SlideShare a Scribd company logo
1 of 1
Download to read offline
Poster template by ResearchPosters.co.za
MODEL FREE KINETICS OF MALAYSIAN COAL AND WASTE PLASTICS
BLENDS DURING CO-PYROLYSIS PROCESS
Nurfarahima Binti Ibrahim and Sharmeela Matali.
Faculty of Chemical Engineering, Universiti Teknologi Mara 40450 Shah Alam, Selangor.
Model free kinetics algorithms was applied to determine
conversion, iso-conversion and activation energy during
co-pyrolysis of high density polyethylene (HDPE) and coal.
The kinetics data were be obtained from TGA data at
different heating rate and model fitting methods. Thermal
behavior of the polyethylene and coal material was be
investigated by knowing thermal degradation kinetics.
Thermal degradation of polymer is a more complex
reaction, thus the reliable model of kinetics models is very
important to get a best results. To estimate the kinetics
data of degradation, the Kissinger’s and Ozawa-Flynn
Wall model free kinetics method were applied. The data
have been obtained via thermogravimetry analysis during
co-pyrolysis of Silantek coal and waste plastics (HDPE)
blends at nitrogen atmosphere, under dynamic conditions
at different heating rate of 10, 20 and 40oC/min. The
kinetics parameters, activation energy (Ea), and pre-
exponential factors (A) of the blends were calculated using
these model equations by function of peak temperature
and heating rate. Therefore, from the calculated results
showed that the activation energy of the coal/HDPE
blends is 238.128 kJ/mol and 250.822 kJ/mol respectively
between temperature range of 300-600oC, and it can be
characterized by first order reaction. DTG curves
coal/HDPE blends are found to be overlapping and due to
possible chemical interaction between these material [1].
1. Sample preparation
Results Results
Conclusion
Recommendation
Acknowledgement
INSERT
LOGO HERE
INSERT
LOGO HERE
Figure 1: Sample Preparation
 To determine the best model free kinetics to represent
experimental data.
 Application of free model kinetics for the co-pyrolysis
process.
2. Proximate Analysis & Thermal Degradation
Kinetics analysis
Figure 2: Thermogravimetric Analyzer (Mettler Toleda/TGA/SDRA51e)
3. Calorific Value Analysis
Mass ratio of blending will affected the calorific value
were increasing amount of mass ratio HDPE reduces
the calorific values of blended samples.
The low degree of branching and required higher energy
to break the stronger intermolecular forces in the
polymeric chain of HDPE [2].
4. Thermogravimetric Profile (TG)
 The weight loss during the pyrolysis process in the
temperature range 300-600°C due mainly to gases
emission gases emission with a mass loss between
60wt%.
 The decomposition curves of the blends are
overlapped which may be due to interactions of solid-
solid and solid-gas [1] .
3. Ultimate Analysis
The model free kinetics study is one of the methods to
obtain the activation energy and pre-exponential factors
from the co-pyrolysis study via thermogravimetric analysis.
Under the pyrolysis conditions, the material decomposition
start to degrade at approximately 300°C with progressively
and rapidly up to 450°C to 600°C.
8. Ozawa-Flynn Wall (OFW) Model Kinetics
4. Model Free Kinetics Methods
Figure 3:Thermo Finnigan Flashed 1112 analyser
 If the percentage of volatile matter increasing, the
percentage of char yield will decrease.
 At range weight ratio 40wt% to 60wt%, the curves of
char yield and volatile matter intersect, assumed that
the char yield produced is inversely proportional to the
volatile matter contents blended sample.
Table 3 : Thermal decomposition of Silantek coal/HDPE blends at different heating rates.
lgβ= log AE/βR -2.315 - 0.4567 E/RT (Eq2)
References
Thank you to my Research Project supervisor, Mdm
Sharmeela Matali and Universiti Teknologi Mara (UiTM).
1. Z. Mikulová and J. F. , Marek Večeř. Study Of Pyrolysisis Of Polymers And
Coal And Co-Pyrolysis Of Their Blends, Kinetics Of The Process. Trans. VŠB –
Tech. Univ. Ostrava, Mech. Ser., vol. LVIII, no. 1, pp. 147–155, 2012.
2. Sharmeela Matali, Mohd Ridzuan Mohatar. Thermogravimetric Analysis of the
Pyrolysis Characteristics on Sarawak Silantek Coal, Waste Tyre, Waste HDPE
and their blends Shah Alam : International Journal of Chemical Engineering,
2015.
Cutting → Drying → Grinding → Sieving
 Kissinger’s Method.
 Ozawa-Flynn Wall Method.
Chemical Element Silantek Coal (wt%) HDPE (wt%)
Carbon 74.186 80.58
Hydrogen 4.350 8.406
Nitrogen 1.454 9.359
Sulphur 0.010 0.526
Oxygen 20.00 1.129
Table 1: Proximate Analysis of Silantek Coal and Waste Plastic HDPE
Figure 4: The Calorific Value of the HDPE, Silantek Coal and their Blends .
0
2
4
6
8
10
12
14
16
18
20
22
0 70 140 210 280 350 420 490 560 630 700 770 840 910 980
WeightRatio(mg)
Temperature (°C)
Thermogravimetric Profile (TG)
HR40
HR20
HR10
Figure 5: Thermogravimetric (TG) Profile of the Silantek Coal and Waste HDPE blends at different heating
