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Char Morphology for Colombian Coals using Image Analysisusing Image Analysis
D. Chavesa, M. Trujilloa, Ed. Garciab, S. Guerrerob, J. Barrazab, Ed Lesterc, M. Barajasc, B. Rodriguezc, M. Romeroc
aMultimedia and Computer Vision Group, Universidad del Valle, Cali, ColombiabChemical Engineering School Universidad del Valle Cali ColombiabChemical Engineering School, Universidad del Valle, Cali, ColombiacColombian Geological Service, Bogota, ColombiadAdvanced Materials Research Group, University of Nottingham, Nottingham, United Kingdom
11th European Conference on Coal Research and its Applications7th September 2016, University of Sheffield, UK
Multimedia and Computer Vision Group o MVC is a research group of the Universidad del Valle in Cali, Colombia
Computer Vision Medical images
COLOMBIACoal images
Coffee leaf images
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Outlineo Introduction
o Char morphologiesA t ti h i l ifi tio Automatic char image classification
o Feature extractiono Classification model
o Experimental resultso Final remarks
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Introduction
Coal
Feed system of coalo The conditions of
CoalPulverised
Pipeline of solid-gas mixture
devolatilisationprocess produces different char morphologies which
Oxygen and nitrogen
rotameters
Tubularfurnace
Ceramic
Temperature controller
morphologies which may be used to determine coal reactivity Ceramic
tube
Char collection system
reactivity
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Pulverised Coal Power Plant CharSimulated coal pulverised combustion
Char morphologies (I)Ch h l i d b th ICCP
o Morphological featuresused for classification:
o Char morphologies proposed by the ICCP
o Unfused materialo Porosityo SphericityMixed Porous Mixed DenseCrassisphree Teniusphere o Sphericityo Wall thickness
p p
Inertoid SolidTenuinetwork Crassisnetwork Mineroid
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Alvarez, D., and Lester, E. Atlas of Char Occurrences. Combustion Working Group, Commission III. ICCP, 2001
Char morphologies (II)o Char morphologies from Colombian coals
o Morphological featuresused for classification:
T i t k
o Unfused materialo Porosityo Sphericity
Mixed Porous Mixed DenseTenuinetwork Crassisnetwork o Sphericityo Wall thickness
Inertoid SolidCrassisphree Teniusphere
Colombian coals has low amount of unfused material
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Inertoid SolidCrassisphree Teniusphere
Automatic char morphology classification
Char classification process is based on image analysis
Image acquisition Preprocessing
segmentation and extraction of particles
ParticlesCamera Classification
ModelParticlesclassification Measuring morphological
features
Automatic char classification process
Microscope
Lester, E., Cloke, M. and Allen, M. Char Characterization Using Image Analysis Techniques. Energy & Fuels, 1996Cloke M Wu T Barranco R and Lester E Char characterisation and its application in a coal burnout model Fuel 2003
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Cloke, M., Wu, T., Barranco,R., and Lester, E. Char characterisation and its application in a coal burnout model. Fuel 2003Wu, T., Lester, E., and Cloke, M. Advanced automated char image analysis techniques. Energy & Fuels, 2006Chaves, D., Reyes, J. M. and Trujillo, P. Clasificación automática de imágenes digitales de carbonizados, LACNEM 2009
Classification model proposed by the ICCPCh l ifi ti t
<50% Ash
<25% Unfused Material
25-75% Unfused Material
>75% Unfused Material
CharParticle
o Char classification tree
Unfused Material Unfused Material Unfused Material
>40%Porosity
<40%Porosity
>60%Porosity
<40%Porosity
60%>Porosity >40%
<5%Porosity
5%< Porosity >40%
>50% Ash
Mineroid
MixedPorous
MixedDense
Inertoid
Solid /Fusinoid
Cenospheric Arrangement Network ArrangementCenospheric Arrangement Network Arrangement
>50% Wall<3umPorosity >80%
>50% Wall>3umPorosity >40%
Tenuisphere Crassisphere
>50% Wall <3umPorosity >70%
>50% Wall>3umPorosity >40%
Tenuinetwork Crassinetwork
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Alvarez, D., and Lester, E. Atlas of Char Occurrences. Combustion Working Group, Commission III. ICCP, 2001
Building a char classification model (I)o A feature vector is obtained from a char particle image and is used to train a classification
model
0. Char high porosity
1. Char low porosity
Char particleMorphological features
Feature extractionFeature vector
ClassificationSupport Vector Machine
Char particle image
