Incorporating Physical and Chemical Characteristics of Fly Ash in Statistical Modeling of Binder Properties Ancona, Italy Prasanth Tanikella and Jan Olek

Incorporating Physical and Chemical Characteristics of Fly Ash in

Statistical Modeling of Binder Properties

Ancona, Italy

Prasanth Tanikella and

Jan OlekPurdue University

1

June 30th , 2010

Jan Olek - Purdue University

Objectives and Hypothesis

• The goal of this research was to:– Characterize two sets of fly ashes (Class C and

Class F)– Statistically verify the importance of their

physical and chemical properties on the performance of binary paste systems

• Scope of the Project (2 Phases)– Phase 1 – Characterization of Fly Ashes– Phase 2 – Effect of Fly Ashes on the Properties of

Binary Paste Systems (cement + fly ash)

Jan Olek - Purdue University 2

Phase 1 – Characterization of Fly Ashes

• Collected 20 different fly ashes (13 Class C and 7 Class F)• 15 of them ( 9 Class C ashes and 6 Class F ashes) are currently on the

INDOT’s list of approved pozzolanic materials• A database summarizing the physical and chemical characteristics of the

collected fly ashes and the impact of these properties on the behavior of binders would benefit the engineers, contractors and concrete producers

Test Methods


Total Chemical Analysis and loss-on ignition ASTM C 311Soluble Sulfates and Alkalis Ion Chromatography

Particle Size DistributionLaser Particle Size

Analyzer and Sedimentation Analysis

Magnetic Particles Teflon coated bar magnet

Crystalling component and glass fraction X-ray DiffractionMorphology SEM

Strength Activity Index ASTM C 311

4

Results

Range of chemical compositions

CLASS

CaO(%)

SiO2

(%)Al2O3

(%)

Fe2O3

(%)Sulfate

(%)

Alkali

Content as Na2O (%)

LOI(%)

F 1 - 9 39 - 56 18-29 5 - 25 0.4 - 2 1.4 – 2.6 1.4 – 2.4

C 17 -28 32 -44 17 - 22 6 - 10 0.05 – 1.3 1.6 - 3.9 0.25 - 0.9


Phase 1

5

Results

Range of physical characteristics

CLASS

Blaine’s Sp. Surface (cm2/g)

Mean Size(micron)

Specific Surface - LPSD(cm2/g)

Strength activity index

(%)

Magnetic Particles

(%)

Specific Gravity

F 2391 – 4088 26.1– 33.24 6344-13012 96.2 – 125.7 3.68 – 37.72 2.22 – 2.68

C 4354 – 7306 13.85 – 32.2 11963-22015 116.7 – 136.7 0 – 3.5 2.56 – 2.84


Phase 1

ResultsXRD – Typical Class F Fly Ash

Typical X-ray patterns for Class F fly ashes

Includes1. Quartz – SiO2

2. Mullite – Al6Si2O13

3. Anhydrite – CaSO4

4. Hematite – Fe2O3

5. Magnetite – Fe3O4

6. Lime – CaO• Measured magnetic content is

generally very high (with two exceptions)

• A hump, representing a silica-type glass with a maximum at 2θ=~25° is visible

• Glass “hump” is generally higher than that observed for Class C ashes Jan Olek - Purdue University 6

XRD pattern for Elmer Smith fly ash

XRD pattern for Miami 7 fly ash

Phase 1

ResultsXRD - Typical Class C Fly Ash

X-ray pattern for a typical Class C fly ash

Includes1. Quartz – SiO2

2. Anhydrite – CaSO4

3. Merwinite – Ca3Mg(SiO4)2

4. Periclase – MgO5. Lime – CaO

• Glass peak is similar for all the ashes of this type

• Magnetite might be present in the fly ash, either in crystalline form or in the glass

• A hump, representing a calcium-aluminate type of glass with a maximum at 2θ=~30° is visible


XRD pattern for Hennepin fly ash

Phase 1

ResultsXRD – Glass Content Estimation

Glass content was empirically estimated by calculating the area under the glass hump

• Three softwares were used for the purpose

• xyExtract – To extract points from the XRD pattern

• LabFit – To fit the curve very precisely through the extracted points

• Sicyon Calculator – To integrate the fitted curve


Phase 1

ResultsParticle Size Distributions

• Class F and Class C ashes form two different bands of PSDs

• The band of Class C ashes is shifted towards the left of the band of Class F ashes


