07. Process Parameters - Ordonez 2015

Using process parameters to assess refractory materials performance REFRA-Training 2015 Hugo Ordóñez

What is performance?

The manner in which or the efficiency with which something reacts or fulfills its intended purpose. What is the intended purpose of refractory materials? To enable production by protecting the equipment from damaging temperatures

What is performance?

The manner in which or the efficiency with which something reacts or fulfills its intended purpose. What is the intended purpose of refractory materials? To enable production by protecting the equipment from damaging temperatures

What is performance?

The manner in which or the efficiency with which something reacts or fulfills its intended purpose. What is the intended purpose of refractory materials? To enable production by protecting the equipment from damaging temperatures

What is performance?

The manner in which or the efficiency with which something reacts or fulfills its intended purpose. What is the intended purpose of refractory materials? To enable production by protecting the equipment from damaging temperatures

What is performance?

The manner in which or the efficiency with which something reacts or fulfills its intended purpose. What is the intended purpose of refractory materials? To enable production by protecting the equipment from damaging temperatures

Refractory Lining Chart

How do we measure refractory material performance? • Lifetime (years, months, weeks) • Specific Refractory Consumption (Kilogram of refractory/Metric Ton of clinker) • Wear rate: – mm/month – cm/month – mm/Metric ton of clinker

Lifetime calculation Lifetime = date of removal-date of instalation (days, months years) Lifetime alone does not inform about: • • • •

The state of the lining by removal. The reasons for the removal. If the kiln produced during all the period. Relevant aspects for lifetime.

Residual thickness measurements HISTORIA DE CATEOS DEL HORNO 5 FECHA METRO 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

OCT 14/2002 0° 180° ZR 5 5 4 4 5 6 7 6 1/2 7 3/4 7 7 7 6 1/2 7 1/2 5 1/2 6 3/4 7 1/4 4 3/4 7 1/2 4 3/4 4 4 1/2 4 6 1/2 4 1/2 6 1/2 5 1/2 6 3 2 1/2 3 1/2 2 3 1/2 5 3 1/2 5

SEP 10/2003 0° 180° ZR

0 4 2 3 5 3 4 5 4 4 4 5 6 4 4 6 4 6 5

1/2 1/2 1/2

1/2 1/2 1/2 1/2

DIC 22/2003 OCT 14/2004 90° 180° 270° Z.R. 0° 180° ZR 4 1/4 4 1/2 4 6 1/2 5 1/2 2 1/2 3 2 3/4 5 3/4 5 3 3 3/4 3 1/2 6 6 5 1/2 5 5 6 1/2 6 4 1/2 4 3/4 5 8 8 4 1/2 5 3 1/2 5 1/2 5 1/2 4 3 1/2 5 1/2 6 4 3/4 4 4 3 1/2 5 1/4 5 1/2 4 1/2 4 1/2 4 1/2 5 8 3 1/2 4 1/2 4 6 1/4 6 1/2 3 1/4 4 3 1/2 6 6 4 4 1/2 4 1/2 6 6 4 1/2 5 3 1/2 5 5 3 1/2 4 1/2 5 1/2 3 3 3 5 1/2 5 1/2 1 1 4 1/2 6 6 1/2 2 3 6 1/2 6 5 1/2 2 2 1/2 6 6 5 1/2 4 2

DIC 21 2004 0° 180 ° ZR 51/2 6 5 5 5 5 51/2 41/2 71/2 63/4 6 61/2 41/2 5 5 43/4 41/2 41/2 41/2 41/2 5 51/4 5 61/4 2 31/2 6 51/2 6 6 5 51/4 5 61/8 41/2 41/2

31.03.2005 0 90° Z.R.

6 5 4 5 4 5 4 6 5

1/2 5 3/4 5 1/2 5 4 4 1/4 1/4 6 3/4 3 1/4 1/4 6 1/2 3/4 6

5 6 5 3/4 5 1/4 5 4 3/4 3 1/2

Wear rate (WR) calulation WR=Wear/time Initial thickness= T1 (mm) Final thickness= T2 (mm) Wear = (T2- T1) mm WR = (T2- T1) /time Time [months]

Thickness

inicial

RELEVANT WEAR final 1/3 hi

WR [ mm/month]

Instalación date

time

Replacement date

Specific refractory consumption (Kg/Metric Ton of clinker) Is calculated from: • Wear rate • Brick density • Kiln dimensions • Klinker production SRC= Kg refractories/Metric tonn clinker • Is the most frequently used measurement of performance. • Different production systems have different typical values • Values fluctuate a lot.