rates at weight ratio blends of 40% Silantek Coal.
5. Derivatives Thermogravimetric Profile (DTG)
 The peak temperature for this thermal event for heating
rate 10°C/min is 479°C with peak height, R of 3.05
mg/min.
 The peak temperature for this thermal event for heating
rate 20°C/min is 497°C with peak height, R of 6.210
mg/min. .
 The peak temperature for this thermal event for heating
rate heating rate 40°C/min is 504°C with peak height, R
of 10.34 mg/min.
-11
-10
-9
-8
-7
-6
-5
-4
-3
-2
-1
0
1
0 70 140 210 280 350 420 490 560 630 700 770 840 910 980
weightratio(mg/min)
Temperature (°C)
Derivatives Thermogravimetric Profile (DTG)
HR40
HR20
HR10
Figure 6: Derivatives Thermogravimetric (TG) Profile of the Silantek Coal and Waste HDPE blends at
different heating rates at weight ratio blends of 40% Silantek Coal.
6. Thermal Degradation
HR Blend TEP 1 R TEP 2 R CY(%) VM(%)
°C/min SC:HDPE Tmax mg/min Tmax mg/min
10 100:0 61.15 0.21 452.10 0.20 45.22 54.78
80:20 60.95 0.21 475.23 0.90 53.30 46.70
60:40 58.35 0.14 482.71 2.13 46.29 53.71
40:60 51.72 0.11 478.88 3.05 42.31 57.69
20:80 49.69 0.01 484.37 4.71 20.21 79.79
0:100 452.09 0.94 44.05 55.95
20 100:0 79.39 0.43 445.52 0.59 49.89 50.11
80:20 497.60 2.60 68.98 31.02
60:40 493.33 2.98 60.78 39.22
40:60 497.39 6.21 37.40 62.60
20:80 495.87 8.08 29.61 70.39
0:100 493.73 9.28 22.09 77.91
40 100:0 441.92 0.91 51.97 48.03
80:20 85.39 0.89 493.83 3.54 59.67 40.34
60:40 85.06 0.85 503.07 6.49 46.74 53.26
40:60 79.28 0.65 503.55 10.34 34.81 65.19
20:80 76.70 0.26 504.23 14.19 30.86 69.14
0:100 101.91 0.11 502.98 19.12 12.40 87.6
Abstract
Results
1. Proximate Analysis
Aspect Silantek Coal (wt%) HDPE (wt%)
Volatile Matter 48.36 87.51
Fixed Carbon 45.22 0
Ash 6.42 12.49
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
100.0% 80.0% 60.0% 40.0% 20.0% 0.0%
Weightrartio%
Weight Ratio % (COAL:HDPE)
Thermal Degradation of Char Yield and Volatile Matter
CR40
CR20
CR10
VM40
VM20
VM10
7. Kissinger’s Model Kinetics.
ln(β/Tm²) = lnAR/E – E/RTm (Eq1)
Where: β = heating rate
Tm = maximum temperature
A =pre-exponential factor
R = rate constant
Ratio Kinetics Data’s
SC:HDPE Slope Intercept Ea(kJ/mol) A(sˉ¹)
100:0 -32191.069 30.949 267.646 8.88E+17
80:20 -39654.125 37.920 329.696 1.17E+21
60:40 -37455.966 34.510 311.420 3.64E+19
40:60 -28640.793 23.002 238.128 2.80E+14
20:80 -39160.646 36.622 325.593 3.15E+20
0:100 -67539.227 73.400 561.541 5.09E+36
Table 4: The kinetic data of Kissinger’s Method.
Blend Kinetic Parameter
% Slope Intercept Ea(kJ/mol) A(sˉ¹)
100 -33612.668 30.949 279.466 9.275E+17
80 -41168.131 37.920 342.284 1.211E+21
60 -38987.946 34.510 324.157 3.787E+19
40 -30167.521 23.002 250.822 2.946E+14
20 -40694.531 36.622 338.347 3.269E+20
0 -69080.905 73.400 574.359 5.208E+36
Table 5: The kinetic data of Ozawa Flynn Wall Method.
267.646
297.222
311.420
238.128
325.593
561.541
279.466
308.988
324.157
250.822
338.347
574.359
200.0
250.0
300.0
350.0
400.0
450.0
500.0
550.0
600.0
0.0% 20.0% 40.0% 60.0% 80.0% 100.0%
ActivationEnergy()kJ/mol
Blend Ratio
Model Kinetic of Co-pyrolysis
Kissinger
OFW
Figure 10: : Comparision of Model Free Kinetics during co-pyrolysis of Silantek Coal and waste plastic
HDPE blends
Model
kinetic
Blends ratio
100:0 80:20 60:40 40:60 20:80 0:100
Kissinger Ea (kJ/mol) 267.6462 297.2219 311.4201 238.1281 325.5934 561.5414
A(sˉ¹) 8.88E+17 1.17E+21 3.64E+19 2.8E+14 3.15E+20 5.09E+36
R² 0.5035 0.959 0.9996 0.9115 0.9896 0.8182
Ozawa-Fynn
Wall
Ea (kJ/mol) 279.4658 308.9883 324.1575 250.8218 338.3465 574.3594
A(sˉ¹) 9.28E+17 1.21E+21 3.79E+19 2.95E+14 3.27E+20 5.21E+36
R² 0.5251 0.9627 0.9997 0.9195 0.9904 0.8248
Table 6 : Comparision of Model Free Kinetics data
2. Ultimate Analysis
Table 2: Ultimate Analysis of Silantek Coal and Waste Plastic HDPE
Figure 7: Thermal Degradation of Char Yield and Volatile Matter
 The lowest Ea is obtained weight ratio of 40:60 and
corresponding R2 value be represent the best fit model
for experimental, calculated were 0.9115 ofr
Kissinger’s model and 0.9195 for OZW model which
are considered acceptable and fits well with there two
models.
Used the various type of the waste material blends with
natural sources and continues for other heating rate for
further study of model kinetics.
Methodology
Objectives
 The lowest activation energy (Ea ) was obtained at
weight ratio of 40:60 with 238 kJ/mol and pre-
exponential factor is 2.8x10¹⁴sˉ¹ were the best blended
ratio of the blended sample.
 Form the table, the activation energy increasing from
the pure Silantek Coal until purely HDPE which is
268kJ/mol ad 561kJ/mol respectively.
 The lowest activation energy (Ea) value was obtained
at weight ratio of 40:60 with 251 kJ/mol and pre-
exponential factors is 2.95x10¹⁴sˉ¹ were the best
blended ratio of the blended sample.
 The activation energy (Ea) value increasing from the
pure Silantek Coal until purely HDPE which is 279
kJ/mol ad 574 kJ/mol respectively.