Class C

Class F

0.0 0.5 5.0 50.0 500.00.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

Comparison of PSDs for Class F and C ashes

Diameter (microns)

Unde

rsize

Per

cent

age

(%)

Phase 1

ResultsDiscrepancies in PSD

Discrepancies observed in PSD

The pipette analysis seems to work well for particles larger than 5 micron

The results below 5 microns seem to diverge from either of the curves

Even though the sedimentation technique does not work well for particles smaller than 5 microns, based on the data it is reasonable to assume that the PSD based on Lab 1 (Purdue) data is accurateJan Olek - Purdue University 10

0.1 1 10 100 10000

10

20

30

40

50

60

70

80

90

100

Fly Ash Petersberg

0.1 1.0 10.0 100.0 1000.00

10

20

30

40

50

60

70

80

90

100

Fly Ash Trimble

Lab 1

Lab 2

Pipette

Phase 1

ResultsMorphology of Class F (Type I) ashes

There is a large variation in the sizes and shapes of the particles

Particles with rugged surface are generally magnetic, contrary to the Class C fly ashes

Many hollow particles present

Relatively smaller number of unburnt carbon particles, but bigger particles have been observed, which is consistent with the higher LOIs values observed in Class F ashes

Jan Olek - Purdue University 11Mill CreekPetersburg

Elmer SmithZimmer

Phase 1

ResultsMorphology of Class C ashes

Wide range of sizes of spherical particles

Many hollow particles with shell generally composed of silica and alumina

Frequent irregularly-shaped particles (often with rugged surfaces) predominantly composed of sulfates or magnesium, or rarely sodium


Rush Island

KenoshaLabadie

Will County

Phase 1

Summary – Phase 1Characterization of fly ashes

• Significant variations in the chemical and physical characteristics of fly ashes observed

• The strength activity index of Class C ashes was higher than Class F ashes

• The glass content for all the Class C ashes was higher than the glass content for all but two Class F ashes, thus indicating that although Class C fly ashes have less glass than these two Class F ashes, the glass in Class C ashes is more reactive

• The morphology of the ashes was similar irrespective of the class, with a few exceptions

• The particle size distributions of class C and class F ashes were significantly different

• All mean particle sizes in class F were larger than mean particle sizes in class C ashes, resulting in a lower surface area of class F ashes

• The LOI values of all class F ashes were higher than that of the C ashes Jan Olek - Purdue University

13

Phase 1

Phase 2 - Evaluation of the hydration characteristics of cement-fly ash binder systems

Binder systems consisted of portland cement with 20% (by weight) replaced by fly ash

Pastes with constant water/binder ratio (0.41) were tested for various properties including,

Initial Time of Set – Vicat needle (ASTM C 191) Heat of Hydration – Isothermal Calorimetry (at a constant

temperature of 21 oC) Amount of Calcium Hydroxide at ages 1, 3, 7 and 28 days

- TGA Non-evaporable water content at 1,3 7 and 28 days –

TGA Rate of strength gain at 1, 3, 7 and 28 days – Strength

activity index (ASTM C 311)Jan Olek - Purdue University 14

Initial Setting Time - Results

Range of set time for Class C ashes – (1 hour to 4.5 hours) Range of set time for Class F ashes – ( 2.5 hours to 3.5 hours)


Phase 2

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5In

itial

Setti

ng ti

me

(Hou

rs)

Flash Set

A Typical Calorimeter Curve

• Data acquired from the calorimeter curve Peak heat of hydration (W/kg) Time of peak heat of hydration (minutes) Total heat of hydration (J/kg) – ( Area under the curve from 60

minutes to 3 days)


Phase 2

Total Heat

Time of Peak Heat

Peak Heat of Hydration - Results

Most ashes tend to reduce the peak heat of hydration compared to cement

Class F ashes in general have a higher peak heat of hydration than Class C ashes

Kenosha, the fly ash with the lowest peak heat of hydration had a flash set


Phase 2

Time of Peak Heat of Hydration - Results

Most ashes tend to delay the occurrence peak heat of hydration compared to cement

Class C ashes in general have a higher time of peak heat than Class C ashes

Kenosha, the fly ash with the lowest peak heat of hydration had longest time of peak heat


Phase 2

Thermo-gravimetric Analysis (TGA)

Calcium hydroxide content and non-evaporable water content were estimated using TGA at various ages (1, 3, 7 and 28 days)

Calcium Hydroxide content between 480oC and 550oC (carbonation taken in to account)

Non-evaporable water content calculated according to Barneyback, 1983.