Factors related to refractory material performance Influences on the part of the cement producer

Refractory installation

Thermal Chemical

Storage

Refractor y selection Installation draw

Lifetime of refractories Productionquality

Kiln burning conditions

Mechanical

Raw material quality Influences on the part of the producer

We are going to focus on the influences being controlled by the technical management! Influences on the part of the cement producer

Thermal Chemical

Lifetime of refractories

Kiln burning conditions

Mechanical

Process parameters and process goals

Process parameters: Mass flows Temperatures Pressures Management/control Chemical compositions Mineralogical compositions Fineness Heating values Etc.

Process goals: Quantity Quality Cost

Basic control loop

set desired value

measure value take control action

YES

Is the difference between measured and desired values acceptable?

NO

Schewhart control chart

Old analog instruments

Modern process control

Clinker chemical composition report (1 analysis/hour = 24 analyses/day = 720 analyses/month = 8.000 analyses/year

Fecha

Hora

Operador

00:00 Lourdes Calla 01:00 Lourdes Calla 02:00 Lourdes Calla 03:00 Lourdes Calla 04:00 Lourdes Calla 05:00 Lourdes Calla 06:00 Yorgan Llerena 08:00 Yorgan Llerena 26.09.2011 10:00 Yorgan Llerena 12:00 Yorgan Llerena 14:00 Yorgan Llerena 16:00 Yorgan Llerena 18:00 Dianne Paco 20:00 Dianne Paco 22:00 Dianne Paco Promedio Desviacion Estandar 00:00 02:00 Dianne Paco 04:00 Dianne Paco 06:00 Yorgan Llerena 08:00 Yorgan Llerena 10:00 Yorgan Llerena 12:00 Yorgan Llerena 27.09.2011 14:00 Yorgan Llerena 16:00 Yorgan Llerena 18:00 20:00 22:00 Promedio Desviacion Estandar

SiO2 (%)

Al2O3 (%)

Fe2O3 (%)

CaO (%)

MgO (%)

SO3 (%)

Na2O (%)

K2O (%)

LSF (%)

C3S (FRX)

C2S (FRX)

C3A (FRX)

C3S (DRX)

C2S (DRX)

C4AF (FRX)

F.L.

M.F

M.H.

M.S

22,12 22,11 22,13 22,12 22,13 22,12 22,05 22,23 22,20 22,32 22,11 22,11 21,98 22,00 22,19 22,13 0,09

4,31 4,29 4,32 4,23 4,21 4,21 4,16 4,25 4,13 4,35 4,26 4,20 4,14 4,21 4,20 4,23 0,06

3,96 4,40 4,38 4,23 4,23 4,22 4,21 4,25 3,95 4,17 4,00 4,15 3,98 4,03 4,13 4,15 0,14

65,38 64,84 64,98 65,05 64,71 64,72 63,99 64,30 64,88 63,56 64,27 63,80 63,92 63,83 65,02 64,48 0,56

2,63 2,60 2,60 2,58 2,61 2,60 2,54 2,55 2,52 2,58 2,56 2,57 2,53 2,60 2,58 2,58 0,03

0,60 0,66 0,54 0,67 0,75 0,76 0,87 0,76 0,65 0,90 0,88 0,98 0,80 0,93 0,51 0,75 0,14

0,11 0,11 0,11 0,11 0,11 0,11 0,12 0,13 0,11 0,14 0,12 0,12 0,11 0,12 0,11 0,12 0,01

0,79 0,87 0,78 0,89 0,96 0,98 1,16 0,99 0,95 1,11 1,14 1,21 1,06 1,19 0,84 1,00 0,14

95,26 94,17 94,23 94,64 94,18 94,24 93,47 93,08 94,41 91,67 93,68 92,99 93,86 93,60 94,44 93,86 0,85

59,32 56,74 57,27 58,06 56,52 56,64 54,07 53,80 58,01 49,00 54,11 51,99 53,45 53,63 58,28 55,39 2,82

18,67 20,58 20,25 19,61 20,81 20,70 22,43 23,15 19,89 27,01 22,58 24,18 22,70 22,61 19,67 21,66 2,17

4,72 3,92 4,05 4,06 4,00 4,01 3,91 4,06 4,27 4,46 4,52 4,12 4,25 4,33 4,15 4,19 0,24

12,04 13,38 13,31 12,86 12,86 12,83 12,81 12,94 12,01 12,70 12,17 12,63 12,10 12,27 12,57 12,63 0,44