More Related Content

What's hot

COMBUSTION OPTIMIZATION IN SPARK IGNITION ENGINES
COMBUSTION OPTIMIZATION IN SPARK IGNITION ENGINESCOMBUSTION OPTIMIZATION IN SPARK IGNITION ENGINES
COMBUSTION OPTIMIZATION IN SPARK IGNITION ENGINESBarhm Mohamad
 
Synthesis of γ alumina by solution combustion method
Synthesis of γ  alumina by solution combustion methodSynthesis of γ  alumina by solution combustion method
Synthesis of γ alumina by solution combustion methodeSAT Publishing House
 
Ultrasonic and Volumetric Investigations of -Butyrolactone with Aliphatic Al...
Ultrasonic and Volumetric Investigations of -Butyrolactone with Aliphatic Al...Ultrasonic and Volumetric Investigations of -Butyrolactone with Aliphatic Al...
Ultrasonic and Volumetric Investigations of -Butyrolactone with Aliphatic Al...IJERA Editor
 
Gas Flare Stack Process
Gas Flare Stack ProcessGas Flare Stack Process
Gas Flare Stack ProcessShad Ibrahim
 
C030102011023
C030102011023C030102011023
C030102011023theijes
 
IA data based, boiling point estimation using molecular model vs carbon fract...
IA data based, boiling point estimation using molecular model vs carbon fract...IA data based, boiling point estimation using molecular model vs carbon fract...
IA data based, boiling point estimation using molecular model vs carbon fract...Lawrence kok
 
Computational and experimental study of engine characteristics using n butanol
Computational and experimental study of engine characteristics using n butanolComputational and experimental study of engine characteristics using n butanol
Computational and experimental study of engine characteristics using n butanolIAEME Publication
 
Tga evaluation mettler toledo
Tga evaluation   mettler toledoTga evaluation   mettler toledo
Tga evaluation mettler toledoDanae Francisco
 
Physical, Spectroscopic and Thermal Characterization of Biofield treated Myri...
Physical, Spectroscopic and Thermal Characterization of Biofield treated Myri...Physical, Spectroscopic and Thermal Characterization of Biofield treated Myri...
Physical, Spectroscopic and Thermal Characterization of Biofield treated Myri...albertdivis
 
IA data based, boiling point estimation fatty acids by molecular weight using...
IA data based, boiling point estimation fatty acids by molecular weight using...IA data based, boiling point estimation fatty acids by molecular weight using...
IA data based, boiling point estimation fatty acids by molecular weight using...Lawrence kok
 
NMeI_salts_JoelSmith
NMeI_salts_JoelSmithNMeI_salts_JoelSmith
NMeI_salts_JoelSmithJoel smith
 
Ept121 lecture 3 thermoanalytical analysis
Ept121 lecture 3   thermoanalytical analysisEpt121 lecture 3   thermoanalytical analysis
Ept121 lecture 3 thermoanalytical analysisKudzai Hamish Ruzvidzo
 

What's hot (18)

COMBUSTION OPTIMIZATION IN SPARK IGNITION ENGINES
COMBUSTION OPTIMIZATION IN SPARK IGNITION ENGINESCOMBUSTION OPTIMIZATION IN SPARK IGNITION ENGINES
COMBUSTION OPTIMIZATION IN SPARK IGNITION ENGINES
 
Synthesis of γ alumina by solution combustion method
Synthesis of γ  alumina by solution combustion methodSynthesis of γ  alumina by solution combustion method
Synthesis of γ alumina by solution combustion method
 
30120140504027 2
30120140504027 230120140504027 2
30120140504027 2
 
Ultrasonic and Volumetric Investigations of -Butyrolactone with Aliphatic Al...
Ultrasonic and Volumetric Investigations of -Butyrolactone with Aliphatic Al...Ultrasonic and Volumetric Investigations of -Butyrolactone with Aliphatic Al...
Ultrasonic and Volumetric Investigations of -Butyrolactone with Aliphatic Al...
 
Gas Flare Stack Process
Gas Flare Stack ProcessGas Flare Stack Process
Gas Flare Stack Process
 
C030102011023
C030102011023C030102011023
C030102011023
 
IA data based, boiling point estimation using molecular model vs carbon fract...
IA data based, boiling point estimation using molecular model vs carbon fract...IA data based, boiling point estimation using molecular model vs carbon fract...
IA data based, boiling point estimation using molecular model vs carbon fract...
 