Phase 2

Calcium Hydroxide Content at 1 day - Results

Most ashes tend to reduce the amount of calcium hydroxide at 1 day compared to plain cement paste (with some exception)

Class F ashes have a slightly higher CH content than Class C ashes at early ages


Phase 2

Calcium Hydroxide Content at 28 days - Results

Most of the ashes show a higher amount of calcium hydroxide at 28 day compared to plain cement paste

Difference in the rates of reactions in the fly ashes


Phase 2

Strength Activity Index at 28 days - Results

All of the Class C ashes show a higher strength at 28 days compared to plain cement paste while Class F ashes show a lower strength comparatively


Phase 2

23

Statistical Modeling of Binary Binders

Phase 2

STEP 1 - Perform linear regression analysis for each of the 16 dependent variables (hydration related properties of ashes) using all the data points (13 Class C and 7 Class F binary pastes)

STEP 2 - Prepare a table with a list of models containing the sets of independent variables that must affect the dependent variables, in a decreasing order of "Adj-R2" (only models with the best 10 adj-R2 values were included)

STEP 3 - Perform linear regression analysis for the same set of 16 dependent variables as in Step 1, but using only those independent variables that were selected based on Step 2 for both Class C and Class F ashes separately


24

Statistical Modeling of Binary Binders

Phase 2

Independent Variables Abbreviations

Physical Properties

Mean Particle Size meansize

Specific surface area measured using Blaine's

apparatus blaines

Specific surface area measured using laser

particle size analyzer

Spsurface

Chemical Properties

Calcium oxide content cao

Sum of silicon, aluminum and iron oxide

contents SAF

Magnesium oxide content mgo

Aluminum oxide content Alumina

Sulfate content sulfate

Physico-chemical PropertiesLoss-on ignition carbon

Glass content measured using X-ray diffractionglass


25

Dependent Variables


26

Ten Models with the highest Adj-R2 – Set Time

Phase 2

Model

Number

Number of Variables in

the modelAdjusted R2 R2 Variables in the model

1 3 0.2447 0.3706 sulfate, alumina, glass

2 5 0.2298 0.4437 sulfate, SAF , mgo, alumina, glass

3 2 0.223 0.3093 sulfate, alumina

4 7 0.2189 0.5226spsurface, meansize, sulfate,

carbon, SAF, alumina, glass

5 1 0.217 0.2605 sulfate

6 7 0.2099 0.5172spsurface, meansize, sulfate,

carbon, cao, alumina, glass

7 6 0.2095 0.473spsurface, sulfate, SAF, mgo,

alumina, glass

8 4 0.2089 0.3847 sulfate, SAF, mgo, alumina

9 2 0.2032 0.2917 sulfate, carbon

105 0.2008 0.4228

spsurface, sulfate, SAF, mgo,

alumina


27

ANOVA Table (Class C Ashes) – Set Time

Phase 2

Source DFSum of

Squares

Mean

SquareF Value p-Value

Model 3 3.269 1.089 1.65 0.2543

Error 8 5.292 0.6615

Total 11 8.561

R2 0.3818

Adj - R2 0.15

Variable DFParameter

Estimate

Standard

Errort-Value p-Value

Intercept 1 4.456 4.112 1.08 0.3101

sulfate 1 1.178 0.644 0.183 0.1048

alumina 1 -0.085 0.235 -0.36 0.7267

glass 1 -0.583 0.619 -0.94 0.3738Jan Olek - Purdue University

28

Observed Vs Predicted (Class C Ashes) – Set Time

1 1.5 2 2.5 3 3.5 4 4.5 51

1.5

2

2.5

3

3.5

4

4.5

5

5.5

Observed Setting Time (Hours)

Pred

icte

d Se

tting

Tim

e (H

ours

)


Phase 2

29

ANOVA Table (Class F Ashes) – Set Time

Source DFSum of

Squares

Mean


Model 3 0.44358 0.14786 1.63 0.3487

Error 3 0.27189 0.09063

Total 6 0.71547

R2 0.62

Adj - R2 0.24


Estimate

Standard


Intercept 1 1.26093 0.99826 1.26 0.2958

sulfate 1 0.46946 0.25233 1.86 0.1598

alumina 1 0.07325 0.0769 0.95 0.4111

glass 1 -0.0845 0.53944 -0.16 0.8855Jan Olek - Purdue University

Phase 2

30

Observed Vs Predicted (Class F Ashes) – Set Time

2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 3.8 42

2.2

2.4

2.6

2.8

3

3.2

3.4

3.6

3.8

4

Observed Set Time (Hours)