58,05 63,82 66,00 61,63 63,53 63,73 62,29 62,60 62,61 61,85 60,85 58,15 67,44 61,71 66,99 62,75 2,72

21,90 14,46 16,05 17,98 15,65 14,30 17,25 16,74 17,90 16,99 19,29 21,69 12,95 16,87 11,73 16,78 2,84

25,95 26,99 26,84 26,45 26,56 26,56 26,66 26,75 25,51 27,16 26,48 26,84 25,87 26,53 25,94 26,47 0,46

1,09 0,98 0,99 1,00 1,00 1,00 0,99 1,00 1,05 1,04 1,07 1,01 1,04 1,04 1,02

2,15 2,11 2,11 2,13 2,12 2,12 2,10 2,09 2,14 2,06 2,12 2,09 2,12 2,11 2,13

22,01 22,04 22,10 22,18 21,91 22,15 22,16

4,14 3,64 64,02 4,19 3,81 65,50 4,56 3,94 62,84 4,08 4,14 64,25 4,11 4,15 63,39 4,16 4,00 64,45 4,05 3,95 64,06 PARO HORNO

2,75 2,65 2,58 2,44 2,41 2,46 2,39

1,02 0,58 0,99 0,78 0,86 0,85 0,94

0,12 0,11 0,12 0,11 0,11 0,12 0,12

1,52 0,93 1,65 1,15 1,24 1,19 1,31

94,44 96,07 91,33 93,43 93,16 93,84 93,41

49,48 61,33 46,34 55,41 53,16 55,72 54,66

25,77 16,93 28,39 21,79 22,70 21,48 22,30

4,81 4,67 5,43 3,81 3,88 4,25 4,03

11,08 11,59 11,97 12,60 12,62 12,18 12,03

48,17 66,66 67,05 60,21 62,43 61,93 62,59

25,33 15,49 14,98 16,79 16,46 18,38 16,68

26,02 25,42 27,88 26,04 26,29 26,10 25,80

1,14 1,10 1,16 0,99 0,99 1,04 1,02

2,15 2,18 2,05 2,11 2,10 2,13 2,12

22,08 0,10

4,18 0,17

2,53 0,14

0,86 0,15

0,12 0,01

1,28 0,24

93,67 1,43

53,73 4,80

22,77 3,60

4,41 0,59

12,01 0,55

61,29 6,31

17,73 3,52

26,22 0,78

3,95 0,18

64,07 0,84

CaO libre, %

P/L

2,68 2,55 2,54 2,62 2,62 2,63 2,63 2,61 2,75 2,62 2,68 2,65 2,71 2,67 2,66

0,59 0,55 0,54 0,56 0,56 0,56 0,58 0,55 0,52 0,59 0,64 0,66 0,96 0,58 0,53 0,60 0,11

1182 1340 1408 1258 1212 1397 1188 1214 1202 1235 1198 1232 1189 1205 1225 1246 74,45

2,83 2,75 2,60 2,70 2,65 2,71 2,77

1,96 0,62 0,60 0,49 0,59 0,54 0,54

920 1194 1224 1207 1204 1206 1154

0,76 0,53

1158 107

We need to use statistical methods to manage the data

Statistical analysis options Searching for adequate tools for statistical analysis can be a very frustrating experience because: • Our knowledge of statistics is normally not very developed. • The amount of available resources is huge and their use require special knowledge. • Most of the frequently used models are not suitable for the analysis of parameters at the cement plants.

Exploratory Data Analysis Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to maximize insight into a data set to: • Uncover underlying structure; • Extract important variables; • Detect outliers and anomalies; • Test underlying assumptions; • Develop parsimonious models (based on as few as possible parameters) • Determine optimal factor settings. There are a number of tools that are useful for EDA, but EDA is characterized more by the attitude taken than by particular techniques.[

How does exploratory data analysis differ from classical data analysis? Three popular data analysis approaches are: • Classical • Exploratory (EDA) • Bayesian These three approaches are similar in that they all start with a general science/engineering problem and all yield science/engineering conclusions. The difference is the sequence and focus of the intermediate steps.

Common to all approaches is: There is a problem!

We want to reach conclusions

There is data available

There are models

Suported by the analyses of the data

The difference is the sequence and focus of the intermediate steps. For classical analysis, the sequence is • Problem => Data => Model => Analysis => Conclusions For Bayesian, the sequence is • Problem => Data => Model => Prior Distribution => Analysis => Conclusions For EDA, the sequence is • Problem => Data => Analysis => Model => Conclusions

Italian Example Analisi chimica farina calda e clinker

Normal distribution for the parameters is assumed, a typical statistical approach based on bayesian models. Is the assumption right?

