone (LS):

Chemical grade limestone is important to the process as it is the primary raw material which

helps remove impurities from the iron ore and produces a slag with low melting point and a

high fluidity. Consistency of the chemical grade limestone for chemistry and sizing is critical

for efficient blast furnace operations and cost control. Limestone will react with the

temperature in the blast furnace as it continues down the furnace to react with sulphur from

the iron and produce a slag with the silica formed from the iron ore. CaO in limestone is used

to remove the sulphur and react with the silica to produce a fluid slag at the bottom of the

furnace.

Consumption of LD slag:

Slag generated during steelmaking in basic oxygen converter (LD-Converter) is one of the

important waste materials in an integrated steel plant. The slag contains various desirable

substances like CaO, Fe and Mn. CaO is an important oxide present in the slag which can be

utilised in other metallurgical processes as flux material instead of lime or lime stone. Use of

this slag with low phosphorous not only replace lime but also avoids heat loss for calcination

of limestone and thus reduce not only the direct steelmaking cost, but also the disposal cost of

the slag.

Manganese ore consumption (Mn)

It is possible to consume efficiently Mn ores with a minimum of 28% Mn. Generally, Mn ore

is composed by manganese oxides (MnO2, Mn2O3, Mn3O4), accompanied by iron oxide, silica

and other oxides. Carbonates have been processed, too; at least in Russia and China. Attention

is paid to Mn/Fe ratio. As iron oxide is fully reduced in the furnace, taking part in the

ferroalloy, a low Mn/Fe ratio may imply that FeMn be below specification in Mn content. The

ratio should be not less than 7.5:1.

Sinter quality Index (SQI):

A relation between sinter usages in burden and productivity are well established. Now a day,

almost all blast furnace usages a sinter in its burden charge. Quality of raw materials (iron ore,

coke and sinter) is the prime factors which attribute the success of iron making producer. The

advantages of higher percentage of sinter in the burden like low silicon in hot metal, higher

productivity and low fuel rate have been well established.

Optimal preparation of basic ferrous material for production of pig iron (sinter) has a

significant impact on the blast furnace process and quality of pig iron. The use of poor-quality

ferrous materials increases the amount of by-products (e.g. slag), worsens the quality of pig

iron, and increases consumption of fuels, the value of which is the main part of production

costs.

Blast Pressure (BP):

Increased top pressure helps good furnace operation and reduced fuel rate by decreasing

velocity of the gases and by increasing retention time for the gas-solid reactions. In addition

to reducing the fuel rate, this measure helps reduce the hot metal silicon variability and

increases productivity.

Blast Volume (BV):

The production rate does not only depend on the oxygen enrichment values but it also

depends on the other variables such as blast temperature, blast volume, and steam injection

rate.



Combined effect of the input materials:

1. Combined effect of BF coke and Nut coke

Coke is a fuel having high carbon content. It is the solid carbonaceous material derived

from destructive distillation of low-ash, low-sulphur bituminous coal. Coke is the most

important factor in blast furnace iron making which alone, other than providing heat, reactants

and mechanical support to burden, accounts for more than 50% of hot metal production cost.

In modern blast furnace operational practices significant efforts are made to decrease the

costly coke consumption mainly by introducing cheaper substitutes through tuyeres. This

alters the in-furnace aerodynamics, reduction conditions, burden distribution and demands on

raw material, particularly coke, quality. The coke charged in blast furnace should have:

i) Adequate cold strength to resist breakage & abrasion by handling and burden

materials in the upper part of furnace.

ii) Adequate hot strength to resist chemical attack and excessive reaction with alkalis, gas

and slag.

iii) Strength to stand against gas kinetic energy and impact of burden descent.

iv) Optimum reactivity to achieve desired reduction rates as well as limit solution loss,

gasification and carburization.

v) Required size and even size distribution to provide better permeability.

