Enhanced Control by Visualisation of Process Characteristics: Video Monitoring of Coal Powder Injection in a Blast Furnace

Jihad Daoud, Igor Nipl

Enhanced Control by Visualisation of Process Characteristics:

Video Monitoring of Coal Powder Injection in a Blast Furnace

MASTER'S THESIS

Civilingenjörsprogrammet Institutionen för Systemteknik Avdelningen för Reglerteknik

2000:076 • ISSN: 1402-1617 • ISRN: LTU-EX--00/076--SE

Characteristics: Video Monitoring of Coal Powder

Injection in a Blast Furnace

Jihad Daoud

Igor Nipl

2000-02-29

Authorsaddress:

LuleåUniversity of Technology

Department of Computer Science andElectrical Engineering

Control Engineering Group

S-971 87LULEÅ,Sweden

jihdao-6@student.luth.se

igonip-6@student.luth.se

blastfurnace. Somedayyouwill become coal,andifyouarelucky,

youmightbeusedinablastfurnace.

Nothingisstatic,notus,notyou,notanyoftheimagesweanalysed.

Fightingagainstchanges,beingstatic,islike.... Noopiniononthat

one!

A language canbeused to control people, a computercanbeused

to control muchmore. It is onlystupidpeople/thingsthat are easy

to control,butagaineverythingisrelative.

Donotaskus aboutthetruth,wearestill searching. Whenwend

it,youwillknowitoryouarealreadydead. Ifyouarenotdead,you

aretoolazyoryouknowsomethingwedonotknow.

Tothepoorpeople.

Jihad

ToLindaandmyparents.

Igor

Thismasterthesis,basedonworkperformedatLuleåUniversityofTechnology

in cooperation withMefos, is aboutmeasurementof pulverisedcoal owinjected

intoablastfurnace,compensatingforsomeoftheusuallyusedcoke. Coalisdrawn

from aninjection vessel andtransported under pressurewiththehelp ofnitrogen

gastoablastfurnace. Itisblownthroughpipestothetuyereswhereitisinjected

intotheironmakingprocess.

Irregular coal supply to the furnace has bad inuence on the quality of the

produced ironso reliablecontrol is needed. In controllingthe ow, it is of great

importancethattheon-lineowmeasurementisaccurate. Enhancing theexisting

measurementwould be benecial for thequality of the produced iron. Therefore

newmeansofblastfurnaceprocesssurveillanceandowmeasurement,usingcam-

erasandimageprocessing,arestudied. Theideabehindcamerasurveillanceisalso

benecial forestimationofotherprocessparameters.

The main goal isobtaining relevantinformation from image data in order to

estimate thepulverisedcoalow. Methodsforachievingthisareinvestigatedand

discussed. Acomparisontooldmeasurementdataismade. Alsovalidationofdata

retrievedwiththehelpofimageprocessingismentioned.

Ithasbeenshownthatvideomonitoringinconjunctionwithimageprocessing

is a feasible option when it comes to coal ow estimation. The images include

potential information for other purposes likedetermining thetemperature of the

ame and how well the coal is distributed inside the blast furnace. This would

solve some of the problems and eliminate obstacles caused by the nature of the

steel makingprocess.

Peoplehavealwaysaskedus: Whystudyautomaticcontrol? Therealquestion

is: Why not? There are not many elds that aect our modern life as much as

automaticcontroldoes,ofcoursemathematicsandpossiblyphysicsarecornerstones

in anynutritiousstudy. Theyarehardtocompetewith. Appliedsciencein allits

forms isthewaytogoto enhanceproductsand toolsthat areessentialin today's

society. Theachievementsin the eld ofautomatic control aresurrounding us in

our everyday lives, no matter if we like it or not. A lot of things out there are

alreadydone,manymorearewaitingto bedone. Thereisalsoalotofnetuning

totakecareof,whichissometimesevenmorechallenging. Wewantedtobeapart

of this evolvingdevelopment. Wewant tothank AndersGrennberg,withouthim

wewouldnotbeclosingloopsthesedays.

Thisworkisapartofabiggerprojectwithinvolvementfromtheindustrialand

research world, backed up byPROSA - Centre for Process and System Automa-

tion 1

. Ourmaster thesiswascarriedoutat theDepartmentofComputerScience

andElectricalEngineering,ControlEngineeringGroupatLuleåUniversityofTech-

nology 2

in cooperationwithMefos 3

. Theworkyouholdin yourhands isbrought

to you by twohuman beings, but is aresult of many more human participants.