Is the normality assumption correct? Hystogram for AR (1.213 analyses)

Gauss Distribution F(avg, s)

Gauss´s normal distribution does not correctly describe the behaviour of most parameters at a Cement Plant!

Because the assumption of normality might lead to false judgments, we prefer tu use EDA (exploratory data analysis)

Some graphical techniques employed in EDA • • • • •

Plotting the raw data (such as time series, histograms, probability plots, lag plots etc.) Plotting control charts for the relevant parameters. Plotting simple statistics such as mean plots, standard deviation plots, box plots and main effects plots of the raw data. Positioning such plots so as to maximize our natural pattern-recognition abilities, such as using multiple plots per page. Using graphic methods that enhance the visualization of the behaviour of the parameter or parameters in relation with the property we want to measure.

THE POWER OF VISUALISATION (GRAPHICAL REPRESENTATION) Example: we want to analyse 4 data sets by means of clasical statistical analysis (lineal regression)

Obs. 1 2 3 4 5 6 7 8 9 10 11 n mean in terc ept slope c orrelation

Data Set 1 X1 Y1 10 8,04 8 6,95 13 7,58 9 8,81 11 8,33 14 9,96 6 7,24 4 4,26 12 10,8 7 4,82 5 5,6 11 11 9 7,5 3 0,5 0,67

D ata Set 2 X2 Y2 10 9,14 8 8,14 13 8,74 9 8,77 11 9,26 14 8,1 6 6,13 4 3,1 12 9,13 7 7,26 5 4,74 11 11 9 7,5 3 0,5 0,67

Data Set 3 X3 Y3 10 7,46 8 6,77 13 12,7 9 7,11 11 7,81 14 8,84 6 6,08 4 5,39 12 8,15 7 6,42 5 5,73 11 11 9 7,5 3 0,5 0,67

Data Set 4 X4 Y4 8 6,58 8 5,76 8 7,71 8 8,84 8 8,47 8 7,04 8 5,25 19 12,5 8 5,56 8 7,91 8 6,89 11 11 9 7,5 3 0,5 0,67

Graphical Representation D ata Set 1

D ata Set 2

D ata Set 3

Data Set 4

OUTLIER

x

Some relevant parameters related to refractory material performance: Lime Saturation Factor (LSF) Silica Ratio (SR) Alumina Ratio (AR) Liquid Phase Quantity (LP) Alkalies-Sulphur Ratio (ASR)

Burnability, coating behaviour Coating behavior, burnability Circulation Phenomena infiltration tendency

Temp. & Thermal load in burning zone

Thermal shock behaviour, coating behaviour

%O2 & %CO2 in combustion gas

Redox behaviour

One parsimonial model for burnability based on LSF and SR. Lime Saturation Factor (LSF) Silica Ratio (SR)

Burnability, coating behaviour

• The underlying assumption for this model is that the burnability of the raw mix is strongly dependent on these two parameters. • We know of course that this is a simplification, as burnability does not depend solely of these parameters, however, it is a useful simplification that enables us to better understand the influences of these two parameters on both, burnability and refractory material performance in the burning zone of the rotary kiln.

Lime saturation factor LSF =

100CaO 2,8SiO2+1,18Al2O3+,65Fe2O3

1.Stoichiometric relationship. 2.Burnability of raw mix

Change in theorethical Sintering Temperature in dependence with LSF

T °C

1510 1500 1490 1480 1470 1460 1450 1440

∆T = 48 °C ∆ LSF=6 89

90

91

92

93

LSF

94

95

96

97

98

Silica ratio SR

=

SiO2 Al2O3 + Fe2O3

Solid Liquid

High SR values decrease burnability due to •Increased probability of having big SiO2 particles in raw meal •Decreased amount of clinker melt •A tendency for decreased homogeneity of raw meal (segregation)

Burnability If less energy is required (T<1450°C) ~1450 °C RAW MIX + ENERGY Free lime

CLINKER

EASY

NORMAL

MAX 2%

If more energy is required (T>1450°C)