Nut coke (10-25 mm) is charged in mixture with the ferrous burden in the blast furnace to

take advantage of better permeability, enhanced reduction kinetics and to lower the expensive

regular coke requirement during smelting. The reason for such a wide variation is poor clarity

on the fundamental behaviour of the nut coke in the blast furnace. The optimum concentration

of the nut coke utilization is a function of size, reactivity, burden chemistry, burden

distribution and its behaviour in hearth.

In blast furnace, total fuel requirement is met with a combination of BF Coke and nut

coke in such proportion that all requirements (Listed above) of BF process can be met. Since

total quantity of fuel is fixed hence to maximise the BF productivity an optimum nut coke is

to be fed at the expense of BF coke. The nut coke utilisation in a mixture with the ferrous

burden is proved beneficial in the ironmaking and its usage varies from few kilograms to as

high as 140 kg/thm. The shaft permeability increases with nut coke size and concentration. In

a multilayer packed bed, porosity was observed minimum at the layer interface. Nut coke

improves the permeability in the cohesive zone, acts as a skeleton for the ferrous burden layer,

and maintains the structure at the cohesive zone. Its utilisation improves the burden softening

and melting properties. Especially at high temperature nut coke utilisation avoids the

„reduction retardation‟ phenomena and enhances the reduction kinetics of the ferrous burden.

Nut coke reactivity is enhanced for its preferential consumption in place of regular coke.

2. Combined effect of Blast coke and pulverized coal injection

Hot metal yield has a significant combined effect of coke and PCI as because with PCI, Coke

volume per charge decreases resulting in lower coke layer thickness at throat & bosh. It has

adverse effect on permeability due to more no of interfaces between ore & coke layers,

situated closely. Productivity will be affected with permeability.

3. Combined effect of Nut coke & Oxygen enrichment

In blast furnace the presence of 79 % N2 by volume in blast restricts the temperature

generated in combustion zone. This temperature can be increased by decreasing the N2



content in blast i.e. by Oxygen enrichment of the blast. Oxygen enrichment results in to lower

fuel consumption. Hence Oxygen enrichment and coke consumption are inter-dependent. So

in case of lower coke consumption the use of coke breeze can be used to limited extent.

4. Combined effect of Sinter Quality index & Oxygen enrichment

In case of oxygen enrichment operations, the amount of coke which burns in front of the

tuyere increases on account of the increase in theconcentration of oxygen in the blast. Thus

the generation of heat and of CO gas is promoted.

The concentration of CO in the case of oxygen enrichment is much higher than that in the

case of non-enrichmentat any level in the furnace. Consequently, the reduction of the sinter

proceeds at a greater rate owing to the high concentration of CO gas.

5. Combined effect of Blast temperature & Blast Pressure

It is well known that Hot metal production isincreased with increasing blast volume.

However, blast volume cannot be increased indefinitely, because very high blast volumes

sometimes cause unfavourable conditions in the furnace, such as extreme increase in pressure

drop, by-passing of the furnace gas and flooding. Also for operation with high blast

temperatures, an increase in productivity and a decrease in coke rate are also expected, since

intensification of capacity for melting the Sinter and an increase in the rate of indirect

reduction of the sinter are caused by an increase in blast temperature.

Blast temperature and Blast pressure are interdependent as high blast temperature

generates a higher RAFT. In Order to control RAFT the Blast pressure is to be regulated i.e.

blast requirement is lowered by oxygen injection and increased blast temperature.

Table 8 R-Squared and the adequate precision values of the model

Std. Dev. 0.0229

R² 0.9992

Mean 3.0827

Adjusted R² 0.9991

C.V. % 0.7437

Predicted R² 0.9988

Adeq Precision 499.0335

From the results it is observed that the model is showing high coefficient of determination

(R-squared value of 0.9992) indicates that there exists a high degree of correlation between

the input parameters and the predicted response of hot metal quality. The "Pred R-Squared" of

0.9988 is in reasonable agreement with the “Adj R-Squared” of 0.9991. The adequate model

discrimination was also clearly visualized from the value of adequate precision (499.0335,

greater than 4. Hence the generated model for the hot metal quality could be deemed fit and

adequate.

