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