Peoplewithoutwhoseknowledgeandwillingnesstohelp,youwouldnotbeableto

read thisreport.

Duringthe time wespent onthis research welearnedto knowseveral people

withdierentbackgroundsfromdierentcompanies,gainedmoreunderstandingof

the complicated coalinjection process in ablastfurnace, and improvedourskills

in imageprocessing.

Wehad greathelpfrom ourexaminerProfessorAlexanderMedvedevandour

supervisorPh. D. OlovMarklund, bothatpresentworkingfortheDepartmentof

Computer Science and Electrical Engineeringat Luleå University of Technology.

Thank you for oering us a part of your valuable time. We would also like to

thank Andreas Johansson, Wolfgang Birk and other researchers at the Control

EngineeringGroup. RolandLindforsattheAV-centre. PerMäkikaltioand others

at the Division of Industrial Electronics and Robotics. Krister Engberg at the

Division ofSignal Processing forputting upwith us. Thesystem administrators,

Mattias Pantzare and Jonas Stahre for their indispensable help. All working at

LuleåUniversityofTechnology. TheFreeSoftwareFoundation 4

oeringtheworld

the best they can achieve, without them we would be dependent on commercial

software, exceptforMATLABwhere wehad notimeto writetheneeded toolbox

forOctave 5

. NottoforgetthehelpfulpeopleatMefos,LKAB,SecuritasandBjörn

OlssonatSSAB. Re-Tekmembersforkeepingupthespirit ofbeingatrociousand

takingthecomputersweborrowedbeforewenishedtheproject. El-Tekmembers

1

PROSA'shomepage. URL:http://www.sm.luth.se/csee/prosa/html/

2

Department of Computer Science and Electrical Engineering's home page. URL:

http://www.sm.luth.se/

3

Mefos'homepage.URL:http://www.mefos.se/

4

FreeSoftwareFoundation'shomepage.URL:http://www.fsf.org/

5

Octave'shomepage.URL:http://www.che.wisc.edu/octave/octave.html

formakingitpossibletobuyprovisionsduringthetimewespentwritingthisreport.

Lastbutnotleast,wewanttothankourfriendsfortheirpsychologicalsupport.

Thankyouall,youmadeusdoit.

Chapter 1. Introduction 11

Chapter 2. ProcessDescription 13

2.1. TheBlastFurnace 13

2.2. CoalInjection 15

2.3. CurrentControl 16

2.3.1. Sensors 16

2.3.2. Controllers 16

2.4. VideoSurveillance 17

Chapter 3. CollectingData 19

3.1. Available SignalsandEquipment 19

3.2. MeasuredSignals 20

3.3. VideoRecording 22

3.4. VideoDigitising 23

Chapter 4. DataProcessing 27

4.1. AnalysisofSignals 27

4.1.1. Filter Identication 33

4.2. Danalyzer 34

Chapter 5. ImageProcessing 35

5.1. ImageDecomposition 35

5.1.1. RGBandHSIspaces 35

5.1.2. ImageQuality 35

5.2. ImageContent 38

5.2.1. ImageHistograms 38

5.2.2. ImageThreshold 41

Chapter 6. Algorithms 45

6.1. FindingtheBackground 45

6.1.1. OneBackgroundApproach 45

6.1.2. CustomisedBackgroundApproach 46

6.2. FindingtheCoalPlume 52

6.3. EstimationofPlume'sArea 55

6.4. EstimationofPlume'sVolume 55

6.4.1. WeightedPixelEstimation 55

6.4.2. RotatedPlumeEstimation 56

6.4.3. ApproximatedShapeEstimation 56

Chapter 7. DataExtractionandValidation 59

7.1. RelationsBetweenAlgorithms 59

7.2. ExtractedDataCharacteristics 60

7.3. AndreasTestProperties 64

Chapter 8. ConclusionsandSuggestions 69

8.1. Conclusions 69

8.2. Suggestions 70

Bibliography 73

AppendixA. MATLABCode 75

A.1. mam.m 75

A.2. mip.m 75

A.3. imframe.m 75

A.4. imends.m 75

A.5. line2pixel.m 76

A.6. imconnect.m 77

A.7. imlter.m 79

A.8. imback.m 79

A.9. dyncrop.m 79

A.10. dynbg.m 80

A.11. bgmulti.m 80

A.12. imarea.m 81

A.13. algox.m 81

A.14. ndame.m 82

A.15. imame.m 82

A.16. algo2vol.m 82

A.17. evalvol.m 83

A.18. countpixels.m 83

A.19. ndvolume2.m 83

AppendixB. CCode 85

B.1. ssnap.c 85

Introduction

Heavy industries are the backbone of our society, any improvements in this

areamean indirectlyabetterstandardof living. Steel andironproduction isone

of these industries. Steelmakinghasevolveddramaticallysince mankindlearned

howto produceit. Yet thereis still alot todo because theprocessitself is quite

complicated and notfully understood. Newtechnology has contributed in many

ways to improve the steel making procedure, where involvement of people with

dierentbackgroundsandacademicknowledgeisessential.