HARD

Clinker burnability chart

more difficult to burn normal burnability easier to burn

How chemical changes affect burnability? Clinker Burnability Acc. Peyre

115

From LSF 93, AR 2,5 to LSF 95; AR 3

Lime Saturation Factor

110

105

100

95

Extrem hard Very hard

90

Hard Normal

85 0,5

1

1,5

2

2,5 Silica Modul

3

3,5

4

4,5

Insufficient raw meal preparation

Burnability graph

4 3 2 1

5

Burnability graph

4 3 2 1

T 5

Of course, burnability also depends on other parameters: • Particle size distribution of the kiln feed • Fineness of kiln feed • Degree of mixing (homogeneity) of kiln feed • Mineralogical composition of raw materials • Segregation, which may happen after homogeneization • Mixing partially calcined dust with fresh meal The influence of these parameters on burnability shall be also understood and should be investigated using additional methods, which in no way diminish the contribution of our parsimonial model based on LSF and SR to UNDERSTANDING AND SEEING WHATS GOING ON

Raw meal: we grind to develop surface, required to promote chemical reactions among the components. Kaolin; picture width 200 micrometer (µ)

Quartz grain; Average Ø 200 µ

Big quartz grains (sourronded by belite ring) do not let sintering to C3S to proceed due to lower reaction rate (low surface area).

1620

Alite crystals

A parsimonial model for coating behaviour based on Liquid Phase Quantity and AR. Alumina Ratio (AR) Liquid Phase Quantity (LP)

Coating behavior, burnability

Current practice shows us that coating behaviour is strongly dependent on these two parameters. We also know that this is a simplification, as other parameters also influence coating behaviour, however; this is a useful simplification that enables us to better understand the influences of chemical conditions on refractory material performance in the burning zone of the rotary kiln.

Alumina ratio

AR

=

Al2O3 Fe2O3

viscous

=

fluid

SOME STATEMENTS RELATED TO AR •

AR values are relevant for the viscosity of the clinker melt.

•

Melt viscosity is relevant for the sinter rate.

•

Alkalies and MgO can lower the viscosity of the clinker melt.

•

At AR=1,38 clinker melt achieves optimun properties to promote

•

sintering at lowest possible temperatures (1280-1340 °C)

% Liquid Phase at 1450 °C Acc. To Lea& Parker: 3Al2O3+2,25Fe2O3+MgO+K2O+Na2O

Coating conditions

thin coating dusty clinker low melt quantity high melt viscosity

AR thin coating dusty clinker low melt quantity low melt viscosity high infiltration of refractories

thick coating nodular clinker high melt quantity high melt viscosity

good coating

thin coating high melt quantity low melt viscosity high infiltration of refractories

Changing coating conditions

AR

Coating conditions

thick coating nodular clinker high melt quantity high melt viscosity

thin coating dusty clinker low melt quantity high melt viscosity

AR

good coating thin coating dusty clinker low melt quantity low melt viscosity high infiltration of refractories

thin coating high melt quantity low melt viscosity high infiltration of refractories

Coating conditions

thick coating nodular clinker high melt quantity high melt viscosity

thin coating dusty clinker low melt quantity high melt viscosity

AR

good coating Period 1 thin coating dusty clinker low melt quantity low melt viscosity high infiltration of refractories

Period 2 thin coating high melt quantity low melt viscosity high infiltration of refractories

Accelerated wear of refractories in lower transition zone due to concurring action of various factors

Combustion conditions

Redox conditions

These reactions are associated with volume changes

Chemical Composition and AFR Feed 1.

2.

Plants invest a great deal of effort (and money) to bring the chemical composition of raw meal fed to the kiln within narrow fluctuation ranges for chemical and physical properties: Prehomo, blending and homogeneizing installations, on line analizers etc. They destroy it by uncontrolled feeding of AFR

RM

AFR

Main burner running with 6 different fuels Solid: Coal Pet Coke Wood Plastic/Fabric/paper Liquid: Waste Solvent Waste oil

Disturbances in LSF due to introduction of AFR

Disturbances in AR due to introduction of AFR

Disturbances in SR due to introduction of AFR

Disturbances in ASR due to introduction of AFR

A reminder: ASR = Alkaly Sulfur Ratio

Na2O K2O + 62 94 ASR = SO3 80

-

Cl 71

Disturbances in ASR due to introduction of AFR

Alkali spalling in the upper transition zone

Is the ASR in kiln feed “under control”?

After the coffe brake we sill look at some „case studies“ using EDA to draw useful conclusions from data analyses.