Computational and experimental study of engine characteristics using n butanol
Computational and experimental study of engine characteristics using n butanolComputational and experimental study of engine characteristics using n butanol
Computational and experimental study of engine characteristics using n butanol
 
my paper-icanm-
my paper-icanm-my paper-icanm-
my paper-icanm-
 
Tga evaluation mettler toledo
Tga evaluation   mettler toledoTga evaluation   mettler toledo
Tga evaluation mettler toledo
 
Physical, Spectroscopic and Thermal Characterization of Biofield treated Myri...
Physical, Spectroscopic and Thermal Characterization of Biofield treated Myri...Physical, Spectroscopic and Thermal Characterization of Biofield treated Myri...
Physical, Spectroscopic and Thermal Characterization of Biofield treated Myri...
 
IA data based, boiling point estimation fatty acids by molecular weight using...
IA data based, boiling point estimation fatty acids by molecular weight using...IA data based, boiling point estimation fatty acids by molecular weight using...
IA data based, boiling point estimation fatty acids by molecular weight using...
 
Jiafei Zhang (Imperial College London) - Influence of Amine Molecular Structu...
Jiafei Zhang (Imperial College London) - Influence of Amine Molecular Structu...Jiafei Zhang (Imperial College London) - Influence of Amine Molecular Structu...
Jiafei Zhang (Imperial College London) - Influence of Amine Molecular Structu...
 
349 d. singh
349 d. singh349 d. singh
349 d. singh
 
Selection of amine solvents for CO2 capture from natural gas power plant
Selection of amine solvents for CO2 capture from natural gas power plantSelection of amine solvents for CO2 capture from natural gas power plant
Selection of amine solvents for CO2 capture from natural gas power plant
 
NMeI_salts_JoelSmith
NMeI_salts_JoelSmithNMeI_salts_JoelSmith
NMeI_salts_JoelSmith
 
Ept121 lecture 3 thermoanalytical analysis
Ept121 lecture 3   thermoanalytical analysisEpt121 lecture 3   thermoanalytical analysis
Ept121 lecture 3 thermoanalytical analysis
 
autoclaved aerated concrete
autoclaved aerated concreteautoclaved aerated concrete
autoclaved aerated concrete
 

Viewers also liked

Java channel allocation and routing in hybrid multichannel multiradio wirele...
Java  channel allocation and routing in hybrid multichannel multiradio wirele...Java  channel allocation and routing in hybrid multichannel multiradio wirele...
Java channel allocation and routing in hybrid multichannel multiradio wirele...ecwayerode
 
From the city to the country
From the city to the countryFrom the city to the country
From the city to the countryMarisa Soares
 
Db aing td2v1
Db aing td2v1Db aing td2v1
Db aing td2v1infcom
 
Tpdba3
Tpdba3Tpdba3
Tpdba3infcom
 
Problemas aditivos
Problemas aditivosProblemas aditivos
Problemas aditivosIsabo Fierro
 
Db aing td3v1
Db aing td3v1Db aing td3v1
Db aing td3v1infcom
 
The Internet of Things 2012 - New Horizon
The Internet of Things 2012 - New HorizonThe Internet of Things 2012 - New Horizon
The Internet of Things 2012 - New HorizonLittle Daisy
 
Exercises chapter 2 Energy, environment and climate second edition
Exercises chapter 2 Energy, environment and climate second editionExercises chapter 2 Energy, environment and climate second edition
Exercises chapter 2 Energy, environment and climate second editionAditya Wibawa
 
Examens Aline Laatiri ISITCOM
Examens Aline Laatiri ISITCOMExamens Aline Laatiri ISITCOM
Examens Aline Laatiri ISITCOMinfcom
 
Examens heykel Tej ISITCOM ingénierie protocoles
Examens heykel Tej ISITCOM ingénierie protocolesExamens heykel Tej ISITCOM ingénierie protocoles
Examens heykel Tej ISITCOM ingénierie protocolesinfcom
 
Examens Zaki Brahmi ISITCOM
Examens Zaki Brahmi ISITCOMExamens Zaki Brahmi ISITCOM
Examens Zaki Brahmi ISITCOMinfcom
 
Java virtual machine : Notions de base
Java virtual machine : Notions de baseJava virtual machine : Notions de base
Java virtual machine : Notions de baseANASYS
 
Wafa kamoun-admin-sec-reseaux
Wafa kamoun-admin-sec-reseauxWafa kamoun-admin-sec-reseaux
Wafa kamoun-admin-sec-reseauxinfcom
 
Oracle ADF : Vue d'ensemble
Oracle ADF : Vue d'ensembleOracle ADF : Vue d'ensemble
Oracle ADF : Vue d'ensembleANASYS
 
POLICE DIPLOMA
POLICE DIPLOMAPOLICE DIPLOMA
POLICE DIPLOMAMlu Tolo
 

Viewers also liked (18)

AggNet: Deep Learning from Crowds
AggNet: Deep Learning from CrowdsAggNet: Deep Learning from Crowds
AggNet: Deep Learning from Crowds
 
Horaris j8
Horaris j8Horaris j8
Horaris j8
 
Java channel allocation and routing in hybrid multichannel multiradio wirele...
Java  channel allocation and routing in hybrid multichannel multiradio wirele...Java  channel allocation and routing in hybrid multichannel multiradio wirele...
Java channel allocation and routing in hybrid multichannel multiradio wirele...
 