Pred

icte

d Se

t Tim

e (H

ours

)


Phase 2

31

ANOVA Table (Class C Ashes) – (SAI) at 28 days


Source DFSum of

SquaresMean Square F Value p-Value

Model 3 470.0203 156.67343 4.444977 0.0407

Error 8 281.9784 35.2472975

Total 11 751.9987

R2 0.625

Adj - R2 0.4844


Estimate

Standard


Intercept 1 142.0434 1.66837 85.13902 0.0036

meansize 1 -1.573 0.10387 -15.1439 0.0246

sulfate 1 -15.7847 0.01841 -857.398 0.0135

SAF 1 0.0496 0.11843 0.418813 0.9266

Phase 2

32

ANOVA Table (Class F Ashes) – (SAI) at 28 days


Source DFSum of

Squares

Mean


Model 3 107.65676 35.88559 40.13 0.0244

Error 2 1.78839 0.894195

Total 5 109.44515

R2 0.9837

Adj - R2 0.9591


Estimate

Standard


Intercept 1 126.13758 17.78975 7.090464 0.0193

meansize 1 -0.67193 0.23397 -2.87186 0.1029

sulfate 1 -9.27674 1.16409 -7.96909 0.0154

SAF 1 -0.00329 0.1415 -0.02325 0.9835

Phase 2

33

Summary of Statistical Procedures for all Dependent Variables


Property ClassModel p-

value Model R2Model

Adjusted R2 p-values

Initial Time of Set

Sulfate Alumina GlassC 0.2543 0.38 0.18 0.1048 0.7267 0.3738F 0.3487 0.62 0.24 0.1598 0.4111 0.8855

Peak Heat Spsurface SAF Glass

C 0.0484 0.5662 0.4216 0.0084 0.0172 0.1447F 0.4564 0.5343 0.0685 0.8168 0.2057 0.2479

Time Peak Spsurface Meansize Mgo

C 0.1722 0.4103 0.2138 0.0533 0.1302 0.1064F 0.0698 0.8778 0.7556 0.0597 0.0656 0.2062

Calcium Hydroxide at

28 days

Blaines Spsurface SulfateC 0.0135 0.719 0.614 0.0021 0.4252 0.1818F 0.1602 0.89 0.725 0.227 0.093 0.1728

Strength at 28 days

Meansize Sulfate SAFC 0.0407 0.625 0.484 0.0246 0.0135 0.9266F 0.0244 0.984 0.96 0.1029 0.0154 0.9835

Phase 2

34

Summary- Phase 2Binary Binder Systems


Property Most Influencing Variables Significant VariablesSet Time Sulfate, alumina, glass None

Peak Heat Spsurface, SAF, glass Spsurface, CaOTimepeak Spsurface, Meansize, MgO Spsurface

Ca(OH)2 28 Day

Blaines, Spsurface, sulfate, cao, glass, carbon, alumina Blaines

SAI 7 Day SAF, CaO, Glass SAF, CaO

SAI 28 Day Meansize, sulfate, SAF Meansize, Sulfate

• Physical characteristics of fly ash had a higher effect than chemical characteristics of fly ash

• Surface area was found to be the most influencing variable affecting most of the properties of the binder system at both early and later ages

• Variables including SAI (at later ages) and time of peak heat of hydration can be predicted accurately using the respective statistical models

Phase 2

Conclusions


• Class C and F ashes were significantly different in both their physical characteristics and chemical composition

• There was significant difference in the effect of the two classes on binder properties

• Both physical and chemical characteristics of fly ash had an effect on the binder systems

• The sets of variables affecting each of the properties were unique

• The signs of the coefficients in the models indeed pointed out the type of effect on the property

• The statistical analysis of the properties of binary binders allowed us to draw inferences about the characteristics of fly ash which held the highest importance

Conclusions


• Some of the properties could not be accurately predicted by the statistical models with good significant as there were errors introduced by the limited number of variables chosen for modeling

• Specific surface area of the fly ash had the highest impact on all the properties of binder systems

37

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Documents

Incorporating Physical and Chemical Characteristics of Fly Ash in Statistical Modeling of Binder Properties Ancona, Italy Prasanth Tanikella and Jan Olek