CASE STUDIE 1 BURNABILITY AND COATING BEHAVIOUR

3 different data sets were distributed NUM

Datum

LSF ()

SR ()

AR ()

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

1.1.12 0:00 1.1.12 1:00 1.1.12 2:00 1.1.12 3:00 1.1.12 4:00 1.1.12 5:00 1.1.12 6:00 1.1.12 7:00 1.1.12 8:00 1.1.12 9:00 1.1.12 10:00 1.1.12 11:00 1.1.12 12:00 1.1.12 13:00 1.1.12 14:00 1.1.12 15:00 1.1.12 16:00 1.1.12 17:00 1.1.12 18:00 1.1.12 19:00 1.1.12 20:00 1.1.12 21:00 1.1.12 22:00 1.1.12 23:00

95,9 95,8

2,04 2,05

1,26 1,26

AVERAGE Maximun Minimum S T A NDA RD DEVIA T ION

Liquid Phase 1450°C (%) 30,1 29,9

95,7

2,06

1,26

29,9

96,5 96,2

2,09 2,06

1,25 1,26

29,6 29,6

96,0

2,08

1,27

29,7

96,3 96,1

2,07 2,07

1,26 1,26

29,7 29,6

95,1

2,04

1,29

30,5

96,5 96,3

2,07 2,06

1,25 1,26

29,6 29,7

96,3

2,08

1,25

29,8

96,5 95,9

2,10 2,07

1,26 1,27

29,3 29,6

95,4

2,05

1,26

30,0

96,4 96,1

2,10 2,07

1,27 1,27

29,2 29,6

95,6

2,09

1,28

29,6

96,2

2,09

1,28

29,5

95,0

2,03

1,29

30,6

96,5

2,11

1,28

29,2

95,8

2,08

1,29

29,6

96,5 95,2

2,11 2,09

1,29 1,31

29,2 29,8

95,99 96,50 94,95 0,455

2,07 2,11 2,03 0,020

1, 27 1, 31 1, 25 0, 015

29, 70 30, 63 29, 19 0, 349

The LSF and SR data are to be ploted in the burnability chart DATA SET 1 Liquid Phase 1450°C (%) 30,1 29,9

NUM

Datum

LSF ()

SR ()

AR ()

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

1.1.12 0:00 1.1.12 1:00 1.1.12 2:00 1.1.12 3:00 1.1.12 4:00 1.1.12 5:00 1.1.12 6:00 1.1.12 7:00 1.1.12 8:00 1.1.12 9:00 1.1.12 10:00 1.1.12 11:00 1.1.12 12:00 1.1.12 13:00 1.1.12 14:00 1.1.12 15:00 1.1.12 16:00 1.1.12 17:00 1.1.12 18:00 1.1.12 19:00 1.1.12 20:00 1.1.12 21:00 1.1.12 22:00 1.1.12 23:00

95,9 95,8

2,04 2,05

1,26 1,26

95,7

2,06

1,26

29,9

96,5 96,2

2,09 2,06

1,25 1,26

29,6 29,6

AV ER AGE Maxi m un M i ni m um S T A NDA RD DEVIA T ION

96,0

2,08

1,27

29,7

96,3 96,1

2,07 2,07

1,26 1,26

29,7 29,6

95,1

2,04

1,29

30,5

96,5 96,3

2,07 2,06

1,25 1,26

29,6 29,7

96,3

2,08

1,25

29,8

96,5 95,9

2,10 2,07

1,26 1,27

29,3 29,6

95,4

2,05

1,26

30,0

96,4 96,1

2,10 2,07

1,27 1,27

29,2 29,6

95,6

2,09

1,28

29,6

96,2

2,09

1,28

29,5

95,0

2,03

1,29

30,6

96,5

2,11

1,28

29,2

95,8

2,08

1,29

29,6

96,5 95,2

2,11 2,09

1,29 1,31

29,2 29,8

95,99 96,50 94,95 0, 455

2,07 2,11 2,03 0,020

1,27 1,31 1,25 0,015

29, 70 30, 63 29, 19 0, 349

DATA SET 1

The first point Point

LSF ()

SR ()

AR ()

95,9

2,04

1,26

95,9

2,04

for example

For the coating behaviour we plot the points in the same way on the coating conditions grap DATA SET 1 Liquid Phase 1450°C (%) 30,1 29,9

NUM

Datum

LSF ()

SR ()

AR ()

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

1.1.12 0:00 1.1.12 1:00 1.1.12 2:00 1.1.12 3:00 1.1.12 4:00 1.1.12 5:00 1.1.12 6:00 1.1.12 7:00 1.1.12 8:00 1.1.12 9:00 1.1.12 10:00 1.1.12 11:00 1.1.12 12:00 1.1.12 13:00 1.1.12 14:00 1.1.12 15:00 1.1.12 16:00 1.1.12 17:00 1.1.12 18:00 1.1.12 19:00 1.1.12 20:00 1.1.12 21:00 1.1.12 22:00 1.1.12 23:00