From the city to the country
From the city to the countryFrom the city to the country
From the city to the country
 
Db aing td2v1
Db aing td2v1Db aing td2v1
Db aing td2v1
 
Expert Advice
Expert AdviceExpert Advice
Expert Advice
 
Tpdba3
Tpdba3Tpdba3
Tpdba3
 
Problemas aditivos
Problemas aditivosProblemas aditivos
Problemas aditivos
 
Db aing td3v1
Db aing td3v1Db aing td3v1
Db aing td3v1
 
The Internet of Things 2012 - New Horizon
The Internet of Things 2012 - New HorizonThe Internet of Things 2012 - New Horizon
The Internet of Things 2012 - New Horizon
 
Exercises chapter 2 Energy, environment and climate second edition
Exercises chapter 2 Energy, environment and climate second editionExercises chapter 2 Energy, environment and climate second edition
Exercises chapter 2 Energy, environment and climate second edition
 
Examens Aline Laatiri ISITCOM
Examens Aline Laatiri ISITCOMExamens Aline Laatiri ISITCOM
Examens Aline Laatiri ISITCOM
 
Examens heykel Tej ISITCOM ingénierie protocoles
Examens heykel Tej ISITCOM ingénierie protocolesExamens heykel Tej ISITCOM ingénierie protocoles
Examens heykel Tej ISITCOM ingénierie protocoles
 
Examens Zaki Brahmi ISITCOM
Examens Zaki Brahmi ISITCOMExamens Zaki Brahmi ISITCOM
Examens Zaki Brahmi ISITCOM
 
Java virtual machine : Notions de base
Java virtual machine : Notions de baseJava virtual machine : Notions de base
Java virtual machine : Notions de base
 
Wafa kamoun-admin-sec-reseaux
Wafa kamoun-admin-sec-reseauxWafa kamoun-admin-sec-reseaux
Wafa kamoun-admin-sec-reseaux
 
Oracle ADF : Vue d'ensemble
Oracle ADF : Vue d'ensembleOracle ADF : Vue d'ensemble
Oracle ADF : Vue d'ensemble
 
POLICE DIPLOMA
POLICE DIPLOMAPOLICE DIPLOMA
POLICE DIPLOMA
 

Similar to POSTER FARAH-

Thermal analysis characterization of polymers and plastics acs webinar
Thermal analysis characterization of polymers and plastics acs webinarThermal analysis characterization of polymers and plastics acs webinar
Thermal analysis characterization of polymers and plastics acs webinarKevin Menard, Ph.D. MBA
 
THERMAL ANALYSIS OF MANGANESE (II) BAKELITE COMPOSITES
THERMAL ANALYSIS OF MANGANESE (II) BAKELITE COMPOSITES THERMAL ANALYSIS OF MANGANESE (II) BAKELITE COMPOSITES
THERMAL ANALYSIS OF MANGANESE (II) BAKELITE COMPOSITES IAEME Publication
 
Thermal analysis of manganese ii bakelite composites
Thermal analysis of manganese  ii bakelite compositesThermal analysis of manganese  ii bakelite composites
Thermal analysis of manganese ii bakelite compositesIAEME Publication
 
2009_Nguyen et al._Journal of Organometallic Chemistry
2009_Nguyen et al._Journal of Organometallic Chemistry2009_Nguyen et al._Journal of Organometallic Chemistry
2009_Nguyen et al._Journal of Organometallic ChemistryHuyen Lyckeskog
 
Meso-Molding Three-Dimensional Macroporous Perovskites
Meso-Molding Three-Dimensional Macroporous PerovskitesMeso-Molding Three-Dimensional Macroporous Perovskites
Meso-Molding Three-Dimensional Macroporous PerovskitesHamid Arandiyan
 
Effect of sintering time on the particle size and dielectric properties of La...
Effect of sintering time on the particle size and dielectric properties of La...Effect of sintering time on the particle size and dielectric properties of La...
Effect of sintering time on the particle size and dielectric properties of La...ijceronline
 
Polli, h.a application of-modelfree-kinetics-to-the-study-of-thermal-degradat...
Polli, h.a application of-modelfree-kinetics-to-the-study-of-thermal-degradat...Polli, h.a application of-modelfree-kinetics-to-the-study-of-thermal-degradat...
Polli, h.a application of-modelfree-kinetics-to-the-study-of-thermal-degradat...Jhon Chique
 
Numerical modeling and experimental evaluation of drying shrinkage of concret...
Numerical modeling and experimental evaluation of drying shrinkage of concret...Numerical modeling and experimental evaluation of drying shrinkage of concret...
Numerical modeling and experimental evaluation of drying shrinkage of concret...Mohamed Moafak
 
Highly stable pt–ru nanoparticles supported on three dimensional cubic ordere...
Highly stable pt–ru nanoparticles supported on three dimensional cubic ordere...Highly stable pt–ru nanoparticles supported on three dimensional cubic ordere...
Highly stable pt–ru nanoparticles supported on three dimensional cubic ordere...madlovescience
 
Highly stable pt–ru nanoparticles supported on three dimensional cubic ordere...
Highly stable pt–ru nanoparticles supported on three dimensional cubic ordere...Highly stable pt–ru nanoparticles supported on three dimensional cubic ordere...
Highly stable pt–ru nanoparticles supported on three dimensional cubic ordere...tshankar20134
 