The above analysis indicates that the model can be suitable for this work. However, poor

or misleading results might be generated for fitting the response surface model. Hence, it is

necessary to check the adequacy of the model. The adequacy of the model was checked

through various diagnoses such as predicted versus actual values.



Figure 2 Predicted versus actual values

From the graph it is observed that the data points on the graph, which is reasonably close

to the line. The predicted value is very close to historical data value, which indicates that the

predicted value is fully consistent with the actual data.

3.1.8. Desirability analysis

Desirability analysis was performed by employing the design expert software using the

desirability function for hot metal quality value. Desirability function uses a dimensionless

desirability value (d). The scale of d varies between 0 and 1. If d =0, then the response is

undesirable to d = 1, where the response is fully desirable. Hence, a value of 1 or closer to 1 is

required for getting a response of perfect target value (Derringer and Suich, 1980). From the

desirability analysis, the optimal level of various input parameters are found and listed in the

Table-9.

Table 9 Optimal level of various input parameters


Deviation Min Max

Optimum

Values

1 BF Coke (kg/thm) 443.6 3.5 453.49 497.96 476.06

2 Nut Coke (kg/thm) 26.2 3.2 16.3 33.7 27.50

3 Pulverised Coal (kg/thm) 63.3 5.4 50.85 74.11 59.90

4 Sinter (kg/thm) 1169.2 72.4 1017.01 1328.07 1274.12

5 Sized Iron Ore (kg/thm) 427.4 11.2 399.95 451.14 402.16

6 Pellets (kg/thm) 64.9 5.1 50.64 74.33 59.88

7 Limestone (kg/thm) 2.2 0.1 2.01 2.47 2.20

8 LD Slag (kg/thm) 2.9 0.041 2.81 3.00 2.96

9 Mn Ore (kg/thm) 0.6 0.040 0.51 0.69 0.58

10 Quartzite (kg/thm) 0.9 0.038 0.80 0.98 0.95

11 Sinter Quality Composite Index 1.6 0.3 0.99 2.94 2.74

12 Blast Temp 1017.1 17.4 983.74 1061.24 1040.28

13 Blast Pressure (kg/cm2) 3.1 0.1 2.84 3.36 3.18

14 Blast Volume (Ncum/min) 5035.6 228.8 4582.81 5561.88 4866.70

15 O2 Enrichment (%) 2.9 0.6 1.87 4.30 3.00

*desirability value = 1.0.



3.1.9. Results and Concluding remarks

The integrated GRA-PCA-RSM approach for the determination of critical blast furnace

parameters has been established methodically to conquer the limitations of single character

performance in multiple performance characteristics problems. The outcomes of this work can

be summarized as follows:

Multiple output parameters of blast furnace are aggregated as single parameters by

defining the PCA score.

Critical process input parameters that impact the aggregated output parameters is

arrived.

Individual input parameters such as Blast furnace Coke (BF), pulverized coal

consumption (PC), sized iron ore (SO), Consumption of Pellets (PE), Consumption of

lime stone (LS), Consumption of LD slag(LD), manganese ore consumption (Mn),

Sinter quality Index (SQI), Blast Pressure (BP) and Blast volume (BV) are obtained

as significant model terms.

Pulverized coal consumption (PC), sized iron ore (SO), Consumption of Pellets (PE),

Consumption of lime stone (LS), Consumption of LD slag(LD), manganese ore

consumption (Mn), Sinter quality Index (SQI), Blast temperature (BT), Blast Pressure

(BP), Blast volume (BV) are obtained as significant model terms.

Combined effect of BF coke (BC) & Nut Coke (NC), blast coke (BC) and pulverized

coal consumption (PC), Nut Coke (NC) & Oxygen enrichment (OE), Sinter Quality

Index (SIQ) & Oxygen enrichment and Blast Temperature and Blast pressure is

arrived as significant model terms.

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STUDY OF THE IMPACT OF OPERATIONAL PARAMETERS ON ......Blast Furnace as factors and PCA score as response to analyze the impact of input parameters on the Blast Furnace performance