95,9 95,8

2,04 2,05

1,26 1,26

95,7

2,06

1,26

29,9

96,5 96,2

2,09 2,06

1,25 1,26

29,6 29,6

AV ERAG E M axim un Mi ni m um S T A NDA RD DEVIA T ION

COUNT

96,0

2,08

1,27

29,7

96,3 96,1

2,07 2,07

1,26 1,26

29,7 29,6

95,1

2,04

1,29

30,5

96,5 96,3

2,07 2,06

1,25 1,26

29,6 29,7

96,3

2,08

1,25

29,8

96,5 95,9

2,10 2,07

1,26 1,27

29,3 29,6

95,4

2,05

1,26

30,0

96,4 96,1

2,10 2,07

1,27 1,27

29,2 29,6

95,6

2,09

1,28

29,6

96,2

2,09

1,28

29,5

95,0

2,03

1,29

30,6

96,5

2,11

1,28

29,2

95,8

2,08

1,29

29,6

96,5 95,2

2,11 2,09

1,29 1,31

29,2 29,8

95,99 96,50 94,95 0, 455 24

2,07 2,11 2,03 0,020 24

1,27 1,31 1,25 0,015 24

29,70 30,63 29,19 0,349 24

Plot the two graphs with the information on your data sets

Please use the next 10 minutes for doing so

Burnability Chart Data Set 1

Burnability Chart Data Set 1, average point

BURNABILITY CHART DATA SET 1 Variable LSF SR

N 24 24

Mean 95,99 2,07

LSF MAX LSF MIN

Minimum 94,95 2,03

Maximum 96,50 2,11

SR MAX

SR MIN

BURNABILITY CHART DATA SET 1

POINTS WITHIN THE REGION OF DIFFICULT BURNABILITY EASY-NORMAL

DIFFICULT

TOTAL

24 100%

0 0%

24 100%

Variable LSF SR

Max-Min 1,54833 0,07667

Area 0,12

Burnability Chart Data Set 2

Variable LFS MS

N 24 24

Mean 88,75 2,05

Minimum 85,82 1,94

Maximum 93,08 2,13

Std Dev 1,48534624 0,05425465

Max-Min 7,254565822 0,185349066

Area 1,3

Variable LFS MS

N 24 24

Mean 98,45 3,94

Minimum 94,50 3,40

Maximum 101,00 4,50

Std Dev 1,59907639 0,44298761

Max-Min 6,5 1,1

Area 7,2

OPC clinker

These 3 clinker types were produced by a single rotaty kiln in a cement plant. Due to current low market demand for OPC in the region, they produce special clinker types to use the installed production capacity of the kiln!

white cement clinker

Cement type II clinker (astm)

Can this „visualization“ help us in judging what kind of refractory bricks we need in the kiln?

Burnability bevaviour

Coating behaviour

ADDITIONAL EXAMPLES

Clinker samples each hour, chemical analysis every 8 hours.

Physical Average (MIX) 1 Analysis per shift

Enero 2013

FEBRERO 2013

Marzo 2013

Abril 2014

Mayo 2013

Junio 2013

Julio 2013

Agosto 2014

septiembre 2013

Octubre 2013

Noviembre 2013

Diciembre 2013

Enero-diciembre 2013 (all analyses)

Enero-diciembre 2013 Average (1 point)

All data Variable LFS MS

N 1004 1004

Variable LFS MS

Mean 92,4 2,6

By looking at the average, we miss the information that all data contains. We can not see it! We make it invisible!

But unlike clinker, raw meal fed to the kiln is analized each hour!

How does it look like if we use It means all these data for our 24 burnability analysis? chemical

HOUR 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

DATE

1.1.14 1.1.14 1.1.14 1.1.14 1.1.14 1.1.14 1.1.14 1.1.14 1.1.14 1.1.14 1.1.14 1.1.14 1.1.14 1.1.14 1.1.14 1.1.14 1.1.14 1.1.14 1.1.14 1.1.14 1.1.14 1.1.14 1.1.14 1.1.14

0:00 0:01 0:02 0:03 0:04 0:05 0:06 0:07 0:08 0:09 0:10 0:11 0:12 0:13 0:14 0:15 0:16 0:17 0:18 0:19 0:20 0:21 0:22 0:23

Sample 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

LSF ()

SR ()

complete complete complete complete complete complete complete complete complete complete complete complete complete complete complete complete complete complete complete complete complete complete complete complete

AR ()

chemical analysis chemical analysis chemical analysis chemical analysis chemical analysis chemical analysis chemical analysis chemical analysis chemical analysis chemical analysis chemical analysis chemical analysis chemical analysis chemical analysis chemical analysis chemical analysis chemical analysis chemical analysis chemical analysis chemical analysis chemical analysis chemical analysis chemical analysis chemical analysis

Liquid Phase 1450°C (%)

analysis per day

Kiln feed burnability enero -diciembre 2013

a v g

Why is the picture so different?