Bczt ceramics prepared from activated powders
Bczt ceramics prepared from activated powdersBczt ceramics prepared from activated powders
Bczt ceramics prepared from activated powdersBéchir Yahmadi
 
Simulation of curing process of carbon/epoxy composite during autoclave degas...
Simulation of curing process of carbon/epoxy composite during autoclave degas...Simulation of curing process of carbon/epoxy composite during autoclave degas...
Simulation of curing process of carbon/epoxy composite during autoclave degas...Darkdragon766
 

Similar to POSTER FARAH- (20)

Thermal analysis characterization of polymers and plastics acs webinar
Thermal analysis characterization of polymers and plastics acs webinarThermal analysis characterization of polymers and plastics acs webinar
Thermal analysis characterization of polymers and plastics acs webinar
 
Ijetcas14 604
Ijetcas14 604Ijetcas14 604
Ijetcas14 604
 
2.pdf
2.pdf2.pdf
2.pdf
 
THERMAL ANALYSIS OF MANGANESE (II) BAKELITE COMPOSITES
THERMAL ANALYSIS OF MANGANESE (II) BAKELITE COMPOSITES THERMAL ANALYSIS OF MANGANESE (II) BAKELITE COMPOSITES
THERMAL ANALYSIS OF MANGANESE (II) BAKELITE COMPOSITES
 
Thermal analysis of manganese ii bakelite composites
Thermal analysis of manganese  ii bakelite compositesThermal analysis of manganese  ii bakelite composites
Thermal analysis of manganese ii bakelite composites
 
2009_Nguyen et al._Journal of Organometallic Chemistry
2009_Nguyen et al._Journal of Organometallic Chemistry2009_Nguyen et al._Journal of Organometallic Chemistry
2009_Nguyen et al._Journal of Organometallic Chemistry
 
Meso-Molding Three-Dimensional Macroporous Perovskites
Meso-Molding Three-Dimensional Macroporous PerovskitesMeso-Molding Three-Dimensional Macroporous Perovskites
Meso-Molding Three-Dimensional Macroporous Perovskites
 
Effect of sintering time on the particle size and dielectric properties of La...
Effect of sintering time on the particle size and dielectric properties of La...Effect of sintering time on the particle size and dielectric properties of La...
Effect of sintering time on the particle size and dielectric properties of La...
 
Thermal
ThermalThermal
Thermal
 
Thermal polysaccharide
Thermal polysaccharideThermal polysaccharide
Thermal polysaccharide
 
Polli, h.a application of-modelfree-kinetics-to-the-study-of-thermal-degradat...
Polli, h.a application of-modelfree-kinetics-to-the-study-of-thermal-degradat...Polli, h.a application of-modelfree-kinetics-to-the-study-of-thermal-degradat...
Polli, h.a application of-modelfree-kinetics-to-the-study-of-thermal-degradat...
 
0001
00010001
0001
 
Numerical modeling and experimental evaluation of drying shrinkage of concret...
Numerical modeling and experimental evaluation of drying shrinkage of concret...Numerical modeling and experimental evaluation of drying shrinkage of concret...
Numerical modeling and experimental evaluation of drying shrinkage of concret...
 
Highly stable pt–ru nanoparticles supported on three dimensional cubic ordere...
Highly stable pt–ru nanoparticles supported on three dimensional cubic ordere...Highly stable pt–ru nanoparticles supported on three dimensional cubic ordere...
Highly stable pt–ru nanoparticles supported on three dimensional cubic ordere...
 
Highly stable pt–ru nanoparticles supported on three dimensional cubic ordere...
Highly stable pt–ru nanoparticles supported on three dimensional cubic ordere...Highly stable pt–ru nanoparticles supported on three dimensional cubic ordere...
Highly stable pt–ru nanoparticles supported on three dimensional cubic ordere...
 
Thesis Defense-2
Thesis Defense-2Thesis Defense-2
Thesis Defense-2
 
Bczt ceramics prepared from activated powders
Bczt ceramics prepared from activated powdersBczt ceramics prepared from activated powders
Bczt ceramics prepared from activated powders
 
G0113647
G0113647G0113647
G0113647
 
Simulation of curing process of carbon/epoxy composite during autoclave degas...
Simulation of curing process of carbon/epoxy composite during autoclave degas...Simulation of curing process of carbon/epoxy composite during autoclave degas...
Simulation of curing process of carbon/epoxy composite during autoclave degas...
 
COAL FIRED BOILER -PRINCIPALS.pdf
COAL FIRED BOILER -PRINCIPALS.pdfCOAL FIRED BOILER -PRINCIPALS.pdf
COAL FIRED BOILER -PRINCIPALS.pdf
 