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Chaves D., Trujillo M., Barraza J.M.: Global and local features for char image classification. IWINAC (2015)
Building a char classification model (II)o Classification groups
Char with high porosity Char with low porosity
Mixed Porous Mixed DenseTenuinetwork Crassisnetwork
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Inertoid SolidCrassisphere Teniusphere
Measuring morphological features (I)
o The unfused material corresponds to brightest pixels
o The porosity is calculated as the ratio between total pore area and total particle area
Fragmented pore reconstruction
Complete pore identification
0 250 255
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reconstruction
Measuring morphological features (II)
o The particle is considered as sphericalif the ratio is greater than 70%
o The wall thickness is calculated drawing lines through the center of the char particle
DiameterFeretMaximumDiameter FeretMinimumSphericity
of the char particle
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Measuring morphological features (III)
o Foreground / Background (F/B) RatioThe foreground area correspond to white color pixels of the binary image
Char particle image
Binary image
Classification: Support Vector Machine, SVMA d l i b ilt th b t h l th t t i th t l do A model is built as the best hyperplane that categorises the two classes and maximises the margin between classes
Obj ti f ti12 2 +
o Objective function:
∙ + ≥ 1 − , ∀Constrains:
≥ 0
+ ≥ 1 , ∀
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Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. COLT ’92 (1992)Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: Liblinear: A library for large linear classification. Journal of Machine Learning Research 9, 1871–1874 (2008)
Experimental setupo Coals from Cundinamarca
o Three temperatures: 800°C, 900°C and 1000°C
o Three residence times: 100ms, 200ms and 300ms
o A set of 1550 char particle images was chosen for building the classification modelo A set of 1550 char particle images was chosen for building the classification modelo A set of 965 char images labelled as high porosity particles o A set of 585 char images labelled as low porosity particles
o Expert criteria were used as a ground truth for qualitative evaluations
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Experiments: image adquisition
Badly polished
Preparation and polishing Camera and microscopyConfiguration LUCIA 5.3o Size: 1600x1200 pixels
Preparation and polishing char blocks
o Zoom: 50xo Exposure: 1/20 so Gain: 1.40xo White balance:
Blur and low content images
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Red:0.75 Green:1.0 Blue:2.75
Char classification results (I) D 1 0 h i l i 96 hi h i d 8 l io Data set: 1550 char particle images, 965 high porosity and 585 low porosity
o Training: 5-fold cross-validation, training 80%-20%, SVM C=5Accuracy I.C. = 0. 7658 +/- 0.0677 Accuracy I.C. = 0. 7665 +/- 0.0680Accuracy I.C. 0. 7658 / 0.0677 ccu acy C 0 665 / 0 0680
Unfused material, Porosity, P it S h i it W ll thi k
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, y,Sphericity, Wall thickness Porosity, Sphericity, Wall thickness
Char classification results (II) D 1 0 h i l i 96 hi h i d 8 l io Data set: 1550 char particle images, 965 high porosity and 585 low porosity
o Training: 5-fold cross-validation, training 80%-20%, SVM C=5Accuracy I.C. = 0. 8039 +/- 0.0485 Accuracy I.C. = 0. 8045 +/- 0.0478Accuracy I.C. 0. 8039 / 0.0485 y
Unfused material, F/B ratio, Porosity, F/B ratio, Porosity, Sphericity, Wall
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Unfused material, F/B ratio, Porosity, Sphericity, Wall thickness
F/B ratio, Porosity, Sphericity, Wall thickness
Final remarkso An automatic char classification model was built using Support Vector Machine for
analysing Colombian coals
Fi e morphological feat res ere tested to train the classifiers nf sed materialo Five morphological features were tested to train the classifiers: unfused material, ratio foreground / background, porosity, sphericity, wall thickness
o An improvement in accuracy of classification models is observed when unfusedp ymaterial was not included
o The use of Foreground/Background ratio increased the accuracy of the classification model
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Manual Char Morphology Classificationo The classification of a char sample requires to analyse at least 500 particles,
over 290 images
ChCharParticle Classification
1 Crassisphere
2 Inertoid
CameraMicroscopeResin
Ch bl k
…
399 Mixed Dense
500 Crassisphere
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Char block