Raw meal looks much more difficult to burn than clinker! RAW MEAL

CLINKER

There are other influences on the chemical composition of clinker Filter dust

Coal Ash

Kiln Feed BURNER

Chemical composition of clinker calculated from chemical composicion of raw meal and coal ash (average values)

L S F SR

How important is the influence of filter dust? Filter dust

Coal Ash

Kiln Feed BURNER

How important is the influence of filter dust? Filter dust

Coal Ash

Kiln Feed BURNER

SOURCE: HOLCIM

Options for kiln dust handling To cement mill Kiln dust

Sold as fertilizer Other uses

X% A Y %B Z %C RAW MATERIALS

HOMO

RAW MILL

SILO

clinker

How does the burnability of the raw meal output looks like?

Did homogeneization improve the burnability of the raw meal? X% A Y %B Z %C

HOMO

RAW MILL

SILO

clinker

How does the burnability of the raw meal output looks like?

How do we measure homogeneization achieved? X% A Y %B Z %C

HOMO

RAW MILL

SILO

clinker

Homogenization degree SD in

X% A Y %B Z %C

SD out

HOMO

RAW MILL

SILO

clinker

Homogenization degree =

SD in SD out

SD in

X% A Y %B Z %C

SD out

HOMO

RAW MILL

SILO

clinker

New definition for Homogeneization degree = A1/A2 ? A1

A2

Coating conditions kiln feed

Coal ash influence is not visible

Coating conditions clinker

Coal ash influence is visible

Clinker melt quantity

Source FLS

THERMAL LOAD

Definition Thermal load is the heat flow throug a cross sectional area. In metric units it is usually expressed in Gcal/m2-Hr It can be calculated from: 1. 2. 3. 1. 2. 3.

Kiln output (MT/Hr) Specific energy consumption of the kiln (Kcal/Kg clinker) kiln diameter. Or from fuel input (Kg/Hr or m3/Hr) the energy content of the fuel (Kcal/Kg or Kcal/m3) kiln diameter

Thermal Load depends mostly from the clinker production system.

Source FLS

Thermal load is normally calculated based on the AVERAGE VALUES Example: In a cement plant the thermal load at the burning zone was given as 3,12 Gcal/m2-Hr. BASED ON HEATING VALUE OF COAL 5.500 Kcal/Kg COAL INYECTION RATE AT THE MAIN BURNER: 50% OF TOTAL COAL

These are average values on long term basis (1 year)

In real life however, flow rate adjustments are made almost on an hourly basis! HOUR

FUEL RATE (TM/Hr)

Meal rate (TM/Hr)

1

8

120

2

8,5

110

3

9

100

4

11

120

5

9

140

6

12

150

7

10

130

Mass flows into the kiln: raw meal and coal

KILN FEED MT/HR

AVERAGE 128

TOTAL COAL FEED MT/HR

AVERAGE 11,19

Does the average value inform us about the situation? KILN FEED MT/HR

AVERAGE 128

TOTAL COAL FEED MT/HR

AVERAGE 11,19

Causes for this: • 8 different local coal suppliers • Every local supplier has his own mine • The coal from each mine has ist own characteristics: • Ash content, Humidity, Calorific value • 3 suppliers deliver imported coal (better quality but more expensive) • The plant does not have coal blending installations • Coal analyses are made once a week

As a result the plant operating personnel can not control energy delivery to the kiln!

Energy content of 1 Kg of coal in dependence with moisture and ash content

Kcal % water

% ash

Calorific value of 1 Kg/Hr of coal with changing ash and water content. (Normal distribution assumed for coal parameters)

Coal mass folw rate constant = 1 Kg/Hr

Thermal load during one kiln turn 3 RPM = 1.5 million turns/year coating Temperature on the internal surface °C

bricks

1 0

Kiln shell

Final remarks • Try to develop your own „parsimonial models“ at your plants for the parameters you would like to analize. • • Whenever possible, do not assume your data follow a model. Instead, use graphs to uncover the behaviour pattern of this parameter IN YOUR PLANT • When possible, use the original data (all) and use statistical models only in the cases where you know the model fits. thanks for your attention and…

enjoy your time at Refratechnik!