POSTER FARAH-

  • 1. Poster template by ResearchPosters.co.za MODEL FREE KINETICS OF MALAYSIAN COAL AND WASTE PLASTICS BLENDS DURING CO-PYROLYSIS PROCESS Nurfarahima Binti Ibrahim and Sharmeela Matali. Faculty of Chemical Engineering, Universiti Teknologi Mara 40450 Shah Alam, Selangor. Model free kinetics algorithms was applied to determine conversion, iso-conversion and activation energy during co-pyrolysis of high density polyethylene (HDPE) and coal. The kinetics data were be obtained from TGA data at different heating rate and model fitting methods. Thermal behavior of the polyethylene and coal material was be investigated by knowing thermal degradation kinetics. Thermal degradation of polymer is a more complex reaction, thus the reliable model of kinetics models is very important to get a best results. To estimate the kinetics data of degradation, the Kissinger’s and Ozawa-Flynn Wall model free kinetics method were applied. The data have been obtained via thermogravimetry analysis during co-pyrolysis of Silantek coal and waste plastics (HDPE) blends at nitrogen atmosphere, under dynamic conditions at different heating rate of 10, 20 and 40oC/min. The kinetics parameters, activation energy (Ea), and pre- exponential factors (A) of the blends were calculated using these model equations by function of peak temperature and heating rate. Therefore, from the calculated results showed that the activation energy of the coal/HDPE blends is 238.128 kJ/mol and 250.822 kJ/mol respectively between temperature range of 300-600oC, and it can be characterized by first order reaction. DTG curves coal/HDPE blends are found to be overlapping and due to possible chemical interaction between these material [1]. 1. Sample preparation Results Results Conclusion Recommendation Acknowledgement INSERT LOGO HERE INSERT LOGO HERE Figure 1: Sample Preparation  To determine the best model free kinetics to represent experimental data.  Application of free model kinetics for the co-pyrolysis process. 2. Proximate Analysis & Thermal Degradation Kinetics analysis Figure 2: Thermogravimetric Analyzer (Mettler Toleda/TGA/SDRA51e) 3. Calorific Value Analysis Mass ratio of blending will affected the calorific value were increasing amount of mass ratio HDPE reduces the calorific values of blended samples. The low degree of branching and required higher energy to break the stronger intermolecular forces in the polymeric chain of HDPE [2]. 4. Thermogravimetric Profile (TG)  The weight loss during the pyrolysis process in the temperature range 300-600°C due mainly to gases emission gases emission with a mass loss between 60wt%.  The decomposition curves of the blends are overlapped which may be due to interactions of solid- solid and solid-gas [1] . 3. Ultimate Analysis The model free kinetics study is one of the methods to obtain the activation energy and pre-exponential factors from the co-pyrolysis study via thermogravimetric analysis. Under the pyrolysis conditions, the material decomposition start to degrade at approximately 300°C with progressively and rapidly up to 450°C to 600°C. 8. Ozawa-Flynn Wall (OFW) Model Kinetics 4. Model Free Kinetics Methods Figure 3:Thermo Finnigan Flashed 1112 analyser  If the percentage of volatile matter increasing, the percentage of char yield will decrease.  At range weight ratio 40wt% to 60wt%, the curves of char yield and volatile matter intersect, assumed that the char yield produced is inversely proportional to the volatile matter contents blended sample. Table 3 : Thermal decomposition of Silantek coal/HDPE blends at different heating rates. lgβ= log AE/βR -2.315 - 0.4567 E/RT (Eq2) References Thank you to my Research Project supervisor, Mdm Sharmeela Matali and Universiti Teknologi Mara (UiTM). 1. Z. Mikulová and J. F. , Marek Večeř. Study Of Pyrolysisis Of Polymers And Coal And Co-Pyrolysis Of Their Blends, Kinetics Of The Process. Trans. VŠB – Tech. Univ. Ostrava, Mech. Ser., vol. LVIII, no. 1, pp. 147–155, 2012. 2. Sharmeela Matali, Mohd Ridzuan Mohatar. Thermogravimetric Analysis of the Pyrolysis Characteristics on Sarawak Silantek Coal, Waste Tyre, Waste HDPE and their blends Shah Alam : International Journal of Chemical Engineering, 2015. Cutting → Drying → Grinding → Sieving  Kissinger’s Method.  Ozawa-Flynn Wall Method. Chemical Element Silantek Coal (wt%) HDPE (wt%) Carbon 74.186 80.58 Hydrogen 4.350 8.406 Nitrogen 1.454 9.359 Sulphur 0.010 0.526 Oxygen 20.00 1.129 Table 1: Proximate Analysis of Silantek Coal and Waste Plastic HDPE Figure 4: The Calorific Value of the HDPE, Silantek Coal and their Blends . 0 2 4 6 8 10 12 14 16 18 20 22 0 70 140 210 280 350 420 490 560 630 700 770 840 910 980 WeightRatio(mg) Temperature (°C) Thermogravimetric Profile (TG) HR40 HR20 HR10 Figure 5: Thermogravimetric (TG) Profile of the Silantek Coal and Waste HDPE blends at different heating rates at weight ratio blends of 40% Silantek Coal. 5. Derivatives Thermogravimetric Profile (DTG)  The peak temperature for this thermal event for heating rate 10°C/min is 479°C with peak height, R of 3.05 mg/min.  