In existing blast furnaces there are many problems, which remain unsolved

despite many yearsof thoroughresearch. New improvements and breakthroughs

are made everyday, but there will always be more work to be done due to the

sophisticated process nature. Eciency, quality, environmental issues and cost

reduction requirements are the main objectives. The fuel used in the furnace is

one of the targets. Changing the kind of fuel used has shown verygood results.

Traditionallycokeisused. Manyotheralternativefuels[10]havebeentested,such

aspulverisedcoal, naturalgas, oilbut alsowastematerials. Thefuture supplyof

coke[1]isanotherproblemthatmightleadtosteadilyincreasingprices. Pulverised

coalhasbecomeagoodalternative. Itis30-40%cheaperandmoreenvironmentally

friendly [12] than coke. Using pulverised coal resulted in a 40% saving in coke

requirementsatBritishSteel,Scunthorpeworks[11],[13]. Inaddition,pulverised

coalhasaquickerimpactonthereactionin theactivezoneofthefurnace.

Beside choosinganalternativefuel,steelmakersneedabetteroverviewof the

process. Controllingthe product quality relies on identifying the process param-

eters from a metallurgical point of view and how well the process is controlled.

Controllingtheprocess,besideunidentiedprocessparameters,runsintoproblems

relatedtoowmeasurement,temperaturemeasurementandfueldistributioninthe

blast furnacewhich are hardto dealwith usingold-fashion techniques becauseof

thehighprocess complexityandtheverydemandingenvironment.

Quick development in computer hardware has opened new perspectives and

possibilities. At present it is an easy task to process alarge amount of data to

extractusefulinformationinordertocontrolandsupervisethesteelproductionin

ablastfurnace. Onewaytodothisistheuseofcameraspointedtothepulverised

coal outlet from thetuyeres into thefurnace. Cameraimages canbeanalysed in

ordertodeterminedierentimportantprocessparameters. Thegoalistocalculate

high qualityparametersthat reect whathappensin thefurnace. This willmake

life easierformetallurgicalandautomaticcontrolpeople.

A prestudy [2] hasshown thatthere is asignicantrelationbetweenthe pul-

verised coalmass owestimation and the size of the coal plume in theanalysed

video recorded series of images. Therecording was donewith a black and white

camera. Deeperinvestigationis requiredto verify theresultsand ndalgorithms

for calculation of the coal plume volume and coal owestimation, but also tem-

perature and coal powder distribution. It is also interesting to study the result

obtainedwithacolourcamerainordertouseseveralindependentestimationchan-

nels. Inthis report we will focus on therst part, i.e. coal ow, but our results

will hopefullybeusefulfortheothertargetstoo.

Process Description

WehadanopportunitytoworkonLKAB'sexperimentalblastfurnaceatMefos

[17],wherewehadasetupofcamerasandapossibilitytocollectneededdata. In

this chapter we will briey describethe dierent parts of the plant and the coal

injection partoftheprocess.

A D B

C

Figure2.0.1. AschematicoverviewoftheplantatMefos.

2.1. The BlastFurnace

TheblastfurnaceatMefosismarkedwithanA inFigure2.0.1. Apictureof

theactualinstallationisinFigure2.1.1. Ithasthreetuyeresandadiameteratthe

tuyerelevelof1.2m. Theworkingvolumeis8.2m 3

. Thehotblastisproducedin

pebbleheaters,capableofsupplying1300 Æ

Cofblasttemperature. Thefurnaceis

designed foroperating withatop pressureof1.5 bar. It hasabell-typecharging

systemwithoutmovablearmour. Thecoalinjectionsystem,seeBinFigure2.0.1,

features individual controlof coalowforeachtuyere. Gascleaning system,part

D ofFigure2.0.1,consistsofdustcatcherandelectrostaticprecipitator. Material

istransportedtothetopoftheblastfurnacethroughCasshowninFigure2.0.1.