The peak temperature for this thermal event for heating rate 20°C/min is 497°C with peak height, R of 6.210 mg/min. .  The peak temperature for this thermal event for heating rate heating rate 40°C/min is 504°C with peak height, R of 10.34 mg/min. -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 0 70 140 210 280 350 420 490 560 630 700 770 840 910 980 weightratio(mg/min) Temperature (°C) Derivatives Thermogravimetric Profile (DTG) HR40 HR20 HR10 Figure 6: Derivatives Thermogravimetric (TG) Profile of the Silantek Coal and Waste HDPE blends at different heating rates at weight ratio blends of 40% Silantek Coal. 6. Thermal Degradation HR Blend TEP 1 R TEP 2 R CY(%) VM(%) °C/min SC:HDPE Tmax mg/min Tmax mg/min 10 100:0 61.15 0.21 452.10 0.20 45.22 54.78 80:20 60.95 0.21 475.23 0.90 53.30 46.70 60:40 58.35 0.14 482.71 2.13 46.29 53.71 40:60 51.72 0.11 478.88 3.05 42.31 57.69 20:80 49.69 0.01 484.37 4.71 20.21 79.79 0:100 452.09 0.94 44.05 55.95 20 100:0 79.39 0.43 445.52 0.59 49.89 50.11 80:20 497.60 2.60 68.98 31.02 60:40 493.33 2.98 60.78 39.22 40:60 497.39 6.21 37.40 62.60 20:80 495.87 8.08 29.61 70.39 0:100 493.73 9.28 22.09 77.91 40 100:0 441.92 0.91 51.97 48.03 80:20 85.39 0.89 493.83 3.54 59.67 40.34 60:40 85.06 0.85 503.07 6.49 46.74 53.26 40:60 79.28 0.65 503.55 10.34 34.81 65.19 20:80 76.70 0.26 504.23 14.19 30.86 69.14 0:100 101.91 0.11 502.98 19.12 12.40 87.6 Abstract Results 1. Proximate Analysis Aspect Silantek Coal (wt%) HDPE (wt%) Volatile Matter 48.36 87.51 Fixed Carbon 45.22 0 Ash 6.42 12.49 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 100.0% 80.0% 60.0% 40.0% 20.0% 0.0% Weightrartio% Weight Ratio % (COAL:HDPE) Thermal Degradation of Char Yield and Volatile Matter CR40 CR20 CR10 VM40 VM20 VM10 7. Kissinger’s Model Kinetics. ln(β/Tm²) = lnAR/E – E/RTm (Eq1) Where: β = heating rate Tm = maximum temperature A =pre-exponential factor R = rate constant Ratio Kinetics Data’s SC:HDPE Slope Intercept Ea(kJ/mol) A(sˉ¹) 100:0 -32191.069 30.949 267.646 8.88E+17 80:20 -39654.125 37.920 329.696 1.17E+21 60:40 -37455.966 34.510 311.420 3.64E+19 40:60 -28640.793 23.002 238.128 2.80E+14 20:80 -39160.646 36.622 325.593 3.15E+20 0:100 -67539.227 73.400 561.541 5.09E+36 Table 4: The kinetic data of Kissinger’s Method. Blend Kinetic Parameter % Slope Intercept Ea(kJ/mol) A(sˉ¹) 100 -33612.668 30.949 279.466 9.275E+17 80 -41168.131 37.920 342.284 1.211E+21 60 -38987.946 34.510 324.157 3.787E+19 40 -30167.521 23.002 250.822 2.946E+14 20 -40694.531 36.622 338.347 3.269E+20 0 -69080.905 73.400 574.359 5.208E+36 Table 5: The kinetic data of Ozawa Flynn Wall Method. 267.646 297.222 311.420 238.128 325.593 561.541 279.466 308.988 324.157 250.822 338.347 574.359 200.0 250.0 300.0 350.0 400.0 450.0 500.0 550.0 600.0 0.0% 20.0% 40.0% 60.0% 80.0% 100.0% ActivationEnergy()kJ/mol Blend Ratio Model Kinetic of Co-pyrolysis Kissinger OFW Figure 10: : Comparision of Model Free Kinetics during co-pyrolysis of Silantek Coal and waste plastic HDPE blends Model kinetic Blends ratio 100:0 80:20 60:40 40:60 20:80 0:100 Kissinger Ea (kJ/mol) 267.6462 297.2219 311.4201 238.1281 325.5934 561.5414 A(sˉ¹) 8.88E+17 1.17E+21 3.64E+19 2.8E+14 3.15E+20 5.09E+36 R² 0.5035 0.959 0.9996 0.9115 0.9896 0.8182 Ozawa-Fynn Wall Ea (kJ/mol) 279.4658 308.9883 324.1575 250.8218 338.3465 574.3594 A(sˉ¹) 9.28E+17 1.21E+21 3.79E+19 2.95E+14 3.27E+20 5.21E+36 R² 0.5251 0.9627 0.9997 0.9195 0.9904 0.8248 Table 6 : Comparision of Model Free Kinetics data 2. Ultimate Analysis Table 2: Ultimate Analysis of Silantek Coal and Waste Plastic HDPE Figure 7: Thermal Degradation of Char Yield and Volatile Matter  The lowest Ea is obtained weight ratio of 40:60 and corresponding R2 value be represent the best fit model for experimental, calculated were 0.9115 ofr Kissinger’s model and 0.9195 for OZW model which are considered acceptable and fits well with there two models. Used the various type of the waste material blends with natural sources and continues for other heating rate for further study of model kinetics. Methodology Objectives  The lowest activation energy (Ea ) was obtained at weight ratio of 40:60 with 238 kJ/mol and pre- exponential factor is 2.8x10¹⁴sˉ¹ were the best blended ratio of the blended sample.  Form the table, the activation energy increasing from the pure Silantek Coal until purely HDPE which is 268kJ/mol ad 561kJ/mol respectively.  The lowest activation energy (Ea) value was obtained at weight ratio of 40:60 with 251 kJ/mol and pre- exponential factors is 2.95x10¹⁴sˉ¹ were the best blended ratio of the blended sample.  The activation energy (Ea) value increasing from the pure Silantek Coal until purely HDPE which is 279 kJ/mol ad 574 kJ/mol respectively.