A tapping machine withdrill andmud gunis installed. Theblastfurnaceis well

equippedwithsensorsandmeasuringdevices,andanadvancedsystemforprocess

control. Probesfortakingmaterialsamplesfrom thefurnaceduring operationare

beingdeveloped.

Figure 2.1.1. Theblastfurnacebody.

LKABusethefurnaceprimarilyfordevelopmentofthenextgenerationofblast

furnace pellets. The furnace performance shows that it is a good tool for other

development projects. An importantarea is recycling of waste oxides. Injection

of wasteoxidesis another research areaaswell asinjection of slagformers. The

interestingpartsoftheplantarepresentedinFigure2.1.2.

Air Lock Vessel

Vessel Coal Injection Slag Vessel

000000000000000 000000000000000 000000000000000 000000000000000 111111111111111 111111111111111 111111111111111 111111111111111

Blast Furnace

Camera Video Tuyeres

Figure 2.1.2. The most relevant parts, for this project, of the

experimental blastfurnaceatMefos.

Figure 2.2.1. Controlscreenforcoalinjection,atMefos.

2.2. Coal Injection

The coalinjection arrangement, Figure 2.2.1,consists of twocoalvesselsand

threepipeseachendingwithatuyere. Thethreetuyeresareevenlyspacedaround

theblastfurnace,asshownin Figure2.2.2.

Blast Furnace Video Camera

Pulverized Coal Pipe

Tuyere

Coal Plume

Furnace Wall

Figure2.2.2. Thethreetuyeresaresurroundingtheblastfurnace

withthesurveillancecamerassupportingframework.

LookingagainatFigure2.1.2. Theuppervessel,theonethatislledwithcoal

when needed, works asan airlock vessel used to pressurisethe coalvessel below.

From the lowervessel, called theinjection vessel, coal is divided and distributed

under pressurethroughpipesto the tuyeres in theblast furnacewith thehelp of

when desired. A big problem is determining the behaviour of the coal particles

travellingthrough the pipes. That is because of the various size of theparticles,

turbulenceeectsinthepipesandpipes'characteristics. Coalparticlescanclogin

apipe,whichcandisturbtheprocessbeforebeingdiscovered. Anotherproblemis

leakageofthecarriergas. Solutionforthelatterisproposedin[6].

2.3. CurrentControl

Theexistingcontrolofthepulverisedcoalowtotheblastfurnaceisbasedon

acontinuouson-linemeasurementofthecoalowitself. Althoughthisistrue,itis

notthewholetruth. Ithasbeenshownthattheowmeasurementdeviceisnotvery

accurate,thatiswhythecurrentcontrolisdependentonaweightmeasurementof

theinjectionvessel.

2.3.1. Sensors. Mainly we cantalk about three ow measurement devices,

everyone ofthem connectedto pipestransporting pulverisedcoal tothe tuyeres.

ThedevicesusedareRamseyDMK270industrialmassowrateandvelocitymea-

suringinstrumentsfornonhomogeneousmedia[16]. Internallythedeviceconsists

ofasolidsconcentrationsensorandavelocitysensorwithtwomeasurementpoints

separatedby adistanceS, basedonthecapacitivemeasuringprinciple. Thepar-

ticle streamismeasuredintwopointswhichthevelocitytransmittercorrelatesto

ndtheclosestsimilaritybetweenthem. Fromthiscorrelationfunction,thetransit

time T from point oneto point two canbe determined. Inthe solid concentra-

tion sensor the change in capacitanceis proportionalto the solids concentration,

this voltage signal is transformedinto afrequency signaland is Pulse Frequency

Modulated(PFM).

TheowrateQ isgivenbyQ=C
V
A

sensor

,whereC istheconcentration

of the medium, V its velocityand A

sensor

is the sensor cross-sectionareaand is

calculatedwithA

sensor

= d

2

sensor



4

,whered

sensor

isthesensordiameter.

TheconcentrationC= Ka

10

(f

PFM f

PFM0

)
K,whereK

a

isanadaptionfactor

to concentration sensor, f

PFM

is the measured frequency of PFM concentration

signal, f

PFM0

is the frequency of PFM signal at concentration zero and K is a

calibration factor. Finallythe velocity is calculatedwith thewell known formula

V = S

T .

Nottoforgetthatthebothvesselsareequippedwithweightgaugesandpressure

meters. Other importantsensorsarethepressuremeasurementdevicesinthecoal

injection pipes.

2.3.2. Controllers. Figure 2.3.1 is a schematic overview of the main con-

trollers. Because the owmeter is not reliable, the on-line ow measurement is

multipliedbyacorrectionfactorcalculatedaccordingtotheinjectionvesselweight

loss deviation, during acertain period of time. See Figure 2.3.2for details. Do-

ing sothe idea ofon-line measurementis lost, in ashort term perspective,while

it is relatively accurate considering a longer period of time. The corrected ow

measurementitself is theoutput of aPI-controllerusing 1 asitssetup valueand

feedbackwiththe owmeters tocoal vessel weight ratio. Beforedividingtheow

measurementbythevesselweightbothsignalsarewindowedwithawindowlength

of 10minutesbefore performingthedivision. This isdonebecausethescaleused

in thevessel has alimitedsensitivityand a bigerror margin, if compared to the

owmeasurementduringashort time.

ThecoalinjectioniscontrolledwiththreePID-controllers,oneforeachtuyere.

An operator issupposed to feedthe systemwith the desiredamountof coalow

needed in the furnace, the amount is divided bythree and the resultserves asa

x

x

x

−

−

−

DMK 270 kg/s

DMK 270 kg/s

DMK 270 kg/s

DIV

− PI

DIV PID

PID PID Flow Meter

Tuyere Valve

3 Operator

Vessel Coal Injection

SUM

XXX

1 Slag Vessel

Setup

WINDOW

Figure 2.3.1. Coalpowderinjectioncontrol.

.

DMK 270 kg/s

Flow Direction Concentration

Velocity

Mass Flow 1

Function

X

Weight Multiplicator

Mass Flow 1 Weight Corrected Coal Pipe 1

Mass Flow 2 Mass Flow 3 Coal Vessel Weight

Slag Vessel Weight

Figure 2.3.2. A principleschemeforpulverisedcoalmassowcalculation.

feedback. Thecontrolsignalfrom each ofthePID-controllers isused tocontrola

valveplacedbeforetheowmeteroneachpipe. Acontrolbasedonthesetermscan

notbeperfecthavinginmindthebadqualityoftheresultingmeasured/calculated

signals. Dependingonhowthemultiplicatorchanges,thecontrolsignalwillbehave

dierently. We will show later in Chapter 4.1 that some control signals have a

strangebehaviour.

2.4. Video Surveillance

Theconditionssurroundingthesteel makingprocedure areratherrough. The

veryhigh temperature of theame and thehigh brightnessfrom inside the blast

furnace makelife hard for those whowant to control or study this process. The

existingcamerasatMefoscannothandletheincominghighlightintensityandthey

thevideopictureviewable. Thelteritselfisadarkgreenpieceofthickglassthat

is placedabit away from thecameralens. Thecamerasthemselvesare mounted

abouttwometersfrom theouterwalloftheblastfurnaceandarebuilt-ininsidea

protectingcover. Thelightfrom inside thefurnace isled to thecamerasthrough

peekholesinthewallneareachtuyereviaprotectingpipes.

Tuyere

Coal Plume

Pulverized Coal Pipe Furnace

Wall

Video Camera

Protecting Cover

Filter

Protecting Pipe

Panasonic

WV−CL410

Figure 2.4.1. Thevideocamerasetup.

For a full comprehension of the whole video camera apparatus, Figure 2.4.1

mightbehelpful forthedevouredreaders. Onitswaytothelens lightpasses the

previously mentioned glass lter. The characteristics of this lter have notbeen

examinedingreatdetailbuthavingacloserlookatthethreecolourbuersshows,

for the human eye, that the red and in particular the green light pass the lter

almost unaected while the blue light is ltered out to the extent that the blue

buer becomesnearlyuseless,asdiscussedin Chapter5. Itisthereforedesiredto

solvethelteringproblem. Wehadthepossibilitytousetransparentglassinstead

ofthegreenone,whichwebelievedwouldleaveallthethreecolourbuersunspoilt.

Bydoingthisweriskedintroducingoverexposuretothevideosurveillancesystem.

Possiblesolutionswillbediscussedlaterinthisreport.

(a)Withgreenglass. (b)Withtransparentglass.

Figure 2.4.2. Sampleimagestakenwithgreenglassaslterand

withtransparentglass.

An example of what is seen with the help of the cameras is in Figure 2.4.2,

where themouthof thetuyere isseentogetherwithadark,ellipticshapedcloud,

thepulverisedcoalinjectedinsidethefurnace.